def non_normal_cluster_expr(trg_ssnames, tf_ssnames, ctype = False, random_tfs = False):
if random_tfs:
tf_kidxs = nu.net_tf_keyidxs()
r =np.random.random_integers(0,len(tf_kidxs.keys()),len(tf_ssnames))
tf_ssnames = []
print 'Randomizing TFs'
for i in r:
tf_ssnames.append(tf_kidxs.keys()[i])
n = nu.parse_net()
ts = nu.load_TS()
if ctype:
cl = nu.load_CL()
tf_vals = array([ts[k] + cl[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] + cl[k] for k in trg_ssnames]).T
else:
tf_vals = array([ts[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] for k in trg_ssnames]).T
tf_vals -= np.mean(tf_vals,0)[:]
tg_vals -= np.mean(tg_vals,0)[:]
tf_vals /= np.std(tf_vals,0)[:]
tg_vals /= np.std(tg_vals,0)[:]
return [tg_vals, tf_vals]
def show_binary(idx = 0):
tsb = nu.expr_TS_binary(reset = 0)
tsvals = nu.load_TS()
net = nu2.get_net()
tgs = net[1]
tfs = net[0]
f = plt.figure(0)
f.clear()
ax = f.add_subplot(111)
for k in tsb.keys()[idx:]:
my_tfs = tgs.get(k,[])
ct = mycolors.getct(len(my_tfs))
tgseries = tsvals[k]
if not my_tfs: continue
for i in range(len(my_tfs)):
tf = my_tfs[i][0]
series = tsvals.get(tf)
if not series: continue
binary = tsb.get(tf)
#if not binary:
# print 'no ts for ' + tg
# continue
npts = len(binary)
xax = tgseries
cmap = equal(binary,0)[:,newaxis]*[1,0,0] + equal(binary,1)[:,newaxis]*[0,1,0]
print my_tfs[i][1]
ax.scatter(xax, series, 500,
color = cmap,
alpha = my_tfs[i][1],
edgecolor = '0')
break
return
def viewclusters(cands, fig = 5):
#clusters = get_candidates(get_candidates(10))
f = plt.figure(fig)
f.clear()
ax1 = f.add_subplot(111)
f2 = plt.figure(fig+1)
f2.clear()
ax2 = f2.add_subplot(111)
for index in cands:
trg_ssnames = get_trg_ss(cluster = index )
tf_ssnames = get_tf_ss( trgnames = trg_ssnames)
n = nu.parse_net()
ts = nu.load_TS()
tf_vals = array([ts[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] for k in trg_ssnames]).T
vals = tf_vals
mvals = np.mean(vals,1)
vals -= mvals[:,newaxis]
svals = np.std(vals,1)
if len(nonzero(equal(svals,0))[0]):
raise Exception('only one tf...')
vals /= svals[:,newaxis]
for v in vals.T:
v -= mean(v)
v /= std(v)
ax1.plot(vals)
break
def normalize_cluster_expr(trg_ssnames, tf_ssnames, ctype = False, random_tfs = False):
if random_tfs:
tf_kidxs = nu.net_tf_keyidxs()
r =np.random.random_integers(0,len(tf_kidxs.keys()),len(tf_ssnames))
tf_ssnames = []
print 'Randomizing TFs'
for i in r:
tf_ssnames.append(tf_kidxs.keys()[i])
n = nu.parse_net()
ts = nu.load_TS()
if ctype:
cl = nu.load_CL()
tf_vals = array([ts[k] + cl[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] + cl[k] for k in trg_ssnames]).T
else:
tf_vals = array([ts[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] for k in trg_ssnames]).T
all_exprs = []
for vstart in [tg_vals, tf_vals]:
vals = vstart
mvals = np.mean(vals,1)
vals -= mvals[:,newaxis]
svals = np.std(vals,1)
if len(nonzero(equal(svals,0))[0]):
raise Exception('only one tf...')
vals /= svals[:,newaxis]
for v in vals.T:
v -= mean(v)
v /= std(v)
all_exprs.append(vals)
#raise Exception()
return all_exprs