def norm(i):
print 'scale %d' % i
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r')
fres_tn = exp.load('%s_%s_time_normalized_scales_mean.npy' %
(strain, feat),
memmap='r+')
fres_tn[:, i, :] = exp.bin_data(fres[:, i, :], sbins)
def run(i):
print 'processing worm %d' % i
fdat = exp.load('%s_%s.npy' % (strain, feat), memmap='r')
signal = fdat[[i], :]
res = average(signal, kernel_flat, scales)
#res = average(signal, kernel_gauss, scales);
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r+')
fres[i, :, :] = res[0]
def plot_shape_trajectory(tstart = 500000, tend = ntime, nfig = 100): xy = exp.load(wid = wid); # head tail positions xycht = np.load(posfile); print xycht.shape # reduce to time window of interest xy = xy[tstart:tend,:]; xycht = xycht[tstart:tend,:]; plt.figure(nfig); plt.clf(); plotTrajectory(xy, cmap = cm.Greys, line = 'gray'); off= [75,75] plotTrajectory(xycht[:,0,:] + xy - off, cmap = cm.Reds, line = 'red'); plotTrajectory(xycht[:,1,:] + xy - off, cmap = cm.Blues, line = None); plotTrajectory(xycht[:,2,:] + xy - off, cmap = cm.Greens, line = None);
def plot_body_line(tstart = 500000, tend = ntime, dt = 1, nfig = 200, lineids = [0]):
xy = exp.load(wid = wid);
# head tail positions
lines = np.load(linefile, mmap_mode = 'r');
#print lines.shape
pos = np.load(posfile);
# reduce to time window of interest
plt.figure(nfig); plt.clf();
off = [75,75];
for i in lineids:
for t in range(tstart, tend, dt):
xyt = xy[t,:];
cl = lines[t,i,:,:];
print xyt
cc = cm.Blues;
plt.plot(cl[0,:] + np.round(xyt[0]) - off[0], -cl[1,:] + np.round(xyt[1]) - off[1], color = cc(1.0 * t/(tend-tstart)));
Created on Tue Jun 21 02:35:04 2016 @author: ckirst """ import os import numpy as np import matplotlib.pyplot as plt from scripts.analyse_wormshape import analyse_shape import experiment as exp; xydata = exp.load(wid= 80) ntime = xydata.shape[0]; #create results data array for traces, points wid = 80; basedir = '/home/ckirst/Science/Projects/CElegansBehaviour/Analysis/Data/2016_06_21_Wormshapes/' linefile = os.path.join(basedir, "shape_wid=%d_lines.npy" % wid); posfile = os.path.join(basedir, "shape_wid=%d_positions.npy" % wid); curfile = os.path.join(basedir, "shape_wid=%d_curvature.npy" % wid); figfile = os.path.join(basedir, "shape_wid=%d_t=%s.png" % (wid, "%d")); # create the files linedata = np.zeros((ntime, 3, 2, 50));
#animate movie fig, ax = plt.subplots(1,3) wid = 96; img = exp.load_img(t=100000, wid = wid)[20:130, 20:130]; img2 =exp.load_img(t=100000+1, wid = wid)[20:130, 20:130]; figimg = ax[0].imshow(img, cmap = 'gray', clim = (0,256), interpolation="nearest"); delta = np.asarray(img2, dtype = float) - np.asarray(img, dtype = float) figimg2 = ax[1].imshow(delta, cmap = 'jet', clim = (-30,60), interpolation="nearest"); plt.show(); xy = exp.load(wid = wid); t0 = 507000; #line, = ax[2].plot([], [], lw=2, color = 'b', marker = 'o'); for t in range(t0, t0 + 15000, 2): img = img2; img2 = exp.load_img(t=t+1, wid = wid)[20:130, 20:130]; delta = np.asarray(img2, dtype = float) - np.asarray(img, dtype = float) figimg.set_data(img); figimg2.set_data(delta); #line.set_data(xy[t0:t,0], xy[t0:t,1]); #ax[2].plot(xy[t-1:t,0], xy[t-1:t,1], color = 'gray'); ax[2].scatter(xy[t,0], -xy[t,1]);
fdat = exp.load('%s_%s.npy' % (strain, feat), memmap='r')
signal = fdat[[i], :]
res = average(signal, kernel_flat, scales)
#res = average(signal, kernel_gauss, scales);
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r+')
fres[i, :, :] = res[0]
from multiprocessing import Pool #, cpu_count;
#pool = Pool(processes = cpu_count()-6);
pool = Pool(processes=12)
pool.map(run, range(data.nworms))
### Time normalize data
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r')
sbins = exp.stage_bins(data, nbins=8192)
res_shape = (fres.shape[0], fres.shape[1], sbins[0].shape[1])
exp.memmap('%s_%s_time_normalized_scales_mean.npy' % (strain, feat),
shape=res_shape,
mode='w+')
def norm(i):
print 'scale %d' % i
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r')
fres_tn = exp.load('%s_%s_time_normalized_scales_mean.npy' %
(strain, feat),
memmap='r+')
fres_tn[:, i, :] = exp.bin_data(fres[:, i, :], sbins)
printExecutionTime()
outputResults()
sys.exit(1)
def printExecutionTime():
print("total time: %s" % showTime(time.time() - startTime))
def outputResults():
with open(args.output, "w") as f:
pickle.dump(exp, f)
pickle.dump(results, f)
exp = experiment.load(args.exp)
experiment.clearHDFS()
results = {}
runNum = 0
totalRuns = len(exp) * args.numruns
startTime = time.time()
signal.signal(signal.SIGINT, exitHandler)
for run in exp:
print("running %s" % run)
s_hat = experiment.Estimate()
margins_hat = [experiment.Estimate() for i in xrange(2)]
# record the complete final info from the history server for each run just in
# case it's useful later.
finalInfo = []
for i in range(args.numruns):
info = logger.AppInfo(args.host, measure=False)
dur_up_min[w,b] = 0;
dur_up_mean[w,b] = 0;
du = durs_dw[w][b];
if len(du) > 0:
dur_dw_max[w,b] = du.max();
dur_dw_min[w,b] = du.min();
dur_dw_mean[w,b] = du.mean();
else:
dur_dw_max[w,b] = 1;
dur_dw_min[w,b] = 0;
dur_dw_mean[w,b] = 0;
dord = exp.load('%s_%s_order.npy' % (strain, feat));
mm = dur_up_mean.max()
dur_up_mean[dur_up_mean == mm] = 0;
plt.figure(2); plt.clf();
dur_names = ['dur_up_max', 'dur_up_min', 'dur_dw_max', 'dur_dw_min', 'dur_up_mean', 'dur_dw_mean', 'ndurs_up'];
fplt.plot_image_array((dur_up_max, dur_up_min, dur_dw_max, dur_dw_min, dur_up_mean, dur_dw_mean, ndurs_up), order = dord, names = dur_names)
plt.tight_layout()
# make histograms for each worm
dbins = 10;
dist_dur_up = np.zeros((nworms, nbins, dbins));
dist_dur_dw = np.zeros((nworms, nbins, dbins));
Matlab to Numpy Data Conversion Routines for Worm Images """ __license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>' __author__ = 'Christoph Kirst <*****@*****.**>' __docformat__ = 'rest' # check cam data: import matplotlib.pyplot as plt; import scipy.io import numpy as np import experiment as exp reload(exp) wxy = exp.load(wid = 96, valid_only=False); i = 0; i = 2; i += 1; #fn = '/data/Science/Projects/CElegansBehaviour/Experiment/CAM814A4/corrdCAM814A4CAM814_2015-11-20-174505-%04d.mat' % i; fn = '/data/Science/Projects/CElegansBehaviour/Experiment/CAM819A3/corrdCAM819A3CAM819_2015-09-14-175453-%04d.mat' % i; data = scipy.io.loadmat(fn) xy = data['x_y_coor'] plt.figure(10); plt.clf(); plt.subplot(3,1,1); plt.plot(xy[:,0] - xy[0,0])
Matlab to Numpy Data Conversion Routines for Worm Images """ __license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>' __author__ = 'Christoph Kirst <*****@*****.**>' __docformat__ = 'rest' # check cam data: import matplotlib.pyplot as plt import scipy.io import numpy as np import experiment as exp reload(exp) wxy = exp.load(wid=96, valid_only=False) i = 0 i = 2 i += 1 #fn = '/data/Science/Projects/CElegansBehaviour/Experiment/CAM814A4/corrdCAM814A4CAM814_2015-11-20-174505-%04d.mat' % i; fn = '/data/Science/Projects/CElegansBehaviour/Experiment/CAM819A3/corrdCAM819A3CAM819_2015-09-14-175453-%04d.mat' % i data = scipy.io.loadmat(fn) xy = data['x_y_coor'] plt.figure(10) plt.clf() plt.subplot(3, 1, 1) plt.plot(xy[:, 0] - xy[0, 0])
#animate movie
fig, ax = plt.subplots(1, 3)
wid = 96
img = exp.load_img(t=100000, wid=wid)[20:130, 20:130]
img2 = exp.load_img(t=100000 + 1, wid=wid)[20:130, 20:130]
figimg = ax[0].imshow(img, cmap='gray', clim=(0, 256), interpolation="nearest")
delta = np.asarray(img2, dtype=float) - np.asarray(img, dtype=float)
figimg2 = ax[1].imshow(delta,
cmap='jet',
clim=(-30, 60),
interpolation="nearest")
plt.show()
xy = exp.load(wid=wid)
t0 = 507000
#line, = ax[2].plot([], [], lw=2, color = 'b', marker = 'o');
for t in range(t0, t0 + 15000, 2):
img = img2
img2 = exp.load_img(t=t + 1, wid=wid)[20:130, 20:130]
delta = np.asarray(img2, dtype=float) - np.asarray(img, dtype=float)
figimg.set_data(img)
figimg2.set_data(delta)
#line.set_data(xy[t0:t,0], xy[t0:t,1]);
#ax[2].plot(xy[t-1:t,0], xy[t-1:t,1], color = 'gray');
ax[2].scatter(xy[t, 0], -xy[t, 1])
