def load(self):
"""Returns worm data as memmap or array"""
return exp.load(strain=self.strain,
dtype=self.dtype,
wid=self.wid,
stage=self.stage,
label=self.label,
valid_only=self.valid_only,
replace_invalid=self.replace_invalid,
memmap=self.memmap)
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)
def align(wid=0, verbose=True):
rv = rd_speed[wid][:rd_data.stage_switch[wid][-1]].copy()
#iv = im_speed[wid];
iv = exp.load(strain='n2', dtype='speed', wid=wid)
print 'worm %d: length: img: %d, rd: %d' % (wid, len(iv), len(rv))
print 'worm %d: invalid img: %d, rd: %d' % (wid, np.sum(
np.isnan(iv)), np.sum(np.isnan(rv)))
# do Shays averaging
ivm, am = alg.average_invalidation_wrong(iv)
# align data set
aa = alg.align_indices(ivm, rv, skip=10, window=500, search=50, start=all)
a = am[aa]
if verbose:
ivmall = alg.average_invalidation_wrong(iv, invalid=False)
fig = plt.figure(10)
plt.clf()
ax = plt.subplot(3, 1, 1)
plt.plot(ivmall[a])
plt.plot(rv)
plt.title('worm %d, exp %s' % (wid, fo.experiment_names[wid]))
plt.subplot(3, 1, 2, sharex=ax)
plt.plot(ivmall[a] - rv)
plt.subplot(3, 1, 3)
plt.plot(iv / 1.0, 'gray')
pp = np.ones(len(ivmall)) * np.nan
sd = np.sort(np.setdiff1d(range(len(ivmall)), a))
pp[sd] = ivmall[sd]
plt.plot(ivmall)
plt.plot(pp, 'r')
sd2 = np.sort(np.setdiff1d(range(len(ivmall)), am))
pp = np.ones(len(ivmall)) * np.nan
pp[sd2] = ivmall[sd2]
plt.plot(pp, 'k')
plt.ylim(-1, 20)
plt.pause(0.01)
fig.savefig('align_%d.png' % wid)
return a
def align(wid = 0, verbose = True):
rv = rd_speed[wid][:rd_data.stage_switch[wid][-1]].copy();
#iv = im_speed[wid];
iv = exp.load(strain = 'n2', dtype = 'speed', wid = wid);
print 'worm %d: length: img: %d, rd: %d' % (wid, len(iv), len(rv))
print 'worm %d: invalid img: %d, rd: %d' % (wid, np.sum(np.isnan(iv)), np.sum(np.isnan(rv)))
# do Shays averaging
ivm, am = alg.average_invalidation_wrong(iv);
# align data set
aa = alg.align_indices(ivm, rv, skip = 10, window = 500, search = 50, start = all)
a = am[aa];
if verbose:
ivmall = alg.average_invalidation_wrong(iv, invalid = False);
fig = plt.figure(10); plt.clf();
ax = plt.subplot(3,1,1);
plt.plot(ivmall[a]);
plt.plot(rv)
plt.title('worm %d, exp %s' % (wid, fo.experiment_names[wid]))
plt.subplot(3,1,2, sharex = ax);
plt.plot(ivmall[a] - rv)
plt.subplot(3,1,3);
plt.plot(iv/1.0, 'gray')
pp = np.ones(len(ivmall)) * np.nan;
sd = np.sort(np.setdiff1d(range(len(ivmall)), a));
pp[sd] = ivmall[sd];
plt.plot(ivmall); plt.plot(pp, 'r')
sd2 = np.sort(np.setdiff1d(range(len(ivmall)), am));
pp = np.ones(len(ivmall)) * np.nan;
pp[sd2] = ivmall[sd2];
plt.plot(pp, 'k')
plt.ylim(-1,20)
plt.pause(0.01);
fig.savefig('align_%d.png' % wid);
return a;
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_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)
strain = 'n2' # 'tph1', 'npr1'
strain = 'daf7'
nworms, exp_names, dir_names = f.filenames(strain = strain);
#%% Convert Worm Images
for wid in range(0,nworms):
#file_data = os.path.join(dir_names[wid], 'short%s-%s.mat' % ('%04d'));
file_data = np.sort(np.unique(np.array(glob.glob(os.path.join(dir_names[wid], 'short*.mat')))));
nf = len(file_data);
file_save = os.path.join(exp.data_directory, 'Images/%s_img_w=%d_s=all.npy' % (strain, wid));
wxy = exp.load(strain = strain, wid = wid);
n = wxy.shape[0];
si = 0;
sf = 0;
dat = scipy.io.loadmat(file_data[sf])['mydata'][0];
imgs = np.zeros((n, 151, 151), dtype = 'uint8')
for i in range(n):
print 'wid: %d step: %d/%d fid:%d,%d' % (wid, i,n, sf, si)
img = dat[si];
#check for format otherwise pad
if img.ndim != 3:
print 'image has different dimensionas: %s, step: %d/%d fid:%d,%d' % (str(img.ndim), i,n, sf, si)
img = np.nan * np.ones((151,151,3), dtype = 'uint8');
import numpy as np import analysis.experiment as exp; import matplotlib.pyplot as plt; plt.rcParams['axes.facecolor'] = 'w' plt.rcParams['figure.facecolor'] = 'w' import analysis.video as v; reload(v); #%% Gui reload(v) td = exp.load(wid = 35, dtype = 'roam'); td[np.isnan(td)] = 0.5; #td = np.random.rand(td.shape[0]); dt = 3; g = v.WormGui(strain = 'n2', wid = 35, time = 400000, trajectory_delta=dt, trajectory_length = 3*60*60 / dt, trajectory_data=td, trajectory_cmap=plt.cm.bwr, trajectory_vmin=0, trajectory_vmax=1); #%% #t = 381500; td = exp.load(wid = 35, dtype = 'roam'); td[np.isnan(td)] = 0;
#%%
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])
im_speed = np.ones((nworms, ntimes)) * np.nan
for wid, u in enumerate(v):
#print '%d / %d' % (wid, nworms)
nstrains = 16;
ntot = 16 * 5 + 14;
strains = ['n2'] * nstrains;
strains.extend(['tph1'] * nstrains);
strains.extend(['tdc1'] * nstrains);
strains.extend(['npr1'] * nstrains);
strains.extend(['daf7'] * 14);
strains.extend(['cat2'] * nstrains);
#wids = np.array(range(nstrains) * 4)[:ntot];
# roamng fraction
wids= [];
for s in ['n2','tph1','tdc1', 'npr1','daf7','cat2']:
roam = exp.load(strain = s, wid = all, dtype = 'speed');
means = np.array([np.nanmean(r) for r in roam]);
ids = np.argsort(means);
print ids;
nworms = len(ids);
if nworms <= 16:
wids.append(ids);
else:
sel = np.array(np.linspace(0, nworms-1, np.min([nworms, 18])), dtype = int);
sel = sel[1:-1];
assert(len(sel) == 16)
wids.append(ids[sel]);
wids = np.hstack(wids);
nworms = len(wids);
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'); np.save(fn_out, v); r = feat.rotation(xy, delta = delta); fn_out = exp.filename(strain = strain, wid = wid, dtype = 'rotation'); np.save(fn_out, r);
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]) im_speed = np.ones((nworms, ntimes)) * np.nan; for wid, u in enumerate(v):
plt.rcParams['axes.facecolor'] = 'w'
plt.rcParams['figure.facecolor'] = 'w'
import analysis.video as v;
reload(v);
#%% All worms
wids = np.arange(124);
wids[90] = 124; # wid = 90 is corrupted data !
# sort via average roaming fraction
roaming = exp.load(strain = 'n2', wid = wids, dtype = 'roam');
roaming_mean = np.array([np.nanmean(r) for r in roaming]);
rorder = np.argsort(roaming_mean);
wids = np.array(wids)[rorder];
#%% persitence based ordering
wids = np.array(np.genfromtxt('/home/ckirst/Science/Projects/CElegans/Analysis/WormBehaviour/Data/persistance_median_order.csv'), dtype = int);
wids = wids - 1;
return x_mean; #%% Parameter rate = 3; # Hz sample rate time_bin = 5*60; # [sec] for averaging # derived nbins = rate * time_bin; # number of time bins to calculate mean ### Create Data Matrices r_all_0 = []; nmax = 0; for wid in range(nworms): r = exp.load(strain = strain, wid = wid, dtype = 'rotation') r_mean = average(r, nbins); r_all_0.append(r_mean); nmax = max(nmax, len(r_mean)); r_all = np.zeros((nworms, nmax)); for wid in range(nworms): r_all[wid,:len(r_all_0[wid])] = r_all_0[wid]; v_all_0 = []; nmax = 0; for wid in range(nworms): v = exp.load(strain = strain, wid = wid, dtype = 'speed') v_mean = average(v, nbins); v_all_0.append(v_mean);
import os import numpy as np import matplotlib.pyplot as plt import copy import time import imageprocessing.active_worm as aw import analysis.experiment as exp wid = 80 xydata = exp.load(wid=wid) ntime = xydata.shape[0] #create results data array for traces, points basedir = '/home/ckirst/Science/Projects/CElegansBehaviour/Analysis/Data/2016_07_23_Wormshapes/' shapefile = os.path.join(basedir, "shape_wid=%d_shapes.npy" % wid) measurefile = os.path.join(basedir, "shape_wid=%d_measures.npy" % wid) figfile = os.path.join(basedir, "shape_wid=%d_t=%s.png" % (wid, "%d")) # create the files npoints = 21 #shapedata = np.zeros((ntime,3*npoints)); #np.save(shapefile, shapedata);
def find_plate(strain, wid=0, verbose=True, save=None, thresholds=[160, 175]):
fns = np.sort(
np.unique(glob.glob(os.path.join(dir_names[wid], 'Traj*.mat'))))
img_plate = scipy.io.loadmat(fns[-1])['Trace']
xy = exp.load(strain=strain, wid=wid, dtype='xy')
### Detect circles
import cv2
gray = 2**8 - np.array(np.sum(img_plate, axis=2) / 3, dtype='uint8')
th1 = thresholds[0]
th2 = thresholds[1]
gray[gray < th1] = th1
gray[gray > th2] = th2
gray = np.array(gray, dtype='uint8')
#plt.figure(8); plt.clf();
#plt.subplot(1,2,1);
#plt.imshow(gray, cmap = plt.cm.gray)
#nn = 1500;
#plt.scatter(np.round(xy[-nn:,0]-1), np.round(xy[-nn:,1]-1))
#plt.subplot(1,2,2);
#plt.imshow(gray > 200)
#cimg = cv2.cvtColor(gray,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(gray,
cv2.HOUGH_GRADIENT,
dp=2,
minDist=100,
minRadius=485,
maxRadius=500,
param1=40,
param2=50)
# detect the correct dish
center = circles[0, :, :2]
radius = circles[0, :, 2]
xy = exp.load(strain=strain, wid=wid, dtype='xy')
xyv = xy[np.logical_not(np.isnan(xy[:, 0]))]
pt = xyv[-1]
if verbose:
if circles is None:
print 'worm %d / %d: no cirlces found!' % (wid, nworms)
else:
print 'worm %d / %d: %d circles found !' % (wid, nworms,
len(circles[0]))
fig = plt.figure(8)
plt.clf()
plt.imshow(gray, cmap=plt.cm.gray)
ax = plt.gcf().gca()
for (x, y, r) in circles[0]:
# draw the outer circle
ax.add_artist(
plt.Circle((x, y), r, color='green', fill=False, linewidth=2))
# draw the center of the circle
ax.add_artist(plt.Circle((x, y), 2, color='green', fill=True))
plt.title('Worm %s, Experiment: %s' % (wid, exp_names[wid]))
plt.show()
plate = np.where(np.linalg.norm(center - pt, axis=1) < 1 * radius)[0]
#if len(plate) == 0:
# plate = np.where(np.linalg.norm(center - pt, axis = 1) < 1.05 * radius)[0];
if len(plate) != 1:
print 'could not find plate for worm %d, %s!' % (wid, strain)
assert False
plate = plate[0]
x = center[plate, 0]
y = center[plate, 1]
r = radius[plate]
if verbose:
ax.add_artist(
plt.Circle((x, y), r, color='red', fill=False, linewidth=2))
ax.add_artist(plt.Circle((x, y), 2, color='red', fill=True))
plt.pause(0.01)
if save is not None:
fig.savefig(save)
return [x, y, r]
nworms = len(exp_times); rate = 3; # Hz sample rate time_bin = 1*30; # s; nbins = rate * time_bin; # number of time bins to calculate mean ntimes = 5 * 24 * 60 * 60 / time_bin; # totla number of time steps in analysis v_24hr = np.zeros((nworms, ntimes)) * np.nan; r_24hr = np.zeros((nworms, ntimes)) * np.nan; # calculate data for wid in range(nworms): xy = exp.load(strain = 'N2', dtype = 'xy', wid = wid, memmap=None); rt = exp.load(strain = 'N2', dtype = 'phi',wid = wid, memmap=None); # invalid positions inv = xy[:,0] == 1.0; xy[inv,:] = np.nan; # calculate speed dxy = xy[1:,:] - xy[:-1,:]; v = np.sqrt(dxy[:,0]**2 + dxy[:,1]**2) * rate; nv = len(v); # bin speed nvm = nv/nbins * nbins; v_mean = v[:nvm]; v_mean = v_mean.reshape([nvm/nbins, nbins]);
rate = 3
# Hz sample rate
time_bin = 1 * 60
# s;
nbins = rate * time_bin
# number of time bins to calculate mean
v_data = []
r_data = []
for wid in range(nworms):
print 'processing %d / %d' % (wid, nworms)
xy = exp.load(strain='N2', dtype='xy', wid=wid, memmap=None)
rt = exp.load(strain='N2', dtype='phi', wid=wid, memmap=None)
# invalid positions
inv = xy[:, 0] == 1.0
xy[inv, :] = np.nan
# calculate speed
dxy = np.diff(xy, axis=0)
v = np.sqrt(dxy[:, 0]**2 + dxy[:, 1]**2) * rate
nv = min(len(v), len(rt))
# bin speed
nvm = nv / nbins * nbins
v_mean = v[:nvm]
v_mean = v_mean.reshape([nvm / nbins, nbins])
__author__ = 'Christoph Kirst <*****@*****.**>' __docformat__ = 'rest' import os import glob import matplotlib.pyplot as plt; import scipy.io import numpy as np import analysis.experiment as exp exp.experiment_directory = '/home/ckirst/Science/Projects/CElegansBehaviour/Experiment/ImageData' xy = exp.load(strain = 'N2', dtype = 'xy', wid = 0) ### Create features from xy positions # xy.shape = (ntimes, 2) def dxy(xy, delta = 1): return (xy[delta:] - xy[:-delta]); def speed(xy, delta = 1, dt = 1): # def distance(xy, delta = 1): d = speed(xy, delta, dt = 1);
nstrains = 16
ntot = 16 * 5 + 14
strains = ['n2'] * nstrains
strains.extend(['tph1'] * nstrains)
strains.extend(['tdc1'] * nstrains)
strains.extend(['npr1'] * nstrains)
strains.extend(['daf7'] * 14)
strains.extend(['cat2'] * nstrains)
#wids = np.array(range(nstrains) * 4)[:ntot];
# roamng fraction
wids = []
for s in ['n2', 'tph1', 'tdc1', 'npr1', 'daf7', 'cat2']:
roam = exp.load(strain=s, wid=all, dtype='speed')
means = np.array([np.nanmean(r) for r in roam])
ids = np.argsort(means)
print ids
nworms = len(ids)
if nworms <= 16:
wids.append(ids)
else:
sel = np.array(np.linspace(0, nworms - 1, np.min([nworms, 18])),
dtype=int)
sel = sel[1:-1]
assert (len(sel) == 16)
wids.append(ids[sel])
wids = np.hstack(wids)
nworms = len(wids)
data_files = [
os.path.join(data_path, 'analysis_2016_10_26_' + n + '.npy')
for n in data_names
]
fig_dir = '/home/ckirst/Desktop/labmeeting'
### match head and tails
data = {n: np.load(f, mmap_mode='r+')
for n, f in zip(data_names, data_files)}
theta = data["theta"]
center = data["center"]
orient = data["orientation"]
pos = exp.load(wid=80, dtype='xy')
#head vs center pos
c_pos = pos + data["xy"]
h_pos = pos + data["center"][:, 0]
t_pos = pos + data["center"][:, -1]
valid = data["width"][:, 0] != -1
plt.figure(1)
plt.clf()
t0 = 0
t1 = 1000
plt.plot(t_pos[t0:t1, 0], t_pos[t0:t1, 1])
plt.plot(h_pos[t0:t1, 0], h_pos[t0:t1, 1])
#match head tail postions
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')
np.save(fn_out, v)
r = feat.rotation(xy, delta=delta)
fn_out = exp.filename(strain=strain, wid=wid, dtype='rotation')
np.save(fn_out, r)
def find_plate(strain, wid = 0, verbose = True, save = None, thresholds = [160, 175]):
fns = np.sort(np.unique(glob.glob(os.path.join(dir_names[wid], 'Traj*.mat'))))
img_plate = scipy.io.loadmat(fns[-1])['Trace'];
xy = exp.load(strain = strain, wid = wid, dtype = 'xy')
### Detect circles
import cv2
gray = 2**8 - np.array(np.sum(img_plate, axis = 2) / 3, dtype = 'uint8');
th1 = thresholds[0]; th2 = thresholds[1];
gray[gray < th1] = th1;
gray[gray > th2] = th2;
gray = np.array(gray, dtype = 'uint8');
#plt.figure(8); plt.clf();
#plt.subplot(1,2,1);
#plt.imshow(gray, cmap = plt.cm.gray)
#nn = 1500;
#plt.scatter(np.round(xy[-nn:,0]-1), np.round(xy[-nn:,1]-1))
#plt.subplot(1,2,2);
#plt.imshow(gray > 200)
#cimg = cv2.cvtColor(gray,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp = 2, minDist = 100, minRadius = 485, maxRadius = 500, param1 = 40, param2 = 50)
# detect the correct dish
center = circles[0, :,:2]
radius = circles[0, :, 2]
xy = exp.load(strain = strain, wid = wid, dtype = 'xy')
xyv = xy[np.logical_not(np.isnan(xy[:,0]))];
pt = xyv[-1]
if verbose:
if circles is None:
print 'worm %d / %d: no cirlces found!' % (wid, nworms)
else:
print 'worm %d / %d: %d circles found !' % (wid, nworms, len(circles[0]))
fig = plt.figure(8); plt.clf();
plt.imshow(gray, cmap = plt.cm.gray)
ax = plt.gcf().gca();
for (x, y, r) in circles[0]:
# draw the outer circle
ax.add_artist(plt.Circle((x,y),r, color = 'green',fill = False, linewidth = 2))
# draw the center of the circle
ax.add_artist(plt.Circle((x,y),2, color = 'green', fill = True))
plt.title('Worm %s, Experiment: %s' % (wid, exp_names[wid]) )
plt.show();
plate = np.where(np.linalg.norm(center - pt, axis = 1) < 1 * radius)[0];
#if len(plate) == 0:
# plate = np.where(np.linalg.norm(center - pt, axis = 1) < 1.05 * radius)[0];
if len(plate) != 1:
print 'could not find plate for worm %d, %s!' % (wid, strain);
assert False;
plate = plate[0];
x = center[plate,0]; y = center[plate,1];
r = radius[plate]
if verbose:
ax.add_artist(plt.Circle((x,y),r, color = 'red',fill = False, linewidth = 2))
ax.add_artist(plt.Circle((x,y),2, color = 'red', fill = True))
plt.pause(0.01)
if save is not None:
fig.savefig(save);
return [x,y,r];
time_data = None, time_size = 0.5, time_cmap = plt.cm.Reds,
border = 60, vmin = 84, vmax = 92,
save = fname, fps = 10, dpi = 360,
time_stamp = True, font_size = 8, font_size_title = 12)
#%% Make Movie of 100 worms
reload(v)
wids = range(110);
wids[90] = 111;
# sort via average roaming fraction
roaming = exp.load(strain = 'n2', wid = wids, dtype = 'roam');
roaming_mean = np.array([np.nanmean(r) for r in roaming]);
rorder = np.argsort(roaming_mean);
wids = np.array(wids)[rorder];
#%%
stimes= exp.load_stage_times(strain = 'n2', wid = wids)[:,:-1];
duration = np.sum(np.diff(stimes, axis = 1), axis = 1);
duration_min = np.min(duration);
dtmin = 1;
n = size[0] * size[1]; return np.array([1.0 * xx.sum() / n, 1.0 * yy.sum() / n]); plt.figure(7); plt.clf(); plt.subplot(1,3,1) plt.imshow(img1t); plt.subplot(1,3,2); plt.imshow(img2t); plt.subplot(1,3,3) plt.imshow(img1t - img2t) ## what is the original position: pos = exp.load(wid = 80) xy10 = pos[t0]; xy20 = pos[t0+1] xy1 = center_of_mass(img1t) xy2 = center_of_mass(img2t) xy10 - xy1 xy20 - xy2 def align_images(img1, img2): def
import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'w'
plt.rcParams['figure.facecolor'] = 'w'
import analysis.video as v
reload(v)
#%% All worms
wids = np.arange(124)
wids[90] = 124
# wid = 90 is corrupted data !
# sort via average roaming fraction
roaming = exp.load(strain='n2', wid=wids, dtype='roam')
roaming_mean = np.array([np.nanmean(r) for r in roaming])
rorder = np.argsort(roaming_mean)
wids = np.array(wids)[rorder]
#%% persitence based ordering
wids = np.array(np.genfromtxt(
'/home/ckirst/Science/Projects/CElegans/Analysis/WormBehaviour/Data/persistance_median_order.csv'
),
dtype=int)
wids = wids - 1
import numpy as np import matplotlib.pyplot as plt; import copy import time import imageprocessing.active_worm as aw; import analysis.experiment as exp; wid = 80; xydata = exp.load(wid = wid) ntime = xydata.shape[0]; #create results data array for traces, points basedir = '/home/ckirst/Science/Projects/CElegansBehaviour/Analysis/Data/2016_07_23_Wormshapes/' shapefile = os.path.join(basedir, "shape_wid=%d_shapes.npy" % wid); measurefile = os.path.join(basedir, "shape_wid=%d_measures.npy" % wid); figfile = os.path.join(basedir, "shape_wid=%d_t=%s.png" % (wid, "%d")); # create the files npoints = 21; #shapedata = np.zeros((ntime,3*npoints)); #np.save(shapefile, shapedata);
return x_mean; #%% Parameter rate = 3; # Hz sample rate time_bin = 5*60; # [sec] for averaging # derived nbins = rate * time_bin; # number of time bins to calculate mean ### Create Data Matrices r_all_0 = []; nmax = 0; for wid in range(nworms): r = exp.load(strain = strain, wid = wid, dtype = 'rotation') r_mean = average(r, nbins); r_all_0.append(r_mean); nmax = max(nmax, len(r_mean)); r_all = np.zeros((nworms, nmax)); for wid in range(nworms): r_all[wid,:len(r_all_0[wid])] = r_all_0[wid]; v_all_0 = []; nmax = 0; for wid in range(nworms): v = exp.load(strain = strain, wid = wid, dtype = 'speed') v_mean = average(v, nbins); v_all_0.append(v_mean);
data_files = [os.path.join(data_path, 'analysis_2016_10_26_' + n + '.npy') for n in data_names];
fig_dir = '/home/ckirst/Desktop/labmeeting'
### match head and tails
data = {n : np.load(f, mmap_mode = 'r+') for n,f in zip(data_names, data_files)};
theta = data["theta"];
center = data["center"]
orient = data["orientation"]
pos = exp.load(wid = 80, dtype = 'xy')
#head vs center pos
c_pos = pos + data["xy"];
h_pos = pos + data["center"][:,0];
t_pos = pos + data["center"][:,-1];
valid = data["width"][:,0] != -1;
plt.figure(1); plt.clf();
t0 = 0; t1 = 1000;
plt.plot(t_pos[t0:t1,0], t_pos[t0:t1,1])
plt.plot(h_pos[t0:t1,0], h_pos[t0:t1,1])
#match head tail postions
def load(self):
"""Returns worm data as memmap or array"""
return exp.load(strain = self.strain, dtype = self.dtype, wid = self.wid, stage = self.stage, label = self.label,
valid_only = self.valid_only, replace_invalid = self.replace_invalid, memmap = self.memmap);
import os
import numpy as np
import analysis.experiment as exp
import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'w'
plt.rcParams['figure.facecolor'] = 'w'
import analysis.video as v
reload(v)
#%% Gui
reload(v)
td = exp.load(wid=35, dtype='roam')
td[np.isnan(td)] = 0.5
#td = np.random.rand(td.shape[0]);
dt = 3
g = v.WormGui(strain='n2',
wid=35,
time=400000,
trajectory_delta=dt,
trajectory_length=3 * 60 * 60 / dt,
trajectory_data=td,
trajectory_cmap=plt.cm.bwr,
trajectory_vmin=0,
trajectory_vmax=1)
