def quantify_intron_retention(event, gene, counts_segments, counts_edges, counts_seg_pos):
cov = sp.zeros((2, ), dtype='float')
sg = gene.splicegraph
segs = gene.segmentgraph
seg_lens = segs.segments[1, :] - segs.segments[0, :]
seg_shape = segs.seg_edges.shape
order = 'C'
offset = 0
### find exons corresponding to event
idx_exon1 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
idx_exon2 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
### find segments corresponding to exons
seg_exon1 = sp.sort(sp.where(segs.seg_match[idx_exon1, :])[1])
seg_exon2 = sp.sort(sp.where(segs.seg_match[idx_exon2, :])[1])
seg_all = sp.arange(seg_exon1[0], seg_exon2[-1])
seg_intron = sp.setdiff1d(seg_all, seg_exon1)
seg_intron = sp.setdiff1d(seg_intron, seg_exon2)
assert(seg_intron.shape[0] > 0)
### compute exon coverages as mean of position wise coverage
# intron_cov
cov[0] = sp.sum(counts_segments[seg_intron] * seg_lens[seg_intron]) / sp.sum(seg_lens[seg_intron])
### check intron confirmation as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([seg_exon1[-1], seg_exon2[0]], seg_shape, order=order) + offset)[0]
cov[1] = counts_edges[idx, 1]
return cov
def quantify_intron_retention(event, gene, counts_segments, counts_edges,
counts_seg_pos, CFG):
cov = sp.zeros((2, ), dtype='float')
sg = gene.splicegraph
segs = gene.segmentgraph
if CFG['is_matlab']:
seg_lens = segs[0, 0][1, :] - segs[0, 0][0, :]
seg_shape = segs[0, 2].shape
order = 'F'
offset = 1
### find exons corresponding to event
idx_exon1 = sp.where((sg[0, 0][0, :] == event.exon1[0])
& (sg[0, 0][1, :] == event.exon1[1]))[0]
idx_exon2 = sp.where((sg[0, 0][0, :] == event.exon2[0])
& (sg[0, 0][1, :] == event.exon2[1]))[0]
### find segments corresponding to exons
seg_exon1 = sp.sort(sp.where(segs[0, 1][idx_exon1, :])[1])
seg_exon2 = sp.sort(sp.where(segs[0, 1][idx_exon2, :])[1])
else:
seg_lens = segs.segments[1, :] - segs.segments[0, :]
seg_shape = segs.seg_edges.shape
order = 'C'
offset = 0
### find exons corresponding to event
idx_exon1 = sp.where((sg.vertices[0, :] == event.exons1[0, 0])
& (sg.vertices[1, :] == event.exons1[0, 1]))[0]
idx_exon2 = sp.where((sg.vertices[0, :] == event.exons1[1, 0])
& (sg.vertices[1, :] == event.exons1[1, 1]))[0]
### find segments corresponding to exons
seg_exon1 = sp.sort(sp.where(segs.seg_match[idx_exon1, :])[1])
seg_exon2 = sp.sort(sp.where(segs.seg_match[idx_exon2, :])[1])
seg_all = sp.arange(seg_exon1[0], seg_exon2[-1])
seg_intron = sp.setdiff1d(seg_all, seg_exon1)
seg_intron = sp.setdiff1d(seg_intron, seg_exon2)
assert (seg_intron.shape[0] > 0)
### compute exon coverages as mean of position wise coverage
# intron_cov
cov[0] = sp.sum(counts_segments[seg_intron] *
seg_lens[seg_intron]) / sp.sum(seg_lens[seg_intron])
### check intron confirmation as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index(
[seg_exon1[-1], seg_exon2[0]], seg_shape, order=order) + offset)[0]
cov[1] = counts_edges[idx, 1]
return cov
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 _clearBadK(self, supervised=False):
goodk = self._goodK()
badk = sp.setdiff1d(sp.arange(self.K), goodk)
if not supervised:
self.rhow[:, badk] = self.bw[:, badk]
self.tauw[:, badk] = 0.0
self.rhoh[badk, :] = self.bh[badk, :]
self.tauh[badk, :] = 0.0
self._compute_expectations(supervised=supervised)
self.Et[badk] = 0.0
def exit_out_of_domain(dom, people, arrays=[], box=None):
"""
Removes individuals who are outside the domain or outside a given box
Parameters
----------
dom: Domain
contains everything for managing the domain
people: numpy array
people coordinates and radius : x,y,r
arrays: list of numpy array
other arrays to resize similarly as people and U
box: numpy array
box coordinates [xmin,xmax,ymin,ymax] which replace the \
domain minimum and maximum coordinates
Returns
-------
people: numpy array
new people array (outside individuals had been removed)
arrays: list of numpy array
new arrays resized similarly as people array
"""
if box is None:
## Remove people who are outside the domain
S = (people[:,0]-people[:,2]<=dom.xmin+dom.pixel_size) + \
(people[:,0]-people[:,2]>=dom.xmax-dom.pixel_size) + \
(people[:,1]-people[:,2]<=dom.ymin+dom.pixel_size) + \
(people[:,1]-people[:,2]>=dom.ymax-dom.pixel_size)
else:
## Remove people who are outside the given box
S = (people[:,0]-people[:,2]<=box[0]+dom.pixel_size) + \
(people[:,0]-people[:,2]>=box[1]-dom.pixel_size) + \
(people[:,1]-people[:,2]<=box[2]+dom.pixel_size) + \
(people[:,1]-people[:,2]>=box[3]-dom.pixel_size)
ind = sp.where(S == False)[0]
people = people[ind, :]
if (len(arrays) > 0):
for a in arrays:
a = a[ind]
## Remove people who are too close to walls or with a masked door distance
I = sp.floor((people[:, 1] - dom.ymin - 0.5 * dom.pixel_size) /
dom.pixel_size).astype(int)
J = sp.floor((people[:, 0] - dom.xmin - 0.5 * dom.pixel_size) /
dom.pixel_size).astype(int)
Dwall = dom.wall_distance[I, J] - people[:, 2]
Ddoor = dom.door_distance[I, J]
indDwall = sp.where(Dwall <= dom.pixel_size)[0]
indDdoor = sp.where(Ddoor.mask == True)[0]
ind = sp.unique(sp.concatenate((indDwall, indDdoor)))
comp_ind = sp.setdiff1d(sp.arange(people.shape[0]), ind)
if (len(arrays) > 0):
return people[comp_ind, :], [a[comp_ind] for a in arrays]
else:
return people[comp_ind, :]
def update(self,net=None):
def logProbkk(k,l):
"""evaluate the probability of C_k and Pi_l"""
pp = C[:,k,:]*Pi[:,l,:]
lpp = SP.log(pp.sum(axis=1))
return lpp.sum()
if (net is None) or (net.permutation_move==False):
return
#do factor permutation if active
#use the marignal indicators to calculate this; I thik they contain all we need; however we need to divide out the prior
C = self.C/self.Pi
Pi = self.Pi
#normalise
Cs = (C+1E-6).sum(axis=2)
C[:,:,0]/=Cs
C[:,:,1]/=Cs
#todo: make this faster
#now evaluate the probability of C under the (network) prior
M = SP.zeros([net.components,net.components])
for k in xrange(net.components):
for l in xrange(net.components):
M[k,l] = logProbkk(k,l)
print "pong"
#greedily select factors
K = random.permutation(net.components)
K = SP.arange(net.components)
F = SP.arange(net.components)
Ipi = SP.zeros(net.components,dtype='int')
for k in K:
#get beset one
Ibest = F[M[k,F].argmax()]
Ipi[k] = Ibest
#remove from list
F = SP.setdiff1d(F,[Ibest])
#keep track of the changes also
self.Ilabel = self.Ilabel[Ipi]
#update the prior Pi
self.Pi = self.Pi[:,Ipi,:]
#and the precalculated log versions:
self.lpC1 = self.lpC1[:,Ipi]
self.lpC0 = self.lpC0[:,Ipi]
pass
def importDataFromMat(self):
print "Importing data ...",
if self.k == 2 :
tmp = spio.loadmat('miniproject_data/norb_binary.mat')
else :
tmp = spio.loadmat('miniproject_data/norb_5class.mat')
size=tmp['train_cat_s'].shape[1]
print size
#Randomize indices
sp.random.seed(1)
#train_set_indices=sp.random.choice(size, 2*size/3, False)
train_set_indices = self.choice(size, 2*size/3)
complete_set_indices=sp.arange(size)
val_set_indices=sp.setdiff1d(complete_set_indices,train_set_indices);
if (self.train_size > 0) & (self.train_size < 2*size/3) :
#train_set_indices=sp.random.choice(train_set_indices, self.train_size, False)
train_set_indices=self.choice(train_set_indices, self.train_size)
if (self.validation_size > 0) & (self.validation_size < size/3) :
#val_set_indices=sp.random.choice(val_set_indices, self.validation_size, False)
val_set_indices=self.choice(val_set_indices, self.validation_size)
#Training Data
self.train_cat=sp.array(tmp['train_cat_s'][:,train_set_indices], dtype='int8')
self.train_left=sp.array(tmp['train_left_s'][:,train_set_indices],dtype=float)
self.train_right=sp.array(tmp['train_right_s'][:,train_set_indices],dtype=float)
#Validation Data
self.val_cat=sp.array(tmp['train_cat_s'][:,val_set_indices], dtype='int8')
self.val_left=sp.array(tmp['train_left_s'][:,val_set_indices],dtype=float)
self.val_right=sp.array(tmp['train_right_s'][:,val_set_indices],dtype=float)
#Test Data
self.test_cat=sp.array(tmp['test_cat_s'], dtype='int8')
self.test_left=sp.array(tmp['test_left_s'], dtype=float)
self.test_right=sp.array(tmp['test_right_s'], dtype=float)
print "OK"
def SRPSO(data, var_info, obj_func, pso_params, user_best):
# Read the data
tr_dat = data['tr_dat']
tr_cls = data['tr_cls']
ts_dat = data['ts_dat']
ts_cls = data['ts_cls']
# Setup PSO parameters
swarm_size = pso_params[0].astype(
int) # Number of particles in an iteration
max_IC = pso_params[1].astype(int) # Maximum number of iterations allowed
IC = 0 # Count of iterations completed
c1 = 1.49445
c2 = 1.49445
# Information regarding the variables to be optimized
optimize_var_idx = sp.nonzero(
var_info[:, 0] != 2)[0] # Index of variables to be optimized
var_count = optimize_var_idx.size # Number of variables to be optimized
int_var_idx = sp.zeros(var_count, dtype=int)
const_params = var_info[var_info[:, 0] == 2,
1] # Value for variables not to be optimized
l_bound = sp.tile(var_info[optimize_var_idx, 1], (swarm_size, 1))
u_bound = sp.tile(var_info[optimize_var_idx, 2], (swarm_size, 1))
# Initialize swarms
swarm = sp.zeros((swarm_size, var_count))
for i in range(optimize_var_idx.size):
current_var = optimize_var_idx[i]
if var_info[current_var, 0] == 0: # For real valued variables
swarm[:, i] = var_info[current_var, 1] + (
var_info[current_var, 2] -
var_info[current_var, 1]) * sp.random.rand(swarm_size)
elif var_info[current_var, 0] == 1: # For integer valued variables
swarm[:, i] = sp.random.randint(var_info[current_var, 1],
var_info[current_var,
2], swarm_size)
int_var_idx[i] = 1
int_var_idx = int_var_idx == 1
history = sp.zeros((max_IC, var_count + 1))
swarm[-1, :] = user_best
# Initialize velocity
vel = sp.zeros((swarm_size, var_count))
max_vel = (var_info[optimize_var_idx, 2] -
var_info[optimize_var_idx, 1]) * 0.100625
max_vel = sp.tile(max_vel, (swarm_size, 1))
# Initialize weight. Weight will vary linearly for w_vary_for iterations.
w = sp.tile(pso_params[2], (swarm_size, var_count))
w_end = pso_params[3]
w_vary_for = sp.floor(pso_params[4] * max_IC)
linear_dec = (pso_params[2] - w_end) / w_vary_for
# Evaluate fitness for each particle
fitness = sp.zeros(swarm_size)
for i in range(swarm_size):
params = sp.concatenate((const_params, swarm[i, :]), axis=1)
fitness[i] = obj_func(tr_dat, tr_cls, ts_dat, ts_cls, params)
g_best_ind = sp.argmax(fitness)
g_best_fitness = fitness[g_best_ind]
g_best = swarm[g_best_ind, :]
p_best = swarm
p_best_fitness = fitness
current_g_best_idx = g_best_ind
history[IC, 0:-1] = g_best
history[IC, -1] = g_best_fitness
swarm_idx = sp.arange(swarm_size)
while IC < max_IC:
rand_num_1 = sp.random.rand(swarm_size, var_count)
rand_num_2 = sp.random.rand(swarm_size, var_count)
non_best_idx = sp.setdiff1d(swarm_idx, current_g_best_idx)
if IC <= w_vary_for:
w[current_g_best_idx, :] = w[current_g_best_idx, :] + linear_dec
w[non_best_idx, :] = w[non_best_idx, :] - linear_dec
vel_update_flag = sp.random.rand(swarm_size - 1, var_count) > 0.5
vel[current_g_best_idx, :] = w[current_g_best_idx, :] * vel[
current_g_best_idx, :]
vel[non_best_idx, :] = w[non_best_idx, :] * vel[non_best_idx, :] + \
c1 * (rand_num_1[non_best_idx, :] * (p_best[non_best_idx, :] - swarm[non_best_idx, :])) + \
c2 * (rand_num_2[non_best_idx, :] * vel_update_flag *
(sp.tile(g_best, (swarm_size - 1, 1)) - swarm[non_best_idx, :]))
vel = sp.minimum(max_vel, sp.maximum(-max_vel, vel))
swarm = swarm + vel
swarm[:, int_var_idx] = sp.around(swarm[:, int_var_idx])
swarm = sp.minimum(u_bound, sp.maximum(l_bound, swarm))
for i in range(swarm_size):
params = sp.concatenate((const_params, swarm[i, :]), axis=1)
fitness[i] = obj_func(tr_dat, tr_cls, ts_dat, ts_cls, params)
update_p_best = fitness > p_best_fitness
p_best[update_p_best, :] = swarm[update_p_best, :]
p_best_fitness[update_p_best] = fitness[update_p_best]
current_g_best_idx = sp.argmax(fitness)
current_g_best_fitness = fitness[current_g_best_idx]
if current_g_best_fitness > g_best_fitness:
g_best_fitness = current_g_best_fitness
g_best = swarm[current_g_best_idx, :]
history[IC, 0:-1] = g_best
history[IC, -1] = g_best_fitness
print('Iteration: ' + str(IC) + 'Best fitness ' + str(g_best_fitness))
print('Params: ' + str(g_best) + '\n\n')
IC = IC + 1
# Read in union of all genes of all gene-wise p-values over all metabolites (files "UniqGeneSymbols.dat", note that the entries are HGNC Gene symbols):
UniqHGNCSymbolsInENGAGEData = scipy.genfromtxt(fname='UniqGeneSymbols.dat',
dtype=str,
delimiter='\t',
skip_header=1,
unpack=True)
# Determine overlap between PINA and ENGAGE set generated by VEGAS:
GeneSymbolsInPINA = scipy.array([])
GeneSymbolsInPINA = scipy.append(GeneSymbolsInPINA,PINAHGNC[0])
GeneSymbolsInPINA = scipy.append(GeneSymbolsInPINA,PINAHGNC[1])
GeneSymbolsInPINA = GeneSymbolsInPINA[scipy.where(GeneSymbolsInPINA!='None')[0]]
GeneSymbolsInPINA = scipy.unique(GeneSymbolsInPINA)
ENGAGEGeneSymbolsNotInPINA = scipy.setdiff1d(ar1=UniqHGNCSymbolsInENGAGEData,
ar2=GeneSymbolsInPINA,
assume_unique=True)
fw = open('UsingUniprotFiles/ENGAGEGeneSymbolsNotInPINA.txt','w')
for i in xrange(len(ENGAGEGeneSymbolsNotInPINA)):
fw.write(ENGAGEGeneSymbolsNotInPINA[i]+'\n')
fw.close()
PINAGeneSymbolsNotInENGAGE = scipy.setdiff1d(ar1=GeneSymbolsInPINA,
ar2=UniqHGNCSymbolsInENGAGEData,
assume_unique=True)
fw = open('UsingUniprotFiles/PINAGeneSymbolsNotInENGAGE.txt','w')
for i in xrange(len(PINAGeneSymbolsNotInENGAGE)):
fw.write(PINAGeneSymbolsNotInENGAGE[i]+'\n')
fw.close()
# Remove unmatched UniprotKBIDs:
if locusTag in essentialGeneLociNames:
essentiality = 'Essential'
else:
essentiality = 'Dispensable'
geneDispensableLocusNameArray.append(locusTag)
geneLocusAndFeatureNameArray.append(
[locusTag, featureName, essentiality])
geneLocusNameArray.append(locusTag)
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Figure out the CDSs that don't have genes, genes that don't have CDSs
genesWithoutCDSs = setdiff1d(geneLocusNameArray, cdsLocusNameArray)
cdssWithoutGenes = setdiff1d(cdsLocusNameArray, geneLocusNameArray)
uniqueGenes = unique(geneLocusNameArray)
uniqueDispensableGenes = unique(geneDispensableLocusNameArray)
uniqueCDSs = unique(cdsLocusNameArray)
uniqueDispensableCDSs = unique(cdsDispensableLocusNameArray)
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Write out data
cdsFileHandle = open(cdsOutputFileName, 'w')
for line in cdsLocusAndFeatureNameArray:
def get_intron_list(genes, options):
introns = sp.zeros((genes.shape[0], 2), dtype = 'object')
introns[:] = None
### collect all possible combinations of contigs and strands
(regions, options) = init_regions(options.bam_fnames, options.confidence, options, sparse_bam=options.sparse_bam)
### form chunks for quick sorting
strands = ['+', '-']
### ignore contigs not present in bam files
keepidx = sp.where(sp.in1d(sp.array([options.chrm_lookup[x.chr] for x in genes]), sp.array([x.chr_num for x in regions])))[0]
genes = genes[keepidx]
c = 0
num_introns_filtered = 0
t0 = time.time()
contigs = sp.array([x.chr for x in genes], dtype='str')
gene_strands = sp.array([x.strand for x in genes])
for contig in sp.unique(contigs):
bam_cache = dict()
for si, s in enumerate(strands):
cidx = sp.where((contigs == contig) & (gene_strands == s))[0]
for i in cidx:
if options.verbose and (c+1) % 100 == 0:
t1 = time.time()
print('%i (%i) genes done (%i introns taken) ... took %i secs' % (c+1, genes.shape[0], num_introns_filtered, t1 - t0), file=sys.stdout)
t0 = t1
gg = sp.array([copy.copy(genes[i])], dtype='object')
assert(gg[0].strand == s)
gg[0].start = max(gg[0].start - 5000, 1)
gg[0].stop = gg[0].stop + 5000
assert(gg[0].chr == contig)
if options.sparse_bam:
if isinstance(options.bam_fnames, str):
[intron_list_tmp] = add_reads_from_sparse_bam(gg[0], options.bam_fnames, contig, options.confidence, types=['intron_list'], filter=options.read_filter, cache=bam_cache, unstranded=options.introns_unstranded)
else:
intron_list_tmp = None
for fname in options.bam_fnames:
[tmp_] = add_reads_from_sparse_bam(gg[0], fname, contig, options.confidence, types=['intron_list'], filter=options.read_filter, cache=bam_cache, unstranded=options.introns_unstranded)
if intron_list_tmp is None:
intron_list_tmp = tmp_
else:
intron_list_tmp = sp.r_[intron_list_tmp, tmp_]
### some merging in case of multiple bam files
if len(options.bam_fnames) > 1:
intron_list_tmp = sort_rows(intron_list_tmp)
rm_idx = []
for i in range(1, intron_list_tmp.shape[0]):
if sp.all(intron_list_tmp[i, :2] == intron_list_tmp[i-1, :2]):
intron_list_tmp[i, 2] += intron_list_tmp[i-1, 2]
rm_idx.append(i-1)
if len(rm_idx) > 0:
k_idx = sp.setdiff1d(sp.arange(intron_list_tmp.shape[0]), rm_idx)
intron_list_tmp = intron_list_tmp[k_idx, :]
else:
[intron_list_tmp] = add_reads_from_bam(gg, options.bam_fnames, ['intron_list'], options.read_filter, options.var_aware, options.primary_only, options.ignore_mismatches, unstranded=options.introns_unstranded, mm_tag=options.mm_tag)
num_introns_filtered += intron_list_tmp.shape[0]
introns[i, si] = sort_rows(intron_list_tmp)
c += 1
for j in range(introns.shape[0]):
if introns[j, 0] is None:
introns[j, 0] = sp.zeros((0, 3), dtype='int')
if introns[j, 1] is None:
introns[j, 1] = sp.zeros((0, 3), dtype='int')
return introns
def test_with_nested_CV(folder='model',
folds=5,
plot=True,
steps=['hashing', 'tfidf']):
'''
Evaluates the classifer by doing nested CV
i.e. keeping 1/folds of the data out of the training and doing training
(including model selection for regularizer) on the training set and testing
on the held-out data
Also prints some stats and figures
INPUT
folder folder with model files
folds number of folds
'''
# start timer
import time
t0 = time.time()
# create bag of words representations
vv = Vectorizer(steps=steps)
# load data
vec = Vectorizer(folder=folder)
data = get_speech_text(folder=folder)
for key in data.keys():
data[key] = vec.transform(data[key])
# create numerical labels
Y = hstack(
map((lambda x: ones(data[data.keys()[x]].shape[0]) * x),
range(len(data))))
# create data matrix
X = vstack(data.values())
# permute data
fsize = len(Y) / folds
randidx = permutation(len(Y))
Y = Y[randidx]
X = X[randidx, :]
idx = reshape(arange(fsize * folds), (folds, fsize))
Y = Y[:fsize * folds]
# allocate matrices for predictions
predicted = zeros(fsize * folds)
predicted_prob = zeros((fsize * folds, len(data)))
# the regularization parameters to choose from
parameters = {'C': (10.**arange(-4, 4, 1.)).tolist()}
# do nested CV
for ifold in range(folds):
testidx = idx[ifold, :]
trainidx = idx[setdiff1d(arange(folds), ifold), :].flatten()
text_clf = LogisticRegression(class_weight='auto', dual=True)
# for nested CV, do folds-1 CV for parameter optimization
# within inner CV loop and use the outer testfold as held-out data
# for model validation
gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1, cv=(folds - 1))
gs_clf.fit(X[trainidx, :], Y[trainidx])
predicted[testidx] = gs_clf.predict(X[testidx, :])
predicted_prob[testidx, :] = gs_clf.predict_proba(X[testidx, :])
print '************ Fold %d *************' % (ifold + 1)
print metrics.classification_report(Y[testidx],
predicted[testidx],
target_names=data.keys())
t1 = time.time()
total_time = t1 - t0
timestr = 'Wallclock time: %f sec\n' % total_time
dimstr = 'Vocabulary size: %d\n' % X.shape[-1]
report = timestr + dimstr
# extract some metrics
print '********************************'
print '************ Total *************'
print '********************************'
report += metrics.classification_report(Y,
predicted,
target_names=data.keys())
# dump metrics to file
open(folder + '/report_%s.txt' % '_'.join(sorted(steps)),
'wb').write(report)
print(report)
conf_mat = metrics.confusion_matrix(Y, predicted)
open(folder + '/conf_mat_%s.txt' % '_'.join(sorted(steps)),
'wb').write(json.dumps(conf_mat.tolist()))
print(conf_mat)
if plot:
# print confusion matrix
import pylab
pylab.figure(figsize=(16, 16))
pylab.imshow(metrics.confusion_matrix(Y, predicted),
interpolation='nearest')
pylab.colorbar()
pylab.xticks(arange(4), [x.decode('utf-8') for x in data.keys()])
pylab.yticks(arange(4), [x.decode('utf-8') for x in data.keys()])
pylab.xlabel('Predicted')
pylab.ylabel('True')
font = {'family': 'normal', 'size': 30}
pylab.rc('font', **font)
pylab.savefig(folder + '/conf_mat.pdf', bbox_inches='tight')
def verify_alt_prime(event, gene, counts_segments, counts_edges, CFG):
# [verified, info] = verify_exon_skip(event, fn_bam, cfg)
# (0) valid, (1) exon_diff_cov, (2) exon_const_cov
# (3) intron1_conf, (4) intron2_conf
info = [1, 0, 0, 0, 0]
verified = [0, 0]
### check validity of exon coordinates (>=0)
if sp.any(event.exons1 < 0) or sp.any(event.exons2 < 0):
info[0] = 0
return (verified, info)
### check validity of intron coordinates (only one side is differing)
if (event.exons1[0, 1] != event.exons2[0, 1]) and (event.exons1[1, 0] !=
event.exons2[1, 0]):
info[0] = 0
return (verified, info)
sg = gene.splicegraph
segs = gene.segmentgraph
### find exons corresponding to event
idx_exon11 = sp.where((sg.vertices[0, :] == event.exons1[0, 0])
& (sg.vertices[1, :] == event.exons1[0, 1]))[0]
if idx_exon11.shape[0] == 0:
segs_exon11 = sp.where((segs.segments[0, :] >= event.exons1[0, 0]) &
(segs.segments[1, :] <= event.exons1[0, 1]))[0]
else:
segs_exon11 = sp.where(segs.seg_match[idx_exon11, :])[1]
idx_exon12 = sp.where((sg.vertices[0, :] == event.exons1[1, 0])
& (sg.vertices[1, :] == event.exons1[1, 1]))[0]
if idx_exon12.shape[0] == 0:
segs_exon12 = sp.where((segs.segments[0, :] >= event.exons1[1, 0]) &
(segs.segments[1, :] <= event.exons1[1, 1]))[0]
else:
segs_exon12 = sp.where(segs.seg_match[idx_exon12, :])[1]
idx_exon21 = sp.where((sg.vertices[0, :] == event.exons2[0, 0])
& (sg.vertices[1, :] == event.exons2[0, 1]))[0]
if idx_exon21.shape[0] == 0:
segs_exon21 = sp.where((segs.segments[0, :] >= event.exons2[0, 0]) &
(segs.segments[1, :] <= event.exons2[0, 1]))[0]
else:
segs_exon21 = sp.where(segs.seg_match[idx_exon21, :])[1]
idx_exon22 = sp.where((sg.vertices[0, :] == event.exons2[1, 0])
& (sg.vertices[1, :] == event.exons2[1, 1]))[0]
if idx_exon22.shape[0] == 0:
segs_exon22 = sp.where((segs.segments[0, :] >= event.exons2[1, 0]) &
(segs.segments[1, :] <= event.exons2[1, 1]))[0]
else:
segs_exon22 = sp.where(segs.seg_match[idx_exon22, :] > 0)[1]
assert (segs_exon11.shape[0] > 0)
assert (segs_exon12.shape[0] > 0)
assert (segs_exon21.shape[0] > 0)
assert (segs_exon22.shape[0] > 0)
if sp.all(segs_exon11 == segs_exon21):
seg_exon_const = segs_exon11
seg_diff = sp.setdiff1d(segs_exon12, segs_exon22)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon22, segs_exon12)
seg_const = sp.intersect1d(segs_exon12, segs_exon22)
elif sp.all(segs_exon12 == segs_exon22):
seg_exon_const = segs_exon12
seg_diff = sp.setdiff1d(segs_exon11, segs_exon21)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon21, segs_exon11)
seg_const = sp.intersect1d(segs_exon21, segs_exon11)
else:
print >> sys.stderr, "ERROR: both exons differ in alt prime event in verify_alt_prime"
sys.exit(1)
seg_const = sp.r_[seg_exon_const, seg_const]
seg_lens = segs.segments[1, :] - segs.segments[0, :]
# exon_diff_cov
info[1] = sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(
seg_lens[seg_diff])
# exon_const_cov
info[2] = sp.sum(counts_segments[seg_const] *
seg_lens[seg_const]) / sp.sum(seg_lens[seg_const])
if info[1] >= CFG['alt_prime']['min_diff_rel_cov'] * info[2]:
verified[0] = 1
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index(
[segs_exon11[-1], segs_exon12[0]], segs.seg_edges.shape))[0]
assert (idx.shape[0] > 0)
info[3] = counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index(
[segs_exon21[-1], segs_exon22[0]], segs.seg_edges.shape))[0]
assert (idx.shape[0] > 0)
info[4] = counts_edges[idx, 1]
if min(info[3], info[4]) >= CFG['alt_prime']['min_intron_count']:
verified[1] = 1
return (verified, info)
def elasticity(N, Y, centered=True, NyqNul=True):
"""
Projection matrix on a space of admissible strain fields
INPUT =
N : ndarray of e.g. stiffness coefficients
d : dimension; d = 2
D : dimension in engineering notation; D = 3
Y : the size of periodic unit cell
OUTPUT =
G1h,G1s,G2h,G2s : projection matrices of size DxDxN
"""
xi = Grid.get_xil(N, Y)
N = np.array(N)
d = N.size
D = d*(d+1)/2
if NyqNul:
Nred = get_Nodd(N)
else:
Nred = N
xi2 = []
for ii in np.arange(d):
xi2.append(xi[ii]**2)
num = np.zeros(np.hstack([d, d, Nred]))
norm2_xi = np.zeros(Nred)
for mm in np.arange(d): # diagonal components
Nshape = np.ones(d)
Nshape[mm] = Nred[mm]
Nrep = np.copy(Nred)
Nrep[mm] = 1
num[mm][mm] = np.tile(np.reshape(xi2[mm], Nshape), Nrep) # numerator
norm2_xi += num[mm][mm]
norm4_xi = norm2_xi**2
ind_center = tuple(Nred/2)
# avoid division by zero
norm2_xi[ind_center] = 1
norm4_xi[ind_center] = 1
for m in np.arange(d): # upper diagonal components
for n in np.arange(m+1, d):
NshapeM = np.ones(d)
NshapeM[m] = Nred[m]
NrepM = np.copy(Nred)
NrepM[m] = 1
NshapeN = np.ones(d)
NshapeN[n] = Nred[n]
NrepN = np.copy(Nred)
NrepN[n] = 1
num[m][n] = np.tile(np.reshape(xi[m], NshapeM), NrepM) \
* np.tile(np.reshape(xi[n], NshapeN), NrepN)
# G1h = np.zeros([D,D]).tolist()
G1h = np.zeros(np.hstack([D, D, Nred]))
G1s = np.zeros(np.hstack([D, D, Nred]))
IS0 = np.zeros(np.hstack([D, D, Nred]))
mean = np.zeros(np.hstack([D, D, Nred]))
Lamh = np.zeros(np.hstack([D, D, Nred]))
S = np.zeros(np.hstack([D, D, Nred]))
W = np.zeros(np.hstack([D, D, Nred]))
WT = np.zeros(np.hstack([D, D, Nred]))
for m in np.arange(d):
S[m][m] = 2*num[m][m]/norm2_xi
for n in np.arange(d):
G1h[m][n] = num[m][m]*num[n][n]/norm4_xi
Lamh[m][n] = np.ones(Nred)/d
Lamh[m][n][ind_center] = 0
for m in np.arange(D):
IS0[m][m] = np.ones(Nred)
IS0[m][m][ind_center] = 0
mean[m][m][ind_center] = 1
if d == 2:
S[0][2] = 2**0.5*num[0][1]/norm2_xi
S[1][2] = 2**0.5*num[0][1]/norm2_xi
S[2][2] = np.ones(Nred)
S[2][2][ind_center] = 0
G1h[0][2] = 2**0.5*num[0][0]*num[0][1]/norm4_xi
G1h[1][2] = 2**0.5*num[0][1]*num[1][1]/norm4_xi
G1h[2][2] = 2*num[0][0]*num[1][1]/norm4_xi
for m in np.arange(d):
for n in np.arange(d):
W[m][n] = num[m][m]/norm2_xi
W[2][m] = 2**.5*num[0][1]/norm2_xi
elif d == 3:
for m in np.arange(d):
S[m+3][m+3] = 1 - num[m][m]/norm2_xi
S[m+3][m+3][ind_center] = 0
for m in np.arange(d):
for n in np.arange(m+1, d):
S[m+3][n+3] = num[m][n]/norm2_xi
G1h[m+3][n+3] = num[m][m]*num[n][n]/norm4_xi
for m in np.arange(d):
for n in np.arange(d):
ind = sp.setdiff1d(np.arange(d), [n])
S[m][n+3] = (0 == (m == n))*2**.5*num[ind[0]][ind[1]]/norm2_xi
G1h[m][n+3] = 2**.5*num[m][m]*num[ind[0]][ind[1]]/norm4_xi
W[m][n] = num[m][m]/norm2_xi
W[n+3][m] = 2**.5*num[ind[0]][ind[1]]/norm2_xi
for m in np.arange(d):
for n in np.arange(d):
ind_m = sp.setdiff1d(np.arange(d), [m])
ind_n = sp.setdiff1d(np.arange(d), [n])
G1h[m+3][n+3] = 2*num[ind_m[0]][ind_m[1]] \
* num[ind_n[0]][ind_n[1]] / norm4_xi
# symmetrization
for n in np.arange(D):
for m in np.arange(n+1, D):
S[m][n] = S[n][m]
G1h[m][n] = G1h[n][m]
for m in np.arange(D):
for n in np.arange(D):
G1s[m][n] = S[m][n] - 2*G1h[m][n]
WT[m][n] = W[n][m]
G2h = 1./(d-1)*(d*Lamh + G1h - W - WT)
G2s = IS0 - G1h - G1s - G2h
if not centered:
for m in np.arange(d):
for n in np.arange(d):
G1h[m][n] = np.fft.ifftshift(G1h[m][n])
G1s[m][n] = np.fft.ifftshift(G1s[m][n])
G2h[m][n] = np.fft.ifftshift(G2h[m][n])
G2s[m][n] = np.fft.ifftshift(G2s[m][n])
G0 = Matrix(name='hG1', val=mean, Fourier=True)
G1h = Matrix(name='hG1', val=G1h, Fourier=True)
G1s = Matrix(name='hG1', val=G1s, Fourier=True)
G2h = Matrix(name='hG1', val=G2h, Fourier=True)
G2s = Matrix(name='hG1', val=G2s, Fourier=True)
if NyqNul:
G0 = G0.enlarge(N)
G1h = G1h.enlarge(N)
G1s = G1s.enlarge(N)
G2h = G2h.enlarge(N)
G2s = G2s.enlarge(N)
return mean, G1h, G1s, G2h, G2s
mfu0.set_fem(gf.Fem('FEM_QK(2,3)'))
mfdu = gf.MeshFem(m, 1)
mfdu.set_fem(gf.Fem('FEM_QK_DISCONTINUOUS(2,2)'))
mf_mult = gf.MeshFem(m, 2)
mf_mult.set_fem(gf.Fem('FEM_QK(2,1)'))
A = gf.asm('volumic', 'V()+=comp()', mim_bound)
#mls.cut_mesh().export_to_pos('mls.pos','cut mesh')
#mf_ls.export_to_pos('mf_ls.pos',ULS,'ULS')
dof_out = mfu0.dof_from_im(mim)
cv_out = mim.convex_index()
cv_in = setdiff1d(m.cvid(), cv_out)
# mfu = gf.MeshFem('partial', mfu0, dof_out, cv_in)
md = gf.Model('real')
md.add_fem_variable('u', mfu0)
md.add_initialized_data('lambda', [1])
md.add_initialized_data('mu', [1])
md.add_isotropic_linearized_elasticity_brick(mim, 'u', 'lambda', 'mu')
md.add_initialized_data('VolumicData', [0, 10])
md.add_source_term_brick(mim, 'u', 'VolumicData')
md.add_multiplier('mult_dir', mf_mult, 'u')
md.add_Dirichlet_condition_with_multipliers(mim_bound, 'u', 'mult_dir', -1)
md.solve()
U = md.variable('u')
def quantify_alt_prime(event, gene, counts_segments, counts_edges):
cov = sp.zeros((2, ), dtype='float')
sg = gene.splicegraph
segs = gene.segmentgraph
seg_lens = segs.segments[1, :] - segs.segments[0, :]
seg_shape = segs.seg_edges.shape[0]
### find exons corresponding to event
idx_exon11 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
if idx_exon11.shape[0] == 0:
segs_exon11 = sp.where((segs.segments[0, :] >= event.exons1[0, 0]) & (segs.segments[1, :] <= event.exons1[0, 1]))[0]
else:
segs_exon11 = sp.where(segs.seg_match[idx_exon11, :])[1]
idx_exon12 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
if idx_exon12.shape[0] == 0:
segs_exon12 = sp.where((segs.segments[0, :] >= event.exons1[1, 0]) & (segs.segments[1, :] <= event.exons1[1, 1]))[0]
else:
segs_exon12 = sp.where(segs.seg_match[idx_exon12, :])[1]
idx_exon21 = sp.where((sg.vertices[0, :] == event.exons2[0, 0]) & (sg.vertices[1, :] == event.exons2[0, 1]))[0]
if idx_exon21.shape[0] == 0:
segs_exon21 = sp.where((segs.segments[0, :] >= event.exons2[0, 0]) & (segs.segments[1, :] <= event.exons2[0, 1]))[0]
else:
segs_exon21 = sp.where(segs.seg_match[idx_exon21, :])[1]
idx_exon22 = sp.where((sg.vertices[0, :] == event.exons2[1, 0]) & (sg.vertices[1, :] == event.exons2[1, 1]))[0]
if idx_exon22.shape[0] == 0:
segs_exon22 = sp.where((segs.segments[0, :] >= event.exons2[1, 0]) & (segs.segments[1, :] <= event.exons2[1, 1]))[0]
else:
segs_exon22 = sp.where(segs.seg_match[idx_exon22, :] > 0)[1]
assert(segs_exon11.shape[0] > 0)
assert(segs_exon12.shape[0] > 0)
assert(segs_exon21.shape[0] > 0)
assert(segs_exon22.shape[0] > 0)
if sp.all(segs_exon11 == segs_exon21):
seg_diff = sp.setdiff1d(segs_exon12, segs_exon22)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon22, segs_exon12)
elif sp.all(segs_exon12 == segs_exon22):
seg_diff = sp.setdiff1d(segs_exon11, segs_exon21)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon21, segs_exon11)
else:
print("ERROR: both exons differ in alt prime event in verify_alt_prime", file=sys.stderr)
sys.exit(1)
# exon_diff_cov
if seg_diff in segs_exon11 or seg_diff in segs_exon12:
cov[0] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
elif seg_diff in segs_exon21 or seg_diff in segs_exon22:
cov[1] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
else:
raise Exception('differential segment not part of any other segment')
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon11[-1], segs_exon12[0]], seg_shape))[0]
assert(idx.shape[0] > 0)
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon21[-1], segs_exon22[0]], seg_shape))[0]
assert(idx.shape[0] > 0)
cov[1] += counts_edges[idx, 1]
return cov
RV_file.append([element_id,count_file_GRCH37,count_file_SNP_maternal,count_file_SNP_paternal,count_file_SV_maternal,count_file_SV_paternal])
continue
#1. load lists
count_GRCH37 = cPickle.load(open(count_file_GRCH37,'rb'))
count_SNP_maternal = cPickle.load(open(count_file_SNP_maternal,'rb'))
count_SNP_paternal = cPickle.load(open(count_file_SNP_paternal,'rb'))
count_SV_maternal = cPickle.load(open(count_file_SV_maternal,'rb'))
count_SV_paternal = cPickle.load(open(count_file_SV_paternal,'rb'))
count_SNP = SP.union1d(count_SNP_maternal,count_SNP_paternal)
count_SV = SP.union1d(count_SV_maternal,count_SV_paternal)
count_intersect_GRCH37_SNP = SP.intersect1d(count_SNP,count_GRCH37)
count_intersect_GRCH37_SV = SP.intersect1d(count_SV,count_GRCH37)
count_intersect_SNP_SV = SP.intersect1d(count_SNP,count_SV)
count_ex_GRCH37_SNP = SP.setdiff1d(count_GRCH37,count_SNP)
count_ex_GRCH37_SV = SP.setdiff1d(count_GRCH37,count_SV)
count_ex_SNP_GRCH37 = SP.setdiff1d(count_SNP,count_GRCH37)
count_ex_SV_GRCH37 = SP.setdiff1d(count_SV,count_GRCH37)
count_ex_SNP_SV = SP.setdiff1d(count_SNP,count_SV)
count_ex_SV_SNP = SP.setdiff1d(count_SV,count_SNP)
#store a couple of things
rv = []
rv = {'element_id': element_id,'count_ref': len(count_GRCH37),'count_SNP_maternal':len(count_SNP_maternal),'count_SNP_paternal':len(count_SNP_paternal),'count_SV_maternal':len(count_SV_maternal),'count_SV_paternal':len(count_SV_paternal),'count_SNP':len(count_SNP),'count_SV':len(count_SV),'count_intersect_GRCH37_SNP':len(count_intersect_GRCH37_SNP),'count_intersect_GRCH37_SV':len(count_intersect_GRCH37_SV),'count_intersect_SNP_SV':len(count_intersect_SNP_SV),'count_ex_GRCH37_SNP':len(count_ex_GRCH37_SNP),'count_ex_GRCH37_SV':len(count_ex_GRCH37_SV),'count_ex_SNP_GRCH37':len(count_ex_SNP_GRCH37),'count_ex_SV_GRCH37':len(count_ex_SV_GRCH37),'count_ex_SNP_SV':len(count_ex_SNP_SV),'count_ex_SV_SNP':len(count_ex_SV_SNP)}
RV.append(rv)
pass
#dump results
RV = pandas.DataFrame(RV)
RV.to_pickle(os.path.join(out_dir,'summary.pickl'))
def generate2D(nx,ny,dx,dy,pLx,pLy,pLz,N):
# to get a nonperiodic ensemble, define extra "ghost" gridpoints
n1 = np.round(1.2*nx)
n2 = np.round(1.2*ny)
n1 = n1+np.mod(n1,2)
n2 = n2+np.mod(n2,2)
# define constants
pi2 = 2.0*pi
deltak = pi2**2./((n1*n2)*dx*dy)
kappa = pi2/((n1)*dx)
kappa2 = kappa**2.
lmbd = pi2/((n2)*dy)
lmbd2 = lmbd**2.
nreal = N
# rescale decorrelation lengths such that we will get the
# following form for the covariance as a function of
# distance delta:
# C(delta)=exp(-3*(delta/Lx)^2)
rx = pLx/np.sqrt(3.0)
ry = pLy/np.sqrt(3.0)
#------------------------------------------------------------------
# solve systems for r1,r2,c
#------------------------------------------------------------------
# define wavenumber indeces p,l, excluding p==l==0
p = np.linspace((-n2/2.+1.),(n2/2.),(n2/2.)-(-n2/2.+1.)+1)
l = np.linspace((-n1/2+1),(n1/2),(n1/2)-(-n1/2+1)+1)
p,l = np.meshgrid(p,l)
# Commented the following lines due to the problem mentioned in LOGS-1
pp = np.array(p).flatten()
ll = np.array(p).flatten()
#ind = sp.setdiff1d(np.linspace(0,p.size-1,p.size-1-0+1),sp.where((p==0) & (l==0)))
ind = sp.setdiff1d(np.linspace(0,p.size-1,p.size-1-0+1),np.r_[sp.where((p==0) & (l==0))])
ind = ind.astype(int)
pn0 = pp[ind]
ln0 = ll[ind]
def ff(ss):
r1,r2 = ss
e = np.exp(-2.0*(kappa2*(ln0**2.)/(r1**2.) + lmbd2*(pn0**2.)/(r2**2.)))
f = np.sum(e*(np.cos(kappa*ln0*rx)-np.exp(-1.)))
g = np.sum(e*(np.cos(lmbd*pn0*ry)-np.exp(-1.)))
return (f,g)
r1,r2 = sp.optimize.fsolve(ff,(3.0/rx,3.0/ry))
summ = np.sum(np.sum(np.exp(-2.0*(kappa2*(l**2.)/(r1**2.)+lmbd2*(p**2.)/(r2**2.)))))
summ = summ-1.0
c = np.sqrt(1.0/(deltak*summ))
# define aij matrices. Note rotation is not enabled in this code
a11 = 1.0/r1**2
a22 = 1.0/r2**2
a12 = 0.0*a11
# define wavenumber indeces following matlab ifft2 convention
l = np.linspace(0,(n1/2),(n1/2)-0+1)
p = np.linspace(0,(n2/2),(n2/2)-0+1)
p,l = np.meshgrid(p,l)
# define amplitudes 'C', in 1st quadrant
e = np.exp(-( a11*kappa2*(l**2.) + 2.0*a12*kappa*lmbd*l*p + a22*lmbd2*(p**2.) ))
C = e*c*np.sqrt(deltak)
C[0,:] = 0.
C[:,0] = 0.
# for each wavenumber (p,l) of each sample (j=1..N)
A = np.zeros((n1,n2,N))
for nn in range (0,int(nreal)):
print "Working on ensemble number " + str(nn)
qhat = np.zeros((n1,n2))+0j
qhat2 = np.zeros((n1,n2))+0j
# 1st quadrant: phase is arbitrary
phi = 2.*pi*np.random.random(C.shape)
phi[:,int(n2)/2] = 0.
phi[int(n1)/2,:] = 0.
qhat[0:int(n1)/2+1,0:int(n2)/2+1] = C*np.exp(cmath.sqrt(-1.)*(phi))
# 3rd quadrant: phase is also arbitrary
phi2 = 2.*pi*np.random.random(C.shape)
phi2[:,int(n2)/2] = 0.
phi2[int(n1)/2,:] = 0.
qhat2[0:int(n1)/2+1,0:int(n2)/2+1] = C*np.exp(cmath.sqrt(-1.)*(phi2))
for j in range (int(n1)/2,int(n1)-1):
for i in range (0,int(n2)/2):
qhat[j+1,i+1] = np.conj(qhat2[(int(n1)-j)+1,i+1])
qhat[int(n1)/2:int(n1)-2,1]=0.
# 2nd and 4th quadrants are set by conjugate symmetry
for i in range (int(n2)/2+1,int(n2)):
for j in range (0,int(n1)):
qhat[j,i] = np.conj(qhat[np.mod(int(n1)-j+1,int(n1)),np.mod(int(n2)-i+1,int(n2)+1)])
#print nn
# Invert the fourier transform to get the sample
A[:,:,nn] = np.fft.ifft2(qhat)*n1*n2
# cut down to desired size
A = A[0:nx,0:ny,:]
# correct mean and variance
AA = np.array([np.tile(np.mean(A,axis=2), (1,1)) for ii in xrange(int(N))])
AA = AA.transpose((1,2,0))
A = A-AA
del AA
AA = np.array([np.tile(np.std(A,axis=2), (1,1)) for ii in xrange(int(N))])
AA = AA.transpose((1,2,0))
A = A/AA*pLz
del AA
return A
fr = open('BioMartUniprotAC_or_ID_to_HGNCSymbol.tsv','r')
BioMartUniprot2HGNCSymbolsHdr = fr.readline().strip().split('\t')
fr.close()
BioMartUniprot2HGNCSymbols = scipy.genfromtxt(fname='BioMartUniprotAC_or_ID_to_HGNCSymbol.tsv',
dtype=str,
delimiter='\t',
skip_header=1,
unpack=True)
# Check if all PINA UniprotKB IDs are reported in the BioMart file:
AllPINAUniprotKBIDs = scipy.unique(PINAUniprot[0])
AllPINAUniprotKBIDs = scipy.append(AllPINAUniprotKBIDs,PINAUniprot[1])
AllPINAUniprotKBIDs = scipy.unique(AllPINAUniprotKBIDs)
AllUNIProtKBIDsInBioMart = scipy.unique(BioMartUniprot2HGNCSymbols[BioMartUniprot2HGNCSymbolsHdr.index('UniProt/SwissProt Accession')])
BioMartUniprotKBIDsNotInPINA = scipy.setdiff1d(ar1=AllPINAUniprotKBIDs,
ar2=AllUNIProtKBIDsInBioMart,
assume_unique=False)
fw = open('BioMartUniprotKBIDsNotInPINA.txt','w')
for i in xrange(len(BioMartUniprotKBIDsNotInPINA)):
fw.write(BioMartUniprotKBIDsNotInPINA[i]+'\n')
fw.close()
PINAUniprotKBIDsNotInBioMart = scipy.setdiff1d(ar1=AllUNIProtKBIDsInBioMart,
ar2=AllPINAUniprotKBIDs,
assume_unique=False)
fw = open('PINAUniprotKBIDsNotInBioMart.txt','w')
for i in xrange(len(PINAUniprotKBIDsNotInBioMart)):
fw.write(PINAUniprotKBIDsNotInBioMart[i]+'\n')
fw.close()
sys.exit()
def _mc_data_config(H, psi0, h_stuff, c_ops, c_stuff, args, e_ops, options):
"""Creates the appropriate data structures for the monte carlo solver
based on the given time-dependent, or indepdendent, format.
"""
#take care of expectation values, if any
if any(e_ops):
odeconfig.e_num = len(e_ops)
for op in e_ops:
if isinstance(op, list):
op = op[0]
odeconfig.e_ops_data.append(op.data.data)
odeconfig.e_ops_ind.append(op.data.indices)
odeconfig.e_ops_ptr.append(op.data.indptr)
odeconfig.e_ops_isherm.append(op.isherm)
odeconfig.e_ops_data = array(odeconfig.e_ops_data)
odeconfig.e_ops_ind = array(odeconfig.e_ops_ind)
odeconfig.e_ops_ptr = array(odeconfig.e_ops_ptr)
odeconfig.e_ops_isherm = array(odeconfig.e_ops_isherm)
#----
#take care of collapse operators, if any
if any(c_ops):
odeconfig.c_num = len(c_ops)
for c_op in c_ops:
if isinstance(c_op, list):
c_op = c_op[0]
n_op = c_op.dag() * c_op
odeconfig.c_ops_data.append(c_op.data.data)
odeconfig.c_ops_ind.append(c_op.data.indices)
odeconfig.c_ops_ptr.append(c_op.data.indptr)
#norm ops
odeconfig.n_ops_data.append(n_op.data.data)
odeconfig.n_ops_ind.append(n_op.data.indices)
odeconfig.n_ops_ptr.append(n_op.data.indptr)
#to array
odeconfig.c_ops_data = array(odeconfig.c_ops_data)
odeconfig.c_ops_ind = array(odeconfig.c_ops_ind)
odeconfig.c_ops_ptr = array(odeconfig.c_ops_ptr)
odeconfig.n_ops_data = array(odeconfig.n_ops_data)
odeconfig.n_ops_ind = array(odeconfig.n_ops_ind)
odeconfig.n_ops_ptr = array(odeconfig.n_ops_ptr)
#----
#--------------------------------------------
# START CONSTANT H & C_OPS CODE
#--------------------------------------------
if odeconfig.tflag == 0:
if odeconfig.cflag:
odeconfig.c_const_inds = arange(len(c_ops))
for c_op in c_ops:
n_op = c_op.dag() * c_op
H -= 0.5j * n_op #combine Hamiltonian and collapse terms into one
#construct Hamiltonian data structures
if options.tidy:
H = H.tidyup(options.atol)
odeconfig.h_data = -1.0j * H.data.data
odeconfig.h_ind = H.data.indices
odeconfig.h_ptr = H.data.indptr
#----
#--------------------------------------------
# START STRING BASED TIME-DEPENDENCE
#--------------------------------------------
elif odeconfig.tflag in array([1, 10, 11]):
#take care of arguments for collapse operators, if any
if any(args):
for item in args.items():
odeconfig.c_args.append(item[1])
#constant Hamiltonian / string-type collapse operators
if odeconfig.tflag == 1:
H_inds = arange(1)
H_tdterms = 0
len_h = 1
C_inds = arange(odeconfig.c_num)
C_td_inds = array(c_stuff[2]) #find inds of time-dependent terms
C_const_inds = setdiff1d(C_inds,
C_td_inds) #find inds of constant terms
C_tdterms = [c_ops[k][1] for k in C_td_inds
] #extract time-dependent coefficients (strings)
odeconfig.c_const_inds = C_const_inds #store indicies of constant collapse terms
odeconfig.c_td_inds = C_td_inds #store indicies of time-dependent collapse terms
for k in odeconfig.c_const_inds:
H -= 0.5j * (c_ops[k].dag() * c_ops[k])
if options.tidy:
H = H.tidyup(options.atol)
odeconfig.h_data = [H.data.data]
odeconfig.h_ind = [H.data.indices]
odeconfig.h_ptr = [H.data.indptr]
for k in odeconfig.c_td_inds:
op = c_ops[k][0].dag() * c_ops[k][0]
odeconfig.h_data.append(-0.5j * op.data.data)
odeconfig.h_ind.append(op.data.indices)
odeconfig.h_ptr.append(op.data.indptr)
odeconfig.h_data = -1.0j * array(odeconfig.h_data)
odeconfig.h_ind = array(odeconfig.h_ind)
odeconfig.h_ptr = array(odeconfig.h_ptr)
#--------------------------------------------
# END OF IF STATEMENT
#--------------------------------------------
#string-type Hamiltonian & at least one string-type collapse operator
else:
H_inds = arange(len(H))
H_td_inds = array(h_stuff[2]) #find inds of time-dependent terms
H_const_inds = setdiff1d(H_inds,
H_td_inds) #find inds of constant terms
H_tdterms = [
H[k][1] for k in H_td_inds
] #extract time-dependent coefficients (strings or functions)
H = array([sum(H[k] for k in H_const_inds)] +
[H[k][0] for k in H_td_inds
]) #combine time-INDEPENDENT terms into one.
len_h = len(H)
H_inds = arange(len_h)
odeconfig.h_td_inds = arange(
1, len_h) #store indicies of time-dependent Hamiltonian terms
#if there are any collpase operators
if odeconfig.c_num > 0:
if odeconfig.tflag == 10: #constant collapse operators
odeconfig.c_const_inds = arange(odeconfig.c_num)
for k in odeconfig.c_const_inds:
H[0] -= 0.5j * (c_ops[k].dag() * c_ops[k])
C_inds = arange(odeconfig.c_num)
C_tdterms = array([])
#-----
else: #some time-dependent collapse terms
C_inds = arange(odeconfig.c_num)
C_td_inds = array(
c_stuff[2]) #find inds of time-dependent terms
C_const_inds = setdiff1d(
C_inds, C_td_inds) #find inds of constant terms
C_tdterms = [
c_ops[k][1] for k in C_td_inds
] #extract time-dependent coefficients (strings)
odeconfig.c_const_inds = C_const_inds #store indicies of constant collapse terms
odeconfig.c_td_inds = C_td_inds #store indicies of time-dependent collapse terms
for k in odeconfig.c_const_inds:
H[0] -= 0.5j * (c_ops[k].dag() * c_ops[k])
else: #set empty objects if no collapse operators
C_const_inds = arange(odeconfig.c_num)
odeconfig.c_const_inds = arange(odeconfig.c_num)
odeconfig.c_td_inds = array([])
C_tdterms = array([])
C_inds = array([])
#tidyup
if options.tidy:
H = array([H[k].tidyup(options.atol) for k in range(len_h)])
#construct data sets
odeconfig.h_data = [H[k].data.data for k in range(len_h)]
odeconfig.h_ind = [H[k].data.indices for k in range(len_h)]
odeconfig.h_ptr = [H[k].data.indptr for k in range(len_h)]
for k in odeconfig.c_td_inds:
odeconfig.h_data.append(-0.5j * odeconfig.n_ops_data[k])
odeconfig.h_ind.append(odeconfig.n_ops_ind[k])
odeconfig.h_ptr.append(odeconfig.n_ops_ptr[k])
odeconfig.h_data = -1.0j * array(odeconfig.h_data)
odeconfig.h_ind = array(odeconfig.h_ind)
odeconfig.h_ptr = array(odeconfig.h_ptr)
#--------------------------------------------
# END OF ELSE STATEMENT
#--------------------------------------------
#set execuatble code for collapse expectation values and spmv
col_spmv_code = "state=odeconfig.colspmv(j,ODE.t,odeconfig.c_ops_data[j],odeconfig.c_ops_ind[j],odeconfig.c_ops_ptr[j],ODE.y"
col_expect_code = "for i in odeconfig.c_td_inds: n_dp.append(odeconfig.colexpect(i,ODE.t,odeconfig.n_ops_data[i],odeconfig.n_ops_ind[i],odeconfig.n_ops_ptr[i],ODE.y"
for kk in range(len(odeconfig.c_args)):
col_spmv_code += ",odeconfig.c_args[" + str(kk) + "]"
col_expect_code += ",odeconfig.c_args[" + str(kk) + "]"
col_spmv_code += ")"
col_expect_code += "))"
odeconfig.col_spmv_code = compile(col_spmv_code, '<string>', 'exec')
odeconfig.col_expect_code = compile(col_expect_code, '<string>',
'exec')
#----
#setup ode args string
odeconfig.string = ""
data_range = range(len(odeconfig.h_data))
for k in data_range:
odeconfig.string += "odeconfig.h_data[" + str(
k) + "],odeconfig.h_ind[" + str(
k) + "],odeconfig.h_ptr[" + str(k) + "]"
if k != data_range[-1]:
odeconfig.string += ","
#attach args to ode args string
if len(odeconfig.c_args) > 0:
for kk in range(len(odeconfig.c_args)):
odeconfig.string += "," + "odeconfig.c_args[" + str(kk) + "]"
#----
name = "rhs" + str(odeconfig.cgen_num)
odeconfig.tdname = name
cgen = Codegen(H_inds,
H_tdterms,
odeconfig.h_td_inds,
args,
C_inds,
C_tdterms,
odeconfig.c_td_inds,
type='mc')
cgen.generate(name + ".pyx")
#----
#--------------------------------------------
# END OF STRING TYPE TIME DEPENDENT CODE
#--------------------------------------------
#--------------------------------------------
# START PYTHON FUNCTION BASED TIME-DEPENDENCE
#--------------------------------------------
elif odeconfig.tflag in array([2, 20, 22]):
#take care of Hamiltonian
if odeconfig.tflag == 2: # constant Hamiltonian, at least one function based collapse operators
H_inds = array([0])
H_tdterms = 0
len_h = 1
else: # function based Hamiltonian
H_inds = arange(len(H))
H_td_inds = array(h_stuff[1]) #find inds of time-dependent terms
H_const_inds = setdiff1d(H_inds,
H_td_inds) #find inds of constant terms
odeconfig.h_funcs = array([H[k][1] for k in H_td_inds])
odeconfig.h_func_args = args
Htd = array([H[k][0] for k in H_td_inds])
odeconfig.h_td_inds = arange(len(Htd))
H = sum(H[k] for k in H_const_inds)
#take care of collapse operators
C_inds = arange(odeconfig.c_num)
C_td_inds = array(c_stuff[1]) #find inds of time-dependent terms
C_const_inds = setdiff1d(C_inds,
C_td_inds) #find inds of constant terms
odeconfig.c_const_inds = C_const_inds #store indicies of constant collapse terms
odeconfig.c_td_inds = C_td_inds #store indicies of time-dependent collapse terms
odeconfig.c_funcs = zeros(odeconfig.c_num, dtype=FunctionType)
for k in odeconfig.c_td_inds:
odeconfig.c_funcs[k] = c_ops[k][1]
odeconfig.c_func_args = args
#combine constant collapse terms with constant H and construct data
for k in odeconfig.c_const_inds:
H -= 0.5j * (c_ops[k].dag() * c_ops[k])
if options.tidy:
H = H.tidyup(options.atol)
Htd = array(
[Htd[j].tidyup(options.atol) for j in odeconfig.h_td_inds])
#setup cosntant H terms data
odeconfig.h_data = -1.0j * H.data.data
odeconfig.h_ind = H.data.indices
odeconfig.h_ptr = H.data.indptr
#setup td H terms data
odeconfig.h_td_data = array(
[-1.0j * Htd[k].data.data for k in odeconfig.h_td_inds])
odeconfig.h_td_ind = array(
[Htd[k].data.indices for k in odeconfig.h_td_inds])
odeconfig.h_td_ptr = array(
[Htd[k].data.indptr for k in odeconfig.h_td_inds])
#--------------------------------------------
# END PYTHON FUNCTION BASED TIME-DEPENDENCE
#--------------------------------------------
#--------------------------------------------
# START PYTHON FUNCTION BASED HAMILTONIAN
#--------------------------------------------
elif odeconfig.tflag == 3:
#take care of Hamiltonian
odeconfig.h_funcs = H
odeconfig.h_func_args = args
#take care of collapse operators
odeconfig.c_const_inds = arange(odeconfig.c_num)
odeconfig.c_td_inds = array([]) #find inds of time-dependent terms
if len(odeconfig.c_const_inds) > 0:
H = 0
for k in odeconfig.c_const_inds:
H -= 0.5j * (c_ops[k].dag() * c_ops[k])
if options.tidy:
H = H.tidyup(options.atol)
odeconfig.h_data = -1.0j * H.data.data
odeconfig.h_ind = H.data.indices
odeconfig.h_ptr = H.data.indptr
def checkDataset(train_set):
batchSize = train_set.batchSize
numSamples = train_set.numSamples
nBatches = numSamples / batchSize
assert nBatches * batchSize == numSamples, "number of samples {} not divisible by batchSize {}".format(
numSamples, batchSize)
nClasses = len(
scipy.setdiff1d(numpy.unique(train_set.y), numpy.array([-1.])))
print("nClasses {}".format(nClasses))
nTripletsPerBatch = train_set.nTripletsPerBatch
si = train_set.sampleInfo
tmplStartIdx = si['tmplBatchDataStartIdx']
sampIdx = si[
'sampIdx'] # number of the sample in original per-class sequence
tmplRots = si['tmplRots']
trainRots = si['trainRots']
#nTrainPerSeq = si['nTrainPerSeq']
zRotInv = si['zRotInv']
print("numSamples {}".format(numSamples))
print("batchSize {}".format(batchSize))
print("nBatches {}".format(nBatches))
#print("train_set.y\n {}".format(train_set.y.reshape((batchSize,nBatches))))
print("train_set.y shape {}".format(train_set.y.shape))
print("numValidSamples {}".format(numpy.sum(train_set.y >= 0)))
#print("tmplStartIdx\n {}".format(tmplStartIdx))
print("sampIdx\n {}".format(sampIdx))
for nBatch in xrange(nBatches):
for i in xrange(nTripletsPerBatch):
tIdx = nBatch * nTripletsPerBatch + i
idx = train_set.tripletIdx[tIdx, :]
# check if idx0 is in the training sample area
if idx[0] >= tmplStartIdx[nBatch, 0]:
print("ERROR: first index must be train sample but {} >= {}".
format(numpy.max(idx[0]), tmplStartIdx[nBatch, 0]))
# check if idx1,idx2 are in the template sample area
if idx[1] < tmplStartIdx[nBatch, 0]:
print(
"ERROR: second index must be template sample but {} < {}".
format(numpy.max(idx[1]), tmplStartIdx[nBatch, 0]))
if idx[2] < tmplStartIdx[nBatch, 0]:
print("ERROR: third index must be template sample but {} < {}".
format(numpy.max(idx[2]), tmplStartIdx[nBatch, 0]))
idx = numpy.copy(idx)
idx = idx + nBatch * batchSize #** it is now a within batch idx. so to index into the whole dataset add offset
# check if idx0 and idx1 are same class
l0 = train_set.y[idx[0]]
l1 = train_set.y[idx[1]]
l2 = train_set.y[idx[2]]
if l0 != l1:
print("ERROR: l0 != l1")
else:
# check if idx2 is also the same
if (
l0 == l2
): # and if yes, if the rotation of the second is bigger than the first
rot0 = trainRots[l0, sampIdx[idx[0]]]
rot1 = tmplRots[l0, sampIdx[idx[1]]]
rot2 = tmplRots[l0, sampIdx[idx[2]]]
sim1 = numpy.dot(rot0, rot1)
sim2 = numpy.dot(rot0, rot2)
if zRotInv[l0] == 2:
sim1 = numpy.maximum(
sim1,
numpy.dot(rot0 * numpy.array([-1, -1, 1]), rot1))
sim2 = numpy.maximum(
sim2,
numpy.dot(rot0 * numpy.array([-1, -1, 1]), rot2))
if sim1 < sim2:
print("ERROR: s2 is more similar to s0 than s1 !!")
print(" idx[0] = {}, [1] = {}, [2] = {}".format(
idx[0], idx[1], idx[2]))
print(" sampIdx[0] = {}, [1] = {}, [2] = {}".format(
sampIdx[idx[0]], sampIdx[idx[1]], sampIdx[idx[2]]))
print(" rot0[0] = {}, 1 = {}, 2 = {}".format(
rot0, rot1, rot2))
def get_intron_list(genes, options):
introns = sp.zeros((genes.shape[0], 2), dtype='object')
introns[:] = None
### collect all possible combinations of contigs and strands
(regions, options) = init_regions(options.bam_fnames,
options.confidence,
options,
sparse_bam=options.sparse_bam)
### form chunks for quick sorting
strands = ['+', '-']
### ignore contigs not present in bam files
keepidx = sp.where(
sp.in1d(sp.array([options.chrm_lookup[x.chr] for x in genes]),
sp.array([x.chr_num for x in regions])))[0]
genes = genes[keepidx]
c = 0
num_introns_filtered = 0
t0 = time.time()
contigs = sp.array([x.chr for x in genes], dtype='str')
gene_strands = sp.array([x.strand for x in genes])
for contig in sp.unique(contigs):
bam_cache = dict()
for si, s in enumerate(strands):
cidx = sp.where((contigs == contig) & (gene_strands == s))[0]
for i in cidx:
if options.verbose and (c + 1) % 100 == 0:
t1 = time.time()
print(
'%i (%i) genes done (%i introns taken) ... took %i secs'
%
(c + 1, genes.shape[0], num_introns_filtered, t1 - t0),
file=sys.stdout)
t0 = t1
gg = sp.array([copy.copy(genes[i])], dtype='object')
assert (gg[0].strand == s)
gg[0].start = max(gg[0].start - 5000, 1)
gg[0].stop = gg[0].stop + 5000
assert (gg[0].chr == contig)
if options.sparse_bam:
if isinstance(options.bam_fnames, str):
[intron_list_tmp] = add_reads_from_sparse_bam(
gg[0],
options.bam_fnames,
contig,
options.confidence,
types=['intron_list'],
filter=options.read_filter,
cache=bam_cache,
unstranded=options.introns_unstranded)
else:
intron_list_tmp = None
for fname in options.bam_fnames:
[tmp_] = add_reads_from_sparse_bam(
gg[0],
fname,
contig,
options.confidence,
types=['intron_list'],
filter=options.read_filter,
cache=bam_cache,
unstranded=options.introns_unstranded)
if intron_list_tmp is None:
intron_list_tmp = tmp_
else:
intron_list_tmp = sp.r_[intron_list_tmp, tmp_]
### some merging in case of multiple bam files
if len(options.bam_fnames) > 1:
intron_list_tmp = sort_rows(intron_list_tmp)
rm_idx = []
for i in range(1, intron_list_tmp.shape[0]):
if sp.all(intron_list_tmp[i, :2] ==
intron_list_tmp[i - 1, :2]):
intron_list_tmp[i,
2] += intron_list_tmp[i -
1, 2]
rm_idx.append(i - 1)
if len(rm_idx) > 0:
k_idx = sp.setdiff1d(
sp.arange(intron_list_tmp.shape[0]),
rm_idx)
intron_list_tmp = intron_list_tmp[k_idx, :]
else:
[intron_list_tmp] = add_reads_from_bam(
gg,
options.bam_fnames, ['intron_list'],
options.read_filter,
options.var_aware,
options.primary_only,
options.ignore_mismatches,
unstranded=options.introns_unstranded,
mm_tag=options.mm_tag)
num_introns_filtered += intron_list_tmp.shape[0]
introns[i, si] = sort_rows(intron_list_tmp)
c += 1
for j in range(introns.shape[0]):
if introns[j, 0] is None:
introns[j, 0] = sp.zeros((0, 3), dtype='int')
if introns[j, 1] is None:
introns[j, 1] = sp.zeros((0, 3), dtype='int')
return introns
def verify_intron_retention(event, gene, counts_segments, counts_edges, counts_seg_pos, CFG):
# [verified, info] = verify_intron_retention(event, fn_bam, CFG)
verified = [0, 0]
# (0) valid, (1) intron_cov, (2) exon1_cov, (3), exon2_cov
# (4) intron_conf, (5) intron_cov_region
info = [1, 0, 0, 0, 0, 0]
### check validity of exon coordinates (>=0)
if sp.any(event.exons1 < 0) or sp.any(event.exons2 < 0):
info[0] = 0
return (verified, info)
### check validity of exon coordinates (start < stop && non-overlapping)
elif sp.any(event.exons1[:, 1] - event.exons1[:, 0] < 1) or sp.any((event.exons2[1] - event.exons2[0]) < 1):
info[0] = 0
return (verified, info)
sg = gene.splicegraph
segs = gene.segmentgraph
### find exons corresponding to event
idx_exon1 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
idx_exon2 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
### find segments corresponding to exons
seg_exon1 = sp.sort(sp.where(segs.seg_match[idx_exon1, :])[1])
seg_exon2 = sp.sort(sp.where(segs.seg_match[idx_exon2, :])[1])
seg_all = sp.arange(seg_exon1[0], seg_exon2[-1])
seg_intron = sp.setdiff1d(seg_all, seg_exon1)
seg_intron = sp.setdiff1d(seg_intron, seg_exon2)
assert(seg_intron.shape[0] > 0)
seg_lens = segs.segments[1, :] - segs.segments[0, :]
### compute exon coverages as mean of position wise coverage
# exon1_cov
info[2] = sp.sum(counts_segments[seg_exon1] * seg_lens[seg_exon1]) / sp.sum(seg_lens[seg_exon1])
# exon2_cov
info[3] = sp.sum(counts_segments[seg_exon2] * seg_lens[seg_exon2]) / sp.sum(seg_lens[seg_exon2])
# intron_cov
info[1] = sp.sum(counts_segments[seg_intron] * seg_lens[seg_intron]) / sp.sum(seg_lens[seg_intron])
# intron_cov_region
info[5] = sp.sum(counts_seg_pos[seg_intron]) / sp.sum(seg_lens[seg_intron])
### check if counts match verification criteria
if info[1] > CFG['intron_retention']['min_retention_cov'] and \
info[5] > CFG['intron_retention']['min_retention_region'] and \
info[1] >= CFG['intron_retention']['min_retention_rel_cov'] * (info[2] + info[3]) / 2:
verified[0] = 1
### check intron confirmation as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([seg_exon1[-1], seg_exon2[0]], segs.seg_edges.shape))[0]
info[4] = counts_edges[idx, 1]
if info[4] >= CFG['intron_retention']['min_non_retention_count']:
verified[1] = 1
return (verified, info)
def quantify_alt_prime(event, gene, counts_segments, counts_edges, CFG):
cov = sp.zeros((2, ), dtype='float')
sg = gene.splicegraph
segs = gene.segmentgraph
if CFG['is_matlab']:
seg_lens = segs[0, 0][1, :] - segs[0, 0][0, :]
seg_shape = segs[0, 2].shape[0]
idx_exon_alt1 = sp.where((sg[0, 0][0, :] == event.exon_alt1[0]) & (sg[0, 0][1, :] == event.exon_alt1[1]))
idx_exon_alt2 = sp.where((sg[0, 0][0, :] == event.exon_alt2[0]) & (sg[0, 0][1, :] == event.exon_alt2[1]))
idx_exon_const = sp.where((sg[0, 0][0, :] == event.exon_const[0]) & (sg[0, 0][1, :] == event.exon_const[1]))
if idx_exon_alt1.shape[0] == 0:
segs_exon_alt1 = sp.where((segs[0, 0][0, :] >= event.exon_alt1[0]) & (segs[0, 0][1, :] >= event.exon_alt1[1]))
else:
segs_exon_alt1 = sp.where(segs[0, 1][idx_exon_alt1, :])[1]
if idx_exon_alt2.shape[0] == 0:
segs_exon_alt2 = sp.where((segs[0, 0][0, :] >= event.exon_alt2[0]) & (segs[0, 0][1, :] >= event.exon_alt2[1]))
else:
segs_exon_alt2 = sp.where(segs[0, 1][idx_exon_alt2, :])[1]
if idx_exon_const.shape[0] == 0:
segs_exon_const = sp.where((segs[0, 0][0, :] >= event.exon_const[0]) & (segs[0, 0][1, :] >= event.exon_const[1]))
else:
segs_exon_const = sp.where(segs[0, 1][idx_exon_const, :])[1]
assert(segs_exon_alt1.shape[0] > 0)
assert(segs_exon_alt2.shape[0] > 0)
assert(segs_exon_const.shape[0] > 0)
cov[1] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
if max(segs_exon_alt1[-1], segs_exon_alt2[-1]) < segs_exon_const[0]:
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_alt1[0], segs_exon_const[-1]], seg_shape))[0] + 1
assert(idx.shape[0] > 0)
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_alt2[0], segs_exon_const[-1]], seg_shape))[0] + 1
assert(idx.shape[0] > 0)
cov[1] += counts_edges[idx, 1]
elif min(segs_exon_alt1[0], segs_exon_alt2[0]) > segs_exon_const[-1]:
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_const[0], segs_exon_alt1[-1]], seg_shape))[0] + 1
assert(idx.shape[0] > 0)
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_const[0], segs_exon_alt2[-1]], seg_shape))[0] + 1
assert(idx.shape[0] > 0)
cov[1] += counts_edges[idx, 1]
else:
seg_lens = segs.segments[1, :] - segs.segments[0, :]
seg_shape = segs.seg_edges.shape[0]
### find exons corresponding to event
idx_exon11 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
if idx_exon11.shape[0] == 0:
segs_exon11 = sp.where((segs.segments[0, :] >= event.exons1[0, 0]) & (segs.segments[1, :] <= event.exons1[0, 1]))[0]
else:
segs_exon11 = sp.where(segs.seg_match[idx_exon11, :])[1]
idx_exon12 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
if idx_exon12.shape[0] == 0:
segs_exon12 = sp.where((segs.segments[0, :] >= event.exons1[1, 0]) & (segs.segments[1, :] <= event.exons1[1, 1]))[0]
else:
segs_exon12 = sp.where(segs.seg_match[idx_exon12, :])[1]
idx_exon21 = sp.where((sg.vertices[0, :] == event.exons2[0, 0]) & (sg.vertices[1, :] == event.exons2[0, 1]))[0]
if idx_exon21.shape[0] == 0:
segs_exon21 = sp.where((segs.segments[0, :] >= event.exons2[0, 0]) & (segs.segments[1, :] <= event.exons2[0, 1]))[0]
else:
segs_exon21 = sp.where(segs.seg_match[idx_exon21, :])[1]
idx_exon22 = sp.where((sg.vertices[0, :] == event.exons2[1, 0]) & (sg.vertices[1, :] == event.exons2[1, 1]))[0]
if idx_exon22.shape[0] == 0:
segs_exon22 = sp.where((segs.segments[0, :] >= event.exons2[1, 0]) & (segs.segments[1, :] <= event.exons2[1, 1]))[0]
else:
segs_exon22 = sp.where(segs.seg_match[idx_exon22, :] > 0)[1]
assert(segs_exon11.shape[0] > 0)
assert(segs_exon12.shape[0] > 0)
assert(segs_exon21.shape[0] > 0)
assert(segs_exon22.shape[0] > 0)
if sp.all(segs_exon11 == segs_exon21):
seg_diff = sp.setdiff1d(segs_exon12, segs_exon22)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon22, segs_exon12)
elif sp.all(segs_exon12 == segs_exon22):
seg_diff = sp.setdiff1d(segs_exon11, segs_exon21)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon21, segs_exon11)
else:
print >> sys.stderr, "ERROR: both exons differ in alt prime event in verify_alt_prime"
sys.exit(1)
# exon_diff_cov
if seg_diff in segs_exon11 or seg_diff in segs_exon12:
cov[0] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
elif seg_diff in segs_exon21 or seg_diff in segs_exon22:
cov[1] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
else:
raise Exception('differential segment not part of any other segment')
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon11[-1], segs_exon12[0]], seg_shape))[0]
assert(idx.shape[0] > 0)
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon21[-1], segs_exon22[0]], seg_shape))[0]
assert(idx.shape[0] > 0)
cov[1] += counts_edges[idx, 1]
return cov
##
# yticks (on the left) are just the level
yticks = range(int(min(level)), int(max(level) + 1))
ax.set_yticks(yticks, ["%d" % (-k) for k in yticks])
ax.set_ylabel('Level', color='k')
ax.set_xlabel('Iteration')
ax.figure.canvas.draw()
##
# Do a second plot of the residual norms, if we have any residuals available
r = data[:, 4]
r[r == -1] = 0
if r.max() > 0:
dr = r[:-1] - r[1:]
r_indices = setdiff1d(
(dr != 0).nonzero()[0],
(r == 0).nonzero()[0] - 1) # x-locations to print
r_to_print = r[r_indices + 1]
r_level = level[r_indices] # corresponding level numbers
ax2 = ax.twinx()
ax2.set_xlim(0, max((2 * nlevels + 2) * niter, len(level)))
ax2.semilogy(r_indices, r_to_print, '-bo')
ax2.set_ylabel('$\| r_k \|$', color='b')
##
# Plot 4 y-ticks on the right for ||r||
tols = data[:, 5]
mi = min(tols[tols > 0].min() / 500., r_to_print.min())
ma = r_to_print.max()
yticks = [
ma, 10**((log10(mi) + log10(ma)) * 1. / 3.),
def quantify_alt_prime(event, gene, counts_segments, counts_edges, CFG):
cov = sp.zeros((2,), dtype="float")
sg = gene.splicegraph
segs = gene.segmentgraph
if CFG["is_matlab"]:
seg_lens = segs[0, 0][1, :] - segs[0, 0][0, :]
seg_shape = segs[0, 2].shape[0]
idx_exon_alt1 = sp.where((sg[0, 0][0, :] == event.exon_alt1[0]) & (sg[0, 0][1, :] == event.exon_alt1[1]))
idx_exon_alt2 = sp.where((sg[0, 0][0, :] == event.exon_alt2[0]) & (sg[0, 0][1, :] == event.exon_alt2[1]))
idx_exon_const = sp.where((sg[0, 0][0, :] == event.exon_const[0]) & (sg[0, 0][1, :] == event.exon_const[1]))
if idx_exon_alt1.shape[0] == 0:
segs_exon_alt1 = sp.where(
(segs[0, 0][0, :] >= event.exon_alt1[0]) & (segs[0, 0][1, :] >= event.exon_alt1[1])
)
else:
segs_exon_alt1 = sp.where(segs[0, 1][idx_exon_alt1, :])[1]
if idx_exon_alt2.shape[0] == 0:
segs_exon_alt2 = sp.where(
(segs[0, 0][0, :] >= event.exon_alt2[0]) & (segs[0, 0][1, :] >= event.exon_alt2[1])
)
else:
segs_exon_alt2 = sp.where(segs[0, 1][idx_exon_alt2, :])[1]
if idx_exon_const.shape[0] == 0:
segs_exon_const = sp.where(
(segs[0, 0][0, :] >= event.exon_const[0]) & (segs[0, 0][1, :] >= event.exon_const[1])
)
else:
segs_exon_const = sp.where(segs[0, 1][idx_exon_const, :])[1]
assert segs_exon_alt1.shape[0] > 0
assert segs_exon_alt2.shape[0] > 0
assert segs_exon_const.shape[0] > 0
cov[1] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
if max(segs_exon_alt1[-1], segs_exon_alt2[-1]) < segs_exon_const[0]:
# intron1_conf
idx = (
sp.where(
counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_alt1[0], segs_exon_const[-1]], seg_shape)
)[0]
+ 1
)
assert idx.shape[0] > 0
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = (
sp.where(
counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_alt2[0], segs_exon_const[-1]], seg_shape)
)[0]
+ 1
)
assert idx.shape[0] > 0
cov[1] += counts_edges[idx, 1]
elif min(segs_exon_alt1[0], segs_exon_alt2[0]) > segs_exon_const[-1]:
# intron1_conf
idx = (
sp.where(
counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_const[0], segs_exon_alt1[-1]], seg_shape)
)[0]
+ 1
)
assert idx.shape[0] > 0
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = (
sp.where(
counts_edges[:, 0] == sp.ravel_multi_index([segs_exon_const[0], segs_exon_alt2[-1]], seg_shape)
)[0]
+ 1
)
assert idx.shape[0] > 0
cov[1] += counts_edges[idx, 1]
else:
seg_lens = segs.segments[1, :] - segs.segments[0, :]
seg_shape = segs.seg_edges.shape[0]
### find exons corresponding to event
idx_exon11 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
if idx_exon11.shape[0] == 0:
segs_exon11 = sp.where(
(segs.segments[0, :] >= event.exons1[0, 0]) & (segs.segments[1, :] <= event.exons1[0, 1])
)[0]
else:
segs_exon11 = sp.where(segs.seg_match[idx_exon11, :])[1]
idx_exon12 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
if idx_exon12.shape[0] == 0:
segs_exon12 = sp.where(
(segs.segments[0, :] >= event.exons1[1, 0]) & (segs.segments[1, :] <= event.exons1[1, 1])
)[0]
else:
segs_exon12 = sp.where(segs.seg_match[idx_exon12, :])[1]
idx_exon21 = sp.where((sg.vertices[0, :] == event.exons2[0, 0]) & (sg.vertices[1, :] == event.exons2[0, 1]))[0]
if idx_exon21.shape[0] == 0:
segs_exon21 = sp.where(
(segs.segments[0, :] >= event.exons2[0, 0]) & (segs.segments[1, :] <= event.exons2[0, 1])
)[0]
else:
segs_exon21 = sp.where(segs.seg_match[idx_exon21, :])[1]
idx_exon22 = sp.where((sg.vertices[0, :] == event.exons2[1, 0]) & (sg.vertices[1, :] == event.exons2[1, 1]))[0]
if idx_exon22.shape[0] == 0:
segs_exon22 = sp.where(
(segs.segments[0, :] >= event.exons2[1, 0]) & (segs.segments[1, :] <= event.exons2[1, 1])
)[0]
else:
segs_exon22 = sp.where(segs.seg_match[idx_exon22, :] > 0)[1]
assert segs_exon11.shape[0] > 0
assert segs_exon12.shape[0] > 0
assert segs_exon21.shape[0] > 0
assert segs_exon22.shape[0] > 0
if sp.all(segs_exon11 == segs_exon21):
seg_diff = sp.setdiff1d(segs_exon12, segs_exon22)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon22, segs_exon12)
elif sp.all(segs_exon12 == segs_exon22):
seg_diff = sp.setdiff1d(segs_exon11, segs_exon21)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon21, segs_exon11)
else:
print >> sys.stderr, "ERROR: both exons differ in alt prime event in verify_alt_prime"
sys.exit(1)
# exon_diff_cov
if seg_diff in segs_exon11 or seg_diff in segs_exon12:
cov[0] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
elif seg_diff in segs_exon21 or seg_diff in segs_exon22:
cov[1] += sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
else:
raise Exception("differential segment not part of any other segment")
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon11[-1], segs_exon12[0]], seg_shape))[0]
assert idx.shape[0] > 0
cov[0] += counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon21[-1], segs_exon22[0]], seg_shape))[0]
assert idx.shape[0] > 0
cov[1] += counts_edges[idx, 1]
return cov
def nonna_lsq_signal_ranking(target, aux, idx, names=(), order=2): """ This function returns the coefficients of the least square prediction of the target signal, using the auxiliary signals and their powers, as specified by the order argument. It also returns a ranking of the signals in terms of their contribution to the reduction of the residual error. Input arguments: target = target signal aux = matrix of auxiliary signals idx = boolean vector to select a subset of the data for the LSQ fit order = order of the polynomial of aux signals to be used in the fit, default is 2 names = list of the auxiliary signal names Output: p = list of coefficients X = matrix of the signals used in the reconstruction cnames = list of the corresponding signals id = list of signal indexes, in order of reducing relevance de = list of the residual error reduction provided by including each signal, in the same order as the list above Note that the mean will be removed from the auxiliary signals. """ if len(names) == 0: # since the user didn't provide signal names, let's build some names = map(lambda x: 'S'+str(x), scipy.arange(naux)+1) if len(idx) == 0: # no index means use all idx = numpy.array(target, dtype=bool) idx[:] = True # first estimation with all channels print 'Calculating LSQ...' p0, X, cnames = nonna_lsq(target, aux, idx=idx, names=names, order=order) # convert B to matrix for convenience and remove the mean (to avoid counting in the # constant term in the ranking) B = scipy.mat(target - scipy.mean(target[idx])) # define the function used to compute the residual error def error(p): return scipy.mean(scipy.square(B[:,idx].T - X[idx,:]*p)) # compute the initial error when all channels are used e0 = error(p0) print '0) initial error %g' % e0 # init variables to store residuals and indexes at each iteration e = scipy.zeros((scipy.shape(X)[1],)) id = scipy.zeros((scipy.shape(X)[1],), dtype=int) # init all indexes to dummy values at the beginning (no channel removed yet) id[:] = -1 # Repeat the estimate of the best fit with all possible reduced set of signals. We'll # remove one at each step print 'Ranking... \n' for i in range(scipy.shape(X)[1]): # this is going to be the list of the new residual errors when we removed one # additional channel newerrors = scipy.zeros((scipy.shape(X)[1],1)) # loop over all channels and remove one by one for j in range(scipy.shape(X)[1]): # check if this channel was already removed if not any(id == j): # remove all the channels that are already in the list, plus the one under # consideration ind = scipy.setdiff1d(range(scipy.shape(X)[1]), id) ind = scipy.setdiff1d(ind, [j]) #originally ind = scipy.setdiff1d(ind, j) # start with an empty set of coefficients pp = scipy.zeros((scipy.shape(X)[1],1)) # compute the best estimate of coefficients if len(ind) != 0: pp[ind] = scipy.linalg.inv(X[idx,:][:,ind].T * X[idx,:][:,ind]) * X[idx,:][:,ind].T * B[:,idx].T # and finally compute the new residual errors newerrors[j] = error(pp) else: # we already used this channel, let's make the error infinite so it won't be # picked later on newerrors[j] = scipy.inf # Now we have to choose the channel that (when removed) still gives the minimum # residual error e[i] = min(newerrors) id[i] = scipy.argmin(newerrors) # Print out some information print '%d) new error %g (removed channel %s)' % (i+1, e[i], cnames[id[i]]) # Final steps, build incremental residual error worsening de = scipy.diff(scipy.concatenate((scipy.array([e0]), e[:]))) # sort them out ii = scipy.argsort(de) de = de[ii[::-1]] id = id[ii[::-1]] # return results return p0, X, cnames, id, de
def verify_alt_prime(event, gene, counts_segments, counts_edges, CFG):
# [verified, info] = verify_exon_skip(event, fn_bam, cfg)
# (0) valid, (1) exon_diff_cov, (2) exon_const_cov
# (3) intron1_conf, (4) intron2_conf
info = [1, 0, 0, 0, 0]
verified = [0, 0]
### check validity of exon coordinates (>=0)
if sp.any(event.exons1 < 0) or sp.any(event.exons2 < 0):
info[0] = 0
return (verified, info)
### check validity of intron coordinates (only one side is differing)
if (event.exons1[0, 1] != event.exons2[0, 1]) and (event.exons1[1, 0] != event.exons2[1, 0]):
info[0] = 0
return (verified, info)
sg = gene.splicegraph
segs = gene.segmentgraph
### find exons corresponding to event
idx_exon11 = sp.where((sg.vertices[0, :] == event.exons1[0, 0]) & (sg.vertices[1, :] == event.exons1[0, 1]))[0]
if idx_exon11.shape[0] == 0:
segs_exon11 = sp.where((segs.segments[0, :] >= event.exons1[0, 0]) & (segs.segments[1, :] <= event.exons1[0, 1]))[0]
else:
segs_exon11 = sp.where(segs.seg_match[idx_exon11, :])[1]
idx_exon12 = sp.where((sg.vertices[0, :] == event.exons1[1, 0]) & (sg.vertices[1, :] == event.exons1[1, 1]))[0]
if idx_exon12.shape[0] == 0:
segs_exon12 = sp.where((segs.segments[0, :] >= event.exons1[1, 0]) & (segs.segments[1, :] <= event.exons1[1, 1]))[0]
else:
segs_exon12 = sp.where(segs.seg_match[idx_exon12, :])[1]
idx_exon21 = sp.where((sg.vertices[0, :] == event.exons2[0, 0]) & (sg.vertices[1, :] == event.exons2[0, 1]))[0]
if idx_exon21.shape[0] == 0:
segs_exon21 = sp.where((segs.segments[0, :] >= event.exons2[0, 0]) & (segs.segments[1, :] <= event.exons2[0, 1]))[0]
else:
segs_exon21 = sp.where(segs.seg_match[idx_exon21, :])[1]
idx_exon22 = sp.where((sg.vertices[0, :] == event.exons2[1, 0]) & (sg.vertices[1, :] == event.exons2[1, 1]))[0]
if idx_exon22.shape[0] == 0:
segs_exon22 = sp.where((segs.segments[0, :] >= event.exons2[1, 0]) & (segs.segments[1, :] <= event.exons2[1, 1]))[0]
else:
segs_exon22 = sp.where(segs.seg_match[idx_exon22, :] > 0)[1]
assert(segs_exon11.shape[0] > 0)
assert(segs_exon12.shape[0] > 0)
assert(segs_exon21.shape[0] > 0)
assert(segs_exon22.shape[0] > 0)
if sp.all(segs_exon11 == segs_exon21):
seg_exon_const = segs_exon11
seg_diff = sp.setdiff1d(segs_exon12, segs_exon22)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon22, segs_exon12)
seg_const = sp.intersect1d(segs_exon12, segs_exon22)
elif sp.all(segs_exon12 == segs_exon22):
seg_exon_const = segs_exon12
seg_diff = sp.setdiff1d(segs_exon11, segs_exon21)
if seg_diff.shape[0] == 0:
seg_diff = sp.setdiff1d(segs_exon21, segs_exon11)
seg_const = sp.intersect1d(segs_exon21, segs_exon11)
else:
print >> sys.stderr, "ERROR: both exons differ in alt prime event in verify_alt_prime"
sys.exit(1)
seg_const = sp.r_[seg_exon_const, seg_const]
seg_lens = segs.segments[1, :] - segs.segments[0, :]
# exon_diff_cov
info[1] = sp.sum(counts_segments[seg_diff] * seg_lens[seg_diff]) / sp.sum(seg_lens[seg_diff])
# exon_const_cov
info[2] = sp.sum(counts_segments[seg_const] * seg_lens[seg_const]) / sp.sum(seg_lens[seg_const])
if info[1] >= CFG['alt_prime']['min_diff_rel_cov'] * info[2]:
verified[0] = 1
### check intron confirmations as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron1_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon11[-1], segs_exon12[0]], segs.seg_edges.shape))[0]
assert(idx.shape[0] > 0)
info[3] = counts_edges[idx, 1]
# intron2_conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index([segs_exon21[-1], segs_exon22[0]], segs.seg_edges.shape))[0]
assert(idx.shape[0] > 0)
info[4] = counts_edges[idx, 1]
if min(info[3], info[4]) >= CFG['alt_prime']['min_intron_count']:
verified[1] = 1
return (verified, info)
def generate2D(nx, ny, dx, dy, pLx, pLy, pLz, N):
# to get a nonperiodic ensemble, define extra "ghost" gridpoints
n1 = np.round(1.2 * nx)
n2 = np.round(1.2 * ny)
n1 = n1 + np.mod(n1, 2)
n2 = n2 + np.mod(n2, 2)
# define constants
pi2 = 2.0 * pi
deltak = pi2**2. / ((n1 * n2) * dx * dy)
kappa = pi2 / ((n1) * dx)
kappa2 = kappa**2.
lmbd = pi2 / ((n2) * dy)
lmbd2 = lmbd**2.
nreal = N
# rescale decorrelation lengths such that we will get the
# following form for the covariance as a function of
# distance delta:
# C(delta)=exp(-3*(delta/Lx)^2)
rx = pLx / np.sqrt(3.0)
ry = pLy / np.sqrt(3.0)
#------------------------------------------------------------------
# solve systems for r1,r2,c
#------------------------------------------------------------------
# define wavenumber indeces p,l, excluding p==l==0
p = np.linspace((-n2 / 2. + 1.), (n2 / 2.),
(n2 / 2.) - (-n2 / 2. + 1.) + 1)
l = np.linspace((-n1 / 2 + 1), (n1 / 2), (n1 / 2) - (-n1 / 2 + 1) + 1)
p, l = np.meshgrid(p, l)
# Commented the following lines due to the problem mentioned in LOGS-1
pp = np.array(p).flatten()
ll = np.array(p).flatten()
#ind = sp.setdiff1d(np.linspace(0,p.size-1,p.size-1-0+1),sp.where((p==0) & (l==0)))
ind = sp.setdiff1d(np.linspace(0, p.size - 1, p.size - 1 - 0 + 1),
np.r_[sp.where((p == 0) & (l == 0))])
ind = ind.astype(int)
pn0 = pp[ind]
ln0 = ll[ind]
def ff(ss):
r1, r2 = ss
e = np.exp(-2.0 * (kappa2 * (ln0**2.) / (r1**2.) + lmbd2 * (pn0**2.) /
(r2**2.)))
f = np.sum(e * (np.cos(kappa * ln0 * rx) - np.exp(-1.)))
g = np.sum(e * (np.cos(lmbd * pn0 * ry) - np.exp(-1.)))
return (f, g)
r1, r2 = sp.optimize.fsolve(ff, (3.0 / rx, 3.0 / ry))
summ = np.sum(
np.sum(
np.exp(-2.0 * (kappa2 * (l**2.) / (r1**2.) + lmbd2 * (p**2.) /
(r2**2.)))))
summ = summ - 1.0
c = np.sqrt(1.0 / (deltak * summ))
# define aij matrices. Note rotation is not enabled in this code
a11 = 1.0 / r1**2
a22 = 1.0 / r2**2
a12 = 0.0 * a11
# define wavenumber indeces following matlab ifft2 convention
l = np.linspace(0, (n1 / 2), (n1 / 2) - 0 + 1)
p = np.linspace(0, (n2 / 2), (n2 / 2) - 0 + 1)
p, l = np.meshgrid(p, l)
# define amplitudes 'C', in 1st quadrant
e = np.exp(-(a11 * kappa2 *
(l**2.) + 2.0 * a12 * kappa * lmbd * l * p + a22 * lmbd2 *
(p**2.)))
C = e * c * np.sqrt(deltak)
C[0, :] = 0.
C[:, 0] = 0.
# for each wavenumber (p,l) of each sample (j=1..N)
A = np.zeros((n1, n2, N))
for nn in range(0, int(nreal)):
print "Working on ensemble number " + str(nn)
qhat = np.zeros((n1, n2)) + 0j
qhat2 = np.zeros((n1, n2)) + 0j
# 1st quadrant: phase is arbitrary
phi = 2. * pi * np.random.random(C.shape)
phi[:, int(n2) / 2] = 0.
phi[int(n1) / 2, :] = 0.
qhat[0:int(n1) / 2 + 1,
0:int(n2) / 2 + 1] = C * np.exp(cmath.sqrt(-1.) * (phi))
# 3rd quadrant: phase is also arbitrary
phi2 = 2. * pi * np.random.random(C.shape)
phi2[:, int(n2) / 2] = 0.
phi2[int(n1) / 2, :] = 0.
qhat2[0:int(n1) / 2 + 1,
0:int(n2) / 2 + 1] = C * np.exp(cmath.sqrt(-1.) * (phi2))
for j in range(int(n1) / 2, int(n1) - 1):
for i in range(0, int(n2) / 2):
qhat[j + 1, i + 1] = np.conj(qhat2[(int(n1) - j) + 1, i + 1])
qhat[int(n1) / 2:int(n1) - 2, 1] = 0.
# 2nd and 4th quadrants are set by conjugate symmetry
for i in range(int(n2) / 2 + 1, int(n2)):
for j in range(0, int(n1)):
qhat[j, i] = np.conj(qhat[np.mod(int(n1) - j + 1, int(n1)),
np.mod(int(n2) - i + 1,
int(n2) + 1)])
#print nn
# Invert the fourier transform to get the sample
A[:, :, nn] = np.fft.ifft2(qhat) * n1 * n2
# cut down to desired size
A = A[0:nx, 0:ny, :]
# correct mean and variance
AA = np.array(
[np.tile(np.mean(A, axis=2), (1, 1)) for ii in xrange(int(N))])
AA = AA.transpose((1, 2, 0))
A = A - AA
del AA
AA = np.array(
[np.tile(np.std(A, axis=2), (1, 1)) for ii in xrange(int(N))])
AA = AA.transpose((1, 2, 0))
A = A / AA * pLz
del AA
return A
def verify_intron_retention(event, gene, counts_segments, counts_edges,
counts_seg_pos, CFG):
# [verified, info] = verify_intron_retention(event, fn_bam, CFG)
verified = [0, 0]
# (0) valid, (1) intron_cov, (2) exon1_cov, (3), exon2_cov
# (4) intron_conf, (5) intron_cov_region
info = [1, 0, 0, 0, 0, 0]
### check validity of exon coordinates (>=0)
if sp.any(event.exons1 < 0) or sp.any(event.exons2 < 0):
info[0] = 0
return (verified, info)
### check validity of exon coordinates (start < stop && non-overlapping)
elif sp.any(event.exons1[:, 1] - event.exons1[:, 0] < 1) or sp.any(
(event.exons2[1] - event.exons2[0]) < 1):
info[0] = 0
return (verified, info)
sg = gene.splicegraph
segs = gene.segmentgraph
### find exons corresponding to event
idx_exon1 = sp.where((sg.vertices[0, :] == event.exons1[0, 0])
& (sg.vertices[1, :] == event.exons1[0, 1]))[0]
idx_exon2 = sp.where((sg.vertices[0, :] == event.exons1[1, 0])
& (sg.vertices[1, :] == event.exons1[1, 1]))[0]
### find segments corresponding to exons
seg_exon1 = sp.sort(sp.where(segs.seg_match[idx_exon1, :])[1])
seg_exon2 = sp.sort(sp.where(segs.seg_match[idx_exon2, :])[1])
seg_all = sp.arange(seg_exon1[0], seg_exon2[-1])
seg_intron = sp.setdiff1d(seg_all, seg_exon1)
seg_intron = sp.setdiff1d(seg_intron, seg_exon2)
assert (seg_intron.shape[0] > 0)
seg_lens = segs.segments[1, :] - segs.segments[0, :]
### compute exon coverages as mean of position wise coverage
# exon1_cov
info[2] = sp.sum(counts_segments[seg_exon1] *
seg_lens[seg_exon1]) / sp.sum(seg_lens[seg_exon1])
# exon2_cov
info[3] = sp.sum(counts_segments[seg_exon2] *
seg_lens[seg_exon2]) / sp.sum(seg_lens[seg_exon2])
# intron_cov
info[1] = sp.sum(counts_segments[seg_intron] *
seg_lens[seg_intron]) / sp.sum(seg_lens[seg_intron])
# intron_cov_region
info[5] = sp.sum(counts_seg_pos[seg_intron]) / sp.sum(seg_lens[seg_intron])
### check if counts match verification criteria
if info[1] > CFG['intron_retention']['min_retention_cov'] and \
info[5] > CFG['intron_retention']['min_retention_region'] and \
info[1] >= CFG['intron_retention']['min_retention_rel_cov'] * (info[2] + info[3]) / 2:
verified[0] = 1
### check intron confirmation as sum of valid intron scores
### intron score is the number of reads confirming this intron
# intron conf
idx = sp.where(counts_edges[:, 0] == sp.ravel_multi_index(
[seg_exon1[-1], seg_exon2[0]], segs.seg_edges.shape))[0]
info[4] = counts_edges[idx, 1]
if info[4] >= CFG['intron_retention']['min_non_retention_count']:
verified[1] = 1
return (verified, info)
def test_with_nested_CV(folder='model',folds=5, plot=True, steps=['hashing','tfidf']):
'''
Evaluates the classifer by doing nested CV
i.e. keeping 1/folds of the data out of the training and doing training
(including model selection for regularizer) on the training set and testing
on the held-out data
Also prints some stats and figures
INPUT
folder folder with model files
folds number of folds
'''
# start timer
import time
t0 = time.time()
# create bag of words representations
vv = Vectorizer(steps=steps)
# load data
vec = Vectorizer(folder=folder)
data = get_speech_text(folder=folder)
for key in data.keys():
data[key] = vec.transform(data[key])
# create numerical labels
Y = hstack(map((lambda x: ones(data[data.keys()[x]].shape[0])*x),range(len(data))))
# create data matrix
X = vstack(data.values())
# permute data
fsize = len(Y)/folds
randidx = permutation(len(Y))
Y = Y[randidx]
X = X[randidx,:]
idx = reshape(arange(fsize*folds),(folds,fsize))
Y = Y[:fsize*folds]
# allocate matrices for predictions
predicted = zeros(fsize*folds)
predicted_prob = zeros((fsize*folds,len(data)))
# the regularization parameters to choose from
parameters = {'C': (10.**arange(-4,4,1.)).tolist()}
# do nested CV
for ifold in range(folds):
testidx = idx[ifold,:]
trainidx = idx[setdiff1d(arange(folds),ifold),:].flatten()
text_clf = LogisticRegression(class_weight='auto',dual=True)
# for nested CV, do folds-1 CV for parameter optimization
# within inner CV loop and use the outer testfold as held-out data
# for model validation
gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1, cv=(folds-1))
gs_clf.fit(X[trainidx,:],Y[trainidx])
predicted[testidx] = gs_clf.predict(X[testidx,:])
predicted_prob[testidx,:] = gs_clf.predict_proba(X[testidx,:])
print '************ Fold %d *************'%(ifold+1)
print metrics.classification_report(Y[testidx], predicted[testidx],target_names=data.keys())
t1 = time.time()
total_time = t1 - t0
timestr = 'Wallclock time: %f sec\n'%total_time
dimstr = 'Vocabulary size: %d\n'%X.shape[-1]
report = timestr + dimstr
# extract some metrics
print '********************************'
print '************ Total *************'
print '********************************'
report += metrics.classification_report(Y, predicted,target_names=data.keys())
# dump metrics to file
open(folder+'/report_%s.txt'%'_'.join(sorted(steps)),'wb').write(report)
print(report)
conf_mat = metrics.confusion_matrix(Y,predicted)
open(folder+'/conf_mat_%s.txt'%'_'.join(sorted(steps)),'wb').write(json.dumps(conf_mat.tolist()))
print(conf_mat)
if plot:
# print confusion matrix
import pylab
pylab.figure(figsize=(16,16))
pylab.imshow(metrics.confusion_matrix(Y,predicted),interpolation='nearest')
pylab.colorbar()
pylab.xticks(arange(4),[x.decode('utf-8') for x in data.keys()])
pylab.yticks(arange(4),[x.decode('utf-8') for x in data.keys()])
pylab.xlabel('Predicted')
pylab.ylabel('True')
font = {'family' : 'normal', 'size' : 30}
pylab.rc('font', **font)
pylab.savefig(folder+'/conf_mat.pdf',bbox_inches='tight')
def elasticity(N, Y, centered=True, NyqNul=True):
"""
Projection matrix on a space of admissible strain fields
INPUT =
N : ndarray of e.g. stiffness coefficients
d : dimension; d = 2
D : dimension in engineering notation; D = 3
Y : the size of periodic unit cell
OUTPUT =
G1h,G1s,G2h,G2s : projection matrices of size DxDxN
"""
xi = Grid.get_xil(N, Y)
N = np.array(N, dtype=np.int)
d = N.size
D = d*(d+1)/2
if NyqNul:
Nred = get_Nodd(N)
else:
Nred = N
xi2 = []
for ii in range(d):
xi2.append(xi[ii]**2)
num = np.zeros(np.hstack([d, d, Nred]))
norm2_xi = np.zeros(Nred)
for mm in np.arange(d): # diagonal components
Nshape = np.ones(d, dtype=np.int)
Nshape[mm] = Nred[mm]
Nrep = np.copy(Nred)
Nrep[mm] = 1
num[mm][mm] = np.tile(np.reshape(xi2[mm], Nshape), Nrep) # numerator
norm2_xi += num[mm][mm]
norm4_xi = norm2_xi**2
ind_center = tuple(Nred/2)
# avoid division by zero
norm2_xi[ind_center] = 1
norm4_xi[ind_center] = 1
for m in np.arange(d): # upper diagonal components
for n in np.arange(m+1, d):
NshapeM = np.ones(d, dtype=np.int)
NshapeM[m] = Nred[m]
NrepM = np.copy(Nred)
NrepM[m] = 1
NshapeN = np.ones(d, dtype=np.int)
NshapeN[n] = Nred[n]
NrepN = np.copy(Nred)
NrepN[n] = 1
num[m][n] = np.tile(np.reshape(xi[m], NshapeM), NrepM) \
* np.tile(np.reshape(xi[n], NshapeN), NrepN)
# G1h = np.zeros([D,D]).tolist()
G1h = np.zeros(np.hstack([D, D, Nred]))
G1s = np.zeros(np.hstack([D, D, Nred]))
IS0 = np.zeros(np.hstack([D, D, Nred]))
mean = np.zeros(np.hstack([D, D, Nred]))
Lamh = np.zeros(np.hstack([D, D, Nred]))
S = np.zeros(np.hstack([D, D, Nred]))
W = np.zeros(np.hstack([D, D, Nred]))
WT = np.zeros(np.hstack([D, D, Nred]))
for m in np.arange(d):
S[m][m] = 2*num[m][m]/norm2_xi
for n in np.arange(d):
G1h[m][n] = num[m][m]*num[n][n]/norm4_xi
Lamh[m][n] = np.ones(Nred)/d
Lamh[m][n][ind_center] = 0
for m in np.arange(D):
IS0[m][m] = np.ones(Nred)
IS0[m][m][ind_center] = 0
mean[m][m][ind_center] = 1
if d == 2:
S[0][2] = 2**0.5*num[0][1]/norm2_xi
S[1][2] = 2**0.5*num[0][1]/norm2_xi
S[2][2] = np.ones(Nred)
S[2][2][ind_center] = 0
G1h[0][2] = 2**0.5*num[0][0]*num[0][1]/norm4_xi
G1h[1][2] = 2**0.5*num[0][1]*num[1][1]/norm4_xi
G1h[2][2] = 2*num[0][0]*num[1][1]/norm4_xi
for m in np.arange(d):
for n in np.arange(d):
W[m][n] = num[m][m]/norm2_xi
W[2][m] = 2**.5*num[0][1]/norm2_xi
elif d == 3:
for m in np.arange(d):
S[m+3][m+3] = 1 - num[m][m]/norm2_xi
S[m+3][m+3][ind_center] = 0
for m in np.arange(d):
for n in np.arange(m+1, d):
S[m+3][n+3] = num[m][n]/norm2_xi
G1h[m+3][n+3] = num[m][m]*num[n][n]/norm4_xi
for m in np.arange(d):
for n in np.arange(d):
ind = sp.setdiff1d(np.arange(d), [n])
S[m][n+3] = (0 == (m == n))*2**.5*num[ind[0]][ind[1]]/norm2_xi
G1h[m][n+3] = 2**.5*num[m][m]*num[ind[0]][ind[1]]/norm4_xi
W[m][n] = num[m][m]/norm2_xi
W[n+3][m] = 2**.5*num[ind[0]][ind[1]]/norm2_xi
for m in np.arange(d):
for n in np.arange(d):
ind_m = sp.setdiff1d(np.arange(d), [m])
ind_n = sp.setdiff1d(np.arange(d), [n])
G1h[m+3][n+3] = 2*num[ind_m[0]][ind_m[1]] \
* num[ind_n[0]][ind_n[1]] / norm4_xi
# symmetrization
for n in np.arange(D):
for m in np.arange(n+1, D):
S[m][n] = S[n][m]
G1h[m][n] = G1h[n][m]
for m in np.arange(D):
for n in np.arange(D):
G1s[m][n] = S[m][n] - 2*G1h[m][n]
WT[m][n] = W[n][m]
G2h = 1./(d-1)*(d*Lamh + G1h - W - WT)
G2s = IS0 - G1h - G1s - G2h
if not centered:
for m in np.arange(d):
for n in np.arange(d):
G1h[m][n] = np.fft.ifftshift(G1h[m][n])
G1s[m][n] = np.fft.ifftshift(G1s[m][n])
G2h[m][n] = np.fft.ifftshift(G2h[m][n])
G2s[m][n] = np.fft.ifftshift(G2s[m][n])
G0 = Matrix(name='hG1', val=mean, Fourier=True)
G1h = Matrix(name='hG1', val=G1h, Fourier=True)
G1s = Matrix(name='hG1', val=G1s, Fourier=True)
G2h = Matrix(name='hG1', val=G2h, Fourier=True)
G2s = Matrix(name='hG1', val=G2s, Fourier=True)
if NyqNul:
G0 = G0.enlarge(N)
G1h = G1h.enlarge(N)
G1s = G1s.enlarge(N)
G2h = G2h.enlarge(N)
G2s = G2s.enlarge(N)
return mean, G1h, G1s, G2h, G2s
def _mc_data_config(H,psi0,h_stuff,c_ops,c_stuff,args,e_ops,options):
"""Creates the appropriate data structures for the monte carlo solver
based on the given time-dependent, or indepdendent, format.
"""
#take care of expectation values, if any
if any(e_ops):
odeconfig.e_num=len(e_ops)
for op in e_ops:
if isinstance(op,list):
op=op[0]
odeconfig.e_ops_data.append(op.data.data)
odeconfig.e_ops_ind.append(op.data.indices)
odeconfig.e_ops_ptr.append(op.data.indptr)
odeconfig.e_ops_isherm.append(op.isherm)
odeconfig.e_ops_data=array(odeconfig.e_ops_data)
odeconfig.e_ops_ind=array(odeconfig.e_ops_ind)
odeconfig.e_ops_ptr=array(odeconfig.e_ops_ptr)
odeconfig.e_ops_isherm=array(odeconfig.e_ops_isherm)
#----
#take care of collapse operators, if any
if any(c_ops):
odeconfig.c_num=len(c_ops)
for c_op in c_ops:
if isinstance(c_op,list):
c_op=c_op[0]
n_op=c_op.dag()*c_op
odeconfig.c_ops_data.append(c_op.data.data)
odeconfig.c_ops_ind.append(c_op.data.indices)
odeconfig.c_ops_ptr.append(c_op.data.indptr)
#norm ops
odeconfig.n_ops_data.append(n_op.data.data)
odeconfig.n_ops_ind.append(n_op.data.indices)
odeconfig.n_ops_ptr.append(n_op.data.indptr)
#to array
odeconfig.c_ops_data=array(odeconfig.c_ops_data)
odeconfig.c_ops_ind=array(odeconfig.c_ops_ind)
odeconfig.c_ops_ptr=array(odeconfig.c_ops_ptr)
odeconfig.n_ops_data=array(odeconfig.n_ops_data)
odeconfig.n_ops_ind=array(odeconfig.n_ops_ind)
odeconfig.n_ops_ptr=array(odeconfig.n_ops_ptr)
#----
#--------------------------------------------
# START CONSTANT H & C_OPS CODE
#--------------------------------------------
if odeconfig.tflag==0:
if odeconfig.cflag:
odeconfig.c_const_inds=arange(len(c_ops))
for c_op in c_ops:
n_op=c_op.dag()*c_op
H -= 0.5j * n_op #combine Hamiltonian and collapse terms into one
#construct Hamiltonian data structures
if options.tidy:
H=H.tidyup(options.atol)
odeconfig.h_data=-1.0j*H.data.data
odeconfig.h_ind=H.data.indices
odeconfig.h_ptr=H.data.indptr
#----
#--------------------------------------------
# START STRING BASED TIME-DEPENDENCE
#--------------------------------------------
elif odeconfig.tflag in array([1,10,11]):
#take care of arguments for collapse operators, if any
if any(args):
for item in args.items():
odeconfig.c_args.append(item[1])
#constant Hamiltonian / string-type collapse operators
if odeconfig.tflag==1:
H_inds=arange(1)
H_tdterms=0
len_h=1
C_inds=arange(odeconfig.c_num)
C_td_inds=array(c_stuff[2]) #find inds of time-dependent terms
C_const_inds=setdiff1d(C_inds,C_td_inds) #find inds of constant terms
C_tdterms=[c_ops[k][1] for k in C_td_inds] #extract time-dependent coefficients (strings)
odeconfig.c_const_inds=C_const_inds#store indicies of constant collapse terms
odeconfig.c_td_inds=C_td_inds#store indicies of time-dependent collapse terms
for k in odeconfig.c_const_inds:
H-=0.5j*(c_ops[k].dag()*c_ops[k])
if options.tidy:
H=H.tidyup(options.atol)
odeconfig.h_data=[H.data.data]
odeconfig.h_ind=[H.data.indices]
odeconfig.h_ptr=[H.data.indptr]
for k in odeconfig.c_td_inds:
op=c_ops[k][0].dag()*c_ops[k][0]
odeconfig.h_data.append(-0.5j*op.data.data)
odeconfig.h_ind.append(op.data.indices)
odeconfig.h_ptr.append(op.data.indptr)
odeconfig.h_data=-1.0j*array(odeconfig.h_data)
odeconfig.h_ind=array(odeconfig.h_ind)
odeconfig.h_ptr=array(odeconfig.h_ptr)
#--------------------------------------------
# END OF IF STATEMENT
#--------------------------------------------
#string-type Hamiltonian & at least one string-type collapse operator
else:
H_inds=arange(len(H))
H_td_inds=array(h_stuff[2]) #find inds of time-dependent terms
H_const_inds=setdiff1d(H_inds,H_td_inds) #find inds of constant terms
H_tdterms=[H[k][1] for k in H_td_inds] #extract time-dependent coefficients (strings or functions)
H=array([sum(H[k] for k in H_const_inds)]+[H[k][0] for k in H_td_inds]) #combine time-INDEPENDENT terms into one.
len_h=len(H)
H_inds=arange(len_h)
odeconfig.h_td_inds=arange(1,len_h)#store indicies of time-dependent Hamiltonian terms
#if there are any collpase operators
if odeconfig.c_num>0:
if odeconfig.tflag==10: #constant collapse operators
odeconfig.c_const_inds=arange(odeconfig.c_num)
for k in odeconfig.c_const_inds:
H[0]-=0.5j*(c_ops[k].dag()*c_ops[k])
C_inds=arange(odeconfig.c_num)
C_tdterms=array([])
#-----
else:#some time-dependent collapse terms
C_inds=arange(odeconfig.c_num)
C_td_inds=array(c_stuff[2]) #find inds of time-dependent terms
C_const_inds=setdiff1d(C_inds,C_td_inds) #find inds of constant terms
C_tdterms=[c_ops[k][1] for k in C_td_inds] #extract time-dependent coefficients (strings)
odeconfig.c_const_inds=C_const_inds#store indicies of constant collapse terms
odeconfig.c_td_inds=C_td_inds#store indicies of time-dependent collapse terms
for k in odeconfig.c_const_inds:
H[0]-=0.5j*(c_ops[k].dag()*c_ops[k])
else:#set empty objects if no collapse operators
C_const_inds=arange(odeconfig.c_num)
odeconfig.c_const_inds=arange(odeconfig.c_num)
odeconfig.c_td_inds=array([])
C_tdterms=array([])
C_inds=array([])
#tidyup
if options.tidy:
H=array([H[k].tidyup(options.atol) for k in range(len_h)])
#construct data sets
odeconfig.h_data=[H[k].data.data for k in range(len_h)]
odeconfig.h_ind=[H[k].data.indices for k in range(len_h)]
odeconfig.h_ptr=[H[k].data.indptr for k in range(len_h)]
for k in odeconfig.c_td_inds:
odeconfig.h_data.append(-0.5j*odeconfig.n_ops_data[k])
odeconfig.h_ind.append(odeconfig.n_ops_ind[k])
odeconfig.h_ptr.append(odeconfig.n_ops_ptr[k])
odeconfig.h_data=-1.0j*array(odeconfig.h_data)
odeconfig.h_ind=array(odeconfig.h_ind)
odeconfig.h_ptr=array(odeconfig.h_ptr)
#--------------------------------------------
# END OF ELSE STATEMENT
#--------------------------------------------
#set execuatble code for collapse expectation values and spmv
col_spmv_code="state=odeconfig.colspmv(j,ODE.t,odeconfig.c_ops_data[j],odeconfig.c_ops_ind[j],odeconfig.c_ops_ptr[j],ODE.y"
col_expect_code="for i in odeconfig.c_td_inds: n_dp.append(odeconfig.colexpect(i,ODE.t,odeconfig.n_ops_data[i],odeconfig.n_ops_ind[i],odeconfig.n_ops_ptr[i],ODE.y"
for kk in range(len(odeconfig.c_args)):
col_spmv_code+=",odeconfig.c_args["+str(kk)+"]"
col_expect_code+=",odeconfig.c_args["+str(kk)+"]"
col_spmv_code+=")"
col_expect_code+="))"
odeconfig.col_spmv_code=compile(col_spmv_code,'<string>', 'exec')
odeconfig.col_expect_code=compile(col_expect_code,'<string>', 'exec')
#----
#setup ode args string
odeconfig.string=""
data_range=range(len(odeconfig.h_data))
for k in data_range:
odeconfig.string+="odeconfig.h_data["+str(k)+"],odeconfig.h_ind["+str(k)+"],odeconfig.h_ptr["+str(k)+"]"
if k!=data_range[-1]:
odeconfig.string+=","
#attach args to ode args string
if len(odeconfig.c_args)>0:
for kk in range(len(odeconfig.c_args)):
odeconfig.string+=","+"odeconfig.c_args["+str(kk)+"]"
#----
name="rhs"+str(odeconfig.cgen_num)
odeconfig.tdname=name
cgen=Codegen(H_inds,H_tdterms,odeconfig.h_td_inds,args,C_inds,C_tdterms,odeconfig.c_td_inds,type='mc')
cgen.generate(name+".pyx")
#----
#--------------------------------------------
# END OF STRING TYPE TIME DEPENDENT CODE
#--------------------------------------------
#--------------------------------------------
# START PYTHON FUNCTION BASED TIME-DEPENDENCE
#--------------------------------------------
elif odeconfig.tflag in array([2,20,22]):
#take care of Hamiltonian
if odeconfig.tflag==2:# constant Hamiltonian, at least one function based collapse operators
H_inds=array([0])
H_tdterms=0
len_h=1
else:# function based Hamiltonian
H_inds=arange(len(H))
H_td_inds=array(h_stuff[1]) #find inds of time-dependent terms
H_const_inds=setdiff1d(H_inds,H_td_inds) #find inds of constant terms
odeconfig.h_funcs=array([H[k][1] for k in H_td_inds])
odeconfig.h_func_args=args
Htd=array([H[k][0] for k in H_td_inds])
odeconfig.h_td_inds=arange(len(Htd))
H=sum(H[k] for k in H_const_inds)
#take care of collapse operators
C_inds=arange(odeconfig.c_num)
C_td_inds=array(c_stuff[1]) #find inds of time-dependent terms
C_const_inds=setdiff1d(C_inds,C_td_inds) #find inds of constant terms
odeconfig.c_const_inds=C_const_inds#store indicies of constant collapse terms
odeconfig.c_td_inds=C_td_inds#store indicies of time-dependent collapse terms
odeconfig.c_funcs=zeros(odeconfig.c_num,dtype=FunctionType)
for k in odeconfig.c_td_inds:
odeconfig.c_funcs[k]=c_ops[k][1]
odeconfig.c_func_args=args
#combine constant collapse terms with constant H and construct data
for k in odeconfig.c_const_inds:
H-=0.5j*(c_ops[k].dag()*c_ops[k])
if options.tidy:
H=H.tidyup(options.atol)
Htd=array([Htd[j].tidyup(options.atol) for j in odeconfig.h_td_inds])
#setup cosntant H terms data
odeconfig.h_data=-1.0j*H.data.data
odeconfig.h_ind=H.data.indices
odeconfig.h_ptr=H.data.indptr
#setup td H terms data
odeconfig.h_td_data=array([-1.0j*Htd[k].data.data for k in odeconfig.h_td_inds])
odeconfig.h_td_ind=array([Htd[k].data.indices for k in odeconfig.h_td_inds])
odeconfig.h_td_ptr=array([Htd[k].data.indptr for k in odeconfig.h_td_inds])
#--------------------------------------------
# END PYTHON FUNCTION BASED TIME-DEPENDENCE
#--------------------------------------------
#--------------------------------------------
# START PYTHON FUNCTION BASED HAMILTONIAN
#--------------------------------------------
elif odeconfig.tflag==3:
#take care of Hamiltonian
odeconfig.h_funcs=H
odeconfig.h_func_args=args
#take care of collapse operators
odeconfig.c_const_inds=arange(odeconfig.c_num)
odeconfig.c_td_inds=array([]) #find inds of time-dependent terms
if len(odeconfig.c_const_inds)>0:
H=0
for k in odeconfig.c_const_inds:
H-=0.5j*(c_ops[k].dag()*c_ops[k])
if options.tidy:
H=H.tidyup(options.atol)
odeconfig.h_data=-1.0j*H.data.data
odeconfig.h_ind=H.data.indices
odeconfig.h_ptr=H.data.indptr
