def classifyWorm(labels, wid, d, s, nbins=256, verbose=True):
XYwdata = XYdata[wid].copy()
w = wd.WormData(XYwdata[:, 0:2],
stage=XYwdata[:, -1],
valid=XYwdata[:, 0] != 1,
label=('x', 'y'),
wid=wid)
w.replaceInvalid()
ds = w.calculateDistances(n=delays[d] + 1, stage=s)
rs = w.calculateRotations(n=delays[d] + 1, stage=s)
ddata = np.vstack([np.log(ds[:, -1]), (rs[:, -1])]).T
#gmmdata = np.vstack([dists[:,j], (rots[:,j])]).T
#ddata.shape
nanids = np.logical_or(np.any(np.isnan(ddata), axis=1),
np.any(np.isinf(ddata), axis=1))
ddata = ddata[~nanids, :]
#ddata.shape
pred2 = -np.ones(rs.shape[0])
pred2nn = pred2[~nanids]
pred2nn.shape
for i in range(2):
ddata[:, i] = ddata[:, i] - ddata[:, i].min()
ddata[:, i] = (ddata[:, i] / ddata[:, i].max()) * (nbins - 1)
ddata = np.asarray(ddata, dtype=int)
for i in range(2):
ddata[ddata[:, i] > (nbins - 1), i] = nbins - 1
for i in xrange(ddata.shape[0]):
pred2nn[i] = labels[ddata[i, 0], ddata[i, 1]]
pred2[~nanids] = pred2nn
#pred2nn.max();
if verbose:
plt.figure(506)
plt.clf()
w.plotTrace(ids=shiftData(pred2, delays[d] / 2, nan=-1), stage=s)
if verbose > 2:
rds = w.calculateRotations(n=delays[d] + 1, stage=s)
plt.figure(510)
plt.clf()
w.plotDataColor(data=shiftData(rds[:, -1], delays[d] / 2, nan=-1),
c=pred2,
lw=0,
s=20,
stage=s,
cmap=cm.rainbow)
dts = w.calculateDistances(n=delays[d] + 1, stage=s)
plt.figure(510)
plt.clf()
w.plotDataColor(data=dts[:, -1],
c=pred2,
lw=0,
s=20,
stage=s,
cmap=cm.rainbow)
plt.figure(507)
plt.clf()
w.plotTrajectory(stage=s,
colordata=shiftData(rds[:, -1] / (delays[d] + 1) /
np.pi,
delays[d] / 2,
nan=-.1))
dist2 = w.calculateLengths(n=200)
plt.figure(511)
plt.clf()
w.plotDataColor(data=dist2[:, -1], c=pred2, lw=0, s=20)
return pred2
import numpy as np
import matplotlib.pyplot as plt
import wormdata as wd
#make face path
#random motion
n1 = 400
n2 = 400
n = n1 + n2
path = np.zeros((n, 2))
path[:n1, :] = np.random.rand(n1, 2)
path[n1:n, :] = 0.1 * np.random.rand(n1, 2) + np.outer(np.linspace(0, 100, n2),
[0, 1])
w = wd.WormData(path, stage=np.zeros(n), label=('x', 'y'), wid=1)
w.replaceInvalid()
plt.figure(1)
plt.clf()
w.plotTrajectory(size=50)
nn = 20
diffs = w.calculateDiffusionParameter(n=nn, parallel=False)
plt.figure(2)
plt.clf()
diffsshift = np.zeros_like(diffs)
diffsshift[nn / 2:, :] = diffs[:-nn / 2, :]
diffsshift[:nn / 2.:] = np.NaN
w.plotTrajectory(colordata=diffsshift[:, 0], size=diffsshift[:, -1] * 1000)
basedir = '/home/ckirst/Science/Projects/CElegansBehaviour/';
filename = os.path.join(basedir, 'Experiment/individuals_N2_X_Y.mat')
data = io.loadmat(filename);
XYdata = data['individual_X_Y'][0];
print XYdata.shape
print XYdata[0].shape
wid = 71;
#wid = 1;
XYwdata = XYdata[wid].copy();
reload(wd)
w = wd.WormData(XYwdata[:,0:2], stage = XYwdata[:,-1], valid = XYwdata[:,0] != 1, label = ('x', 'y'), wid = wid);
w.replaceInvalid();
plt.figure(1); plt.clf();
w.plotData();
plt.figure(2); plt.clf();
w.plotTrajectory()
plt.figure(3); plt.clt();
w.plotTrace(ids = w.stage())
##############################################################################
# small test set
reload(wd)
import wormdata as wd
## test work data
reload(wd)
wid = 1
xyd = np.array(
[[0, 0, 0, 0, 1, 1, 1, 2, 2.1, 2.2, 2.3, 2.1, 2.4, 2.6],
[0, 0.1, 1, 1.1, 1.2, 2, 3, 3.1, 3.2, 3.1, 3.3, 3.4, 3.1, 3.7]],
dtype=float).T
nn = xyd.shape[0]
w = wd.WormData(xyd,
stage=np.ones(nn),
valid=np.ones(nn, dtype=bool),
label=('x', 'y'),
wid=wid)
w.replaceInvalid()
w.plotTrajectory()
rads = [0.1, 0.5, 1., 2]
npath = 4
pe = w.calculatePathEntropy(radius=rads, n=npath)
print pe
plt.clf()
nr = pe.shape[1]
for i in range(nr):