def main():
# Loading any data in the
# phase space from the appropriate folder
fname = "out_fil.h5"
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
# all functions here, iterate parameter set by set
for folder in folders:
fils, sim = read_data(folder, fname)
traj(fils)
plt.show()
return
def main():
# Loading any data in the
# phase space from the appropriate folder
fname = "out_fil.h5"
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [0.4, 0.6, 0.8, 1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
# all functions here, iterate parameter set by set
for pai, folder in zip(pa, folders):
fils, sim = read_data(folder, fname)
for i, t in enumerate([0.3]):
#traj_distance(fils,t=t)
#traj_time(fils,t=t)
sigma = 50
#traj(fils)
traj_time_vel_corr(fils, t=0.3, sigma=50, pai=pai)
traj_distance_vel_corr(fils, t=0.3, sigma=50)
#traj(fils,t=t,sigma=sigma)
#traj_distance(fils,t=t, sigma=sigma)
#traj_time(fils,t=t, sigma=sigma)
plt.show()
return
def main():
# Loading any data in the
# phase space from the appropriate folder
fname="out_fil.h5"
base ="/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density=[0.4]; pa=[1.0]; pp=[0.0];
folders=[]
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base+'density_'+str(di)+'/pa_'+str(pai)+'/pp_'+str(ppi)+'/')
# all functions here, iterate parameter set by set
for folder in folders:
fils, sim = read_data(folder, fname)
data = fils.xi.transpose(2,1,0) # Adopt Guglielmo's convention here to avoid confusion
pkl.dump(data, open('com.pkl','wb'))
data = fils.ori.transpose(2,1,0) # Adopt Guglielmo's convention here to avoid confusion
pkl.dump(data, open('ori.pkl','wb'))
return
def disp(fname):
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
for folder in folders:
cells, sim = read_data(folder, fname)
rodids = [0, 10]
f, (ax1, ax2) = plt.subplots(1, 2)
for rodid in rodids:
traj = cells.xi[5000:, :, rodid]
print(traj.shape)
disp = traj - traj[0]
disp2 = np.sqrt(np.square(disp[:, 0]) + np.square(disp[:, 1]))
ax1.plot(disp2)
ax2.plot(traj[:, 0], traj[:, 1])
ax2.set_aspect('equal')
def main():
### get the data folder
parser = argparse.argumentparser()
parser.add_argument("-sdfl", "--simdatafolder", \
help="folder containing the simulation data")
parser.add_argument("-dfl", "--datafolder", \
help="folder containing analysis data")
parser.add_argument("-sfl", "--savefolder", \
help="folder to save the resulting analysis data inside")
parser.add_argument("-figfl", "--figfolder", \
help="folder to save the figures inside")
parser.add_argument("-ti", "--inittime", nargs="?", const=10, \
type=int, help="initial time step")
parser.add_argument("-tf", "--fintime", nargs="?", const=100, \
type=int, help="final timestep")
parser.add_argument("-s","--savepdf", action="store_true", \
help="decide whether to save in pdf or not")
args = parser.parse_args()
### read the data and general information from the folder
beads, sim = read_write.read_data(args.simdatafolder)
beads.calc_img_pos(sim.lx)
rcut = 15. # size of the interrogation circle
dcut = 0.1 # defect strength cut
dcrit = 9. # clustering distance threshold criteria
ncrit = 15 # clustering size threshold criteria
for step in range(args.inittime, args.fintime):
print 'step / last_step: ', step, args.fintime
### load the possible defect points
sfilepath = args.datafolder + 'possible_defect_pts_cpp_' + str(
step) + '.h5'
possible_defect_pts = read_write.load_h5_data(sfilepath)
### cluster the possible defect points and plot the cluster
xcm, ycm = cluster_analysis(possible_defect_pts, dcrit, ncrit, sim, step, \
beads.xi[step, 0, :], beads.xi[step, 1, :], beads.cid)
### for each of the defect points found by clustering
# calculate the defect strength and plot each point
defect_pts = find_defects.recompute_defects(\
xcm, ycm, beads, sim, rcut, dcut, step, args.figfolder)
### save the ultimate defect points
sfilepath = args.savefolder + 'defect_pts_' + str(step) + '.txt'
read_write.save_data(defect_pts, sfilepath)
return
def disp(fname):
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
for folder in folders:
rodids = [0]
f, (ax1, ax2, ax3) = plt.subplots(3, 1)
for rodid in rodids:
cells, sim = read_data(folder, fname)
traj = cells.xi[5000:, :, rodid]
print(traj.shape)
disp = traj - traj[0]
disp2 = np.sqrt(np.square(disp[:, 0]) + np.square(disp[:, 1]))
ax3.plot(traj[:1000, 0], traj[:1000, 1], c='b')
ax3.plot(traj[1000:2000, 0], traj[1000:2000, 1], c='y')
ax3.plot(traj[2000:3000, 0], traj[2000:3000, 1], c='g')
ax3.plot(traj[3000:4000, 0], traj[3000:4000, 1], c='r')
ax3.plot(traj[4000:5000, 0], traj[4000:5000, 1], c='k')
ax3.set_aspect('equal')
lag = 100
thresh = 10
slope = disp2[:-lag] - disp2[lag:]
ax1.plot(np.abs(slope))
#ax2.plot(np.abs(slope))
ax2.plot(np.abs(slope) > thresh)
plotter = slope
plotter[np.abs(slope) > thresh] = 1
plotter[np.abs(slope) < thresh] = 0
def gug(fname):
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
for folder in folders:
cells, sim = read_data(folder, fname)
traj = cells.xi[:, :, :50]
fp = open('./gug.pkl', 'wb')
pkl.dump(traj, fp)
fp.close()
def vacf(fname):
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [1.0]
pp = [0.0]
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
lag = 1
plotter0 = np.zeros((50, 2))
plotter1 = np.zeros((50, 2))
for fi, folder in enumerate(folders):
cells, sim = read_data(folder, fname)
lags_v = [int(l) for l in np.logspace(0, np.log10(len(cells.xi)), 100)]
plotter = np.zeros((len(lags_v), 2))
traj = cells.xi[:, :, :]
v = traj[:-lag] - traj[lag:]
for i, lagv in enumerate(lags_v):
v_corr = np.sum(v[:-lagv] * v[lagv:], axis=1)
plotter[i, 0] = lagv
if len(v_corr) > 0:
plotter[i, 1] = v_corr.mean()
else:
plotter[i, 1] = 0
x, y = lags_v, plotter[:, 1]
plt.loglog(x, y)
def levy(fname):
base = "/work/jiff26/jiff2611/PROJECTS/effective/Jobs/170731-Active_Diffusion/rotation_data/aspect_ratio_10.0/"
density = [0.4]
pa = [0.2, 1.0]
pp = [0.0]
powerlaw = lambda x, amp, index: amp * (x**index)
folders = []
for di in density:
for pai in pa:
for ppi in pp:
folders.append(base + 'density_' + str(di) + '/pa_' +
str(pai) + '/pp_' + str(ppi) + '/')
for folder in folders:
rodids = [153]
#lags = (10, 100, 500)
lags = (100, )
long_time = 10
for lag in lags:
for rodid in rodids:
cells, sim = read_data(folder, fname)
traj = cells.xi[5000:, :, :]
dr = traj[:-lag] - traj[lag:]
disps = np.sqrt(np.square(dr[:, 0]) + np.square(dr[:, 1]))
hist, be = np.histogram(disps / 10, bins=100, normed=True)
bw = be[1] - be[0]
bc = be[1:] - bw / 2
popt, pcov = curve_fit(powerlaw, bc[long_time:], hist[long_time:])
plt.loglog(bc, hist)
plt.loglog(bc[long_time:], powerlaw(bc[long_time:], *popt), c='k')
print(*popt)
if sent[hyphen_index - 1] != " ":
answer += " "
answer += sent[hyphen_index] + " "
else:
answer += sent[hyphen_index]
start = hyphen_index + 1
answer += sent[start:]
return answer
if __name__ == "__main__":
model = build_model(
configs.morpho_tagger.UD2_0.morpho_ru_syntagrus_pymorphy_lemmatize,
download=True)
ud_model = udModel.load(ud_model_path)
sents, answers = read_data(train_path)
symbols = sorted(set(a for sent in sents for a in sent))
# with open("dump/symbols.out", "w", encoding="utf8") as fout:
# fout.write("\n".join(symbols))
# sys.exit()
sents = [sanitize(sent) for sent in sents]
# print("Tagging...")
# tokenized_data = tokenizer.process("\n\n".join(sents))
# data = parse_ud_output(tokenized_data)
# source = [[elem[1] for elem in sent] for sent in data]
# tagged_data = call_model(model, sents, batch_size=64)
if tokenize:
tokenized_data, for_tagging = "", []
tokenizer = Pipeline(ud_model, "tokenize", Pipeline.NONE,
Pipeline.NONE, "conllu")
for start in range(0, len(sents), 40):
def prepare_data(train_file, parse_file, max_sents=-1, **kwargs):
(source, answers) = read_data(train_file, max_sents=max_sents)
sents = read_parse_file(parse_file, max_sents=max_sents, parse=False)
return _prepare_data(source, sents, answers, **kwargs)
