def main():
# 2d where some fxpt not on every path. happens at glancing nullclines
network_sizes, num_samples, test_data=fe.load_test_data('dl2.npz')
N, s = 2, 1
W = test_data['N_%d_W_%d'%(N,s)]
samp = 10
ang = np.linspace(0,np.pi,samp)
C = np.array([np.sin(ang),np.cos(ang)])
v_ax = plt.subplot(1,2,1)
a_ax = plt.subplot(1,2,2)
for a in [0]:#range(samp):
status, fxV, VA, c, deltas, s_mins = rd.traverse(W, c=C[:,[a]])
print(a, fxV.shape[1])
ptr.plot(v_ax, VA[:N,:], '-', c=str(1.0*a/samp))
ptr.scatter(v_ax, fxV, c='r')
a_ax.plot(VA[N,:], '-', c=str(1.0*a/samp))
a_ax.plot(range(VA.shape[1]),np.zeros(VA.shape[1]),'--b')
# nullclines
v = np.linspace(-1,1,100)[1:-1]
v_ax.plot(v, (np.arctanh(v)-W[0,0]*v)/W[0,1], '-b')
v_ax.plot((np.arctanh(v)-W[1,1]*v)/W[1,0], v, '-b')
ptr.set_lims(v_ax, np.array([[-2,2],[-2,2]]))
ptr.set_lims(a_ax, np.array([[0,VA.shape[1]],[-.5,.5]]))
plt.show()
def percent_timeout(xlabel, data, fix=None):
xs = list(set(int(d[xlabel]) for d in data if d[xlabel] != xlabel))
timeouts = []
distribs = []
averages = []
for x in xs:
count = 0
times = []
total = 0
for d in data:
if d[xlabel] == xlabel: continue
if int(d[xlabel]) != int(x): continue
if fix is not None and int(d[fix[0]]) != int(d[fix[0]]): continue
total += 1
times.append(min(float(d["time"]), args.timeout))
if d["succeed"] != "true": count += 1
if len(times) == 0:
continue
timeouts.append(float(count) / float(total))
distribs.append(times)
averages.append(sum(times) / len(times))
title_prc = "Percent_Timeout_100Edits_32bits_%s" % xlabel
title_avg = "Mean_Time_100Edits_32bits_%s" % xlabel
title_viol = "Violin_100Edits_32bits_%s" % xlabel
if fix is not None:
app_str = str(fix[0]) + str(fix[1])
title_prc += app_str
title_avg += app_str
assert len(xs) == len(averages)
plotter.scatter(args.output,
xs,
timeouts,
title_prc,
xlabel,
"% timeout",
ylim=(0.0, 1.0))
plotter.scatter(args.output,
xs,
averages,
title_avg,
xlabel,
"time(s)",
ylim=(0.0, args.timeout))
plotter.violins(args.output,
xs,
distribs,
title_viol,
xlabel,
"time (s)",
ylim=(0.0, args.timeout))
def plot_scatter(x, y, label, data, maxy=None, fix=None):
xs = []
ys = []
for d in data:
if d[fix[0]] == fix[0]: continue
if fix is None or int(d[fix[0]]) == int(fix[1]):
xs.append(int(d[x]))
if maxy is not None and float(d[y]) > float(maxy):
ys.append(float(maxy))
else:
ys.append(float(d[y]))
print "data for", fix, len(xs), len(ys)
if maxy is None:
ylim = None
else:
ylim = (0, maxy)
plotter.scatter(args.output, xs, ys, label, x, y, ylim=ylim)
def scatter_color(xlabel, ylabel, data, maxtime, xlim=None, ylim=None):
xs = []
ys = []
cs = []
for d in random.sample(data, k=10000):
if d[xlabel] == xlabel: continue
xs.append(float(d[xlabel]))
ys.append(float(d[ylabel]))
time = float(d["time"])
if time > maxtime:
time = maxtime
cs.append(time)
title = "{}_vs_{}_vs_time".format(xlabel, ylabel)
print "generating"
plotter.scatter(args.output,
xs,
ys,
title,
xlabel,
ylabel,
ylim=ylim,
xlim=xlim,
colors=cs)
def fiber_fig():
"""
Show an example of a directional fiber for a two-neuron network
"""
# mpl.rcParams['mathtext.default'] = 'regular'
mpl.rcParams['mathtext.default'] = 'it'
mpl.rcParams.update({'font.size': 18})
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
W = np.array([[1.5, 0.05], [-0.01, 1.5]])
c = np.array([[1.], [2]])
_, fxV, VA, _, _, _, _ = rfx.traverse(W, c=c)
fxV, _ = rfx.post_process_fxpts(W, fxV)
VA = np.concatenate((-VA[:2, ::-1], VA[:2, :]), axis=1)
plt.figure(figsize=(7, 7))
ptr.plot(plt.gca(), VA[:2, :], '-k')
ptr.scatter(plt.gca(), fxV, 75, c=((0, 0, 0), ))
V = ptr.lattice([-1.25, -1.25], [1.25, 1.25], 20)
C = np.tanh(W.dot(V)) - V
ptr.quiver(plt.gca(),
V,
C,
scale=.008,
units='dots',
width=1,
color=((.7, .7, .7), ),
headwidth=7)
V = VA[:2, :]
V = V[:, ((-1.25 < V) & (V < 1.25)).all(axis=0)]
C = np.tanh(W.dot(V)) - V
ptr.quiver(plt.gca(), V, C, scale=.005, units='dots', width=2, headwidth=5)
plt.xlim([-1.25, 1.25])
plt.xlabel('$v_1$', fontsize=24)
plt.ylim([-1.25, 1.25])
plt.ylabel('$v_2$', fontsize=24, rotation=0)
# plt.tight_layout()
plt.show()
def c_space_fig():
"""
Plot both the set of bad c and some nearby fibers
"""
mpl.rcParams['mathtext.default'] = 'regular'
# mpl.rcParams.update({'figure.autolayout': True})
mpl.rcParams.update({'font.size': 14})
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
N = 3
num_C = 8 # should be even or else land exactly on W[i,:]c=0
# known loop:
W = np.array([[ 1.20005258, -0.09232104, -0.12745533],
[-0.03161879, 1.26916341, -0.11257691],
[-0.03246188, 0.08548894, 1.24386724]])
# c1 = np.array([[.7,.5,2]]).T
#c2 = np.array([[.1,1.5,2]]).T
c1 = np.array([[.3,.7,.7]]).T
c2 = np.array([[.7,.5,.7]]).T
# # Random W, C
# W = 1.1*np.eye(N) + 0.1*np.random.randn(N,N)
# c1 = mldivide(W, np.array([[+0.5,0.3,1]]).T)
# c2 = mldivide(W, np.array([[-0.5,0.3,1]]).T)
interp = np.arange(num_C)/(num_C-1.0)
C = c1*interp[np.newaxis,:] + c2*(1-interp[np.newaxis,:])
C = C/np.sqrt((C*C).sum(axis=0))
bright_cap = .7
# show c space partition
if not os.path.exists('lork_c_3d_data.npz'):
_ = find_low_rank_c_3d_data(W)
npz = np.load('lork_c_3d_data.npz')
fig = plt.figure(figsize=(4.5,9))
ax = fig.add_subplot(2,1,1,projection='3d')
for colorful in range(0):
colors = ['b','g','r','c','m','y','k','w']
# numerical
for s in npz['signs'].shape[1]:
# if colors[s] not in ['b','m','y']: continue
# if s not in [0,4,5]: continue
# for p in range(len(c_path[s])):
for p in range(npz['lens'][s]):
c_path_s_p = npz['c_path_%d_%d'%(s,p)]
pltr.plot(ax, c_path_s_p, colors[s])
#pltr.quiver(ax, a_path[s][p], c_path_s_p)
# pltr.plot(ax, c_path_s_p + 0.05*a_path[s][p], 'k--')
pltr.plot(ax, -c_path_s_p, colors[s])
# pltr.plot(ax, -c_path_s_p - 0.05*a_path[s][p], 'k--')
# pltr.plot(ax, c_path_inf[s][p], colors[s])
# pltr.plot(ax, -c_path_inf[s][p], colors[s])
# pltr.plot(ax, c[s], colors[s]+'+')
# Wc = 0
angles = np.linspace(0,2*np.pi,100)
for i in range(N):
_, _, Z = np.linalg.svd(W[[i],:]) # for null-space
c_i = Z[-2:,:].T.dot(np.array([np.sin(angles), np.cos(angles)]))
pltr.plot(ax, c_i, 'k--')
for summary in range(1):
# numerical
for s in range(npz['signs'].shape[1]):
if s not in [0,4,5]: continue
for p in range(npz['lens'][s]):
c_path_s_p = npz['c_path_%d_%d'%(s,p)]
pltr.plot(ax, c_path_s_p, 'k')
pltr.plot(ax, -c_path_s_p, 'k')
# if c_path[s][p].shape[1] > 0:
# print(c_path[s][p].shape)
# print(int(c_path[s][p].shape[1]/2))
# print(c_path[s][p][:,[int(c_path[s][p].shape[1]/2)]])
# print(['%d,%d'%(s,p)])
# pltr.text(ax, c_path[s][p][:,[int(c_path[s][p].shape[1]/2)]],['+%d,%d'%(s,p)])
# # pltr.text(ax, -c_path[s][p][:,[int(c_path[s][p].shape[1]/2)]],['-%d,%d'%(s,p)])
# Wc = 0
angles = np.linspace(0,2*np.pi,100)
for i in range(N):
_, _, Z = np.linalg.svd(W[[i],:]) # for null-space
c_i = Z[-2:,:].T.dot(np.array([np.sin(angles), np.cos(angles)]))
pltr.plot(ax, c_i, 'k--')
# plt.xticks([-1,0,1])
plt.yticks([-1,0,1])
# ax.set_zticks([-1,0,1])
pltr.set_lims(ax, 1*np.array([[1],[1],[1]])*np.array([[-1,1]]))
# ax.set_xlabel('$c_1$')
# ax.set_ylabel('$c_2$')
# ax.set_zlabel('$c_3$')
# ax.set_title('(a)')
mpl.rcParams['mathtext.default'] = 'it'
# mpl.rcParams['font.family'] = 'serif'
# ax.set_title('$\frac{c}{||c||}\in\mathrm{\mathbb{S}}^2$')
ax.set_title('$c/||c||\in\mathrm{\mathbf{S}}^2$') # mathbb incompatible with truetype
pltr.scatter(ax, C, c=np.array([[bright_cap*g for _ in range(3)] for g in interp]),edgecolor='face')
# pltr.scatter(ax, C, s=20, c=[str(bright_cap*g) for g in interp],edgecolor='face')
# pltr.quiver(ax, c1, c1-c2)
# ax.azim, ax.elev = 16, 51
ax.azim, ax.elev = -74, 2
# plt.show()
# return None
# show paths
if not os.path.exists('brute_fiber_data.npz'):
_ = c_space_fig_fiber_data()
npz = np.load('brute_fiber_data.npz')
# npz['fxV_%d_%d'%(p,i)] = fxV[i]
# npz['VA_%d_%d'%(p,i)] = VA[i]
# npz['lens_%d'%p] = len(fxV)
# np.savez('brute_fiber_data.npz',**npz)
ax = fig.add_subplot(2,1,2,projection='3d')
for p in range(0,len(interp),2):
for i in range(npz['lens_%d'%p]):
fxV_i = npz['fxV_%d_%d'%(p,i)]
VA_i = npz['VA_%d_%d'%(p,i)]
for s in [1]:#[-1,1]:
col = np.array([bright_cap*interp[p] for _ in range(3)])
pltr.plot(ax, s*VA_i[:N, (np.fabs(VA_i[:N,:]) < 3).all(axis=0)], color=col)
pltr.plot(ax, s*fxV_i, 'ko')
# ax.set_title('c_%d'%p)
pltr.set_lims(ax, 1.5*np.array([[1,1,1]]).T*np.array([[-1,1]]))
ax.azim, ax.elev = -94, 8
# ax.set_xlabel('$v_1$') #,rotation=0)
# ax.set_ylabel('$v_2$') #,rotation=0)
# ax.set_zlabel('$v_3$') #,rotation=0)
plt.xticks([-1,0,1])
plt.yticks([-1,0,1])
ax.set_zticks([-1,0,1])
ax.set_title('$v\in\mathrm{\mathbf{R}}^3$')
plt.tight_layout()
plt.show()
def c_space_fig_old():
"""
Plot both the set of bad c and some nearby fibers
"""
mpl.rcParams['mathtext.default'] = 'regular'
# mpl.rcParams.update({'figure.autolayout': True})
mpl.rcParams.update({'font.size': 14})
N = 3
num_C = 8 # should be even or else land exactly on W[i,:]c=0
# known loop:
W = np.array([[ 1.20005258, -0.09232104, -0.12745533],
[-0.03161879, 1.26916341, -0.11257691],
[-0.03246188, 0.08548894, 1.24386724]])
# c1 = np.array([[.7,.5,2]]).T
#c2 = np.array([[.1,1.5,2]]).T
c1 = np.array([[.3,.7,.7]]).T
c2 = np.array([[.7,.5,.7]]).T
# # Random W, C
# W = 1.1*np.eye(N) + 0.1*np.random.randn(N,N)
# c1 = mldivide(W, np.array([[+0.5,0.3,1]]).T)
# c2 = mldivide(W, np.array([[-0.5,0.3,1]]).T)
interp = np.arange(num_C)/(num_C-1.0)
C = c1*interp[np.newaxis,:] + c2*(1-interp[np.newaxis,:])
C = C/np.sqrt((C*C).sum(axis=0))
bright_cap = .7
print('low rank c...')
# show c space partition
fig = plt.figure(figsize=(11,6))
ax = fig.add_subplot(1,2,1,projection='3d')
_ = find_low_rank_c_3d(W)
# ax.set_title('(a)')
mpl.rcParams['mathtext.default'] = 'it'
# mpl.rcParams['font.family'] = 'serif'
ax.set_title('$c/||c||\in\mathrm{\mathbb{S}}^2$')
pltr.scatter(ax, C, c=np.array([[bright_cap*g for _ in range(3)] for g in interp]),edgecolor='face')
# pltr.scatter(ax, C, s=20, c=[str(bright_cap*g) for g in interp],edgecolor='face')
# pltr.quiver(ax, c1, c1-c2)
# ax.azim, ax.elev = 16, 51
ax.azim, ax.elev = -74, 2
# plt.show()
# return None
# show paths
ax = fig.add_subplot(1,2,2,projection='3d')
for p in range(2):
print('fxpath brute %d...'%p)
fxV, VA = rfx.brute_fiber(W, C[:,[p*-1]])
for i in range(len(VA)):
for s in [1]:#[-1,1]:
pltr.plot(ax, s*VA[i][:N, (np.fabs(VA[i][:N,:]) < 3).all(axis=0)], color=str(bright_cap*interp[p*-1]))
pltr.plot(ax, s*fxV[i], 'ko')
# ax.set_title('c_%d'%p)
pltr.set_lims(ax, 3*np.array([[1,1,1]]).T*np.array([[-1,1]]))
ax.azim, ax.elev = -94, 8
# ax.set_xlabel('$v_1$') #,rotation=0)
# ax.set_ylabel('$v_2$') #,rotation=0)
# ax.set_zlabel('$v_3$') #,rotation=0)
ax.set_title('$v\in\mathrm{\mathbb{R}}^3$')
plt.tight_layout()
plt.show()
# ax.xaxis.label.set_rotation(0)
# ax.yaxis.label.set_rotation(0)
# ax.zaxis.label.set_rotation(0)
return fxV, VA, W, C
#if not (person[3:7] == '0713' and int(person[7:]) < 300):
# continue
if audio not in ['06', '07']:
continue
print(person, label, filename)
cnt += 1
l, r = basic_endpoint_detection(sig, rate)
#l,r= 0,len(sig)
sig = preemphasis(sig, coeff=0.97)
frames = to_frames(sig[l:r], rate)
feature = pitch_feature(sig[l:r], rate)
p.append((feature[0], feature[-1]))
X.append(feature)
labels.append(label)
if cnt == 200: break
scatter(p, labels, '111', True)
cnt = 0
p = []
for idx, l, (sig, rate), label, filename in val_itr:
reg = re.compile('(\d+)-(\d+)-(\d+).wav')
audio = reg.match(filename).group(2)
person = reg.match(filename).group(1)
if audio not in ['06', '07']:
continue
print(person, label, filename)
cnt += 1
l, r = basic_endpoint_detection(sig, rate)
sig = preemphasis(sig, coeff=0.97)
frames = to_frames(sig[l:r], rate)
feature = pitch_feature(sig[l:r], rate)