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prePostTools.py
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prePostTools.py
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# coding: utf-8
# In[1]:
M1 = ["D","Dv","D-D","D-DvL","DvR-DvL","D-D-D", "D-D-DvL","D-DvL-DvR","D-D-DvL-DvR",
"DvL-DvR-DvR1","D-DvL-DvR-DvR1","D-D-DvL-DvR-DvR1","sD",
"D-sD",
"sD-sD",
"sD-DvL",
"sD-D-D",
"sD-sD-D",
"sD-sD-sD",
"sD-D-DvL",
"sD-sD-DvL",
"sD-DvL-DvR",
"sD-D-DvL-DvR",
"sD-sD-DvL-DvR",
"sD-DvL-DvR-DvR1",
"sD-D-DvL-DvR-DvR1",
"sD-sD-DvL-DvR-DvR1"]
M1b = ["D","Dv","2D","D-Dv","2Dv","3D", "2D-Dv","D-2Dv","2D-2Dv",
"3Dv","D-3Dv","2D-3Dv","sD",
"D-sD",
"2sD",
"sD-Dv",
"sD-2D",
"2sD-D",
"3sD",
"sD-D-Dv",
"2sD-DvL",
"sD-2Dv",
"sD-D-2Dv",
"2sD-2Dv",
"sD-3Dv",
"sD-D-3Dv",
"2sD-3Dv"]
M0 = ["D","Dv","D-D","D-DvL","DvR-DvL","D-D-D", "D-D-DvL","D-DvL-DvR","DvL-DvR-DvR1","D-D-DvL-DvR",
"D-DvL-DvR-DvR1","D-D-DvL-DvR-DvR1"]
# In[11]:
def get_parameters(traj,segmentation,pixel,time,ndim):
deltas = traj[1:] - traj[:-1]
cat = list(set(segmentation))
cat.sort()
#print deltas.shape
Vals = []
for subcat in cat:
if np.isnan(subcat):
continue
wh = np.array(segmentation) == subcat
Mean = np.mean(deltas[wh],axis=0)
#print "inside",subcat,sum(wh)
if subcat in [0,1,2,6,7,8]:
Vals.append([subcat,np.sum(wh),[Mean,np.mean(np.sqrt(deltas[wh]**2),axis=0),
np.mean(np.sum(deltas[wh]**2,axis=1))*pixel**2/time/(2*ndim),
np.mean(np.sum(deltas[wh]**2,axis=1))**0.5]])
else:
Mean = np.mean(deltas[wh],axis=0)
Vals.append([subcat,np.sum(wh),[Mean,np.mean(np.sqrt((deltas[wh]-Mean)**2),axis=0),
np.mean(np.sum((deltas[wh]-Mean)**2,axis=1))*pixel**2/time/(2*ndim),
np.mean(np.sum((deltas[wh]-Mean)**2,axis=1))**0.5]])
return Vals
if __name__ == "__main__":
traj = [[0,0,0],[1,1,1],[1,1,1],[2,2,2],[2,2,2]]
traj = np.array(traj)
segmentation = np.array([0,0,0,np.nan])
print get_parameters(traj,segmentation,1,1,2)
# In[16]:
import numpy as np
from numpy import histogram
import copy
def clean(traj_proba,class_traj,fight=True,options=[],sub=False,append_steady=False):
StateN = {"Ra0": 0,"Ra1":1,"Ra2":2,"Le0":3,"Ri0":4,"Ri1":5,"sRa0":6,"sRa1":7,"sRa2":8,"Steady":9}#,"Ra3": 3,"Ra4":4,"Ra5":5,"Le0":6}
iStateN = {v:k for k,v in StateN.items()}
if not sub:
StateN["Steady"] = 6
Model_type = {"D":[0,["Ra0"]],
"Dv":[1,["Le0"]],
"D-D":[2,["Ra0","Ra1"]],
"D-DvL":[3,["Ra0","Le0"]],
"DvR-DvL":[4,["Le0","Ri0"]],
"D-D-D":[5,["Ra0","Ra1","Ra2"]],
"D-D-DvL":[6,["Ra0","Ra1","Le0"]],
"D-DvL-DvR":[7,["Ra0","Le0","Ri0"]],
"D-D-DvL-DvR":[8,["Ra0","Ra1","Le0","Ri0"]],
"DvL-DvR-DvR1":[9,["Le0","Ri0","Ri1"]],
"D-DvL-DvR-DvR1":[10,["Ra0","Le0","Ri0","Ri1"]],
"D-D-DvL-DvR-DvR1":[11,["Ra0","Ra1","Le0","Ri0","Ri1"]]}
if fight:
Model_type["DvL-DvR-DvR1"][0] = 8
Model_type["D-D-DvL-DvR"][0] = 9
if sub:
Model_type1 = {"sD":[12,["sRa0"]],
"D-sD":[13,["Ra0","sRa0"]],
"sD-sD":[14,["sRa0","sRa1"]],
"sD-DvL":[15,["sRa0","Le0"]],
"sD-D-D":[16,["sRa0","Ra0","Ra1"]],
"sD-sD-D":[17,["sRa0","sRa1","Ra0"]],
"sD-sD-sD":[18,["sRa0","sRa1","sRa2"]],
"sD-D-DvL":[19,["sRa0","Ra0","Le0"]],
"sD-sD-DvL":[20,["sRa0","sRa1","Le0"]],
"sD-DvL-DvR":[21,["sRa0","Le0","Ri0"]],
"sD-D-DvL-DvR":[22,["sRa0","Ra0","Le0","Ri0"]],
"sD-sD-DvL-DvR":[23,["sRa0","sRa1","Le0","Ri0"]],
"sD-DvL-DvR-DvR1":[24,["sRa0","Le0","Ri0","Ri1"]],
"sD-D-DvL-DvR-DvR1":[25,["sRa0","Ra0","Le0","Ri0","Ri1"]],
"sD-sD-DvL-DvR-DvR1":[26,["sRa0","sRa1","Le0","Ri0","Ri1"]]
}
Model_type = dict(Model_type.items()+Model_type1.items())
if append_steady:
for k in Model_type.keys():
Model_type[k][-1].append("Steady")
cats = np.argmax(traj_proba,axis=-1)
if 4 in cats and not 3 in cats:
traj_proba[::,3:5] = traj_proba[::,3:5][::,::-1]
cats = np.argmax(traj_proba,axis=-1)
if 5 in cats and not 3 in cats:
t2 = traj_proba.copy()
t2[::,3] = traj_proba[::,5]
t2[::,5] = traj_proba[::,3]
traj_proba = t2
elif 5 in cats and not 4 in cats:
traj_proba[::,4:6] = traj_proba[::,4:6][::,::-1]
iModel = {v[0]:k for k,v in Model_type.items()}
inside =[StateN[name] for name in Model_type[iModel[class_traj]][1]]
#print inside
traj_max_restricted = np.argmax(traj_proba[::,inside],axis=-1)
seq = [inside[itraj] for itraj in traj_max_restricted]
return np.array(seq)
# In[3]:
def traj_to_dist(traji,bins=20,random_rotation=0,ndim=2):
trajip = traji[1:,:ndim]-traji[:-1,:ndim]
norm = np.sqrt(np.sum(trajip[::]**2,axis=1))[::,np.newaxis]
trajip /= norm
avoid = np.isnan(trajip)
trajip[avoid] = 0
theta = np.arccos(trajip[::,0])
theta [ trajip[::,1] <0] = 2*3.14 - theta [ trajip[::,1] <0]
theta = theta[~avoid[::,0]]
#print trajip[:10,0]
#hist(theta,bins=bins)
count,pos = histogram(theta,bins=bins)
directionp = pos[np.argmax(count)]
"""
count2,pos2 = histogram(theta,bins=2*bins)
directionp2 = pos2[np.argmax(count2)]
"""
count4,pos4 = histogram(theta,bins=4*bins)
directionp4 = pos4[np.argmax(count4)]
#print directionp, directionp4
#if abs(directionp4 - directionp) < 0.3 or abs(directionp4 - directionp) >6.:
# directionp = directionp4
#print directionp, abs(directionp4 - directionp)
#print directionp
#rirectionp=3.14*2-0.1
if ndim == 2:
axis = [[np.cos(directionp),np.cos(directionp+3.14/2)],
[np.sin(directionp),np.sin(directionp+3.14/2)]]
if ndim == 3:
axis = [[np.cos(directionp),np.cos(directionp+3.14/2),0],
[np.sin(directionp),np.sin(directionp+3.14/2),0],
[0,0,1]]
axis=np.array(axis)
#print axis
avoid = np.isnan(traji)
newtraj = (traji-np.mean(traji[~avoid[::,0]].T,axis=1)).T
alligned_traj = np.dot(axis.T,newtraj).T
dist = np.sqrt( np.sum((alligned_traj[1:]-alligned_traj[:-1])**2,axis=1))
#print coeff,latent
normed= [copy.deepcopy(dist),copy.deepcopy(dist)]
normed.append((alligned_traj[1:,0]-alligned_traj[:-1,0])/dist)
normed.append((alligned_traj[1:,1]-alligned_traj[:-1,1])/dist)
if ndim == 3:
normed.append((alligned_traj[1:,2]-alligned_traj[:-1,2])/dist)
normed.append([ len(dist)/100. for i in range(len(dist))])
normed = np.array(normed).T
normed[::,0] = normed[::,0]-np.mean(normed[::,0])
normed[::,0] /= np.std(normed[::,0])
normed[::,1] /= np.std(normed[::,1])
#Zero = normalized - mean
#One = normalized
normed[np.isnan(normed)] = 0
return alligned_traj,normed,directionp,axis
# In[2]:
from Toolv1 import traj_to_dist2
def filter_same(traj):
new_traj = []
#i = 1
zeros=[]
falsezeros = []
nnan = []
nn = 0
addzero=False
#First replace single point by nan
for i in range(1,len(traj)-1):
if np.isnan(traj[i-1][0]) and np.isnan(traj[i+1][0]):
traj[i] = traj[i] * np.nan
#Then remove the nan from the trajectory
for i in range(len(traj)):
if not np.isnan(traj[i][0]):
if addzero:
addzero = False
falsezeros.append(True)
zeros.append(len(new_traj))
nnan.append(nn)
new_traj.append(traj[i])
falsezeros.append(False)
nn = 0
else:
addzero = True
nn += 1
if addzero:
addzero = False
falsezeros.append(True)
nnan.append(nn)
#Not needed
zeros.append(len(new_traj))
return np.array(new_traj),zeros,nnan,falsezeros
def clean_initial_trajectory(traj0,v=1):
traj,zeros,nans,falsezeros = filter_same(traj0)
added0 = False
#We need trajectory that can be subsampled by two
if len(traj) % 2 == 0:
#traj = np.concatenate((traj,np.zeros_like(traj[0:1,::])),axis=0)
traj = np.concatenate((traj,traj[-1:,::]),axis=0)
added0 = True
zeros.append(len(traj)-1)
if v == 1:
alligned_traj,normed,directionp,_ = traj_to_dist(traj)
if v == 2:
alligned_traj,normed = traj_to_dist2(traj)
for i in zeros:
if i == 0:
continue
if i-1 < len(normed):
normed[i-1,::] = 0
return traj,alligned_traj,normed,zeros,nans,added0
def put_back_nan(cat,zeros,nans):
for izeros in zeros:
if izeros <= len(cat) and izeros != 0:
cat[izeros-1] = np.nan
if 0 in zeros:
cat.insert(0,np.nan)
if len(zeros)>=1 and len(cat) <= zeros[-1]:
cat.append(np.nan)
#Middle nan should be increased by one
for i,iz in enumerate(zeros):
if iz != 0 and iz < len(cat)-1:
#print iz,len(cat)
nans[i] += 1
#print nans , zeros
start = 0
newcat = []
for c in cat:
if np.isnan(c):
try:
for inan in range(nans[start]):
newcat.append(np.nan)
except:
newcat.append(np.nan)
start += 1
else:
newcat.append(c)
return newcat
def test_nans():
trajs = [ [[0.2,0.2,0.2],[False,False]],
[[0.2,0.2,0.2,0.2],[False,False,False]],
[[0.2,0.2,np.nan,0.2],[False,True,True]],
[[0.2,0.2,0.2,0.2],[False,False,False]],
[[np.nan,np.nan,0.2,1,0.3,np.nan,0.4,0.4,0.2,np.nan],
[True,True,False,False,True,True,False,False,True]],
[[np.nan,np.nan,0.2,1,0.3,np.nan,0.4,0.4,0.2,np.nan,np.nan],
[True,True,False,False,True,True,False,False,True,True]],
[[np.nan,np.nan,0.2,1,0.3,np.nan,0.4,0.4,0.2,0.2,np.nan],
[True,True,False,False,True,True,False,False,False,True]],
[[0.2,1,0.3,np.nan,0.4,0.4,0.2,0.2,np.nan],
[False,False,True,True,False,False,False,True]],
[[0.2,1,0.3,np.nan,np.nan,0.4,0.4,0.2,0.2,np.nan],
[False,False,True,True,True,False,False,False,True]],
[[0.2,1,0.3,np.nan,np.nan,0.4,0.4,0.2,0.2,0.2,np.nan],
[False,False,True,True,True,False,False,False,False,True]],
[[0.2,1,0.3,0.1,np.nan,np.nan,np.nan,0.4,0.4,0.2,0.2,0.2,np.nan],
[False,False,False,True,True,True,True,False,False,False,False,True]],
[[0.2,1,0.3,0.1,np.nan,np.nan,np.nan,0.4,0.4,0.2,0.2,0.2,np.nan,0.1],
[False,False,False,True,True,True,True,False,False,False,False,True,True]],
[[0.2,1,0.3,0.1,np.nan,np.nan,np.nan,0.4,0.4,0.2,0.2,0.2,np.nan,0.1,np.nan],
[False,False,False,True,True,True,True,False,False,False,False,True,True,True]],
[[0.2,1,0.3,0.1,np.nan,0.1,np.nan,0.4,np.nan,0.2,0.2,0.2,np.nan,0.1,np.nan],
[False,False,False,True,True,True,True,True,True,False,False,True,True,True]]]
for traj0,isnan in trajs:
traj1 = np.array([traj0,traj0]).T
traj,alligned_traj,normed,zeros,nans,added0 = clean_initial_trajectory(traj1)
#graph produce a cat with same size than normed
cat = [1 for i in range(len(normed))]
if added0:
zeros.pop(-1)
cat.pop(-1)
#print "Before"
#print cat
cat = put_back_nan(cat,zeros,nans)
#print traj0
#print np.isnan(cat),isnan
assert np.all(np.isnan(cat) == np.array(isnan))
if __name__ == "__main__":
test_nans()