def run(TFIntervaldict,pad,threshold,bins):
distances = dict()
for TF in TFIntervaldict:
x = list()
for array in TFIntervaldict[TF]:
for interval in array:
for position in interval:
if position[2] != np.inf and position[2] > threshold:
x.append((position[0]+position[1]/2)-pad)
if len(x) > 0:
counts,edges = np.histogram(x, bins=bins)
edges = edges[1:]
X = np.zeros((len(counts), 2))
X[:,0] = edges
X[:,1] = counts
w = em.fit(X)
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,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('/')[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