def plot_quad_mesh(pts, quads):
"""Plot a quadrilateral mesh."""
def plot_quad(pts):
"""Plot one quad."""
plot(np.r_[pts[:,0], pts[0,0]],
np.r_[pts[:,1], pts[0,1]],
lw=1.5, color="black")
hold(True)
for k,q in enumerate(quads):
plot_quad(pts[q])
if len(quads) < 400:
for k,q in enumerate(quads):
center = pts[q].sum(axis=0) / 4.0
text(center[0], center[1], "%d" % k, color="black",
fontsize=10,
horizontalalignment='center', verticalalignment='center')
if len(pts) < 200:
for k,p in enumerate(pts):
text(p[0], p[1], "%d" % k, color="black",
fontsize=10,
bbox=dict(boxstyle = "round", fc = "white"),
horizontalalignment='center', verticalalignment='center')
def plot_xc(t_years):
"""Plot the location of the calving front."""
x = x_c(t_years * secpera) / 1000.0 # convert to km
_, _, y_min, y_max = axis()
hold(True)
plot([x, x], [y_min, y_max], '--g')
def plot_bpm_file(pool):
bpm = pool.descriptors['tempotap_bpm']['values'][0]
intervals = pool.descriptors['tempotap_intervals']['values'][0]
bpm_periods = [60./interval for interval in intervals]
ticks = pool.descriptors['tempotap_ticks']['values'][0]
rubato_start = pool.descriptors['tempotap_rubato_start']['values'][0]
rubato_stop = pool.descriptors['tempotap_rubato_stop']['values'][0]
print 'bpm', bpm
print 'ticks', ticks
print 'rubato_start', rubato_start
print 'rubato_stop', rubato_stop
print 'intervals', intervals
import pylab
pylab.plot(ticks,[bpm_periods[0]] + bpm_periods,'r+-')
pylab.hold(True)
pylab.plot([ticks[0],ticks[-1]],[bpm]*2,'g-')
pylab.plot(rubato_start,[bpm]*len(rubato_start),'b+')
pylab.plot(rubato_stop,[bpm]*len(rubato_stop),'b|')
# ground truth
if 'gt_ticks' in pool.descriptors.keys():
gt_ticks = pool.descriptors['gt_ticks']['values'][0]
if len(gt_ticks) > 1:
gt_bpm_periods = [60./(gt_ticks[i] - gt_ticks[i-1]) for i in range(1,len(gt_ticks))]
p1 = pylab.plot(gt_ticks,[gt_bpm_periods[0]] + gt_bpm_periods,'rx:')
p2 = pylab.plot(gt_ticks,[gt_bpm_periods[0]] + gt_bpm_periods,'rx:')
#pylab.legend((p1[0],p2[0]),('Men','Women'))
pylab.hold(False)
pylab.show()
def makeBoxPlot(outputDir, distances): plt = figure() ax = axes() hold(True) bp = boxplot(distances) savefig(outputDir + "/boxplot.png")
def _test_graph():
i = 10000
x = np.linspace(0,3.7*pi,i)
y = (0.3*np.sin(x) + np.sin(1.3 * x) + 0.9 * np.sin(4.2 * x) + 0.06 *
np.random.randn(i))
y *= -1
x = range(i)
_max, _min = peakdetect(y,x,750, 0.30)
xm = [p[0] for p in _max]
ym = [p[1] for p in _max]
xn = [p[0] for p in _min]
yn = [p[1] for p in _min]
plot = pylab.plot(x,y)
pylab.hold(True)
pylab.plot(xm, ym, 'r+')
pylab.plot(xn, yn, 'g+')
_max, _min = peak_det_bad.peakdetect(y, 0.7, x)
xm = [p[0] for p in _max]
ym = [p[1] for p in _max]
xn = [p[0] for p in _min]
yn = [p[1] for p in _min]
pylab.plot(xm, ym, 'y*')
pylab.plot(xn, yn, 'k*')
pylab.show()
def plot_spectrum():
#get the data...
a_0=struct.unpack('>1024l',fpga.read('even',1024*4,0))
a_1=struct.unpack('>1024l',fpga.read('odd',1024*4,0))
interleave_a=[]
for i in range(1024):
interleave_a.append(a_0[i])
interleave_a.append(a_1[i])
pylab.figure(num=1,figsize=(10,10))
pylab.ioff()
pylab.plot(interleave_a)
#pylab.semilogy(interleave_a)
pylab.title('Integration number %i.'%prev_integration)
pylab.ylabel('Power (arbitrary units)')
pylab.grid()
pylab.xlabel('Channel')
pylab.xlim(0,2048)
pylab.ioff()
pylab.hold(False)
pylab.show()
pylab.draw()
def PlotTorques(robot, traj0, traj1, dt=0.001, taumin=[], taumax=[],
figstart=0):
from pylab import figure, clf, hold, gca, plot, axis, title, xlabel, ylabel
colorcycle = ['r', 'g', 'b', 'm', 'c', 'y', 'k']
colorcycle = colorcycle[0:traj0.dimension]
Tmax = max(traj0.duration, traj1.duration)
tvect0, tauvect0 = ComputeTorques(traj0, robot, dt)
tvect1, tauvect1 = ComputeTorques(traj1, robot, dt)
figure(figstart)
clf()
hold('on')
ax = gca()
ax.set_color_cycle(colorcycle)
plot(tvect0, tauvect0, '--', linewidth=2)
ax.set_color_cycle(colorcycle)
plot(tvect1, tauvect1, linewidth=2)
ax.set_color_cycle(colorcycle)
for a in taumax:
plot([0, Tmax], [a, a], '-.')
ax.set_color_cycle(colorcycle)
for a in taumin:
plot([0, Tmax], [a, a], '-.')
if len(taumax) > 0:
axis([0, Tmax, 1.2 * min(taumin), 1.2 * max(taumax)])
title('Joint torques', fontsize=20)
xlabel('Time (s)', fontsize=18)
ylabel('Joint torques (Nm)', fontsize=18)
def demo():
from pylab import hold, linspace, subplot, plot, legend, show
hold(True)
#y = [9,6,1,3,8,4,2]
#y = [9,11,13,3,-2,0,2]
y = [9, 11, 2, 3, 8, 0]
#y = [9,9,1,3,8,2,2]
x = linspace(0, 1, len(y))
t = linspace(x[0], x[-1], 400)
subplot(211)
plot(t, bspline(y, t, clamp=False), '-.y',
label="unclamped bspline") # bspline
# bspline
plot(t, bspline(y, t, clamp=True), '-y', label="clamped bspline")
plot(sorted(x), y, ':oy', label="control points")
legend()
#left, right = _derivs(t, bspline(y, t, clamp=False))
#print(left, (y[1] - y[0]) / (x[1] - x[0]))
subplot(212)
xt, yt = pbs(x, y, t, clamp=False)
plot(xt, yt, '-.b', label="unclamped pbs") # pbs
xt, yt = pbs(x, y, t, clamp=True)
plot(xt, yt, '-b', label="clamped pbs") # pbs
#xt,yt = pbs(x,y,t,clamp=True, parametric=True)
# plot(xt,yt,'-g') # pbs
plot(sorted(x), y, ':ob', label="control points")
legend()
show()
def _smooth_demo():
from numpy import linspace, sin, ones
from pylab import subplot, plot, hold, axis, legend, title, show, randn
t = linspace(-4, 4, 100)
x = sin(t)
xn = x + randn(len(t)) * 0.1
y = smooth(x)
ws = 31
subplot(211)
plot(ones(ws))
windows = ["flat", "hanning", "hamming", "bartlett", "blackman"]
hold(True)
for w in windows[1:]:
eval("plot(" + w + "(ws) )")
axis([0, 30, 0, 1.1])
legend(windows)
title("The smoothing windows")
subplot(212)
plot(x)
plot(xn)
for w in windows:
plot(smooth(xn, 10, w))
l = ["original signal", "signal with noise"]
l.extend(windows)
legend(l)
title("Smoothing a noisy signal")
show()
def plotResults(self, titlestr="", ylimits=[0.5,1.05], plotfunc = pl.semilogx, ylimitsB=[0,101],
legend_loc=3, show=True ):
pl.figure(num=None, figsize=(15,5))
xvals = range(1, (1+len(self.removed)) )
#Two subplots. One the left is the test accuracy vs. iteration
pl.subplot(1,2,1)
plotfunc(xvals, self.test_acc_list, "b", label="Test Accuracy")
pl.hold(True)
plotfunc(xvals, self.getRollingAvgTestAcc(window_size=10), "r", label="Test Acc (rolling avg)")
plotfunc(xvals, self.getRollingAvgTrainAcc(window_size=10), "g--", label="Train Acc (rolling avg)")
pl.ylim(ylimits)
if titlestr == "":
pl.title("Iterative Feature Removal")
else:
pl.title(titlestr)
pl.ylabel("Test Accuracy")
pl.xlabel("Iteration")
pl.legend(loc=legend_loc) #3=lower left
pl.hold(False)
#second subplot. On the right is the number of features removed per iteration
pl.subplot(1,2,2)
Ns = [ len(lst) for lst in self.removed ]
pl.semilogx(xvals, Ns, "bo", label="#Features per Iteration")
pl.xlabel("Iteration")
pl.ylabel("Number of Features Selected")
pl.title("Number of Features Removed per Iteration")
pl.ylim(ylimitsB)
pl.subplots_adjust(left=0.05, bottom=0.15, right=0.95, top=0.90, wspace=0.20, hspace=0.20)
if show: pl.show()
def plotRes_varyingTrees( data_dict, dataset_name, max_correct=3000 , show=True):
'''
Plots the results of a varyingNumTrees() experiment, using a dictionary
structure to hold the data. See the loadRes_varyingTrees() comments on the
dictionary layout.
'''
xvals = data_dict['NumTrees']
#prox forest trials
pf_avg = data_dict['PF'].mean(axis=0)
pf_std = data_dict['PF'].std(axis=0)
pf_95_conf = 1.96 * pf_std / math.sqrt(data_dict['PF'].shape[0])
#kdt forest trials
kdt_avg = data_dict['KDT'].mean(axis=0)
kdt_std = data_dict['KDT'].std(axis=0)
kdt_95_conf = 1.96 * kdt_std / math.sqrt(data_dict['KDT'].shape[0])
#plot average results of each, bounded by lower and upper bounds of 95% conf intv
pl.hold(True)
pl.errorbar(xvals, pf_avg/max_correct, yerr=pf_95_conf/max_correct, fmt='-r',
label="PF")
pl.errorbar(xvals, kdt_avg/max_correct, yerr=kdt_95_conf/max_correct, fmt='-.b',
label="KDT")
pl.ylim([0,1.05])
pl.title(dataset_name)
pl.xlabel("Number of Trees in Forest")
pl.ylabel("Percent Correct")
pl.legend(loc='lower right')
if show: pl.show()
def test_radial_profiles():
arr = random_periodic_upsample(128, 16, seed=0)
mask = np.zeros(arr.shape, dtype=np.bool_)
arr_x = vcalc.cderivative(arr, 'X_DIR')
arr_y = vcalc.cderivative(arr, 'Y_DIR')
arr_div = np.sqrt(arr_x**2 + arr_y**2)
surf = _cp.TopoSurface(arr)
rprofs = radial_profiles(surf, threshold=25, expand_regions=1, other_arr=arr_div, mask=mask)
arr[mask] = 2 * arr.max()
pl.imshow(arr, interpolation='nearest')
pl.figure()
pl.imshow(arr_div)
pl.figure()
pl.hold(True)
linreg_xy = ([], [])
for minmax, (rprof, region) in rprofs.items():
# minmax_flux = arr_div[minmax]
pts, fluxes, avg_fluxes, avg_fluxes_errs, avg_dists, avg_dists_errs = \
zip(*rprof)
linreg_xy[0].extend(fluxes)
linreg_xy[1].extend(avg_fluxes)
# fluxes = np.abs(np.array(fluxes) - minmax_flux)
# avg_fluxes = np.abs(np.array(avg_fluxes) - minmax_flux)
# pl.plot(avg_dists, avg_fluxes, 'd-')
pl.plot(avg_dists, avg_fluxes, 'd-')
pl.grid()
slope, intercept, rval, pval, stderr = stats.linregress(*linreg_xy)
print
print "slope: %f" % slope
print "intercept: %f" % intercept
print "rval: %f" % rval
print "pval: %f" % pval
print "stderr: %f" % stderr
import pdb; pdb.set_trace()
def plot_the_overview(samples, i, j, output_image_file):
pylab.hold(True)
pylab.scatter(samples[:,i], samples[:,j])
pylab.draw()
pylab.savefig(output_image_file, dpi=150)
pylab.close()
def Wave2DShow(ufield, ds, vel=None, vmin=None, vmax=None):
r"""
Show a 2D pressure field at some instant of time.
As background is shown velocity field.
Same dimension as ufield.
* ufield : 2d pressure field at an instant of time
* ds : space discretization
* vel : 2d background velocity field
* vmin/vmax : vmin/vmax of imshow
"""
#max index time and max index space
maxt = np.shape(snapshots)[0]
maxk = np.shape(snapshots)[1]
maxi = np.shape(snapshots)[2]
print "vmin : ", vmin, "vmax : ", vmax
# space axis starting at 0 in x and z (using y coz' plotting)
# extents of the picture,
xmin, xmax = 0, ds*maxi
ymin, ymax = 0, ds*maxk
extent= xmin, xmax, ymax, ymin
py.hold(True)
if not vel == None:
py.imshow(vel, interpolation='bilinear', cmap=cm.jet, extent=extent, origin='upper', aspect='auto')
py.imshow(ufield, interpolation='bilinear', cmap=cm.Greys_r, alpha=0.8, extent=extent, origin='upper', aspect='auto', vmin=vmin, vmax=vmax)
py.hold(False)
# optional cmap=cm.jet, apect='auto' adjust aspect to the previous plot
py.show()
def pinwheel_overlay(pinwheels, contours=None, style='wo',linewidth=1,mmap=None):
"""
Plots the pinwheel locations and optionally the real and imaginary
pinwheel contours. Designed to be overlayed over an OR map.
"""
fig = plt.figure(frameon=False)
fig.patch.set_alpha(0.0)
ax = plt.subplot(111, aspect='equal', frameon=True)
ax.patch.set_alpha(0.0)
plt.hold(True)
plt.imshow(mmap,cmap='hsv',extent=(0, 1.0, 0, 1.0))
(recontours, imcontours) = contours if contours else ([],[])
for recontour in recontours:
plt.plot(recontour[:,0], recontour[:,1],'k',linewidth=linewidth)
for imcontour in imcontours:
plt.plot(imcontour[:,0], imcontour[:,1],'w', linewidth=linewidth)
Xs, Ys = zip(*pinwheels)
plt.plot(np.array(Xs), np.array(Ys), style)
plt.xlim((0.0,1.0)); plt.ylim((0.0,1.0))
ax.xaxis.set_ticks([]); ax.yaxis.set_ticks([])
ax.xaxis.set_ticklabels([]); ax.yaxis.set_ticklabels([])
return fig
def plot_bars(pos_count, title='', max_pathway_length=8, legend_loc='upper right'):
n_labels = len(pos_count)
ind = np.arange(max_pathway_length)
width = 0.2
fig = pylab.figure()
pylab.hold(True)
ax = fig.add_subplot(111)
colors = {'No known regulation':'grey', 'Activated':'green', 'Inhibited':'red', 'Mixed regulation':'blue'}
plot_order = ['Inhibited', 'Mixed regulation', 'Activated', 'No known regulation']
i = 0
for label in plot_order:
curr_vals = pos_count[label][1:max_pathway_length+1]
if (sum(curr_vals) < 20):
n_labels -= 1
continue
ax.bar(ind + i * width, tuple([j * 1.0 /sum(curr_vals) for j in curr_vals]), width, color=colors[label], label=('%s (%d)' % (label, sum(curr_vals))))
i += 1
ax.set_ylabel('Fraction of reactions per type')
ax.set_xlabel('Position in pathway')
ax.set_xticks(ind+ width * n_labels/2)
ax.set_xticklabels( ind + 1 )
legendfont = matplotlib.font_manager.FontProperties(size=11)
pylab.legend(loc=legend_loc, prop=legendfont)
pylab.title(title)
pylab.hold(False)
return fig
def genSensitivityResults():
''' Generates sensitivity results regarding number of tree levels and how
increasing the number of estimation parameters affects convergence.
Didn't have space to include these figures in the conference paper.
'''
shahx_a,shahy_a = genConvergencePlots("shah", numLevels=4)
shahx_b,shahy_b = genConvergencePlots("shah", numLevels=10)
shahx_c,shahy_c = genConvergencePlots("shah", numLevels=25)
shahx_d,shahy_d = genConvergencePlots("shah", numLevels=50)
compare_fig = pl.figure('Convergence of different metrics')
pl.hold(True)
shaha = pl.plot(shahx_a,shahy_a[:,0],'k-',label="# Shah Levels = 2",linewidth=3)
shahb = pl.plot(shahx_b,shahy_b[:,0],'k-',label="# Shah Levels = 4",linewidth=1)
shahc = pl.plot(shahx_c,shahy_c[:,0],'b--',label="# Shah Levels = 10",linewidth=3)
shahd = pl.plot(shahx_d,shahy_d[:,0],'b--',label="# Shah Levels = 50",linewidth=1)
pl.hold(False)
pl.xlabel("Number of A/B Comparisons used in training")
pl.ylabel("Prediction accuracy")
pl.title("Comparison of various metrics")
pl.ylim((.5,1.0))
pl.xlim((0,shahx_a[-1]))
pl.legend(loc=4)
# Uncomment if you want interactive plotting
#pl.show()
pl.savefig(plot_path+"sensitivity_convergence_comparison.pdf")
def plot_arm_speed(axis, startTime=-1):
rootName = 'siemensSensors'
f = netcdf.netcdf_file(rootName+'Data.nc', 'r')
data1 = f.variables[rootName+'.data.'+'carouselSpeedSetpoint'].data[startSample:]
data2 = f.variables[rootName+'.data.'+'carouselSpeedSmoothed'].data[startSample:]
ts_trigger = f.variables[rootName+'.data.ts_trigger'].data[startSample:]*1.0e-9
# Load the actual arm speed from the arm gyro
rootName = 'armboneLisaSensors'
fiile = netcdf.netcdf_file(rootName+'Data.nc', 'r')
rawdata4 = fiile.variables['armboneLisaSensors.GyroState.gr'].data[startSample:]
ts_trigger4 = fiile.variables['armboneLisaSensors.GyroState.ts_trigger'].data[startSample:]*1.0e-9
#fullscale = 2000 # deg/sec
#data4 = -1.0 * rawdata4 / (2**15) * fullscale * pi/180 - 0.0202 # Rad/s
data4 = rawdata4
if startTime == -1:
startTime = ts_trigger[0]
times = ts_trigger-startTime
times4 = ts_trigger4-startTime
pylab.hold(True)
plot(times, data2, '.-', label='On Motor Side of Belt')
plot(times4, data4,'.-', label='From Gyro on Arm')
plot(times, data1, '.-', label='Setpoint (Echoed)')
ylabel('Arm rotation speed [Rad/s]')
xlabel('Time [s]')
#legend(['Setpoint (Echoed)', 'Setpoint (Sent)', 'On Motor Side of Belt', 'From Gyro on Arm'])
title('Plot of Signals Related to Arm Speed')
return startTime
def plot_coord_mapping(mapper,sheet,style='b-'):
"""
Plot a coordinate mapping for a sheet.
Given a CoordinateMapperFn (as for a CFProjection) and a sheet
of the projection, plot a grid showing where the sheet's units
are mapped.
"""
from pylab import plot,hold,ishold
xs = sheet.sheet_rows()
ys = sheet.sheet_cols()
hold_on = ishold()
if not hold_on:
plot()
hold(True)
for y in ys:
pts = [mapper(x,y) for x in xs]
plot([u for u,v in pts],
[v for u,v in pts],
style)
for x in xs:
pts = [mapper(x,y) for y in ys]
plot([u for u,v in pts],
[v for u,v in pts],
style)
hold(hold_on)
def pyData2C( delta = 1e-2 ):
#Load all the simulated trajectories
file_name = os.path.join(RESULTS_DIR, 'OU_Xs.N=10.npy')
trajectoryBank = load(file_name)
#Select an arbitrary trajectory: (here the 2nd)
figure(); hold(True);
n_thin = int(delta / dt); print n_thin, delta
idx = 3;
ts, Xs = trajectoryBank[:,0], trajectoryBank[:,idx]
#Select sampling rate:
#Generate sampled data, by sub-sampling the fine trajectory:
ts_thin = ts[::n_thin]; Xs_thin = Xs[::n_thin]
file_name = os.path.join(RESULTS_DIR, 'Xdata.txt',);
# outfile = open(file_name, 'w')
# outfile.print(N);
# outfile.print(delta);
# for X in Xs_thin:
# outfile.print(X)
#
# outfile.close()
savetxt(file_name, Xs_thin, "%f");
print 'Saved to ', file_name
print 'N, delta,', len(Xs_thin), delta
def run_demo(with_plots=True):
"""
An example on how to simulate a model using the DAE simulator. The result
can be compared with that of sim_rlc.py which has solved the same problem
using dymola. Also writes information to a file.
"""
curr_dir = os.path.dirname(os.path.abspath(__file__))
model_name = "RLC_Circuit"
mofile = curr_dir + "/files/RLC_Circuit.mo"
jmu_name = compile_jmu(model_name, mofile)
model = JMUModel(jmu_name)
init_res = model.initialize()
(E_dae, A_dae, B_dae, F_dae, g_dae, state_names, input_names, algebraic_names, dx0, x0, u0, w0, t0) = linearize_dae(
init_res.model
)
(A_ode, B_ode, g_ode, H_ode, M_ode, q_ode) = linear_dae_to_ode(E_dae, A_dae, B_dae, F_dae, g_dae)
res1 = model.simulate()
jmu_name = compile_jmu("RLC_Circuit_Linearized", mofile)
lin_model = JMUModel(jmu_name)
res2 = lin_model.simulate()
c_v_1 = res1["capacitor.v"]
i_p_i_1 = res1["inductor.p.i"]
i_p1_i_1 = res1["inductor1.p.i"]
t_1 = res1["time"]
c_v_2 = res2["x[1]"]
i_p_i_2 = res2["x[2]"]
i_p1_i_2 = res2["x[3]"]
t_2 = res2["time"]
assert N.abs(res1.final("capacitor.v") - res2.final("x[1]")) < 1e-3
if with_plots:
p.figure(1)
p.hold(True)
p.subplot(311)
p.plot(t_1, c_v_1)
p.plot(t_2, c_v_2, "g")
p.ylabel("c.v")
p.legend(("original model", "linearized ODE"))
p.grid()
p.subplot(312)
p.plot(t_1, i_p_i_1)
p.plot(t_2, i_p_i_2, "g")
p.ylabel("i.p.i")
p.grid()
p.subplot(313)
p.plot(t_1, i_p1_i_1)
p.plot(t_2, i_p1_i_2, "g")
p.ylabel("i.p1.i")
p.grid()
p.show()
def estimateHarness( delta = 1e-1,
alpha = .0,
num_samples=10,
Tf_sample = Tf ):
#Load all the simulated trajectories
file_name = os.path.join(RESULTS_DIR,
'OU_Xs.a=%.3f_N=%d.npy'%(alpha,
num_samples));
trajectoryBank = load(file_name)
#Select an arbitrary trajectory: (here the 2nd)
figure(); hold(True);
n_thin = int(delta / dt); print n_thin
N_sample = int(Tf_sample / dt)
# for idx in xrange(1,10):
# for idx in [2]: #xrange(3,4):
for idx in xrange(1,num_samples+1):
ts, Xs = trajectoryBank[:N_sample,0], trajectoryBank[:N_sample,idx]
#Select sampling rate:
#Generate sampled data, by sub-sampling the fine trajectory:
ts_thin = ts[::n_thin];
Xs_thin = Xs[::n_thin];
#Obtain estimator
# est_params = estimateParams(Xs_thin, delta)
# print 'est original: %.4f,%.4f, %.4f'%(est_params[0],est_params[1],est_params[2])
est_params = estimateParamsBeta(Xs_thin, delta, alpha)
print 'est reduced: %.4f,%.4f, %.4f'%(est_params[0],est_params[1],est_params[2])
plot(ts_thin, Xs_thin);
print 'true param values:', [mu, beta, sigma]
def BetavsT_AP_P(folder,keys):
AP = Analysis.AnalyseFile()
P = Analysis.AnalyseFile()
I = 300e-6
for f in folder:
a=Analysis.AnalyseFile(f)
fit= a.curve_fit(quad,'Current','Voltage',bounds=lambda x,y:x,result=True,header='Fit',asrow=True)
if f['iterator'] == 7:
AP.add_column(fit,str(f['iterator']))
AP['Sample Temp'] = f['Sample Temp']
Spc = ((-0.01411*f['Sample Temp'])-0.11185)*1e-6
T_d = -fit[0]*(I*I)/Spc
AP['DeltaTemp'] = T_d
elif f['iterator'] == 6:
P.add_column(fit,str(f['iterator']))
P['Sample Temp'] = f['Sample Temp']
Spc = ((-0.01411*f['Sample Temp'])-0.11185)*1e-6
T_d = -fit[0]*(I*I)/Spc
P['DeltaTemp'] = T_d
plt.hold(True)
plt.title(r'$\beta$ coef of NLIV vs Temp')
plt.xlabel('Temperture (K)')
plt.ylabel(r'$\beta$ (V/A$^2$)')
plt.plot(f['IVtemp'],P['DeltaTemp']-AP['DeltaTemp'],'ok')
def NormDeltaRvT(folder,keys):
if folder[0]['IVtemp']<250 and folder[0]['IVtemp']>5:
APiterator = [5,10]
AP = Analysis.AnalyseFile()
P = Analysis.AnalyseFile()
tsum = 0.0
for f in folder:
if f['iterator'] in APiterator:
AP.add_column(f.column('Voltage'),str(f['iterator']))
else:
P.add_column(f.column('Voltage'),str(f['iterator']))
tsum = tsum + f['Sample Temp']
AP.apply(func,0,replace=False,header='Mean NLV')
AP.add_column(f.Current,column_header = 'Current')
P.apply(func,0,replace=False,header='Mean NLV')
P.add_column(f.Current,column_header = 'Current')
APfit= AP.curve_fit(quad,'Current','Mean NLV',bounds=lambda x,y:x,result=True,header='Fit',asrow=True)
Pfit = P.curve_fit(quad,'Current','Mean NLV',bounds=lambda x,y:x,result=True,header='Fit',asrow=True)
DeltaR = Pfit[2] - APfit[2]
ErrDeltaR = numpy.sqrt((Pfit[3]**2)+(APfit[3]**2))
Spinsig.append(DeltaR/Res_Cu(tsum/10))
Spinsig_error.append(ErrDeltaR)
Temp.append(tsum/10)
plt.hold(True)
plt.title('$\Delta$R$_s$ vs T from linear coef of\nNLIV fit for '+f['Sample ID'],verticalalignment='bottom')
plt.xlabel('Temperture (K)')
plt.ylabel(r'$\Delta$R$_s$/$\rho$')
plt.errorbar(f['IVtemp'],1e3*DeltaR,1e3*ErrDeltaR,ecolor='k',marker='o',mfc='r', mec='k')
#plt.plot(f['IVtemp'],ErrDeltaR,'ok')
return Temp, Spinsig
def anim_slices(dir="bckreact_b5_n01_E1_M14_t4d3",out=[2,3,4,5,6,7,8,9],end=[1.,1.,1.],N=None,var='d',log=True,fig=1,over=False, rmax=10.0):
#def anim_slices(dir="backreaction_b5_3d3yr",out=[3,6],end=[1.,1.,1.],N=None,var='d',log=True,fig=1,over=False, rmax=15.0, mp4="dens_maps.mp4"):
# reading time intervals
tseq = read_times(dir=dir+'/')
pylab.close(fig)
figure = matplotlib.pyplot.figure(num=fig)
#a = matplotlib.pyplot.gca()
a = None
ims = []
for i in out:
print 'output #',i, ' age:',tseq[i-1]
connect(dir=dir,out=i,var=[var])
load_slices()
[map,img, jet] = show_slice(var=var,log=False,rmax=rmax,ax=a,time=tseq[i-1])
ann = pylab.annotate(tseq[i-1]+' yrs', xy=(.8, .9), xycoords='axes fraction', horizontalalignment='center', verticalalignment='center', color="white")
ims.append((img,ann,))
a = jet.ax
#ims.append((img,ann,jet,))
# artificially expand the longer steps
#if eval(tseq[i-1])>(5.e2-1.0):
#ims.append((img,ann))
# if eval(tseq[i-1])>1.5e3-1.0: ims.append((img,ann))
fname = '_tmp%03d.png'%i
print 'Saving frame', fname
figure.savefig(basedir+dir+'/'+fname)
pylab.hold(False)
matplotlib.pyplot.xlabel('r, pc')
matplotlib.pyplot.ylabel('r, pc')
im_ani = anim.ArtistAnimation(figure, ims, interval=200, repeat_delay=500, blit=True)
def DRIVplot(folder,keys):
T = 281
APiterator = [5,10]
AP = Analysis.AnalyseFile()
P = Analysis.AnalyseFile()
if folder[0]['IVtemp'] == T:
scale = 1e6
plt.hold(True)
plt.title('NLIV in P and AP at ' + str(T) + 'K')
plt.xlabel('Current ($\mu$A)')
plt.ylabel('V$_{NL}$ ($\mu$V)')
for f in folder:
if f['iterator'] in APiterator:
AP.add_column(f.Voltage,str(f['iterator']))
else:
P.add_column(f.Voltage,str(f['iterator']))
AP.apply(func,0,replace=False,header='Mean NLVoltage')
P.apply(func,0,replace=False,header='Mean NLVoltage')
I = numpy.arange(-295e-6,295e-6,1e-6)
ap = interpolate.interp1d(f.column('Current'),AP.column('Mean NLV'))
p = interpolate.interp1d(f.column('Current'),P.column('Mean NLV'))
print P
plt.title(' ',verticalalignment='bottom')
plt.xlabel('Current ($\mu$A)')
#plt.ylabel('V$_{NL}$/|I| (V/A)')
plt.ylabel('$\Delta$V$_{NL}$/|I| (mV/A)')
plt.plot(f.column('Current')*scale,1e3*(P.column('Mean NLV')-AP.column('Mean NLV'))/abs(f.column('Current')),label =''+str(T)+ ' K')
#plt.plot(f.column('Current')*scale,1e3*(P.column('Mean NLV'))/abs(f.column('Current')),label ='P at '+str(T)+ ' K')
#plt.plot(f.column('Current')*scale,1e3*(AP.column('Mean NLV'))/abs(f.column('Current')),label ='AP at '+str(T)+ ' K')
plt.legend(loc='upper left')
else:
return 1
def plot_groups_at_time_point(self,t, feat1, feat2):
markers = ['ro', 'go', 'bo', 'yo', 'ko', 'mo', 'co']
cp_list = self.cell_tracker.list_of_cell_profiles_per_timestamp[t].list_of_cell_profiles
fig = pylab.figure( facecolor='white')
counter = -1
for group_name in self.groups.keys():
counter +=1
gr = self.groups[group_name][t]
feat1_vals = []
feat2_vals = []
for idx in gr:
feat1_vals.append(cp_list[idx].dict_of_features[feat1])
feat2_vals.append(cp_list[idx].dict_of_features[feat2])
pylab.plot(feat1_vals, feat2_vals, markers[counter], label = group_name)
pylab.hold(True)
fig.canvas.set_window_title("Time point %s" % t)
pylab.legend(loc="best")
pylab.xlabel(feat1)
pylab.ylabel(feat2)
pylab.grid()
def plot_dist_to_nucleus_hists(self):
fig = pylab.figure(figsize=(7,5), facecolor='white')
fig.canvas.set_window_title("Distance to nucleus histograms")
num_bins = len(self.cell_tracker.list_of_cell_profiles_per_timestamp[0].list_of_cell_profiles[0].dict_of_features["border_to_nucleus_dist_hist"])
color_idx = np.linspace(0, 1, num_bins )
bins = np.arange(0,1, 1/float(num_bins))
# histogram of cell sizes
for colorshift, i in zip(color_idx, xrange(len(self.cell_tracker.list_of_cell_profiles_per_timestamp))):
ts = self.cell_tracker.list_of_cell_profiles_per_timestamp[i].time_stamp
hists = self.cell_tracker.list_of_cell_profiles_per_timestamp[i].list_of_cell_profiles[0].dict_of_features["border_to_nucleus_dist_hist"]
for idx in xrange(1,len(self.cell_tracker.list_of_cell_profiles_per_timestamp[i].list_of_cell_profiles)):
cp_item = self.cell_tracker.list_of_cell_profiles_per_timestamp[i].list_of_cell_profiles[idx]
hist = cp_item.dict_of_features["border_to_nucleus_dist_hist"]
hist = np.array(hist) / float(np.sum(hist))
hists = np.vstack((hists, hist))
hist = hists.mean(0)
pylab.plot(bins, hist , linewidth=3.0, color=pylab.cm.winter(colorshift), label="t = "+str(ts))
pylab.hold(True)
pylab.xlabel("")
pylab.ylabel("")
fig.canvas.set_window_title("Distance to nucleus histograms")
pylab.legend()
def plotTrackingHigh(trackResults, settings):
fig = pylab.figure()
fig.clf()
if (settings.plotTrackingNumPts > len(trackResults[0].I_P)):
x_pts = [i*0.001 for i in range(len(trackResults[0].I_P))]
else:
x_pts = [i*0.001 for i in range(settings.plotTrackingNumPts)]
colors = [(0,0,0),\
(0,0,1),\
(0,1,0),\
(0,1,1),\
(1,0,0),\
(1,0,1),\
(1,1,0),\
(0,0,0.5),\
(0,0.5,0),\
(0,0.5,0.5),\
(0.5,0,0),\
(0.5,0,0.5),\
(0.5,0.5,0),\
(0.5,0.5,0.5)]
pylab.title("Prompt correlation magnitude of each channel")
pylab.xlabel("Time")
pylab.hold(True)
for channelNr in range(len(trackResults)):
pylab.plot(x_pts,\
np.sqrt(np.square(trackResults[channelNr].I_P[0:len(x_pts)])\
+ np.square(trackResults[channelNr].Q_P[0:len(x_pts)])),\
color=colors[channelNr], label=("PRN %2d" % (trackResults[channelNr].PRN)))
pylab.legend()
pylab.hold(False)
return fig
def plot(isDraw=True):
# return
pylab.subplot(3, 1, 1)
pylab.title("State")
pylab.plot([x[0, 0] for x in kal.xhist], "k-", label="Estimated state")
pylab.hold(True)
pylab.plot([t[0, 0] for t in states[1:]], "r-", label="True state")
# pylab.legend(loc="lower right")
pylab.hold(False)
pylab.subplot(3, 1, 2)
pylab.plot([t[0, 0] for t in measures], label="Measurement")
pylab.hold(False)
pylab.legend()
pylab.hold(False)
pylab.subplot(3, 1, 3)
pylab.plot([tmpx[0, 0] - t[0, 0] for tmpx, t in zip(kal.xhist, states[1:])], label="Estimation error")
pylab.hold(False)
pylab.legend()
if isDraw:
pylab.draw()
else:
pylab.show()
def plot_profile(self,
axis,
index,
n_bins=100,
range_plot=None,
true_value=None,
estimated_value=None,
figure=None,
cmap=None,
color_line=None,
title="Profile"):
# FIXME: implementation is 2-D only, extend to 3-D
if axis != 0 and axis != 1:
raise ("axis: 0 or 1")
if cmap is None:
cdict = {
'red': ((0.0, 1.0, 1.0), (1.0, 0.7, 0.7)),
'green': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0))
}
cmap = pylab.matplotlib.colors.LinearSegmentedColormap(
'my_colormap', cdict, 256)
shape = self._tracer.shape()
n_points = shape[1 - axis]
M = np.zeros((n_points, n_bins))
if range_plot is None:
range_plot = (self._tracer.min(), self._tracer.max())
for i in range(n_points):
if axis == 0:
M[i, :] = self._tracer.histogram((index, i),
n_bins=100,
range_histogram=range_plot)[0]
else:
M[i, :] = self._tracer.histogram((i, index),
n_bins=100,
range_histogram=range_plot)[0]
if axis == 0:
p = self._tracer.get_samples()[:, index, :]
if true_value is not None:
p_t = true_value[index, :]
if estimated_value is not None:
p_e = estimated_value[index, :]
else:
p = self._tracer.get_samples()[:, :, index]
if true_value is not None:
p_t = true_value[:, index]
if estimated_value is not None:
p_e = estimated_value[:, index]
p_m = np.mean(p, 0)
if color_line is None:
color_line = hsv_to_rgb(0.2, 0.0, 0.3)
if not figure:
pass # figure = pylab.figure()
else:
pylab.figure(figure.number)
pylab.hold(1)
pylab.plot((n_bins / (range_plot[1] - range_plot[0])) * p_m,
color=color_line,
linewidth=1,
linestyle='dashed')
if true_value is not None:
pylab.plot((n_bins / (range_plot[1] - range_plot[0])) * p_t,
color=color_line,
linewidth=1,
linestyle='solid')
if estimated_value is not None:
pylab.plot((n_bins / (range_plot[1] - range_plot[0])) * p_e,
color=color_line,
linewidth=1,
linestyle='dotted')
pylab.imshow(M.transpose(), cmap=cmap, origin="lower", aspect="auto")
pylab.title(title)
pylab.draw()
def Wave2DAnim(snapshots,
ds,
dt,
vel,
filename='wave2danim',
norm=True,
vmin=None,
vmax=None,
anim="avi",
fps=15):
r"""
Create an animation file from a matrix resulting from a simulation of a 2d wave field.
Creates many intermediate files to achieve that, uses ImageMagick.
Z is downward.
* snapshots : is a 3d matrix [time][nz][nx] - pressure field
* ds : space equal in x/y axis
* dt : time increment between simulation steps
* vel : 2d background velocity field
* filename : file name for the animation file
* anim : animation type (gif or avi)
* fps : frames per second
* norm : scale the values getting the general max and min (vmax/vmin)
* vmin : global minimum of snapshots
* vmax : global maximum of snapshots
"""
py.ion()
#max index time and max index space
maxt = np.shape(snapshots)[0]
maxk = np.shape(snapshots)[1]
maxi = np.shape(snapshots)[2]
if norm:
# get the maximum and minimum values of the last 5%
# snapshots to not blow the scale during the animation
# get the maximum and minimum values of the last 5%
# snapshots to not blow the scale during the animation
snaptmp = snapshots[-int(0.05 * maxt):]
vmax = snaptmp.max()
vmin = snaptmp.min()
print "vmin : ", vmin, "vmax : ", vmax
# space axis starting at 0 in x and z (using y coz' plotting)
# extents of the picture,
xmin, xmax = 0, ds * maxi
ymin, ymax = 0, ds * maxk
extent = xmin, xmax, ymax, ymin
# font position
width = xmax - xmin
height = ymax - ymin
posx = 0.8 * width + xmin
posz = 0.8 * height + ymin
# not working?
# verticalalignment='top',
# horizontalalignment='right'
_ClearTempImages(filename, "png") # clear any previous existing
for t in xrange(maxt):
py.hold(True)
py.imshow(vel,
interpolation='bilinear',
cmap=cm.jet,
extent=extent,
origin='upper',
aspect='auto')
py.imshow(snapshots[t],
interpolation='bilinear',
cmap=cm.Greys_r,
alpha=0.8,
extent=extent,
origin='upper',
aspect='auto',
vmin=vmin,
vmax=vmax)
# optional cmap=cm.jet, apect='auto' adjust aspect to the previous plot
py.show()
# draw time
py.text(posx,
posz,
"{0:1.5f}".format(t * dt),
alpha=0.8,
style='italic',
color='b')
# since its just math objects would be perfect
# will be something like Wave1DAnim001.png
py.savefig(filename + "{0:03d}".format(t) + '.png', dpi=150)
sys.stderr.write("\r progressing .. %.1f%%" %
(100.0 * float(t) / maxt))
sys.stderr.flush()
py.clf()
sys.stdout.write(" done! \n")
py.hold(False)
py.ioff()
py.close()
if (anim == "gif"):
AnimFromPng(filename, fps=fps)
else:
AnimFromPng(filename, False, fps)
_ClearTempImages(filename, "png")
#REFERENCE
#listofalltimes[0] = times for density 25
#listofalltimes[1] = times for density 30
#listofalltimes[2] = times for density 35
#listofalltimes[3] = times for density 40
#listofalltimes[4] = times for density 45
#listofalltimes[5] = times for density 50
#listofalltimes[0][0] = times for density 25 with u = 2
#listofalltimes[0][1] = times for density 25 with u = 3
#listofalltimes[0][2] = times for density 25 with u = 4
fig = figure()
ax = axes()
hold(True)
def boxsettings(bp):
color = [(0.9769448735268946, 0.6468696110452877, 0.2151452804329661),
(0.37645505989354233, 0.6875228836084111, 0.30496111115768654),
(0.6151274326753975, 0.4961389476149738, 0.15244053646953548)]
i = 0
for box in bp['boxes']:
box.set(color='#000000', linewidth=1)
box.set(facecolor=color[i])
i = i + 1
for whisker in bp['whiskers']:
whisker.set(color='#000000', linewidth=1, ls='-')
a_sine += (a_random - 0.5) * 1.0 # Loudia's solution # --------------------------------- # window = loudia.Window(frameSize, loudia.Window.HAMMING) fft = loudia.FFT(fftSize) peaks = loudia.PeakDetectionComplex(5, 4) peaksinterp = loudia.PeakInterpolationComplex() trajs = loudia.PeakTracking(5, 4, 3) r_sine_windowed = window.process(a_sine) r_sine_mag = fft.process(r_sine_windowed) r_sine_peakpos, r_sine_peakmag, r_sine_peakphase = peaks.process(r_sine_mag) r_sine_peakipos, r_sine_peakimag, r_sine_peakphasei = peaksinterp.process(r_sine_mag, r_sine_peakpos, r_sine_peakmag, r_sine_peakphase) r_sine_trajpos, r_sine_trajmag = trajs.process(r_sine_mag, r_sine_peakipos, r_sine_peakimag) # -------------------------------------------------------- # print r_sine_mag print r_sine_peakpos print r_sine_peakmag print r_sine_trajpos, r_sine_trajmag import pylab pylab.hold(True) pylab.plot(abs(r_sine_mag[0,:])) pylab.hold(True) pylab.scatter(r_sine_peakpos[0,:], r_sine_peakmag[0,:]) pylab.show()
numCondsToPlot = 3
S_top = np.log10(plotDepthResults(top, what, numCondsToPlot) / 1e-6) * 20.0
S_bottom = np.log10(
plotDepthResults(bottom, what, numCondsToPlot) / 1e-6) * 20.0
m = [0, -4.0, -8.0, -12.0]
top_mu = S_top[:5, :].mean(axis=0)
top_err = S_top[:5, :].std(axis=0) / (len(top)**0.5)
bottom_mu = S_bottom[-5:, :].mean(axis=0)
bottom_err = S_bottom[-5:, :].std(axis=0) / (len(bottom)**0.5)
pl.close('all')
pl.figure()
pl.errorbar(m, top_mu, yerr=top_err, linewidth=3)
pl.hold(True)
pl.errorbar(m, bottom_mu, yerr=bottom_err, color='r', linewidth=3)
pl.plot(m, S_top.T, '--', linewidth=2, color=[0.5, 0.5, 0.5])
pl.plot(m, S_bottom.T, '--', linewidth=2, color=[0.5, 0.5, 0.5])
pl.xlabel('Modulation Depth (dB re: 100%)', fontsize=20)
pl.ylabel('EFR magnitude (dB re: 1uV)', fontsize=20)
pl.xlim((-12.1, 0.1))
pl.show()
allsubj = top + bottom
S_all = np.log10(plotDepthResults(allsubj, what, numCondsToPlot) / 1e-6) * 20
N_all = np.log10(plotDepthResults(allsubj, 'N', numCondsToPlot) / 1e-6) * 20
correction = 24.1
all_mu = S_all.mean(axis=0) - correction
all_err = S_all.std(axis=0) / (len(allsubj)**0.5)
all_mu_n = N_all.mean() - correction
def plot_frame(features_array, ground_truth, sampleRate, hop_size, acf,
periods, phases, bin2hz, minlag, maxlag, mcomb):
import numpy
from pylab import figure, subplot, imshow, plot, axis, hold, clf, show
# norm features, acf and peaks
pfeatures = numpy.array(features_array).transpose()
maxis = pfeatures.max(axis=1)
for i in range(len(maxis)):
pfeatures[i] /= maxis[i]
normed_acf = []
for i in range(len(acf)):
normed_acf.append(acf[i] / max(acf[i]))
acf = normed_acf
# begin plot
figure()
clf()
subplot(311)
hold(True)
for i in range(len(pfeatures)):
plot(pfeatures[i] + i)
hold(False)
axis('tight')
subplot(312)
hold(True)
for i in range(len(mcomb)):
plot(mcomb[i] / mcomb[i].max() + i)
plot([mcomb[i].argmax()] * 2, [i, i + 1])
plot([periods[i]] * 2, [i, i + 1])
# plot the ground truth
if ground_truth != None:
plot([bpmtolag(ground_truth, sampleRate, hop_size)] * 2,
[0., len(acf)], 'r-')
# plot the bpm estimate
for i in range(len(periods)):
plot([periods[i]] * 2, [i, i + 1], 'g-')
hold(False)
axis('tight')
subplot(313)
hold(True)
for i in range(len(pfeatures)):
periodnum = 4
phout = [0. for j in range(len(pfeatures[i]) / periodnum)]
if periods[i] == 0: continue
for j in range(len(phout)):
for a in range(periodnum):
phout[j] += pfeatures[i][a * periods[i] + j]
plot(phout / max(phout) + i)
phase = phases[i]
if phase >= periods[i]:
while phase >= periods[i]:
phase -= periods[i]
while phase < len(pfeatures[i]):
plot([phase] * 2, [i, i + 1], 'r-')
phase += periods[i]
hold(False)
axis([0., len(pfeatures[i]), 0., len(pfeatures)])
show()
def peakdetect_fft(y_axis, x_axis, pad_len=5):
"""
Performs a FFT calculation on the data and zero-pads the results to
increase the time domain resolution after performing the inverse fft and
send the data to the 'peakdetect' function for peak
detection.
Omitting the x_axis is forbidden as it would make the resulting x_axis
value silly if it was returned as the index 50.234 or similar.
Will find at least 1 less peak then the 'peakdetect_zero_crossing'
function, but should result in a more precise value of the peak as
resolution has been increased. Some peaks are lost in an attempt to
minimize spectral leakage by calculating the fft between two zero
crossings for n amount of signal periods.
The biggest time eater in this function is the ifft and thereafter it's
the 'peakdetect' function which takes only half the time of the ifft.
Speed improvementd could include to check if 2**n points could be used for
fft and ifft or change the 'peakdetect' to the 'peakdetect_zero_crossing',
which is maybe 10 times faster than 'peakdetct'. The pro of 'peakdetect'
is that it resutls in one less lost peak. It should also be noted that the
time used by the ifft function can change greatly depending on the input.
keyword arguments:
y_axis -- A list containg the signal over which to find peaks
x_axis -- A x-axis whose values correspond to the y_axis list and is used
in the return to specify the postion of the peaks.
pad_len -- (optional) By how many times the time resolution should be
increased by, e.g. 1 doubles the resolution. The amount is rounded up
to the nearest 2 ** n amount (default: 5)
return -- two lists [max_peaks, min_peaks] containing the positive and
negative peaks respectively. Each cell of the lists contains a tupple
of: (position, peak_value)
to get the average peak value do: np.mean(max_peaks, 0)[1] on the
results to unpack one of the lists into x, y coordinates do:
x, y = zip(*tab)
"""
# check input data
x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis)
zero_indices = zero_crossings(y_axis, window=11)
#select a n amount of periods
last_indice = -1 - (1 - len(zero_indices) & 1)
# Calculate the fft between the first and last zero crossing
# this method could be ignored if the begining and the end of the signal
# are discardable as any errors induced from not using whole periods
# should mainly manifest in the beginning and the end of the signal, but
# not in the rest of the signal
fft_data = fft(y_axis[zero_indices[0]:zero_indices[last_indice]])
padd = lambda x, c: x[:len(x) // 2] + [0] * c + x[len(x) // 2:]
n = lambda x: int(log(x) / log(2)) + 1
# padds to 2**n amount of samples
fft_padded = padd(list(fft_data),
2**n(len(fft_data) * pad_len) - len(fft_data))
# There is amplitude decrease directly proportional to the sample increase
sf = len(fft_padded) / float(len(fft_data))
# There might be a leakage giving the result an imaginary component
# Return only the real component
y_axis_ifft = ifft(fft_padded).real * sf #(pad_len + 1)
x_axis_ifft = np.linspace(x_axis[zero_indices[0]],
x_axis[zero_indices[last_indice]],
len(y_axis_ifft))
# get the peaks to the interpolated waveform
max_peaks, min_peaks = peakdetect(y_axis_ifft,
x_axis_ifft,
500,
delta=abs(np.diff(y_axis).max() * 2))
#max_peaks, min_peaks = peakdetect_zero_crossing(y_axis_ifft, x_axis_ifft)
# store one 20th of a period as waveform data
data_len = int(np.diff(zero_indices).mean()) / 10
data_len += 1 - data_len & 1
fitted_wave = []
for peaks in [max_peaks, min_peaks]:
peak_fit_tmp = []
index = 0
for peak in peaks:
index = np.where(x_axis_ifft[index:] == peak[0])[0][0] + index
x_fit_lim = x_axis_ifft[index - data_len // 2:index +
data_len // 2 + 1]
y_fit_lim = y_axis_ifft[index - data_len // 2:index +
data_len // 2 + 1]
peak_fit_tmp.append([x_fit_lim, y_fit_lim])
fitted_wave.append(peak_fit_tmp)
#pylab.plot(range(len(fft_data)), fft_data)
#pylab.show()
pylab.plot(x_axis, y_axis)
pylab.hold(True)
pylab.plot(x_axis_ifft, y_axis_ifft)
#for max_p in max_peaks:
# pylab.plot(max_p[0], max_p[1], 'xr')
pylab.show()
return [max_peaks, min_peaks]
def plotDepthResults(subjlist,
what,
numCondsToPlot,
summary=False,
max=False,
ch_sel=30):
# froot = '/home/hari/Documents/PythonCodes/research/DepthResults/'
froot = '/home/hari/Documents/DepthResults/'
nsubjs = len(subjlist)
condlist = [[1, 7], [2, 8], [3, 9], [4, 10]]
condstemlist = ['_0dB', '_m4dB', '_m8dB', '_m12dB']
nconds = len(condlist)
whatever_all = np.zeros((nsubjs, nconds))
for k, subj in enumerate(subjlist):
fpath = froot + subj + '/'
whatever = np.zeros(len(condlist))
for condind, cond in enumerate(condlist):
condstem = condstemlist[condind]
load_name = fpath + subj + condstem + '.mat'
dat = io.loadmat(load_name)
f = dat['f']
f_ind = np.argmin(abs(f - 100))
if (what == 'cplv'):
summary = False
whatever[condind] = dat[what][f_ind]
else:
if (summary):
if (max):
whatever[condind] = np.max(dat[what][:, f_ind])
else:
f_pca = (np.logical_and(f > 80, f < 115)).squeeze()
C = np.cov(dat[what][:, f_pca])
lambdas, wts = linalg.eigh(C)
w = wts[:, -1] / (wts[:, -1]).sum()
whatever[condind] = np.dot(w, dat[what])[f_ind]
else:
whatever[condind] = dat[what][ch_sel, f_ind]
whatever_all[k, :] = whatever
pl.plot(20 * np.log10(whatever[0:numCondsToPlot] / 1e-6),
'o-',
linewidth=2)
pl.ylabel(what + ' (dB re: 1 sq.micro.V)', fontsize=20)
pl.xlabel('Modulation Depth', fontsize=20)
pl.hold(True)
ax = pl.gca()
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(20)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(20)
pl.show()
pl.legend(subjlist)
return whatever_all