def run(bidirfile, fimodir):
distances = dict()
directorylist = [fimodir + '/' + item for item in os.listdir(fimodir) if 'fimo_out' in item]
for item in directorylist:
print item
TF = item.split('/')[5].split('_')[0]
x = Functions.get_distances_pad_v3(bidirfile, item + "/fimo.cut.txt", True, 1500)
if len(x) != 0:
start = min(x)
stop = max(x)
sigma = np.std(x)
mu = np.mean(x)
N = len(x)
y = np.random.uniform(start, stop, N)
z = mu/(sigma/math.sqrt(N))
p = 1 - scipy.special.ndtr(z)
k = scipy.stats.ks_2samp(x,y)
m = scipy.stats.mode(x)[0][0]
if -0.25 < m < 0.25:
m = 0
else:
m = 1
distances[TF] = [k[1],p,m,x]
return distances
def run(bidirfile, fimodir):
distances = dict()
directorylist = [fimodir + '/' + item for item in os.listdir(fimodir) if 'fimo_out' in item]
for item in directorylist:
print item
TF = item.split('/')[7].split('_')[0]
x = Functions.get_distances_pad_v3(bidirfile, item + "/fimo.cut.txt", True, 1500)
for i in range(len(x)):
x[i] = x[i]*1500
#if len(x) != 0:
# counts,edges = np.histogram(x, bins=200)
# edges = edges[1:]
# X = np.zeros((len(counts), 2))
# X[:,0] = edges
# X[:,1] = counts
# w = em.fit(X)
# ks = list()
# for a in range(1000):
# d = ds.simulate
# ks.append(scipy.stats.ks_2samp(x,d))
# d = np.mean(ks)
# start = min(x)
# stop = max(x)
# sigma = np.std(x)
# mu = np.mean(x)
# N = len(x)
# y = np.random.uniform(start, stop, N)
# y = np.linspace(start,stop,N)
# z = mu/(sigma/math.sqrt(N))
# p = 1 - scipy.special.ndtr(z)
# k = scipy.stats.ks_2samp(x,y)
# m = scipy.stats.mode(x)[0][0]
# if -0.25 < m < 0.25:
# m = 0
# else:
# m = 1
# print w,k[1]
#distances[TF] = [w,k[1],d,p,m,x]
distances[TF] = x
return distances
def run(bidirfile, fimodir):
distances = dict()
directorylist = [fimodir + '/' + item for item in os.listdir(fimodir) if 'fimo_out' in item]
for item in directorylist:
print item
TF = item.split('/')[6].split('_')[0]
x = Functions.get_distances_pad_v3(bidirfile, item + "/fimo.cut.txt", True, 1500)
for i in range(len(x)):
x[i] = x[i]*1500
if len(x) != 0:
counts,edges = np.histogram(x, bins=200)
edges = edges[1:]
X = np.zeros((len(counts), 2))
X[:,0] = edges
X[:,1] = counts
w = em.fit(X)
w2 = ds2.get_w(X)
ks = list()
for a in range(1000):
d = ds.simulate
ks.append(scipy.stats.ks_2samp(x,d))
d = np.mean(ks)
start = min(x)
stop = max(x)
sigma = np.std(x)
mu = np.mean(x)
N = len(x)
y = np.random.uniform(start, stop, N)
y = np.linspace(start,stop,N)
z = mu/(sigma/math.sqrt(N))
p = 1 - scipy.special.ndtr(z)
k = scipy.stats.ks_2samp(x,y)
m = scipy.stats.mode(x)[0][0]
if -0.25 < m < 0.25:
m = 0
else:
m = 1
distances[TF] = [w,w2,k[1],d,p,m,x]
return distances
def run(bidirfile, fimodir):
distances = dict()
directorylist = [fimodir + '/' + item for item in os.listdir(fimodir) if 'fimo_out' in item]
bidirsites = Functions.create_site_bidir(bidirfile)
for item in directorylist:
print item
TF = item.split('/')[5].split('_')[0]
fimodict = Functions.create_tup_fimo(item + "/fimo.cut.txt", True)
for key in bidirsites:
start,stop,chrom = key
fimotree = fimodict[chrom]
fimotree = node.tree(fimotree)
intervalsearch = []
for item in fimotree.searchInterval(key):
start2,stop2,pval = item
i = (start+stop)/2
x = (start2+stop2)/2
intervalsearch.append((i-x,pval))
bidirsites[key].append((TF,intervalsearch))
x = Functions.get_distances_pad_v3(bidirfile, item + "/fimo.cut.txt", True, 1500)
if len(x) != 0:
start = min(x)
stop = max(x)
sigma = np.std(x)
mu = np.mean(x)
N = len(x)
#y = np.random.uniform(start, stop, N)
y = np.linspace(start,stop,N)
z = mu/(sigma/math.sqrt(N))
p = 1 - scipy.special.ndtr(z)
k = scipy.stats.ks_2samp(x,y)
m = scipy.stats.mode(x)[0][0]
if -0.25 < m < 0.25:
m = 0
else:
m = 1
distances[TF] = [k[1],p,m]
return distances,bidirsites
