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)
fig = plt.figure(180)
plt.clf()
for i in range(9):
tt = pca_comp[i]
#tta = tt - np.mean(tt);
plt.subplot(3, 3, i + 1)
plt.plot(tt, linewidth=2, c='r')
plt.title('PCA %d' % (i + 1))
plt.ylim(-0.6, 0.6)
fig.savefig(os.path.join(fig_dir, 'theta_pca_9_plot.png'), facecolor='white')
pcs = pca.Y
pcs.shape
aplt.plot_array(pcs.T)
fig = plt.figure(190)
plt.clf()
#plt.imshow(pca.Wt, interpolation = 'none', aspect = 'auto', cmap = 'viridis')
#plt.colorbar(pad = 0.01,fraction = 0.01)
#plt.title('pca vectors');
plt.plot(np.cumsum(pca.fracs), 'k', linewidth=1)
plt.ylim(0, 1)
fig.savefig(os.path.join(fig_dir, 'theta_pca_variance.png'), facecolor='white')
## time evolution of components
fig = plt.figure(200)
plt.clf()
# v = exp.load(strain = strain, wid = wid, dtype = 'speed')
# #transitions_id_fine[wid][1:-1] = find_transitions(v, transitions_id[wid][1:-1], window = 20000, bin_width = 3 * 60)
# transitions_id_fine[wid][1:-1] = find_transitions(v, transitions_id[wid][1:-1], window = 10000, bin_width = 3 * 60 * 15)
# not fine tuning really required !
#%% Plot original data with transition points
r_full_plt = r_full.copy();
r_full_plt[np.isnan(r_full)] = 0;
r_th = np.percentile(r_full_plt.flat, [95]);
r_full_plt[r_full_plt > r_th] = r_th;
for wid in range(nworms):
for s in range(6):
r_full_plt[wid, max(0, transitions_id_fine[wid,s]-200):min(r_full_plt.shape[1], transitions_id_fine[wid,s]+200)] = 1.2*r_th
fplt.plot_array(r_full_plt)
v_full_plt = v_full.copy();
v_full_plt[np.isnan(v_full)] = 0;
v_th = np.percentile(v_full_plt.flat, [95]);
v_full_plt[v_full_plt > v_th] = v_th;
for wid in range(nworms):
for s in range(6):
v_full_plt[wid, max(0, transitions_id_fine[wid,s]-200):min(v_full_plt.shape[1], transitions_id_fine[wid,s]+200)] = 1.2*v_th
fplt.plot_array(v_full_plt)
#%%############################################################################
### Save data
r_full = np.zeros((nworms, lr)); for wid in range(nworms): v = exp.load(strain = strain, dtype = 'speed', wid = wid, memmap = None); nv = len(v); v_full[wid,:nv] = v; r = exp.load(strain = strain, dtype = 'rotation', wid = wid, memmap = None); nr = r.shape[0]; r_full[wid,:nr] = r; #%% r_plt = r_all.copy(); r_plt[np.isnan(r_plt)] = 0.0; fplt.plot_array(r_plt) #%% Determine Lethargus phases l_pks_0 = []; k = 0; verbose = True; for wid in range(nworms): r = exp.load(strain = strain, wid = wid, dtype = 'rotation') r_mean = average(r, nbins); # smooth roaming curve rf = sig.savgol_filter(r_mean, 51, 7)
plt.clf() plt.hist(curv, bins=256) plt.figure(1) plt.clf() plt.hist(bend, bins=256) # resolve for stages etc mt = np.max(np.abs(theta), axis=1) idx = mt < 1 theta_red = theta[idx] theta_red.shape aplt.plot_array(theta_red.T) ### PCA plt.figure(12) plt.clf() aplt.plot_pca(theta) pca = aplt.PCA(theta) pca_comp = pca.Wt import worm.model as wm import worm.geometry as wgeo w = wm.WormModel(npoints=22) fig = plt.figure(17)
strain = 'daf7' nworms, exp_names, dir_names = f.filenames(strain = strain); #%% straind = 'daf-7' strain = 'daf7'; rd_data = dexp.load_data(strain = straind); rd_speed = rd_data.speed; rd_speed_th = rd_speed.copy(); th = np.nanpercentile(rd_speed, 95); rd_speed_th[rd_speed_th > th] = th; fplt.plot_array(rd_speed_th) v = []; for wid in range(nworms): v.append(exp.load(strain = strain, dtype = 'speed', wid = wid, memmap = None)); ntimes = max([len(vv) for vv in v]) #v = []; #for wid in range(nworms): # print '%d / %d' % (wid, nworms); # xy = exp.load(strain = 'N2', dtype = 'xy', wid = wid, memmap = None); # xy = xy[np.logical_not(np.any(np.isnan(xy), axis = 1))] # vv = np.linalg.norm(xy[3:,:] - xy[:-3,:], axis = 1); # v.append(vv)
#tta = tt - np.mean(tt);
plt.subplot(3,3,i+1)
plt.plot(tt, linewidth = 2, c = 'r')
plt.title('PCA %d' % (i+1))
plt.ylim(-0.6, 0.6)
fig.savefig(os.path.join(fig_dir, 'theta_pca_9_plot.png'), facecolor='white')
pcs = pca.Y;
pcs.shape
aplt.plot_array(pcs.T)
fig = plt.figure(190); plt.clf();
#plt.imshow(pca.Wt, interpolation = 'none', aspect = 'auto', cmap = 'viridis')
#plt.colorbar(pad = 0.01,fraction = 0.01)
#plt.title('pca vectors');
plt.plot(np.cumsum(pca.fracs), 'k', linewidth = 1)
plt.ylim(0,1)
fig.savefig(os.path.join(fig_dir, 'theta_pca_variance.png'), facecolor='white')
## time evolution of components
fig = plt.figure(200); plt.clf();
"""
__author__ = 'Christoph Kirst <*****@*****.**>'
__license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>'
__docformat__ = 'rest'
import numpy as np
import scipy.io as io
import analysis.experiment as exp
import analysis.plot as plt
dat = exp.load_stage_binned(dtype='roam', nbins_per_stage=75)
dat[np.isnan(dat)] = 0
plt.plot_array(dat)
mdict = {'roaming': dat}
io.savemat(
'/home/ckirst/Science/Projects/CElegansBehaviour/Analysis/WormBehaviour/Data/roaming_auto_stages.mat',
mdict)
dat = exp.load_stage_binned(dtype='roam', nbins_per_stage=10)
dat[np.isnan(dat)] = 0
plt.plot_array(dat)
mdict = {'roaming': dat}
io.savemat(
import numpy as np import matplotlib.pyplot as plt import analysis.experiment as exp import analysis.plot as aplt import worm.model as wm; # load image img = exp.load_img(wid = 80, t= 500000, smooth = 1.0); aplt.plot_array(img); from skimage.filters import threshold_otsu threshold_factor = 0.95; level = threshold_factor * threshold_otsu(img); from imageprocessing.masking import mask_to_phi phi_img = mask_to_phi(img < level); ### worm -> Phi w = wm.WormModel(npoints = 20); w.from_image(img, sigma = None) w.plot(image = img);
__license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>' __author__ = 'Christoph Kirst <*****@*****.**>' __docformat__ = 'rest' import numpy as np import matplotlib.pyplot as plt import analysis.experiment as exp import analysis.plot as aplt import worm.model as wm # load image img = exp.load_img(wid=80, t=500000, smooth=1.0) aplt.plot_array(img) from skimage.filters import threshold_otsu threshold_factor = 0.95 level = threshold_factor * threshold_otsu(img) from imageprocessing.masking import mask_to_phi phi_img = mask_to_phi(img < level) ### worm -> Phi w = wm.WormModel(npoints=20) w.from_image(img, sigma=None)
strain = 'daf7'
nworms, exp_names, dir_names = f.filenames(strain=strain)
#%%
straind = 'daf-7'
strain = 'daf7'
rd_data = dexp.load_data(strain=straind)
rd_speed = rd_data.speed
rd_speed_th = rd_speed.copy()
th = np.nanpercentile(rd_speed, 95)
rd_speed_th[rd_speed_th > th] = th
fplt.plot_array(rd_speed_th)
v = []
for wid in range(nworms):
v.append(exp.load(strain=strain, dtype='speed', wid=wid, memmap=None))
ntimes = max([len(vv) for vv in v])
#v = [];
#for wid in range(nworms):
# print '%d / %d' % (wid, nworms);
# xy = exp.load(strain = 'N2', dtype = 'xy', wid = wid, memmap = None);
# xy = xy[np.logical_not(np.any(np.isnan(xy), axis = 1))]
# vv = np.linalg.norm(xy[3:,:] - xy[:-3,:], axis = 1);
# v.append(vv)
ntimes = max([len(u) for u in v])