def test():
import glob
import analysis.experiment as exp
reload(exp)
fn = exp.filename()
print fn
fn = filename(dtype = 'img')
print fn
print glob.glob(fn)
data = exp.load(wid = 0);
print data.shape
import matplotlib.pyplot as plt
plt.figure(1); plt.clf();
img = exp.load_img(t=200000);
plt.imshow(img, cmap = 'gray')
#animate movie
import time
fig, ax = plt.subplots()
figimg = ax.imshow(img, cmap = 'gray');
plt.show();
for t in range(200000, 300000):
figimg.set_data(exp.load_img(t=t));
ax.set_title('t=%d' % t);
time.sleep(0.001)
fig.canvas.draw()
fig.canvas.flush_events()
reload(exp)
sbins = exp.stage_bins(wid = [0,1])
d = exp.load_stage_binned(wid = [0,1], nbins_per_stage=10)
a = exp.load_aligned(wid = all, align='time', dtype = 'speed')
a_th = np.nanpercentile(a, 85);
a[a> a_th] = a_th;
a[np.isnan(a)] = -1.0;
import analysis.plot as fplt;
fplt.plot_array(a)
a = exp.load_aligned(wid = all, align='L2', dtype = 'speed')
a_th = np.nanpercentile(a, 85);
a[a> a_th] = a_th;
a[np.isnan(a)] = -1.0;
import analysis.plot as fplt;
fplt.plot_array(a)
def test():
import glob
import analysis.experiment as exp
reload(exp)
fn = exp.filename()
print fn
fn = filename(dtype='img')
print fn
print glob.glob(fn)
data = exp.load(wid=0)
print data.shape
import matplotlib.pyplot as plt
plt.figure(1)
plt.clf()
img = exp.load_img(t=200000)
plt.imshow(img, cmap='gray')
#animate movie
import time
fig, ax = plt.subplots()
figimg = ax.imshow(img, cmap='gray')
plt.show()
for t in range(200000, 300000):
figimg.set_data(exp.load_img(t=t))
ax.set_title('t=%d' % t)
time.sleep(0.001)
fig.canvas.draw()
fig.canvas.flush_events()
reload(exp)
sbins = exp.stage_bins(wid=[0, 1])
d = exp.load_stage_binned(wid=[0, 1], nbins_per_stage=10)
a = exp.load_aligned(wid=all, align='time', dtype='speed')
a_th = np.nanpercentile(a, 85)
a[a > a_th] = a_th
a[np.isnan(a)] = -1.0
import analysis.plot as fplt
fplt.plot_array(a)
a = exp.load_aligned(wid=all, align='L2', dtype='speed')
a_th = np.nanpercentile(a, 85)
a[a > a_th] = a_th
a[np.isnan(a)] = -1.0
import analysis.plot as fplt
fplt.plot_array(a)
@author: ckirst
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import analysis.experiment as exp
import analysis.plot as aplt
import worm.model as wmodel
npts = 22
w = wmodel.WormModel(npoints=npts)
fn = exp.filename(strain='n2', wid=80, dtype='img')
imgdata = np.load(fn, mmap_mode='r')
ntimes = imgdata.shape[0]
theta, orientation, xy, length = w.theta()
ntheta = theta.shape[0]
data_path = '/home/ckirst/Desktop'
data_names = ['theta', 'orientation', 'xy', 'length', 'center', 'width']
data_sizes = [(ntimes, ntheta), ntimes, (ntimes, 2), ntimes, (ntimes, npts, 2),
(ntimes, npts)]
data_files = [
os.path.join(data_path, 'analysis_2016_10_26_' + n + '.npy')
for n in data_names
coords = []
for i, fn in enumerate(fns):
#print 'processing %d / %d' % (i, len(fns));
data = scipy.io.loadmat(fn)
xy_part = data['x_y_coor']
coords.append(xy_part)
xy = np.vstack(coords)
# (1,1) is invalid coordinates
inv = np.logical_and(xy[:, 0] == 1.0, xy[:, 1] == 1.0)
xy[inv, :] = np.nan
print 'worm %d: len = %d' % (wid, xy.shape[0])
fn_out = exp.filename(strain=strain, wid=wid)
np.save(fn_out, xy)
#%% Precalculate Speed
import analysis.features as feat
delta = 3
for wid in range(nworms):
print 'processing worm %d/%d' % (wid, nworms)
xy = exp.load(strain=strain, wid=wid, memmap=None)
v = feat.speed(xy, delta=delta)
fn_out = exp.filename(strain=strain, wid=wid, dtype='speed')
fig.savefig('align_%d.png' % wid);
a[wid] = aan;
### Save mapping to file
fn = os.path.join(exp.data_directory, 'n2_roam_align_w=%d_s=all.npy');
for wid in range(nworms):
print '%d / %d' % (wid, nworms)
np.save(fn % wid, a[wid])
fn = exp.filename(strain = 'n2', wid = 0, dtype = 'roam_align')
### Generate Roaming Data
for wid in range(nworms):
print '%d / %d' % (wid, nworms)
roam = np.array(rd_data.roam[wid][:rd_data.stage_switch[wid][-1]].copy(), dtype = bool);
fn = exp.filename(strain = 'n2', wid = wid, dtype = 'roam');
roamall = np.ones(len(v[wid])) * np.nan;
roamall[a[wid]] = roam;
np.save(fn, roamall)
@author: ckirst """ import os import numpy as np import matplotlib.pyplot as plt import analysis.experiment as exp import analysis.plot as aplt import worm.model as wmodel npts = 22; w = wmodel.WormModel(npoints = npts); fn = exp.filename(strain = 'n2', wid = 80, dtype = 'img') imgdata = np.load(fn, mmap_mode = 'r'); ntimes = imgdata.shape[0]; theta, orientation, xy, length = w.theta(); ntheta = theta.shape[0]; data_path = '/home/ckirst/Desktop'; data_names = ['theta', 'orientation', 'xy', 'length', 'center', 'width']; data_sizes = [(ntimes, ntheta), ntimes, (ntimes,2), ntimes, (ntimes, npts, 2), (ntimes, npts)]; data_files = [os.path.join(data_path, 'analysis_2016_10_26_' + n + '.npy') for n in data_names]; fig_dir = '/home/ckirst/Desktop/labmeeting'
nbins = 80
plt.figure(10)
plt.clf()
plt.subplot(1, 2, 1)
res = plt.hist2d(r_data, v_data, bins=nbins, range=[[0, r_th], [0, v_th]])
plt.subplot(1, 2, 2)
res = plt.hist2d(r_data,
np.log(v_data),
bins=nbins,
range=[[0, r_th], [np.nanmin(np.log(v_data)),
np.log(v_th)]])
### Classify into Roaming and Dwelling ?
plt.figure(11)
plt.clf()
plt.subplot(1, 2, 1)
plt.hist(r_data, bins=128, range=[0, r_th])
plt.subplot(1, 2, 2)
plt.hist(v_data, bins=128, range=[0, v_th])
for wid in range(nworms):
rt = exp.load(strain='N2', dtype='phi', wid=wid, memmap=None)
roam = rt < 1.42
exp.experiment_directory = '/home/ckirst/Science/Projects/CElegansBehaviour/Experiment/ImageData'
fn_out = exp.filename(strain='N2', wid=wid, dtype='roam')
np.save(fn_out, roam)
coords = [];
for i,fn in enumerate(fns):
#print 'processing %d / %d' % (i, len(fns));
data = scipy.io.loadmat(fn)
xy_part = data['x_y_coor'];
coords.append(xy_part);
xy = np.vstack(coords);
# (1,1) is invalid coordinates
inv = np.logical_and(xy[:,0] == 1.0, xy[:,1] == 1.0);
xy[inv,:] = np.nan;
print 'worm %d: len = %d' % (wid, xy.shape[0])
fn_out = exp.filename(strain = strain, wid = wid);
np.save(fn_out, xy);
#%% Precalculate Speed
import analysis.features as feat;
delta = 3;
for wid in range(nworms):
print 'processing worm %d/%d' % (wid, nworms);
xy = exp.load(strain = strain, wid = wid, memmap = None);
pp[sd2] = ivmall[sd2]
plt.plot(pp, 'k')
plt.ylim(-1, 20)
plt.pause(0.01)
fig.savefig('align_%d.png' % wid)
a[wid] = aan
### Save mapping to file
fn = os.path.join(exp.data_directory, 'n2_roam_align_w=%d_s=all.npy')
for wid in range(nworms):
print '%d / %d' % (wid, nworms)
np.save(fn % wid, a[wid])
fn = exp.filename(strain='n2', wid=0, dtype='roam_align')
### Generate Roaming Data
for wid in range(nworms):
print '%d / %d' % (wid, nworms)
roam = np.array(rd_data.roam[wid][:rd_data.stage_switch[wid][-1]].copy(),
dtype=bool)
fn = exp.filename(strain='n2', wid=wid, dtype='roam')
roamall = np.ones(len(v[wid])) * np.nan
roamall[a[wid]] = roam
np.save(fn, roamall)