def demo():
from pylab import hold, linspace, plot, show
hold(True)
#y = [9,6,1,3,8,4,2]
#y = [9,11,13,3,-2,0,2]
y = [9,11,2,3,8,0,2]
#y = [9,9,1,3,8,2,2]
xeq = linspace(0,1,len(y))
x = xeq+0
#x[1],x[-2] = x[0],x[-1]
#x[1],x[-2] = x[2],x[-3]
#x[1],x[2] = x[2],x[1]
#x[1],x[-2] = x[2]-0.001,x[-2]+0.001
#x[1],x[-2] = x[1]-x[1]/2,x[-1]-x[1]/2
t = linspace(x[0],x[-1],400)
plot(xeq,y,':oy')
plot(t,bspline(y,t,clamp=False),'-.y') # bspline
plot(t,bspline(y,t,clamp=True),'-y') # bspline
xt,yt = pbs(x,y,t,clamp=False)
plot(xt,yt,'-.b') # pbs
xt,yt = pbs(x,y,t,clamp=True)
plot(xt,yt,'-b') # pbs
#xt,yt = pbs(x,y,t,clamp=True, parametric=True)
#plot(xt,yt,'-g') # pbs
plot(sorted(x),y,':ob')
show()
def zeroPaddData(self,desiredLength,paddmode='zero',where='end'):
#zero padds the time domain data, it is possible to padd at the beginning,
#or at the end, and further gaussian or real zero padding is possible
#might not work for gaussian mode!
desiredLength=int(desiredLength)
#escape the function
if desiredLength<0:
return 0
#calculate the paddvectors
if paddmode=='gaussian':
paddvec=py.normal(0,py.std(self.getPreceedingNoise())*0.05,desiredLength)
else:
paddvec=py.ones((desiredLength,self.tdData.shape[1]-1))
paddvec*=py.mean(self.tdData[-20:,1:])
timevec=self.getTimes()
if where=='end':
#timeaxis:
newtimes=py.linspace(timevec[-1],timevec[-1]+desiredLength*self.dt,desiredLength)
paddvec=py.column_stack((newtimes,paddvec))
longvec=py.row_stack((self.tdData,paddvec))
else:
newtimes=py.linspace(timevec[0]-(desiredLength+1)*self.dt,timevec[0],desiredLength)
paddvec=py.column_stack((newtimes,paddvec))
longvec=py.row_stack((paddvec,self.tdData))
self.setTDData(longvec)
def contourFromFunction(XYfunction,plotPoints=100,\ xrange=None,yrange=None,numContours=20,alpha=1.0, contourLines=None): """ Given a 2D function, plots constant contours over the given range. If the range is not given, the current plotting window range is used. """ # set up x and y ranges currentAxis = pylab.axis() if xrange is not None: xvalues = pylab.linspace(xrange[0],xrange[1],plotPoints) else: xvalues = pylab.linspace(currentAxis[0],currentAxis[1],plotPoints) if yrange is not None: yvalues = pylab.linspace(yrange[0],yrange[1],plotPoints) else: yvalues = pylab.linspace(currentAxis[2],currentAxis[3],plotPoints) #coordArray = _coordinateArray2D(xvalues,yvalues) # add extra dimension to this to make iterable? # bug here! need to fix for contour plots z = map( lambda y: map(lambda x: XYfunction(x,y), xvalues), yvalues) if contourLines: pylab.contour(xvalues,yvalues,z,contourLines,alpha=alpha) else: pylab.contour(xvalues,yvalues,z,numContours,alpha=alpha)
def griddata( X, Y, Z, xl, yl, xr, yr, dx):
# define grid.
xi, yi = p.meshgrid( p.linspace(xl,xr, int((xr-xl)/dx)+1), p.linspace(yl,yr, int((yr-yl)/dx)+1))
# grid the data.
zi = mgriddata(X,Y,Z,xi,yi)
New = grid( zi, xl, yl, dx)
return New
def data2fig(data, X, options, legend_title, xlabel, ylabel=r'Reachability~$\reachability$'):
if options['grayscale']:
colors = options['graycm'](pylab.linspace(0, 1, len(data.keys())))
else:
colors = options['color'](pylab.linspace(0, 1, len(data.keys())))
fig = MyFig(options, figsize=(10, 8), xlabel=r'Sources~$\sources$', ylabel=ylabel, grid=False, aspect='auto', legend=True)
for j, nhdp_ht in enumerate(sorted(data.keys())):
d = data[nhdp_ht]
try:
mean_y = [scipy.mean(d[n]) for n in X]
except KeyError:
logging.warning('key \"%s\" not found, continuing...', nhdp_ht)
continue
confs_y = [confidence(d[n])[2] for n in X]
poly = [conf2poly(X, list(numpy.array(mean_y)+numpy.array(confs_y)), list(numpy.array(mean_y)-numpy.array(confs_y)), color=colors[j])]
patch_collection = PatchCollection(poly, match_original=True)
patch_collection.set_alpha(0.3)
patch_collection.set_linestyle('dashed')
fig.ax.add_collection(patch_collection)
fig.ax.plot(X, mean_y, label='$%d$' % nhdp_ht, color=colors[j])
fig.ax.set_xticks(X)
fig.ax.set_xticklabels(['$%s$' % i for i in X])
fig.ax.set_ylim(0,1)
fig.legend_title = legend_title
return fig
def bistability_analysis():
f2_range = linspace(0, 0.4, 41)
t = linspace(0, 50000, 1000)
ion()
ss_aBax_vals_up = []
ss_aBax_vals_down = []
for f2 in f2_range:
model.parameters['Bid_0'].value = f2 * 1e-1
bax_total = 2e-1
# Do "up" portion of hysteresis plot
model.parameters['aBax_0'].value = 0
model.parameters['cBax_0'].value = bax_total
x = odesolve(model, t)
figure('up')
plot(t, x['aBax_']/bax_total)
ss_aBax_vals_up.append(x['aBax_'][-1]/bax_total)
# Do "down" portion of hysteresis plot
model.parameters['aBax_0'].value = bax_total
model.parameters['cBax_0'].value = 0
x = odesolve(model, t)
figure('down')
plot(t, x['aBax_']/bax_total)
ss_aBax_vals_down.append(x['aBax_'][-1]/bax_total)
figure()
plot(f2_range, ss_aBax_vals_up, 'r')
plot(f2_range, ss_aBax_vals_down, 'g')
def test_operation_approx():
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * pl.cos(phi_p)*pl.cos(phi_m) - alpha * pl.cos(phi_ext - 2*phi_p)
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
phi_m = pl.linspace(0, 2*np.pi, 100)
phi_p = pl.linspace(0, 2*np.pi, 100)
X,Y = pl.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
# the diagram creatinos
from diagram.operations.computations import multiply
from diagram.ternary import AEV3DD
aevdd = AEV3DD()
diagram3 = aevdd.create(Z, 0, True)
diagram4 = aevdd.create(Z, 0, True)
aevdd_mat = multiply(diagram3, diagram4, 9).to_matrix(77, True)
aevdd_mat_approx = multiply(diagram3, diagram4, 9, approximation_precision=1, in_place='1').to_matrix(27, True)
pl.plt.figure()
fig, ax = pl.plt.subplots()
p = ax.pcolor(X/(2*pl.pi), Y/(2*pl.pi), Z, cmap=pl.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)
p = ax.pcolor(X/(2*pl.pi), Y/(2*pl.pi), Z, cmap=pl.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)
# cnt = ax.contour(Z, cmap=pl.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
pl.show()
def plotDistribution(self):
# plot frequency count for the entire vocabulary
threshold = 1000
size = len(self.listOfDict[0])
x = linspace(1, size, size)
y = sorted(self.listOfDict[0].values(), reverse=True)
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, y, 'r')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('title');
plt.show()
#plot frequency count for all words with count over a given threshold (e.g. number of files read)
threshold = self.numFilesRead
size = len(self.listOfDict[0])
size_relevant = sum(1 for i in self.listOfDict[0].values() if i>threshold)
x = linspace(1, size_relevant, size_relevant)
y = sorted([i for i in self.listOfDict[0].values() if i>threshold], reverse=True)
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, y, 'r')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('title');
plt.show()
def Plot_field_gp():
r = pl.linspace(-1.*mm,1.*mm,50)
z = pl.linspace(0,g,50)
X, Y = np.meshgrid(z, r)
for z,r in zip(np.ravel(X), np.ravel(Y)):
print z*1000,r*1000,SpaceChargeField(r,0,z,0,0,0.5*mm)*0.001
def plot_interfaces(current_data):
from pylab import linspace, plot
xl = linspace(xp1,xp2,100)
yl = linspace(yp1,yp2,100)
plot(xl,yl,'g')
xl = linspace(xlimits[0],xlimits[1],100)
plot(xl,0.0*xl,'b')
def mk_grid(llx, ulx, nx, lly, uly, ny):
# Get the Galaxy info
#galaxies = mk_galaxy_struc()
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies = pyl.asarray(filter(lambda galaxy: galaxy.ICD_IH < 0.5, galaxies))
# Make the low mass grid first
x = [galaxy.Mass for galaxy in galaxies]
y = [galaxy.ICD_IH *100 for galaxy in galaxies]
bins_x =pyl.linspace(llx, ulx, nx)
bins_y = pyl.linspace(uly, lly, ny)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
for j in range(bins_y.size-1):
ymax = bins_y[j]
ymin = bins_y[j+1]
cond=[cond1 and cond2 and cond3 and cond4 for cond1, cond2, cond3,
cond4 in zip(x>=xmin, x<xmax, y>=ymin, y<ymax)]
grid.append(galaxies.compress(cond))
return grid
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 plot_elecs_and_neurons(neuron_dict, ext_sim_dict, neural_sim_dict):
pl.close('all')
fig_all = pl.figure(figsize=[15,15])
ax_all = fig_all.add_axes([0.1, 0.1, 0.8, 0.8], frameon=False)
for elec in xrange(len(ext_sim_dict['elec_z'])):
ax_all.plot(ext_sim_dict['elec_z'][elec], ext_sim_dict['elec_y'][elec], color='b',\
marker='$E%i$'%elec, markersize=20 )
legends = []
for i, neur in enumerate(neuron_dict):
folder = os.path.join(neural_sim_dict['output_folder'], neuron_dict[neur]['name'])
coor = np.load(os.path.join(folder,'coor.npy'))
x,y,z = coor
n_compartments = len(x)
fig = pl.figure(figsize=[10, 10])
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], frameon=False)
# Plot the electrodes
for elec in xrange(len(ext_sim_dict['elec_z'])):
ax.plot(ext_sim_dict['elec_z'][elec], ext_sim_dict['elec_y'][elec], color='b',\
marker='$%i$'%elec, markersize=20 )
# Plot the neuron
xmid, ymid, zmid = np.load(folder + '/coor.npy')
xstart, ystart,zstart = np.load(folder + '/coor_start.npy')
xend, yend, zend = np.load(folder + '/coor_end.npy')
diam = np.load(folder + '/diam.npy')
length = np.load(folder + '/length.npy')
n_compartments = len(diam)
for comp in xrange(n_compartments):
if comp == 0:
xcoords = pl.array([xmid[comp]])
ycoords = pl.array([ymid[comp]])
zcoords = pl.array([zmid[comp]])
diams = pl.array([diam[comp]])
else:
if zmid[comp] < 0.400 and zmid[comp] > -.400:
xcoords = pl.r_[xcoords, pl.linspace(xstart[comp],
xend[comp], length[comp]*3*1000)]
ycoords = pl.r_[ycoords, pl.linspace(ystart[comp],
yend[comp], length[comp]*3*1000)]
zcoords = pl.r_[zcoords, pl.linspace(zstart[comp],
zend[comp], length[comp]*3*1000)]
diams = pl.r_[diams, pl.linspace(diam[comp], diam[comp],
length[comp]*3*1000)]
argsort = pl.argsort(-xcoords)
ax.scatter(zcoords[argsort], ycoords[argsort], s=20*(diams[argsort]*1000)**2,
c=xcoords[argsort], edgecolors='none', cmap='gray')
ax_all.plot(zmid[0], ymid[0], marker='$%i$'%i, markersize=20, label='%i: %s' %(i, neur))
#legends.append('%i: %s' %(i, neur))
ax.axis(ext_sim_dict['plot_range'])
ax.axis('equal')
ax.axis(ext_sim_dict['plot_range'])
ax.set_xlabel('z [mm]')
ax.set_ylabel('y [mm]')
fig.savefig(os.path.join(neural_sim_dict['output_folder'],\
'neuron_figs', '%s.png' % neur))
ax_all.axis('equal')
ax.axis(ext_sim_dict['plot_range'])
ax_all.set_xlabel('z [mm]')
ax_all.set_ylabel('y [mm]')
ax_all.legend()
fig_all.savefig(os.path.join(neural_sim_dict['output_folder'], 'fig.png'))
def main():
"""
This shows the use of SynChan with Izhikevich neuron. This can be
used for creating a network of Izhikevich neurons.
"""
simtime = 200.0
stepsize = 10.0
model_dict = make_model()
vm, inject, gk, spike = setup_data_recording(model_dict['neuron'],
model_dict['pulse'],
model_dict['synapse'],
model_dict['spike_in'])
mutils.setDefaultDt(elecdt=0.01, plotdt2=0.25)
mutils.assignDefaultTicks(solver='ee')
moose.reinit()
mutils.stepRun(simtime, stepsize)
pylab.subplot(411)
pylab.plot(pylab.linspace(0, simtime, len(vm.vector)), vm.vector, label='Vm (mV)')
pylab.legend()
pylab.subplot(412)
pylab.plot(pylab.linspace(0, simtime, len(inject.vector)), inject.vector, label='Inject (uA)')
pylab.legend()
pylab.subplot(413)
pylab.plot(spike.vector, pylab.ones(len(spike.vector)), '|', label='input spike times')
pylab.legend()
pylab.subplot(414)
pylab.plot(pylab.linspace(0, simtime, len(gk.vector)), gk.vector, label='Gk (mS)')
pylab.legend()
pylab.show()
def gfe4():
x2=plt.linspace(1e-20,.13,90000)
xmin2=((4*np.pi*(SW.R)**3)/3)*1e-20
xmax2=((4*np.pi*(SW.R)**3)/3)*.13
xff2 = plt.linspace(xmin2,xmax2,90000)
thigh=100
plt.figure()
plt.title('Grand free energy per volume vs ff @ T=%0.4f'%Tlist[thigh])
plt.ylabel('Grand free energy per volume')
plt.xlabel('filling fraction')
plt.plot(xff2,SW.phi(Tlist[thigh],x2,nR[thigh]),color='#f36118',linewidth=3)
#plt.axvline(nL[thigh])
#plt.axvline(nR[thigh])
#plt.axhline(SW.phi(Tlist[thigh],nR[thigh]))
#plt.plot(x2,x2-x2,'c')
plt.plot(nL[thigh]*((4*np.pi*(SW.R)**3)/3),SW.phi(Tlist[thigh],nL[thigh],nR[thigh]),'ko')
plt.plot(nR[thigh]*((4*np.pi*(SW.R)**3)/3),SW.phi(Tlist[thigh],nR[thigh],nR[thigh]),'ko')
plt.axhline(SW.phi(Tlist[thigh],nR[thigh],nR[thigh]),color='c',linewidth=2)
print(Tlist[100])
print(nL[100],nR[100])
plt.savefig('figs/gfe_cotangent.pdf')
plt.figure()
plt.plot(xff2,SW.phi(Tlist[thigh],x2,nR[thigh]),color='#f36118',linewidth=3)
plt.plot(nL[thigh]*((4*np.pi*(SW.R)**3)/3),SW.phi(Tlist[thigh],nL[thigh],nR[thigh]),'ko')
plt.plot(nR[thigh]*((4*np.pi*(SW.R)**3)/3),SW.phi(Tlist[thigh],nR[thigh],nR[thigh]),'ko')
plt.axhline(SW.phi(Tlist[thigh],nR[thigh],nR[thigh]),color='c',linewidth=2)
plt.xlim(0,0.0003)
plt.ylim(-.000014,0.000006)
print(Tlist[100])
print(nL[100],nR[100])
plt.savefig('figs/gfe_insert_cotangent.pdf')
def draw_bandstructure(
jobname, kspace, band, ext=".csv", format="pdf", filled=True, levels=15, lines=False, labeled=False, legend=False
):
# clf()
fig = figure(figsize=fig_size)
ax = fig.add_subplot(111, aspect="equal")
x, y, z = loadtxt(jobname + ext, delimiter=", ", skiprows=1, usecols=(1, 2, 4 + band), unpack=True)
if kspace.dimensions == 1:
pylab.plot(x, y, z)
elif kspace.dimensions == 2:
xi = linspace(-0.5, 0.5, kspace.x_res)
yi = linspace(-0.5, 0.5, kspace.y_res)
zi = griddata(x, y, z, xi, yi)
if filled:
cs = ax.contourf(xi, yi, zi, levels, **contour_filled)
legend and colorbar(cs, **colorbar_style)
cs = lines and ax.contour(xi, yi, zi, levels, **contour_lines)
labeled and lines and clabel(cs, fontsize=8, inline=1)
else:
cs = ax.contour(xi, yi, zi, levels, **contour_plain)
legend and colorbar(cs, **colorbar_style)
labeled and clabel(cs, fontsize=8, inline=1)
ax.set_xlim(-0.5, 0.5)
ax.set_ylim(-0.5, 0.5)
savefig(jobname + format, format=format, transparent=True)
def grid(x, y, z , resX=90, resY=90):
"Convert 3 column data to matplotlib grid"
xi = pl.linspace(min(x), max(x), resX)
yi = pl.linspace(min(y), max(y), resY)
Z = pl.griddata(x, y, z, xi, yi , interp='linear')
X, Y = pl.meshgrid(xi, yi )
return X, Y, Z
def _plot_bar3d(self):
logging.debug('')
if self.dimension > 0:
return
array = self._data_to_array()
# width, depth, and height of the bars (array == height values == dz)
dx = list(numpy.array([1.0/len(array[0]) for x in range(0, array.shape[1])]))
dy = list(numpy.array([1.0/len(array) for x in range(0, array.shape[0])]))
dx *= len(array)
dy *= len(array[0])
dz = array.flatten()+0.00000001 # dirty hack to cirumvent ValueError
# x,y,z position of each bar
x = pylab.linspace(0.0, 1.0, len(array[0]), endpoint=False)
y = pylab.linspace(0.0, 1.0, len(array), endpoint=False)
xpos, ypos = pylab.meshgrid(x, y)
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = numpy.zeros(array.shape).flatten()
fig = MyFig(self.options, xlabel='Probability p', ylabel='Fraction of Nodes', zlabel='Fraction of Executions', ThreeD=True)
fig.ax.set_zlim3d(0.0, 1.01)
fig.ax.set_xlim3d(0.0, 1.01)
fig.ax.set_ylim3d(0.0, 1.01)
fig.ax.set_autoscale_on(False)
assert(len(dx) == len(dy) == len(array.flatten()) == len(xpos) == len(ypos) == len(zpos))
fig.ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=['#CCBBDD'])
try:
self.figures['bar3d'] = fig.save('bar3d' + str(self.data_filter))
except ValueError, err:
logging.warning('%s', err)
def plot_wire_surface_pcolor(self):
"""
Plot the fraction of executions as a function of the fraction of nodes for
each source. Three plots are created: wireframe, surface, and pseudocolor.
"""
logging.debug('')
if self.dimension > 0:
return
array = self._data_to_array()
x = pylab.linspace(0.0, 1.0, len(array[0])+1)
y = pylab.linspace(0.0, 1.0, len(array)+1)
X, Y = pylab.meshgrid(x, y)
#fig_wire = MyFig(self.options, xlabel='Probability p', ylabel='Fraction of Nodes', ThreeD=True)
#fig_surf = MyFig(self.options, xlabel='Probability p', ylabel='Fraction of Nodes', ThreeD=True)
fig_pcol = MyFig(self.options, xlabel='Probability p', ylabel='Fraction of Nodes')
#fig_wire.ax.plot_wireframe(X, Y, array)
#fig_surf.ax.plot_surface(X, Y, array, rstride=1, cstride=1, linewidth=1, antialiased=True)
pcolor = fig_pcol.ax.pcolor(X, Y, array, cmap=cm.jet, vmin=0.0, vmax=1.0)
cbar = fig_pcol.fig.colorbar(pcolor, shrink=0.8, aspect=10)
cbar.ax.set_yticklabels(pylab.linspace(0.0, 1.0, 11), fontsize=0.8*self.options['fontsize'])
#for ax in [fig_wire.ax, fig_surf.ax]:
#ax.set_zlim3d(0.0, 1.01)
#ax.set_xlim3d(0.0, 1.01)
#ax.set_ylim3d(0.0, 1.01)
#self.figures['wireframe'] = fig_wire.save('wireframe_' + str(self.data_filter))
#self.figures['surface'] = fig_surf.save('surface_' + str(self.data_filter))
self.figures['pcolor'] = fig_pcol.save('pcolor_' + str(self.data_filter))
def showSF( N, label = '' ):
fig = P.figure()
ax1 = fig.add_subplot(1, 2, 1, projection='3d')
ax2 = fig.add_subplot(1, 2, 2)
Nx = 21
Ny = 21
nLevels = 12
tix = P.linspace( 0.0, 1.0, Nx )
tiy = P.linspace( 0.0, 1.0, Ny )
(X,Y) = P.meshgrid( tix, tiy )
z = g.RVector( len( X.flat ) )
for i, x in enumerate( X.flat ):
p = g.RVector3( X.flat[ i ], Y.flat[ i ] )
z[ i ] = N(p)
Z = P.ma.masked_where( z == -99., z )
Z = Z.reshape( Ny, Nx )
ax2.contourf( X, Y, Z )
ax2.set_aspect( 'equal' )
surf = ax1.plot_surface( X, Y, Z, rstride = 1, cstride = 1, cmap=P.cm.jet,linewidth=0 )
ax2.set_title( label + N.__str__() )
fig.colorbar( surf )
def int_f(a, fs=1.):
"""
A fourier-based integrator.
===========
Parameters:
===========
a : *array* (1D)
The array which should be integrated
fs : *float*
sampling time of the data
========
Returns:
========
y : *array* (1D)
The integrated array
"""
if False:
# version with "mirrored" code
xp = hstack([a, a[::-1]])
int_fluc = int_f0(xp, float(fs))[:len(a)]
baseline = mean(a) * arange(len(a)) / float(fs)
return int_fluc + baseline - int_fluc[0]
# old version
baseline = mean(a) * arange(len(a)) / float(fs)
int_fluc = int_f0(a, float(fs))
return int_fluc + baseline - int_fluc[0]
# old code - remove eventually (comment on 02/2014)
# periodify
if False:
baseline = linspace(a[0], a[-1], len(a))
a0 = a - baseline
m = a0[-1] - a0[-2]
b2 = linspace(0, -.5 * m, len(a))
baseline -= b2
a0 += b2
a2 = hstack([a0, -1. * a0[1:][::-1]]) # "smooth" periodic signal
dbase = baseline[1] - baseline[0]
t_vec = arange(len(a)) / float(fs)
baseint = baseline[0] * t_vec + .5 * dbase * t_vec ** 2
# define frequencies
T = len(a2) / float(fs)
freqs = 1. / T * arange(len(a2))
freqs[len(freqs) // 2 + 1 :] -= float(fs)
spec = fft.fft(a2)
spec_i = zeros_like(spec, dtype=complex)
spec_i[1:] = spec[1:] / (2j * pi* freqs[1:])
res_int = fft.ifft(spec_i).real[:len(a0)] + baseint
return res_int - res_int[0]
def cplot(f, re=[-5,5], im=[-5,5], points=2000, color=default_color_function,
verbose=False, file=None, dpi=None):
"""
Plots the given complex-valued function *f* over a rectangular part
of the complex plane specified by the pairs of intervals *re* and *im*.
For example::
cplot(lambda z: z, [-2, 2], [-10, 10])
cplot(exp)
cplot(zeta, [0, 1], [0, 50])
By default, the complex argument (phase) is shown as color (hue) and
the magnitude is show as brightness. You can also supply a
custom color function (*color*). This function should take a
complex number as input and return an RGB 3-tuple containing
floats in the range 0.0-1.0.
To obtain a sharp image, the number of points may need to be
increased to 100,000 or thereabout. Since evaluating the
function that many times is likely to be slow, the 'verbose'
option is useful to display progress.
NOTE: This function requires matplotlib (pylab).
"""
import pylab
pylab.clf()
rea, reb = re
ima, imb = im
dre = reb - rea
dim = imb - ima
M = int(sqrt(points*dre/dim)+1)
N = int(sqrt(points*dim/dre)+1)
x = pylab.linspace(rea, reb, M)
y = pylab.linspace(ima, imb, N)
# Note: we have to be careful to get the right rotation.
# Test with these plots:
# cplot(lambda z: z if z.real < 0 else 0)
# cplot(lambda z: z if z.imag < 0 else 0)
w = pylab.zeros((N, M, 3))
for n in xrange(N):
for m in xrange(M):
z = mpc(x[m], y[n])
try:
v = color(f(z))
except plot_ignore:
v = (0.5, 0.5, 0.5)
w[n,m] = v
if verbose:
print n, "of", N
pylab.imshow(w, extent=(rea, reb, ima, imb), origin='lower')
pylab.xlabel('Re(z)')
pylab.ylabel('Im(z)')
if file:
pylab.savefig(file, dpi=dpi)
else:
pylab.show()
def create_figure():
psd = test_eigenfre_music()
f = linspace(-0.5, 0.5, len(psd))
plot(f, 10 * log10(psd/max(psd)), '--',label='MUSIC 15')
savefig('psd_eigenfre_music.png')
psd = test_eigenfre_ev()
f = linspace(-0.5, 0.5, len(psd))
plot(f, 10 * log10(psd/max(psd)), '--',label='EV 15')
savefig('psd_eigenfre_ev.png')
def potAddStreamParticles(self): """ Takes the dimensions of plot widget and plots stream lines by advecting the particles for a small time step dt """ noOfParticlesAtX = int(self.axisRange[3] - self.axisRange[2])*3 noOfParticlesAtY = int(self.axisRange[1] - self.axisRange[0])*2 self.potStreakParticles = [particle(x, y) for x in linspace(self.axisRange[1], self.axisRange[0], \ noOfParticlesAtY) for y in linspace(self.axisRange[2]+0.1, self.axisRange[3]-0.1, noOfParticlesAtX)] self.graphicWidget.item.grid(False) self.graphicWidget.plotStreakParticles(self.potStreakParticles, tag = "velMagnitude") self.graphicWidget.fig.canvas.draw()
def mandel_serial():
m = zeros((N,N))
i=-1;
for x in linspace(-2, 1, num=N):
i += 1
j = -1
for y in linspace(-2, 1, num=N):
j += 1
m[j,i] = mandel_pixel(x, y)
return m
def displayPlots():
clock = moose.Clock( '/clock' ) # look up global clock
totR = moose.element( '/model/graphs/conc1/tot_PSD_R.Co' )
PP1 = moose.element( '/model/moregraphs/conc4/PP1_dash_active.Co' )
Ca = moose.element( '/model/graphs/conc1/Ca.Co' )
pylab.plot( pylab.linspace( 0, clock.currentTime, len( totR.vector )), totR.vector, label='membrane Receptor' )
pylab.plot( pylab.linspace( 0, clock.currentTime, len( PP1.vector ) ), PP1.vector, label='active PP1' )
pylab.plot( pylab.linspace( 0, clock.currentTime, len( Ca.vector ) ), Ca.vector, label='Ca' )
pylab.legend()
pylab.show()
def plot_fig7(vm):
ax_7 = pylab.subplot(111)
ax_7.plot(pylab.linspace(0, simtime/tau, len(vm[0].vector)),
(vm[0].vector-Em)/(Ek - Em), label='(1,2)->(3,4)->(5,6)->(7,8)')
ax_7.plot(pylab.linspace(0, simtime/tau, len(vm[1].vector)),
(vm[1].vector-Em)/(Ek - Em), label='(7,8)->(5,6)->(3,4)->(1,2)')
ax_7.plot(pylab.linspace(0, simtime/tau, len(vm[2].vector)),
(vm[2].vector-Em)/(Ek - Em), label='control')
pylab.legend()
pylab.show()
def plot_risetimes(a, b, **kwargs):
# plt.ion()
# if kwargs is not None:
# for key, value in kwargs.iteritems():
# if key == 'file_list':
# file_list = value
# if key == 'scan_line':
# scan_line = value
# varray = plt.array(get_value_from_cfg(file_list, scan_line))
n_files = a.shape[-1]
cmap = plt.get_cmap('jet')
c = [cmap(i) for i in plt.linspace(0, 1, n_files)]
fig1, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
[ax.set_color_cycle(c) for ax in (ax1, ax2)]
r = []
for i in xrange(n_files):
x, y = a[:,i], b[:,i]
# xo, yo = x, y #, get_envelope(x, y)
xo, yo = get_envelope(x, y)
p = plt.polyfit(xo, np.log(yo), 1)
# Right way to fit... a la Nicolas - the fit expert!
l = ax1.plot(x, plt.log(plt.absolute(y)))
lcolor = l[-1].get_color()
ax1.plot(xo, plt.log(yo), color=lcolor, marker='o', mec=None)
ax1.plot(x, p[1] + x * p[0], color=lcolor, ls='--', lw=3)
l = ax2.plot(x, y)
lcolor = l[-1].get_color()
ax2.plot(xo, yo, 'o', color=lcolor)
xi = plt.linspace(plt.amin(x), plt.amax(x))
yi = plt.exp(p[1] + p[0] * xi)
ax2.plot(xi, yi, color=lcolor, ls='--', lw=3)
print p[1], p[0], 1 / p[0]
# plt.draw()
# ax1.cla()
# ax2.cla()
r.append(1/p[0])
ax2.set_ylim(0, 1000)
plt.figure(2)
plt.plot(r, lw=3, c='purple')
# plt.gca().set_ylim(0, 10000)
# ax3 = plt.subplot(111)
# ax3.semilogy(x, y)
# ax3.semilogy(xo, yo)
return r
def ideal():
x2=plt.linspace(1e-20,.13,40000)
tt=plt.linspace(1e-20,1.2,4000)
plt.figure()
plt.title('ideal gas')
plt.ylabel('f')
plt.xlabel('n')
#plt.plot(x2,SW.fid(Tlist[100],x2),color='#f36118')
#plt.plot(tt,SW.fid(tt,nR[100]))
plt.show()
def eval_critical(options, tuples_for_pb, ps, cepsilon=0.01):
fig_abs = MyFig(options, figsize=(10, 8), xlabel=r'Probability $p_s$', ylabel=r'abs', aspect='auto', legend=True, grid=False)
fig_mean = MyFig(options, figsize=(10, 8), xlabel=r'Probability $p_s$', ylabel=r'mean', aspect='auto', legend=True, grid=False)
fig_majority = MyFig(options, figsize=(10, 8), xlabel=r'Probability $p_s$', ylabel=r'Majority', aspect='auto', legend=True, grid=False)
if options['grayscale']:
colors = options['graycm'](pylab.linspace(0, 1.0, len(tuples_for_pb)))
else:
colors = options['color'](pylab.linspace(0, 1.0, len(tuples_for_pb)))
crit_ranges = options['crit_range']
p_c = list()
for i, pb in enumerate(sorted(tuples_for_pb.keys())):
if '%.2f' % pb not in crit_ranges:
continue
minps = crit_ranges['%.2f' % pb][0]
maxps = crit_ranges['%.2f' % pb][1]
rs = tuples_for_pb[pb]
tuples = zip(*rs)
if len(tuples) == 0:
continue
crit_abs = [max(t)-min(t) for t in tuples]
crit_abs_min = min(crit_abs)
crit_abs_ps = [pos for pos, c in enumerate(crit_abs) if abs(c - crit_abs_min) < cepsilon]
fig_abs.ax.plot(ps, crit_abs, label='%.2f' % pb, color=colors[i])
crit_mean = [scipy.mean(t) for t in tuples]
crit_mean_min = min(crit_mean)
crit_mean_ps = [pos for pos, c in enumerate(crit_mean) if abs(c - crit_mean_min) < cepsilon]
fig_mean.ax.plot(ps, crit_mean, label='%.2f' % pb, color=colors[i])
majority = list()
for t in tuples:
all_pairs = list(k_subsets(t, 2))
c = [abs(a-b) for a,b in all_pairs]
counter = Counter(c)
times = set(counter.values())
y = scipy.mean([val for (val, occ) in counter.iteritems() if occ == max(times)])
#y = scipy.mean(c)
majority.append(y)
fig_majority.ax.plot(ps, majority, label='%.2f' % pb, color=colors[i])
start = list(ps).index(minps)
stop = min(list(ps).index(maxps)+1, len(ps))
metric = majority
pc = ps[list(metric).index(min(metric[start:stop]))]
p_c.append((pb, pc))
fig_abs.legend_title = '$p_b$'
fig_abs.save('value-eval-abs')
fig_mean.legend_title = '$p_b$'
fig_mean.save('value-eval-pairs')
fig_majority.legend_title = '$p_b$'
fig_majority.save('value-eval-pairs')
return p_c
def cplot(ctx,
f,
re=[-5, 5],
im=[-5, 5],
points=2000,
color=None,
verbose=False,
file=None,
dpi=None,
axes=None):
"""
Plots the given complex-valued function *f* over a rectangular part
of the complex plane specified by the pairs of intervals *re* and *im*.
For example::
cplot(lambda z: z, [-2, 2], [-10, 10])
cplot(exp)
cplot(zeta, [0, 1], [0, 50])
By default, the complex argument (phase) is shown as color (hue) and
the magnitude is show as brightness. You can also supply a
custom color function (*color*). This function should take a
complex number as input and return an RGB 3-tuple containing
floats in the range 0.0-1.0.
To obtain a sharp image, the number of points may need to be
increased to 100,000 or thereabout. Since evaluating the
function that many times is likely to be slow, the 'verbose'
option is useful to display progress.
.. note :: This function requires matplotlib (pylab).
"""
if color is None:
color = ctx.default_color_function
import pylab
if file:
axes = None
fig = None
if not axes:
fig = pylab.figure()
axes = fig.add_subplot(111)
rea, reb = re
ima, imb = im
dre = reb - rea
dim = imb - ima
M = int(ctx.sqrt(points * dre / dim) + 1)
N = int(ctx.sqrt(points * dim / dre) + 1)
x = pylab.linspace(rea, reb, M)
y = pylab.linspace(ima, imb, N)
# Note: we have to be careful to get the right rotation.
# Test with these plots:
# cplot(lambda z: z if z.real < 0 else 0)
# cplot(lambda z: z if z.imag < 0 else 0)
w = pylab.zeros((N, M, 3))
for n in xrange(N):
for m in xrange(M):
z = ctx.mpc(x[m], y[n])
try:
v = color(f(z))
except ctx.plot_ignore:
v = (0.5, 0.5, 0.5)
w[n, m] = v
if verbose:
print(str(n) + ' of ' + str(N))
rea, reb, ima, imb = [float(_) for _ in [rea, reb, ima, imb]]
axes.imshow(w, extent=(rea, reb, ima, imb), origin='lower')
axes.set_xlabel('Re(z)')
axes.set_ylabel('Im(z)')
if fig:
if file:
pylab.savefig(file, dpi=dpi)
else:
pylab.show()
def foo(t):
if t < 10.0:
return 0.0
elif 10.0 <= t < 10.0 * np.pi:
return 1.0
else:
return 10.0 * sin(t * 0.5)
def values_before_zero(t):
return array([0.0, 0.0])
def model(Y, t):
x, y = Y(t)
x_tau, y_tau = Y(t - tau)
return array([foo(t), x_tau])
tt = linspace(0, 100, 1000)
yy = ddeint(model, values_before_zero, tt)
fig, ax = subplots(1, figsize=(8, 4))
ax.plot(tt, [x[0] for x in yy], label="trace1")
ax.plot(tt, [x[1] for x in yy], label="trace2")
fig.legend(loc='upper center', borderaxespad=2.0)
fig.show()
def plot_histogram(self):
configurations_per_variant = self.configurations_per_variant
colors = self.options['color'](pylab.linspace(
0, 0.8, configurations_per_variant))
labels = []
for li_of_frac in self.label_info:
s = str()
for i, (param, value) in enumerate(li_of_frac):
if i > 0:
s += '\n'
s += '%s=%s' % (_cname_to_latex(param), value)
labels.append(s)
labels *= len(self.configurations['p'])
ps = list(
pylab.flatten(self.configurations_per_p * [p]
for p in self.configurations['p']))
#################################################################
# histogram plot
#################################################################
fig2 = MyFig(self.options,
rect=[0.1, 0.2, 0.8, 0.75],
figsize=(max(self.length(), 10), 10),
xlabel='Probability p',
ylabel='Fraction of Nodes',
aspect='auto')
patches = []
for i, fracs in enumerate(self.fraction_of_nodes):
hist, bin_edges = numpy.histogram(fracs,
bins=pylab.linspace(
0, 1, self.options['bins']))
hist_norm = hist / float(len(fracs)) * 0.4
yvals = list(bin_edges.repeat(2))[1:-1]
yvals.extend(list(bin_edges.repeat(2))[1:-1][::-1])
xvals = list((i + 1 + hist_norm).repeat(2))
xvals.extend(list((i + 1 - hist_norm).repeat(2))[::-1])
i = (i % self.configurations_per_p)
poly = Polygon(zip(xvals, yvals),
edgecolor='black',
facecolor=colors[i],
closed=True)
patches.append(poly)
patch_collection = PatchCollection(patches, match_original=True)
fig2.ax.add_collection(patch_collection)
fig2.ax.set_xticks(range(1, self.length() + 1))
fig2.ax.set_xticklabels(ps, fontsize=self.options['fontsize'] * 0.6)
for x in range(0, self.length(), self.configurations_per_p):
fig2.ax.plot([x + 0.5, x + 0.5], [0.0, 1.0],
linestyle='dotted',
color='red',
alpha=0.8)
fig2.ax.set_ylim(0, 1)
#################################################################
# create some dummy elements for the legend
#################################################################
if configurations_per_variant > 1:
proxies = []
#for i in range(0, len(self.configurations['gossip'])):
for i in range(0, configurations_per_variant):
r = Rectangle((0, 0),
1,
1,
facecolor=colors[i % configurations_per_variant],
edgecolor='black')
proxies.append((r, labels[i]))
fig2.ax.legend([proxy for proxy, label in proxies],
[label for proxy, label in proxies],
loc='lower right')
self.figures['histogram'] = fig2.save('histogram_' +
str(self.data_filter))
def plot_distribution(self):
logging.debug('')
if self.dimension > 0:
return
probabilities = pylab.linspace(0, 1, 20)
distributions = {}
for name, func, extra_args in [('data', None, []),
('normal', stats.norm, [])]:
#('chi2', stats.chi2), ('gamma', stats.gamma)]:
fig_cdf = MyFig(self.options,
xlabel='Fraction of Nodes',
ylabel='CDF',
grid=True,
legend=True)
fig_cdf.ax.plot([0.0, 1.0], [0.0, 1.0],
color='black',
linestyle='solid')
fig_qq = MyFig(self.options,
xlabel='Fraction of Nodes',
ylabel='%s Distribution' % name,
grid=True,
legend=True)
fig_qq.ax.plot([0.0, 1.0], [0.0, 1.0],
color='black',
linestyle='solid')
fig_qq.ax.set_xlim(0.0, 1.0)
fig_qq.ax.set_ylim(0.0, 1.0)
distributions[name] = (func, fig_cdf, fig_qq, extra_args)
#fig_cdf_data = MyFig(self.options, xlabel='Fraction of Nodes', ylabel='CDF', grid=True, legend=True)
#fig_cdf_data.ax.plot([0.0, 1.2], [0.0, 1.2], color='black', linestyle='solid')
#########################################
#fig_qq_normal = MyFig(self.options, xlabel='Fraction of Nodes', ylabel='Normal Distribution', grid=True, legend=True)
#fig_qq_normal.ax.plot([0.0, 1.0], [0.0, 1.0], color='black', linestyle='solid')
#fig_qq_normal.ax.set_xlim(0.0, 1.0)
#fig_qq_normal.ax.set_ylim(0.0, 1.0)
#fig_cdf_normal = MyFig(self.options, xlabel='x', ylabel='CDF', grid=True, legend=True)
#fig_cdf_normal.ax.plot([0.0, 1.2], [0.0, 1.2], color='black', linestyle='solid')
#########################################
#fig_qq_gamma = MyFig(self.options, xlabel='Fraction of Nodes', ylabel='Gamma Distribution', grid=True, legend=True)
#fig_qq_gamma.ax.plot([0.0, 1.0], [0.0, 1.0], color='black', linestyle='solid')
#fig_qq_gamma.ax.set_xlim(0.0, 1.0)
#fig_qq_gamma.ax.set_ylim(0.0, 1.0)
#fig_cdf_gamma = MyFig(self.options, xlabel='x', ylabel='CDF', grid=True, legend=True, aspect='auto')
#fig_cdf_gamma.ax.set_xlim(0.0, 1.0)
#########################################
#fig_qq_2normal = MyFig(self.options, xlabel='Fraction of Nodes', ylabel='Normal Distribution', grid=True, legend=True)
#fig_qq_2normal.ax.plot([0.0, 1.0], [0.0, 1.0], color='black', linestyle='solid')
#fig_qq_2normal.ax.set_xlim(0.0, 1.0)
#fig_qq_2normal.ax.set_ylim(0.0, 1.0)
#fig_cdf_2normal = MyFig(self.options, xlabel='x', ylabel='CDF', grid=True, legend=True)
#fig_cdf_2normal.ax.plot([0.0, 1.2], [0.0, 1.2], color='black', linestyle='solid')
#########################################
#fig_qq_2chi2 = MyFig(self.options, xlabel='Fraction of Nodes', ylabel='2x Chi Square Distribution', grid=True, legend=True)
#fig_qq_2chi2.ax.plot([0.0, 1.0], [0.0, 1.0], color='black', linestyle='solid')
#fig_qq_2chi2.ax.set_xlim(0.0, 1.0)
#fig_qq_2chi2.ax.set_ylim(0.0, 1.0)
#fig_cdf_2chi2 = MyFig(self.options, xlabel='x', ylabel='CDF', grid=True, legend=True)
#fig_cdf_2chi2.ax.plot([0.0, 1.2], [0.0, 1.2], color='black', linestyle='solid')
colors = self.options['color'](pylab.linspace(0, 0.8, self.length()))
markers = self.options['markers']
for j, data in enumerate(self.fraction_of_nodes):
label = 'p=%.2f' % self.configurations['p'][j]
avr = scipy.average(data)
sigma = scipy.std(data)
quantiles_data = stats.mstats.mquantiles(data, prob=probabilities)
for name in distributions:
func, fig_cdf, fig_qq, extra_args = distributions[name]
if func:
quantiles_stat = func.ppf(probabilities,
*extra_args,
loc=avr,
scale=sigma)
fig_qq.ax.plot(quantiles_data,
quantiles_stat,
'o',
color=colors[j],
linestyle='-',
label=label,
marker=markers[j])
else:
quantiles_stat = quantiles_data
fig_cdf.ax.plot(quantiles_stat,
probabilities,
'o',
color=colors[j],
linestyle='-',
label=label,
marker=markers[j])
#fig_cdf_data.ax.plot(quantiles_data, probabilities, 'o', color=colors[j], linestyle='-', label=label)
###########################################################################
# Normal Distribution
###########################################################################
#quantiles_normal = stats.norm.ppf(probabilities, loc=avr, scale=sigma)
#fig_cdf_normal.ax.plot(quantiles_normal, probabilities, 'o', color=colors[j], linestyle='-', label=label)
#fig_qq_normal.ax.plot(quantiles_data, quantiles_normal, 'o', color=colors[j], linestyle='-', label=label)
###########################################################################
# Gamma Distribution
###########################################################################
#quantiles_gamma = stats.gamma.ppf(probabilities, 0.4, loc=avr, scale=sigma)
#_quantiles_gamma = []
#for x in quantiles_gamma:
#if x != numpy.infty:
#_quantiles_gamma.append(min(1.0,x))
#else:
#_quantiles_gamma.append(x)
#quantiles_gamma = numpy.array(_quantiles_gamma)
#fig_cdf_gamma.ax.plot(quantiles_gamma, probabilities, 'o', color=colors[j], linestyle='-', label=label)
#fig_qq_gamma.ax.plot(quantiles_data, quantiles_gamma, 'o', color=colors[j], linestyle='-', label=label)
###########################################################################
# 2x Chi Square Distribution
###########################################################################
#data_2chi2 = []
#for i in range(0, 5000):
#sigma_2chi2 = p*0.5
#dv = 0.5
#if random.normalvariate(0.6, 0.1) < p:
#exp_2chi2 = 1.0
#d = stats.chi2.rvs(dv, loc=exp_2chi2, scale=sigma_2chi2, size=1)
#d = exp_2chi2-abs(exp_2chi2-d)
#else:
#exp_2chi2 = 0.05
#d = stats.chi2.rvs(dv, loc=exp_2chi2, scale=sigma_2chi2, size=1)
#data_2chi2.append(max(min(d[0], 1.0), 0.0))
#quantiles_data_2chi2 = stats.mstats.mquantiles(data_2chi2, prob=probabilities)
#fig_cdf_2chi2.ax.plot(quantiles_data_2chi2, probabilities, 'o', linestyle='-', label='p=%.2f' % p, color=colors[j])
#fig_qq_2chi2.ax.plot(quantiles_data, quantiles_data_2chi2, 'o', color=colors[j], linestyle='-', label=label)
###########################################################################
# 2x Normal Distribution
###########################################################################
#data_2normal = []
#for i in range(0, 2000):
#if random.uniform(0.0, 1.0) < p:
#exp_2normal = 1.0
#sigma_2normal = 0.05
#d = stats.norm.rvs(loc=exp_2normal, scale=sigma_2normal, size=1)
#d = exp_2normal-abs(exp_2normal-d)
#else:
#exp_2normal = 0.05
#sigma_2normal = 0.05
#d = stats.norm.rvs(loc=exp_2normal, scale=sigma_2normal, size=1)
#data_2normal.append(max(min(d[0], 1.0), 0.0))
#quantiles_data_2normal = stats.mstats.mquantiles(data_2normal, prob=probabilities)
#fig_cdf_2normal.ax.plot(quantiles_data_2normal, probabilities, 'o', linestyle='-', label='p=%.2f' % p, color=colors[j])
#fig_qq_2normal.ax.plot(quantiles_data, quantiles_data_2normal, 'o', color=colors[j], linestyle='-', label=label)
for name in distributions:
func, fig_cdf, fig_qq, extra_args = distributions[name]
if func:
fig_qq.save('distribution-qq_%s_%s' %
(name, str(self.data_filter)))
fig_cdf.save('distribution-cdf_%s_%s' %
(name, str(self.data_filter)))
def _main():
parser = argparse.ArgumentParser()
parser.add_argument("-m",
"--model",
default=[],
nargs='*',
type=str,
help="Frozen model file to test")
parser.add_argument("-fd_dih",
"--full_dimension_dih",
default=9,
type=int,
help="The dimensionality of FES")
parser.add_argument("-fd_dist",
"--full_dimension_dist",
default=3,
type=int,
help="The dimensionality of FES")
parser.add_argument("-ns",
"--num_step",
default=1000000,
type=int,
help="number of mc step")
parser.add_argument("-nw",
"--num_walker",
default=3000,
type=int,
help="number of walker")
args = parser.parse_args()
model = args.model
fd_dih = args.full_dimension_dih
fd_dist = args.full_dimension_dist
ns = args.num_step
nw = args.num_walker
positons = []
energies = []
forces = []
graph = load_graph(model[0])
bins = 30
xx = pylab.linspace(0, 200, bins)
yy = pylab.linspace(0.2, 4.1, bins)
pp_hist1cv1 = np.zeros((1, len(xx)))
pp_hist1cv2 = np.zeros((1, len(yy)))
pp_hist2d = np.zeros((1, len(xx), len(yy)))
delta1 = 200.0 / bins
delta2 = (4.1 - 0.2) / bins
with tf.Session(graph=graph) as sess:
walker = Walker(fd_dih, fd_dist, nw, sess)
for ii in range(100):
pp, ee, ff = walker.sample(compute_ef)
for ii in range(ns + 1):
pp, ee, ff = walker.sample(compute_ef)
##all 1d
##certain 2d
pp_hist_new2d, pp_hist_new1cv1, pp_hist_new1cv2 = my_hist2d(
pp, xx, yy, delta1, delta2, fd_dih, fd_dist)
pp_hist2d = (pp_hist2d * ii + pp_hist_new2d) / (ii + 1)
pp_hist1cv1 = (pp_hist1cv1 * ii + pp_hist_new1cv1) / (ii + 1)
pp_hist1cv2 = (pp_hist1cv2 * ii + pp_hist_new1cv2) / (ii + 1)
if np.mod(ii, 50000) == 0:
zz1 = -np.log(pp_hist1cv1 + 1e-7) / beta
zz1 *= f_cvt / 4.184 ##kcal
zz1 = zz1 - np.min(zz1)
fp = open("1CV1_index0.dat", "a")
for temp in zz1[0]:
fp.write(str(temp) + ' ')
fp.write('\n')
fp.close()
zz2 = -np.log(pp_hist1cv2 + 1e-7) / beta
zz2 *= f_cvt / 4.184 ##kcal
zz2 = zz2 - np.min(zz2)
fp = open("1CV2_index1.dat", "a")
for temp in zz2[0]:
fp.write(str(temp) + ' ')
fp.write('\n')
fp.close()
zz2d = np.transpose(-np.log(pp_hist2d + 1e-10),
(0, 2, 1)) / beta
zz2d *= f_cvt / 4.184
zz2d = zz2d - np.min(zz2d)
np.savetxt("2d_step%d.dat" % ii, zz2d[0])
np.savetxt("position%d.dat" % ii, pp)
1:2 * ny:2] = (9 * c7) / 16.0 + (9 * c6) / 16.0 + (3 * c5) / 16.0 + (
3 * c4) / 16.0 - (3 * c2) / 16.0 - c1 / 8.0 - (3 * c0) / 16.0
return Xn, Yn, qn
fh = tables.openFile("s120-pb-advection-rb_chi_1.h5")
grid = fh.root.StructGrid
lower = grid._v_attrs.vsLowerBounds
upper = grid._v_attrs.vsUpperBounds
cells = grid._v_attrs.vsNumCells
dx = (upper[0] - lower[0]) / cells[0]
dy = (upper[1] - lower[1]) / cells[1]
Xc = pylab.linspace(lower[0] + 0.5 * dx, upper[0] - 0.5 * dx, cells[0])
Yc = pylab.linspace(lower[1] + 0.5 * dy, upper[1] - 0.5 * dy, cells[1])
T = pylab.linspace(0, 4 * math.pi, 41)
for i in range(41):
print "Workin on %d" % i
fh = tables.openFile("s120-pb-advection-rb_chi_%d.h5" % i)
# get solution
q = fh.root.StructGridField
Xn, Yn, qn_1 = projectOnFinerGrid_f3(Xc, Yc, q)
pylab.pcolormesh(Xn, Yn, pylab.transpose(qn_1), vmin=0.0, vmax=0.5)
pylab.title('T=%f' % T[i])
pylab.axis('image')
# Andreas Müller, 2008 # [email protected] # # this code may be freely used under GNU GPL conditions """ Simulate channel with sampling frequency offset """ import math, random, pylab import dab_tb, ber NUM_BYTES = 1000000 MODES = [1, 2, 3, 4] MODES = [1] SAMPLE_RATE_ERROR = pylab.linspace(0.99, 1.01, 50) PLOT_FORMAT = ['-', '-x', '--x', '-.x', ':x'] # initialise test flowgraph tb = dab_tb.dab_ofdm_testbench(autocorrect_sample_rate=True, ber_sink=True) tb.gen_random_bytes(NUM_BYTES) # prepeare plot pylab.xlabel("Sampling frequency offset (ratio)") pylab.ylabel("BER") # open logfile logfile = open("sampling_frequency_offset_ber_log.txt", 'w') logfile.write("number of bytes: " + str(NUM_BYTES) + "\nRange of sampling rate ratios: " + str(SAMPLE_RATE_ERROR) +
A, b, V = lowpass(10000.0, 10000.0, 1e-9, 1e-9, 1.586, 1.0)
Vo = V[3]
print simplify(Vo)
w = p.logspace(0, 8, 801)
ss = 1j * w
hf = lambdify(s, Vo, 'numpy')
v = hf(ss)
p.loglog(w, abs(v), lw=2)
p.title('Low pass filter')
p.xlabel('$\omega(rad/s)$')
p.ylabel('$Magnitude$')
p.grid(True)
p.show()
#unit step response
H = sp.lti([0.1], [6.31517e-12, 8.9455e-7, 0.0631517, 0])
t, x = sp.impulse(H, None, p.linspace(0, 0.0001, 1001))
p.title('unit step response of low pass filter')
p.xlabel('t')
p.ylabel('$V_o(t)$')
p.plot(t, x)
p.grid(True)
p.show()
#for sinusoidal input
H = sp.lti([0.1], [6.31517e-12, 8.9455e-7, 0.0631517])
t = p.linspace(0.0, 1e-2, 10001)
u = p.sin(2e3 * p.pi * t) + p.cos(2e6 * p.pi * t)
t, y, svec = sp.lsim(H, u, t)
p.title('response of low pass filter for sinusoidal input')
p.xlabel('t')
p.ylabel('$V_o(t)$')
non_linear=0.5)
return img
# Get the Galaxy info
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies = pyl.asarray(filter(lambda galaxy: galaxy.ICD_IH < 0.5, galaxies))
# Make the low mass grid first
x = [galaxy.Mass for galaxy in galaxies]
y = [galaxy.ICD_IH * 100 for galaxy in galaxies]
ll = 8.5
ul = 12
bins_x = pyl.linspace(ll, ul, 8)
bins_y = pyl.linspace(50, 0, 6)
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
for j in range(bins_y.size - 1):
ymax = bins_y[j]
ymin = bins_y[j + 1]
cond = [
cond1 and cond2 and cond3
and cond4 for cond1, cond2, cond3, cond4 in zip(
x >= xmin, x < xmax, y >= ymin, y < ymax)
from math import asin, sin
from matplotlib import pyplot as plt
from pylab import linspace
from qc import Calc
#构建点集
h = linspace(0.01, 17, 10000)
#初始化函数
a = Calc()
a.set_raw_values(u1=0, hm=17)
#计算h/hm
y = []
for i in linspace(0.01, 17, 10000):
y.append(i / a.get_raw_values('hm'))
#计算D光
LD = []
DetalLD = []
a.set_raw_values(r1=105.1175, r2=-74.7353, r3=-215.38763374564763)
a.set_raw_values(d1=5.32, d2=2.5)
a.set_raw_values(n1=1, n1s=1.51633, n2=1.51633, n2s=1.6727, n3=1.6727, n3s=1)
a.do_update()
ld = a.get_raw_values('l')
for i in h:
L3s = a.get_L3s(i)
DetalLD.append(L3s - ld)
LD.append(L3s)
print("h/hm=0.707时球差：", a.get_L3s(0.707 * a.get_raw_values('hm')) - ld)
print("h/hm=1时球差：", a.get_L3s(a.get_raw_values('hm')) - ld)
def plot_color_vs_mass_hist():
galaxies = mk_galaxy_struc()
# Definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# Add the figures
# Mass vs color plot I-H
f1 = pyl.figure(1, figsize=(8, 8))
f1s1 = f1.add_axes(rect_scatter)
f1s2 = f1.add_axes(rect_histx)
f1s3 = f1.add_axes(rect_histy)
# Mass vs color plot J-H
f2 = pyl.figure(2, figsize=(8, 8))
f2s1 = f2.add_axes(rect_scatter)
f2s2 = f2.add_axes(rect_histx)
f2s3 = f2.add_axes(rect_histy)
#f2s1 = f2.add_subplot(111)
# Mass vs color plot Z-H
f3 = pyl.figure(3, figsize=(8, 8))
f3s1 = f3.add_axes(rect_scatter)
f3s2 = f3.add_axes(rect_histx)
f3s3 = f3.add_axes(rect_histy)
#f3s1 = f3.add_subplot(111)
mass1 = []
color1 = []
mass2 = []
color2 = []
mass3 = []
color3 = []
for i in range(len(galaxies)):
# Color vs Mass Plots
if galaxies[i].ston_I > 30.0:
if galaxies[i].Mips >= 10.0:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass1.append(galaxies[i].Mass) # Get the right mass
color1.append(galaxies[i].Imag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_IH > 0.1:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_I and galaxies[i].ston_I < 30.0:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
if galaxies[i].ston_Z > 30.0:
if galaxies[i].Mips >= 10.0:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass2.append(galaxies[i].Mass) # Get the right mass
color2.append(galaxies[i].Zmag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_ZH > 0.05:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_Z and galaxies[i].ston_Z < 30.0:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
if galaxies[i].ston_J > 30.0:
if galaxies[i].Mips >= 10.0:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass3.append(galaxies[i].Mass) # Get the right mass
color3.append(galaxies[i].Jmag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_JH > 0.03:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_J and galaxies[i].ston_J < 30.0:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
############
# FIGURE 1 #
############
pyl.figure(1)
f1s1.set_xscale('log')
f1s1.set_xlim(3e7, 1e12)
f1s1.set_ylim(0, 4.5)
f1s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f1s1.set_ylabel("$(I-H)_{Observed}$", fontsize=20)
f1s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f1s1.get_ylim()[0], f1s1.get_ylim()[1] + 0.25)
f1s2.hist(mass1, bins=binsx)
f1s2.set_xlim(f1s1.get_xlim())
f1s2.tick_params(labelbottom='off')
f1s2.set_xscale('log')
f1s3.hist(color1, bins=binsy, orientation='horizontal')
f1s3.set_ylim(f1s1.get_ylim())
f1s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_IH.eps')
############
# FIGURE 2 #
############
pyl.figure(2)
f2s1.set_xscale('log')
f2s1.set_xlim(3e7, 1e12)
f2s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f2s1.set_ylabel("$(Z-H)_{Observed}$", fontsize=20)
f2s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f2s1.get_ylim()[0], f2s1.get_ylim()[1] + 0.25)
f2s2.hist(mass2, bins=binsx)
f2s2.set_xlim(f2s1.get_xlim())
f2s2.tick_params(labelbottom='off')
f2s2.set_xscale('log')
f2s3.hist(color2, bins=binsy, orientation='horizontal')
f2s3.set_ylim(f2s1.get_ylim())
f2s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_ZH.eps')
############
# FIGURE 3 #
############
pyl.figure(3)
f3s1.set_xscale('log')
f3s1.set_xlim(3e7, 1e12)
f3s1.set_ylim(-0.5, 2)
f3s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f3s1.set_ylabel("$(J-H)_{Observed}$", fontsize=20)
f3s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f3s1.get_ylim()[0], f3s1.get_ylim()[1] + 0.25)
f3s2.hist(mass3, bins=binsx)
f3s2.set_xlim(f3s1.get_xlim())
f3s2.tick_params(labelbottom='off')
f3s2.set_xscale('log')
f3s3.hist(color3, bins=binsy, orientation='horizontal')
f3s3.set_ylim(f3s1.get_ylim())
f3s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_JH.eps')
pyl.show()
from pylab import subplot, plot, linspace, savefig
from numpy import sin, cos, sinh, cosh, pi
x = linspace(-pi, pi, 100)
subplot(221)
plot(x, sin(x))
subplot(222)
plot(x, cos(x))
subplot(223)
plot(x, sinh(x))
subplot(224)
plot(x, cosh(x))
savefig('figuraejemplo3.pdf', format='pdf')
def splot(ctx, f, u=[-5,5], v=[-5,5], points=100, keep_aspect=True, \
wireframe=False, file=None, dpi=None, axes=None):
"""
Plots the surface defined by `f`.
If `f` returns a single component, then this plots the surface
defined by `z = f(x,y)` over the rectangular domain with
`x = u` and `y = v`.
If `f` returns three components, then this plots the parametric
surface `x, y, z = f(u,v)` over the pairs of intervals `u` and `v`.
For example, to plot a simple function::
>>> from sympy.mpmath import *
>>> f = lambda x, y: sin(x+y)*cos(y)
>>> splot(f, [-pi,pi], [-pi,pi]) # doctest: +SKIP
Plotting a donut::
>>> r, R = 1, 2.5
>>> f = lambda u, v: [r*cos(u), (R+r*sin(u))*cos(v), (R+r*sin(u))*sin(v)]
>>> splot(f, [0, 2*pi], [0, 2*pi]) # doctest: +SKIP
.. note :: This function requires matplotlib (pylab) 0.98.5.3 or higher.
"""
import pylab
import mpl_toolkits.mplot3d as mplot3d
if file:
axes = None
fig = None
if not axes:
fig = pylab.figure()
axes = mplot3d.axes3d.Axes3D(fig)
ua, ub = u
va, vb = v
du = ub - ua
dv = vb - va
if not isinstance(points, (list, tuple)):
points = [points, points]
M, N = points
u = pylab.linspace(ua, ub, M)
v = pylab.linspace(va, vb, N)
x, y, z = [pylab.zeros((M, N)) for i in xrange(3)]
xab, yab, zab = [[0, 0] for i in xrange(3)]
for n in xrange(N):
for m in xrange(M):
fdata = f(ctx.convert(u[m]), ctx.convert(v[n]))
try:
x[m, n], y[m, n], z[m, n] = fdata
except TypeError:
x[m, n], y[m, n], z[m, n] = u[m], v[n], fdata
for c, cab in [(x[m, n], xab), (y[m, n], yab), (z[m, n], zab)]:
if c < cab[0]:
cab[0] = c
if c > cab[1]:
cab[1] = c
if wireframe:
axes.plot_wireframe(x, y, z, rstride=4, cstride=4)
else:
axes.plot_surface(x, y, z, rstride=4, cstride=4)
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_zlabel('z')
if keep_aspect:
dx, dy, dz = [cab[1] - cab[0] for cab in [xab, yab, zab]]
maxd = max(dx, dy, dz)
if dx < maxd:
delta = maxd - dx
axes.set_xlim3d(xab[0] - delta / 2.0, xab[1] + delta / 2.0)
if dy < maxd:
delta = maxd - dy
axes.set_ylim3d(yab[0] - delta / 2.0, yab[1] + delta / 2.0)
if dz < maxd:
delta = maxd - dz
axes.set_zlim3d(zab[0] - delta / 2.0, zab[1] + delta / 2.0)
if fig:
if file:
pylab.savefig(file, dpi=dpi)
else:
pylab.show()
""" DDE where the delay depends on Y(t). """
from pylab import cos, linspace, subplots
from ddeint import ddeint
def model(Y, t):
return -Y(t - 3 * cos(Y(t))**2)
def values_before_zero(t):
return 1
tt = linspace(0, 30, 2000)
yy = ddeint(model, values_before_zero, tt)
fig, ax = subplots(1, figsize=(4, 4))
ax.plot(tt, yy)
ax.figure.savefig("variable_delay.jpeg")
def solve(self):
"""
"""
s = '::: solving TransientSolver :::'
text = colored(s, 'blue')
print text
firn = self.firn
config = self.config
fe = self.fe
fv = self.fv
fd = self.fd
if config['age']['on']:
fa = self.fa
t0 = config['t_start']
tm = config['t_mid']
tf = config['t_end']
dt = config['time_step']
dt_list = config['dt_list']
if dt_list != None:
numt1 = (tm-t0)/dt_list[0] + 1 # number of time steps
numt2 = (tf-tm)/dt_list[1] + 1 # number of time steps
times1 = linspace(t0,tm,numt1) # array of times to evaluate in seconds
times2 = linspace(tm,tf,numt2) # array of times to evaluate in seconds
dt1 = dt_list[0] * ones(len(times1))
dt2 = dt_list[1] * ones(len(times2))
times = hstack((times1,times2))
dts = hstack((dt1, dt2))
else:
numt = (tf-t0)/dt + 1 # number of time steps
times = linspace(t0,tf,numt) # array of times to evaluate in seconds
dts = dt * ones(len(times))
firn.t = t0
self.times = times
self.dts = dts
for t,dt in zip(times[1:], dts[1:]):
# update timestep :
firn.dt = dt
firn.dt_v.assign(dt)
# update boundary conditions :
firn.update_Hbc()
firn.update_rhoBc()
firn.update_wBc()
#firn.update_omegaBc()
# newton's iterative method :
fe.solve()
fd.solve()
fv.solve()
if config['age']['on']:
fa.solve()
# update firn object :
firn.update_vars(t)
firn.update_height_history()
if config['free_surface']['on']:
if dt_list != None:
if t > tm+dt:
firn.update_height()
else:
firn.update_height()
# update model parameters :
if t != times[-1]:
firn.H_1.assign(firn.H)
firn.U_1.assign(firn.U)
firn.omega_1.assign(firn.omega)
firn.w_1.assign(firn.w)
firn.a_1.assign(firn.a)
firn.m_1.assign(firn.m)
# update the plotting parameters :
if config['plot']['on']:
self.plot.update_plot()
#plt.draw()
s = '>>> Time: %i yr <<<'
text = colored(s, 'red', attrs=['bold'])
print text % (t / firn.spy)
if config['plot']['on']:
pass
# [email protected] # # ********************************************* import pylab as pl import PyOFTK LMBD = (1.0526 + 0.00047) / (2 * 1.45) LMBD2 = 0.34485 LNGTH = 100 fbg1 = PyOFTK.apodizedFBG(3.0, 62.5, 0.04, 0.0, 1e-1, 1e-2, LMBD2) fbg2 = PyOFTK.apodizedFBG(3.0, 62.5, 0.04, 0.0, 1e-1, 5e-3, LMBD2) fbg3 = PyOFTK.apodizedFBG(3.0, 62.5, 0.04, 0.0, 1e-1, 1e-3, LMBD2) print "Longueur d'onde de Bragg: " + str(fbg1.braggWavelength) + " um" wvl1 = pl.linspace(fbg1.braggWavelength - 0.00047, fbg1.braggWavelength - 0.00050, LNGTH) wvl2 = pl.linspace(fbg1.braggWavelength + 0.010, fbg1.braggWavelength + 0.050, LNGTH) beta2Grating1 = pl.zeros(LNGTH, float) beta2Grating2 = pl.zeros(LNGTH, float) beta2Grating3 = pl.zeros(LNGTH, float) beta3Grating1 = pl.zeros(LNGTH, float) beta3Grating2 = pl.zeros(LNGTH, float) beta3Grating3 = pl.zeros(LNGTH, float) for i in range(LNGTH): beta2Grating1[i] = fbg1.gBeta2(wvl2[i]) * 1e24 beta2Grating2[i] = fbg2.gBeta2(wvl2[i]) * 1e24 beta2Grating3[i] = fbg3.gBeta2(wvl2[i]) * 1e24
return pylab.log(x) + 2*pylab.log10(x)
def t(x):
return pylab.sin(pylab.sqrt(abs(5*x)))
def u(x):
return pylab.maximum(pylab.sin(x), pylab.cos(x)**2)
def v(x):
return pylab.minimum(pylab.sin(x), pylab.cos(2*x))'''
#Setting the range of function
a, b, n = -2 * pylab.pi, 2 * pylab.pi, 1000
#在 [a,b] 間產生 n 個點存到 xs
xs = pylab.linspace(a, b, n)
#畫圖
pylab.plot(xs, f(xs), 'blue')
pylab.plot(xs, g(xs), 'green')
pylab.grid()
pylab.xlabel("X")
pylab.ylabel("Y")
pylab.title(
"f(x)=max( abs(x sin(x)),abs(x cos(x)) ),g(x)=min( abs(x sin(x)), abs(x cos(x)) )"
)
pylab.savefig('Output/homework.png')
def plot_redundancy(self):
"""
Plot the fraction of (unique) packets received from the source (0.0 - 1.0) over the
total number of packets received as fraction (0.0 - infty).
A cubic curve is fit to the data points.
"""
if self.dimension > 0:
return
cursor = self.options['db_conn'].cursor()
max_total, = cursor.execute('''
SELECT MAX(total)
FROM eval_fracsOfHosts
''').fetchone()
fig_all = MyFig(self.options,
xlabel='Fraction of rx Packets (incl. duplicates)',
ylabel='Fraction of rx Packets sent by %s' % self.src,
legend=True,
aspect='auto')
fig_all.ax.plot([1, 1], [0, 1], linestyle='dashed', color='grey')
fig_all_median = MyFig(
self.options,
xlabel='Fraction of rx Packets (incl. duplicates)',
ylabel='Fraction of rx Packets sent by %s' % self.src,
legend=True,
aspect='auto')
fig_all_median.ax.plot([1, 1], [0, 1],
linestyle='dashed',
color='grey')
colors = self.options['color'](pylab.linspace(0, 0.8,
len(self.tag_keys)))
markers = self.options['markers']
max_x = 0
ellipses = []
for j, tag_key in enumerate(self.tag_keys):
fig = MyFig(self.options,
xlabel='Fraction of rx Packets (incl. duplicates)',
ylabel='Fraction of rx Packets sent by %s' % self.src,
aspect='auto')
results = cursor.execute(
'''
SELECT total, frac
FROM eval_fracsOfHosts
WHERE src=? AND tag_key=?
''', (self.src, tag_key)).fetchall()
assert (len(results))
fig.ax.plot([1, 1], [0, 1], linestyle='-', color='grey')
xvals = [x[0] for x in results]
yvals = [y[1] for y in results]
label = 'p=%.2f' % self.configurations['p'][j]
fig.ax.scatter(xvals, yvals, s=15, color=colors[j])
fig.ax.set_xlim((0, max_total))
max_x = max(max_x, max_total)
fig.ax.set_ylim((0, 1))
z = numpy.polyfit(xvals, yvals, 3)
poly = numpy.poly1d(z)
median_y = scipy.median(yvals)
mean_y = scipy.mean(yvals)
ci_y = confidence(yvals)
median_x = scipy.median(xvals)
mean_x = scipy.mean(xvals)
ci_x = confidence(xvals)
ellipse = Ellipse((mean_x, mean_y),
ci_x[1] - ci_x[0],
ci_y[1] - ci_y[0],
edgecolor=colors[j],
facecolor=colors[j],
alpha=0.5)
ellipses.append(ellipse)
selected_xvals = numpy.arange(min(xvals), max(xvals), 0.4)
fig.ax.plot(selected_xvals,
poly(selected_xvals),
"-",
color=colors[j])
fig.ax.plot([0.0, 10], [median_y, median_y],
linestyle="dashed",
color=colors[j])
fig_all.ax.plot(selected_xvals,
poly(selected_xvals),
"-",
color=colors[j],
label=label,
marker=markers[j])
fig_all.ax.plot([0.0, 10], [median_y, median_y],
linestyle="dashed",
color=colors[j],
alpha=0.6,
marker=markers[j])
fig_all_median.ax.plot(ci_x, [mean_y, mean_y],
color=colors[j],
label=label,
marker=markers[j])
fig_all_median.ax.plot([mean_x, mean_x],
ci_y,
color=colors[j],
marker=markers[j])
fig.save('redundancy_%s_%s' % (label, str(self.data_filter)))
fig_all.ax.axis((0, max(max_x, 1), 0, 1))
fig_all_median.ax.axis((0, max(max_x, 1), 0, 1))
patch_collection = PatchCollection(ellipses, match_original=True)
fig_all_median.ax.add_collection(patch_collection)
fig_all.save('redundancy_%s' % str(self.data_filter))
fig_all_median.save('redundancy_median_%s' % str(self.data_filter))
def evaluate(self, x, derivative=0, smooth=0, simple='auto'):
"""
smooth=0 is how much to smooth the spline data
simple='auto' is whether we should just use straight interpolation
you may want smooth > 0 for this, when derivative=1
"""
if simple == 'auto': simple = self.simple
# make it into an array if it isn't one, and remember that we did
is_array = True
if not type(x) == type(_pylab.array([])):
x = _pylab.array([x])
is_array = False
if simple:
# loop over all supplied x data, and come up with a y for each
y = []
for n in range(0, len(x)):
# get a window of data around x
if smooth:
[xtemp, ytemp,
etemp] = _fun.trim_data(self.xdata, self.ydata, None,
[x[n] - smooth, x[n] + smooth])
else:
i1 = _fun.index_nearest(x[n], self.xdata)
# if the nearest data point is lower than x, use the next point to interpolate
if self.xdata[i1] <= x[n] or i1 <= 0: i2 = i1 + 1
else: i2 = i1 - 1
# if we're at the max, extrapolate
if i2 >= len(self.xdata):
print x[n], "is out of range. extrapolating"
i2 = i1 - 1
x1 = self.xdata[i1]
y1 = self.ydata[i1]
x2 = self.xdata[i2]
y2 = self.ydata[i2]
slope = (y2 - y1) / (x2 - x1)
xtemp = _numpy.array([x[n]])
ytemp = _numpy.array([y1 + (x[n] - x1) * slope])
# calculate the slope based on xtemp and ytemp (if smoothing)
# or just use the raw slope if smoothing=0
if derivative == 1:
if smooth:
y.append(
(_numpy.average(xtemp * ytemp) -
_numpy.average(xtemp) * _numpy.average(ytemp)) /
(_numpy.average(xtemp * xtemp) -
_numpy.average(xtemp)**2))
else:
y.append(slope)
# otherwise just average (even with one element)
elif derivative == 0:
y.append(_numpy.average(ytemp))
if is_array: return _numpy.array(y)
else: return y[0]
if smooth:
y = []
for n in range(0, len(x)):
# take 20 data points from x+/-smooth
xlow = max(self.xmin, x[n] - smooth)
xhi = min(self.xmax, x[n] + smooth)
xdata = _pylab.linspace(xlow, xhi, 20)
ydata = _interpolate.splev(xdata, self.pfit, derivative)
y.append(_numpy.average(ydata))
if is_array: return _numpy.array(y)
else: return y[0]
else:
return _interpolate.splev(x, self.pfit, derivative)
def _plot_propagation(options):
"""
Plot the fraction of packets each router received from its neighbors.
This can be used to (visually) detect routers that depend on a particular
neighbors.
tags: Can be used with any tag/configuration. One plot is generated for each!
"""
locs = options['locs']
cursor = options['db_conn'].cursor()
colors = options['color2'](pylab.linspace(0, 1, 101))
units2meter = options['units2meter']
#################################################################################
## Get mapping of hosts and interface addresses
#################################################################################
cursor.execute('''
SELECT DISTINCT(host), rx_if
FROM rx
''')
addr2host = {}
for host, rx_if in cursor.fetchall():
addr2host[rx_if] = host
################################################################################
# Evaluate for all sources
################################################################################
for i, src in enumerate(options['src']):
logging.info('src=%s (%d/%d)', src, i + 1, len(options['src']))
options['prefix'] = src
#################################################################################
## Get all hostnames
#################################################################################
#cursor.execute('SELECT host FROM addr')
#dsts = sorted([str(d[0]) for d in cursor.fetchall()])
################################################################################
# Evaluate received packets for each tag for each node
################################################################################
tags = cursor.execute('''
SELECT key, id
FROM tag
''').fetchall()
for j, (tag_key, tag_id) in enumerate(tags):
logging.info('\ttag=%s (%d/%d)', tag_id, j + 1, len(tags))
results = cursor.execute(
'''
SELECT host, total, frac
FROM eval_fracsOfHosts
WHERE src=? AND tag_key=?
''', (src, tag_key)).fetchall()
################################################################################
# Draw figure for current tag
################################################################################
fig = MyFig(options,
rect=[0.1, 0.1, 0.8, 0.7],
xlabel='x Coordinate',
ylabel='y Coordinate')
fig3d = MyFig(options,
rect=[0.1, 0.1, 0.8, 0.7],
xlabel='x Coordinate',
ylabel='y Coordinate',
zlabel='z Coordinate',
ThreeD=True)
fig.ax.set_autoscalex_on(False)
fig.ax.set_autoscaley_on(False)
min_x = min_y = numpy.infty
max_x = max_y = max_z = 0
circ_max = 5
line_max = 10
floor_factor = 2
floor_skew = -0.25
line_min = 1
# first draw the links....
for host, _total, _frac in results:
try:
xpos, ypos, zpos = locs[host]
except KeyError:
logging.warning('no position found for node %s', host)
continue
xpos = xpos * units2meter
ypos = ypos * units2meter
zpos = zpos * units2meter
prevs = cursor.execute(
'''
SELECT prev, frac
FROM eval_prevHopFraction
WHERE src=? AND tag_key=? and cur=?
''', (src, tag_key, host)).fetchall()
for prev, frac in prevs:
try:
prev_xpos, prev_ypos, prev_zpos = locs[addr2host[prev]]
except KeyError:
logging.warning('no position found for node %s', prev)
continue
prev_xpos = prev_xpos * units2meter
prev_ypos = prev_ypos * units2meter
prev_zpos = prev_zpos * units2meter
fig.ax.plot([
xpos + zpos * floor_skew * floor_factor,
prev_xpos + prev_zpos * floor_skew * floor_factor
], [
ypos + zpos * floor_factor,
prev_ypos + prev_zpos * floor_factor
],
linestyle='-',
color=colors[frac * 100],
linewidth=max(line_max * frac, line_min),
alpha=0.3)
fig3d.ax.plot([xpos, prev_xpos], [ypos, prev_ypos],
[zpos, prev_zpos],
linestyle='-',
color=colors[frac * 100],
linewidth=max(line_max * frac, line_min),
alpha=0.3)
# ...then draw the nodes
for host, _total, frac in results:
try:
xpos, ypos, zpos = locs[host]
except KeyError:
logging.warning('no position found for node %s', host)
continue
xpos = xpos * units2meter
ypos = ypos * units2meter
zpos = zpos * units2meter
max_x = max(xpos, max_x)
max_y = max(ypos, max_y)
min_x = min(xpos, min_x)
min_y = min(ypos, min_y)
max_z = max(zpos, max_z)
fig.ax.plot(xpos + zpos * floor_skew * floor_factor,
ypos + zpos * floor_factor,
'o',
color=colors[int(frac * 100)],
ms=max(frac * circ_max, 1))
fig3d.ax.plot([xpos], [ypos], [zpos],
'o',
color=colors[int(frac * 100)],
ms=max(frac * circ_max, 1))
drawBuildingContours(fig3d.ax, options)
fig.ax.axis((min_x - 10, max_x + 10, min_y - 10,
max_y + 10 + max_z * floor_factor + 10))
colorbar_ax = fig.fig.add_axes([0.1, 0.875, 0.8, 0.025])
colorbar_ax3d = fig3d.fig.add_axes([0.1, 0.875, 0.8, 0.025])
alinspace = numpy.linspace(0, 1, 100)
alinspace = numpy.vstack((alinspace, alinspace))
for tax in [colorbar_ax, colorbar_ax3d]:
tax.imshow(alinspace, aspect='auto', cmap=options['color2'])
tax.set_xticks(range(0, 101, 25))
tax.set_xticklabels(numpy.arange(0.0, 101.0, 0.25),
fontsize=0.8 * options['fontsize'])
tax.set_yticks([])
tax.set_title(tag_id, size=options['fontsize'])
fig.save('propagation2d_%s' % tag_id)
fig3d.save('propagation3d_%s' % tag_id)
#!/usr/local/bin/python
import sys
pi = 3.14159265359
def cs(eta):
return (1 + eta + eta**2 - eta**3) / (1 - eta)**3 * eta * 6. / pi
if __name__ == "__main__":
nopt = len(sys.argv)
if nopt < 2:
print "\n!! Provide an input packing fraction !!"
if nopt == 2:
P = cs(float(sys.argv[1]))
print P
if nopt == 3:
import pylab as pl
etas = pl.linspace(float(sys.argv[1]), float(sys.argv[2]), 100)
pl.plot(etas, cs(etas))
pl.show()
Energy = 10000
#struct = pyasf.unit_cell("1521772")
struct = pyasf.unit_cell("cif/LiNbO3_28294.cif") #Li Nb O3
Sub = reflectivity.Substrate(struct)
v_par = sp.Matrix([0, 0, 1])
v_perp = sp.Matrix([1, 0, 0]) #2,1,0
Sub.calc_orientation(v_par, v_perp)
layer1 = reflectivity.Epitaxial_Layer(struct, thickness)
layer1.calc_orientation(v_par, v_perp)
crystal = reflectivity.Sample(Sub, layer1)
crystal.set_Miller(R)
crystal.calc_g0_gH(Energy)
thBragg = float(
layer1.calc_Bragg_angle(Energy).subs(layer1.structure.subs).evalf())
angle = pl.linspace(0.9955, 1.0045, 501) * thBragg
crystal.calc_reflectivity(angle, Energy)
layer1.calc_amplitudes(angle, Energy)
Sub.calc_amplitudes(angle, Energy)
XRl = layer1.XR
XRs = Sub.XR
XT = layer1.XT
crystal.print_values(angle, Energy)
pl.plot(data[:, 0], data[:, 1], label='GID_sl', color='red')
# pl.plot(angle-thBragg,abs(XT)**2-1)
pl.plot(pl.degrees(angle - thBragg),
abs(XRl)**2,
def _main():
parser = argparse.ArgumentParser()
parser.add_argument("-m",
"--model",
default=[],
nargs='*',
type=str,
help="Frozen model file to test")
parser.add_argument("-fd",
"--full_dimension",
default=3,
type=int,
help="The dimensionality of FES")
parser.add_argument("-ns",
"--num_step",
default=1000000,
type=int,
help="number of mc step")
parser.add_argument("-nw",
"--num_walker",
default=2000,
type=int,
help="number of walker")
parser.add_argument("-cv1",
"--cv1_index",
default=1,
type=int,
help="cv1 index")
parser.add_argument("-cv2",
"--cv2_index",
default=2,
type=int,
help="cv2 index")
args = parser.parse_args()
model = args.model
fd = args.full_dimension
ns = args.num_step
nw = args.num_walker
cv1 = args.cv1_index
cv2 = args.cv2_index
positons = []
energies = []
forces = []
graph = load_graph(model[0])
bins = 25
xx = pylab.linspace(0, 2 * np.pi, bins)
yy = pylab.linspace(0, 2 * np.pi, bins)
pp_hist = np.zeros((fd, len(xx)))
pp_hist2d = np.zeros((1, len(xx), len(yy)))
delta = 2.0 * np.pi / bins
with tf.Session(graph=graph) as sess:
walker = Walker(fd, nw, sess)
for ii in range(100):
pp, ee, ff = walker.sample(compute_ef)
for ii in range(ns):
pp, ee, ff = walker.sample(compute_ef)
##all 1d
pp_hist_new = my_hist1d(pp, xx, delta, fd)
pp_hist = (pp_hist * ii + pp_hist_new) / (ii + 1)
##certain 2d
pp_hist_new2d = my_hist2d(pp, xx, yy, delta, cv1, cv2)
pp_hist2d = (pp_hist2d * ii + pp_hist_new2d) / (ii + 1)
if np.mod(ii, 50000) == 0:
zz = -np.log(pp_hist + 1e-7) / beta
zz *= f_cvt / 4.184 ##kcal
zz = zz - np.min(zz)
for jj in range(fd):
fp = open("1CV_index%d.dat" % jj, "a")
for temp in zz[jj]:
fp.write(str(temp) + ' ')
fp.write('\n')
fp.close()
zz2d = np.transpose(-np.log(pp_hist2d + 1e-10),
(0, 2, 1)) / beta
zz2d *= f_cvt / 4.184
zz2d = zz2d - np.min(zz2d)
np.savetxt("2CV_step%d.dat" % ii, zz2d[0])
#VMT = 4*pi*RMuffinTin**3/3. # Volume of MT
VMT = (4 / 3.) * pi * pow(RMuffinTin, 3)
Vinter = fcc.Volume - VMT # Volume of the interstitial region
print "Muffin-Tin radius = ", RMuffinTin
print "Volume of the MT sphere = ", VMT
print "Volume of the unit cell = ", fcc.Volume
print "Volume of the interstitial = ", Vinter
fcc.GenerateReciprocalVectors(
4, CutOffK
) # Reciprocal bravais lattice is built, K points taken into account only for |K|<CutOff
fcc.ChoosePointsInFBZ(
nkp,
0) # Choose the path in the 1BZ or the k-points in the irreducible 1BZ
# Radial mesh -- only linear mesh can be used in connection to Numerov algorithm.
R0 = linspace(0, RMuffinTin, N)
R0[0] = 1e-10
R = R0[::-1]
# Interstital overlap does not change through iterations
Olap_I = ComputeInterstitialOverlap(fcc.Km, RMuffinTin, fcc.Volume)
# We interpolate atomic charge on the new mesh within Muffin-Tin sphere
TotRho = interpolate.splev(R0, AtomRhoSpline)
for itt in range(Nitt): # self-consistent loop
print '%d) Preparing potential' % itt
UHartree = SolvePoisson(Z, R0, TotRho)
# Adding exchange-correlation part
Vxc = [XC.Vx(rsi) + XC.Vc(rsi) for rsi in rs(TotRho)]
def plot(self, cmap, filename=None,
starttime=T1, endtime=T2,
show_percentiles=False, percentiles=[10, 50, 90],
show_class_models=True, grid=True, title_comment=False):
"""
Plot the QC resume figure
If a filename is specified the plot is saved to this file, otherwise
a plot window is shown.
:type filename: str (optional)
:param filename: Name of output file
:type show_percentiles: bool (optional)
:param show_percentiles: Enable/disable plotting of approximated
percentiles. These are calculated from the binned histogram and
are not the exact percentiles.
:type percentiles: list of ints
:param percentiles: percentiles to show if plotting of percentiles is
selected.
:type show_class_models: bool (optional)
:param show_class_models: Enable/disable plotting of class models.
:type grid: bool (optional)
:param grid: Enable/disable grid in histogram plot.
:type cmap: cmap
:param cmap: Colormap for PPSD.
"""
# COMMON PARAMETERS
psd_db_limits = (-180, -110)
psdh_db_limits = (-200, -90)
f_limits = (5e-3, 20)
per_left = (10, 1, .1)
per_right = (100, 10, 1)
# -----------------
# Select Time window
# -----------
times_used = array(self.times_used)
starttime = max(min(times_used), starttime)
endtime = min(max(times_used), endtime)
bool_times_select = (times_used > starttime) & (times_used < endtime)
times_used = times_used[bool_times_select]
psd = self.psd[bool_times_select, :]
spikes = self.spikes[bool_times_select]
hist_stack = self._QC__get_ppsd(time_lim=(starttime, endtime))
Hour = arange(0, 23, 1)
HourUsed = array([t.hour for t in times_used])
Day_span = (endtime - starttime) / 86400.
# -----------
# FIGURE and AXES
fig = plt.figure(figsize=(9.62, 13.60), facecolor='w', edgecolor='k')
ax_ppsd = fig.add_axes([0.1, 0.68, 0.9, 0.28])
ax_coverage = fig.add_axes([0.1, 0.56, 0.64, 0.04])
ax_spectrogram = fig.add_axes([0.1, 0.31, 0.64, 0.24])
ax_spectrogramhour = fig.add_axes([0.76, 0.31, 0.20, 0.24])
ax_freqpsd = fig.add_axes([0.1, 0.18, 0.64, 0.12])
ax_freqpsdhour = fig.add_axes([0.76, 0.18, 0.20, 0.12])
ax_spikes = fig.add_axes([0.1, 0.05, 0.64, 0.12])
ax_spikeshour = fig.add_axes([0.76, 0.05, 0.20, 0.12])
ax_col_spectrogram = fig.add_axes([0.76, 0.588, 0.20, 0.014])
ax_col_spectrogramhour = fig.add_axes([0.76, 0.57, 0.20, 0.014])
########################### COVERAGE
ax_coverage.xaxis_date()
ax_coverage.set_yticks([])
# plot data coverage
starts = date2num([a.datetime for a in times_used])
ends = date2num([a.datetime for a in times_used + PPSD_LENGTH])
for start, end in zip(starts, ends):
ax_coverage.axvspan(start, end, 0, 0.7, alpha=0.5, lw=0)
# plot data really available
aa = [(start, end) for start, end in self.times_data if (
(end - start) > PPSD_LENGTH)] # avoid very small gaps otherwise very long to plot
for start, end in aa:
start = date2num(start.datetime)
end = date2num(end.datetime)
ax_coverage.axvspan(start, end, 0.7, 1, facecolor="g", lw=0)
# plot gaps
aa = [(start, end) for start, end in self.times_gaps if (
(end - start) > PPSD_LENGTH)] # avoid very small gaps otherwise very long to plot
for start, end in aa:
start = date2num(start.datetime)
end = date2num(end.datetime)
ax_coverage.axvspan(start, end, 0.7, 1, facecolor="r", lw=0)
# Compute uncovered periods
starts_uncov = ends[:-1]
ends_uncov = starts[1:]
# Keep only major uncovered periods
ga = (ends_uncov - starts_uncov) > (PPSD_LENGTH) / 86400
starts_uncov = starts_uncov[ga]
ends_uncov = ends_uncov[ga]
ax_coverage.set_xlim(starttime.datetime, endtime.datetime)
# labels
ax_coverage.xaxis.set_ticks_position('top')
ax_coverage.tick_params(direction='out')
ax_coverage.xaxis.set_major_locator(mdates.AutoDateLocator())
if Day_span > 5:
ax_coverage.xaxis.set_major_formatter(DateFormatter('%D'))
else:
ax_coverage.xaxis.set_major_formatter(DateFormatter('%D-%Hh'))
for label in ax_coverage.get_xticklabels():
label.set_fontsize(10)
for label in ax_coverage.get_xticklabels():
label.set_ha("right")
label.set_rotation(-25)
########################### SPECTROGRAM
ax_spectrogram.xaxis_date()
t = date2num([a.datetime for a in times_used])
f = 1. / self.per_octaves
T, F = np.meshgrid(t, f)
spectro = ax_spectrogram.pcolormesh(
T, F, transpose(psd), cmap=spectro_cmap)
spectro.set_clim(*psd_db_limits)
spectrogram_colorbar = colorbar(spectro, cax=ax_col_spectrogram,
orientation='horizontal',
ticks=linspace(psd_db_limits[0],
psd_db_limits[1], 5),
format='%i')
spectrogram_colorbar.set_label("dB")
spectrogram_colorbar.set_clim(*psd_db_limits)
spectrogram_colorbar.ax.xaxis.set_ticks_position('top')
spectrogram_colorbar.ax.xaxis.label.set_position((1.1, .2))
spectrogram_colorbar.ax.yaxis.label.set_horizontalalignment('left')
spectrogram_colorbar.ax.yaxis.label.set_verticalalignment('bottom')
ax_spectrogram.grid(which="major")
ax_spectrogram.semilogy()
ax_spectrogram.set_ylim(f_limits)
ax_spectrogram.set_xlim(starttime.datetime, endtime.datetime)
ax_spectrogram.set_xticks(ax_coverage.get_xticks())
setp(ax_spectrogram.get_xticklabels(), visible=False)
ax_spectrogram.yaxis.set_major_formatter(FormatStrFormatter("%.2f"))
ax_spectrogram.set_ylabel('Frequency [Hz]')
ax_spectrogram.yaxis.set_label_coords(-0.08, 0.5)
########################### SPECTROGRAM PER HOUR
#psdH=array([array(psd[HourUsed==h,:]).mean(axis=0) for h in Hour])
psdH = zeros((size(Hour), size(self.per_octaves)))
for i, h in enumerate(Hour):
a = array(psd[HourUsed == h, :])
A = ma.masked_array(
a, mask=~((a > psdh_db_limits[0]) & (a < psdh_db_limits[1])))
psdH[i, :] = ma.getdata(A.mean(axis=0))
psdH = array([psdH[:, i] - psdH[:, i].mean()
for i in arange(0, psdH.shape[1])])
H24, F = np.meshgrid(Hour, f)
spectroh = ax_spectrogramhour.pcolormesh(H24, F, psdH, cmap=cm.RdBu_r)
spectroh.set_clim(-8, 8)
spectrogram_per_hour_colorbar = colorbar(spectroh,
cax=ax_col_spectrogramhour,
orientation='horizontal',
ticks=linspace(-8, 8, 5),
format='%i')
spectrogram_per_hour_colorbar.set_clim(-8, 8)
ax_spectrogramhour.semilogy()
ax_spectrogramhour.set_xlim((0, 23))
ax_spectrogramhour.set_ylim(f_limits)
ax_spectrogramhour.set_xticks(arange(0, 23, 4))
ax_spectrogramhour.set_xticklabels(arange(0, 23, 4), visible=False)
ax_spectrogramhour.yaxis.set_ticks_position('right')
ax_spectrogramhour.yaxis.set_label_position('right')
ax_spectrogramhour.yaxis.grid(True)
ax_spectrogramhour.xaxis.grid(False)
########################### PSD BY PERIOD RANGE
t = date2num([a.datetime for a in times_used])
ax_freqpsd.xaxis_date()
for pp in zip(per_left, per_right):
mpsd = self._QC__get_psd(time_lim=(starttime, endtime), per_lim=pp)
mpsdH = zeros(size(Hour)) + NaN
for i, h in enumerate(Hour):
a = array(mpsd[HourUsed == h])
A = ma.masked_array(
a, mask=~((a > psdh_db_limits[0]) & (a < psdh_db_limits[1])))
mpsdH[i] = ma.getdata(A.mean())
ax_freqpsd.plot(t, mpsd)
ax_freqpsdhour.plot(Hour, mpsdH - mpsdH.mean())
ax_freqpsd.set_ylim(psd_db_limits)
ax_freqpsd.set_xlim(starttime.datetime, endtime.datetime)
ax_freqpsd.set_xticks(ax_coverage.get_xticks())
setp(ax_freqpsd.get_xticklabels(), visible=False)
ax_freqpsd.set_ylabel('Amplitude [dB]')
ax_freqpsd.yaxis.set_label_coords(-0.08, 0.5)
ax_freqpsd.yaxis.grid(False)
ax_freqpsd.xaxis.grid(True)
########################### PSD BY PERIOD RANGE PER HOUR
ax_freqpsdhour.set_xlim((0, 23))
ax_freqpsdhour.set_ylim((-8, 8))
ax_freqpsdhour.set_yticks(arange(-6, 7, 2))
ax_freqpsdhour.set_xticks(arange(0, 23, 4))
ax_freqpsdhour.set_xticklabels(arange(0, 23, 4), visible=False)
ax_freqpsdhour.yaxis.set_ticks_position('right')
ax_freqpsdhour.yaxis.set_label_position('right')
########################### SPIKES
ax_spikes.xaxis_date()
ax_spikes.bar(t, spikes, width=1. / 24)
ax_spikes.set_ylim((0, 50))
ax_spikes.set_xlim(starttime.datetime, endtime.datetime)
ax_spikes.set_yticks(arange(10, 45, 10))
ax_spikes.set_xticks(ax_coverage.get_xticks())
#setp(ax_spikes.get_xticklabels(), visible=False)
ax_spikes.set_ylabel("Detections [#/hour]")
ax_spikes.yaxis.set_label_coords(-0.08, 0.5)
ax_spikes.yaxis.grid(False)
ax_spikes.xaxis.grid(True)
# labels
ax_spikes.xaxis.set_ticks_position('bottom')
ax_spikes.tick_params(direction='out')
ax_spikes.xaxis.set_major_locator(mdates.AutoDateLocator())
if Day_span > 5:
ax_spikes.xaxis.set_major_formatter(DateFormatter('%D'))
else:
ax_spikes.xaxis.set_major_formatter(DateFormatter('%D-%Hh'))
for label in ax_spikes.get_xticklabels():
label.set_fontsize(10)
for label in ax_spikes.get_xticklabels():
label.set_ha("right")
label.set_rotation(25)
########################### SPIKES PER HOUR
mspikesH = array([array(spikes[[HourUsed == h]]).mean() for h in Hour])
ax_spikeshour.bar(Hour, mspikesH - mspikesH.mean(), width=1.)
ax_spikeshour.set_xlim((0, 23))
ax_spikeshour.set_ylim((-8, 8))
ax_spikeshour.set_xticks(arange(0, 23, 4))
ax_spikeshour.set_yticks(arange(-6, 7, 2))
ax_spikeshour.set_ylabel("Daily variation")
ax_spikeshour.set_xlabel("Hour [UTC]")
ax_spikeshour.yaxis.set_ticks_position('right')
ax_spikeshour.yaxis.set_label_position('right')
ax_spikeshour.yaxis.set_label_coords(1.3, 1)
########################### plot gaps
for start, end in zip(starts_uncov, ends_uncov):
ax_spectrogram.axvspan(
start, end, 0, 1, facecolor="w", lw=0, zorder=100)
ax_freqpsd.axvspan(
start, end, 0, 1, facecolor="w", lw=0, zorder=100)
ax_spikes.axvspan(start, end, 0, 1,
facecolor="w", lw=0, zorder=100)
# LEGEND
leg = [str(xx) + '-' + str(yy) + ' s' for xx,
yy in zip(per_left, per_right)]
hleg = ax_freqpsd.legend(
leg, loc=3, bbox_to_anchor=(-0.015, 0.75), ncol=size(leg))
for txt in hleg.get_texts():
txt.set_fontsize(8)
# PPSD
X, Y = np.meshgrid(self.xedges, self.yedges)
ppsd = ax_ppsd.pcolormesh(X, Y, hist_stack.T, cmap=cmap)
ppsd_colorbar = plt.colorbar(ppsd, ax=ax_ppsd)
ppsd_colorbar.set_label("PPSD [%]")
color_limits = (0, 30)
ppsd.set_clim(*color_limits)
ppsd_colorbar.set_clim(*color_limits)
ax_ppsd.grid(b=grid, which="major")
if show_percentiles:
hist_cum = self.__get_normalized_cumulative_histogram(
time_lim=(starttime, endtime))
# for every period look up the approximate place of the percentiles
for percentile in percentiles:
periods, percentile_values = self.get_percentile(
percentile=percentile, hist_cum=hist_cum, time_lim=(starttime, endtime))
ax_ppsd.plot(periods, percentile_values, color="black")
# Noise models
model_periods, high_noise = get_nhnm()
ax_ppsd.plot(model_periods, high_noise, '0.4', linewidth=2)
model_periods, low_noise = get_nlnm()
ax_ppsd.plot(model_periods, low_noise, '0.4', linewidth=2)
if show_class_models:
classA_periods, classA_noise, classB_periods, classB_noise = get_class()
ax_ppsd.plot(classA_periods, classA_noise, 'r--', linewidth=3)
ax_ppsd.plot(classB_periods, classB_noise, 'g--', linewidth=3)
ax_ppsd.semilogx()
ax_ppsd.set_xlim(1. / f_limits[1], 1. / f_limits[0])
ax_ppsd.set_ylim((-200, -80))
ax_ppsd.set_xlabel('Period [s]')
ax_ppsd.get_xaxis().set_label_coords(0.5, -0.05)
ax_ppsd.set_ylabel('Amplitude [dB]')
ax_ppsd.xaxis.set_major_formatter(FormatStrFormatter("%.2f"))
# TITLE
title = "%s %s -- %s (%i segments)"
title = title % (self.id, starttime.date, endtime.date,
len(times_used))
if title_comment:
fig.text(0.82, 0.978, title_comment, bbox=dict(
facecolor='red', alpha=0.5), fontsize=15)
ax_ppsd.set_title(title)
# a=str(UTCDateTime().format_iris_web_service())
plt.draw()
if filename is not None:
plt.savefig(filename)
plt.close()
else:
plt.show()
def Atom_charge(Z,
core,
mix=0.3,
RmaxAtom=10.,
Natom=3001,
precision=1e-5,
Nitt=100):
#def Atom_charge(Z, core, mix=0.3, RmaxAtom=10., Natom=3001, precision=1e-5, Nitt=1000):
""" Computes Atomic electronic density and atomic Energy
Input:
Z -- Nucleolus charge
core -- States treated as core in LAPW (example: [3,2,0] # 1s,2s,3s, 1p,2p, no-d)
mix -- Mixing parameter for density
RmaxAtom -- The end of the radial mesh (maximum r)
Natom -- Number of points in radial mesh
precision -- How precise total energy we need
Nitt -- Maximum number of itterations
"""
XC = excor.ExchangeCorrelation(5)
#XC = excor.ExchangeCorrelation(3) # Exchange correlations class; VWN seems to be the best (look http://physics.nist.gov/PhysRefData/DFTdata/Tables/ptable.html)
R0 = linspace(1e-10, RmaxAtom, Natom) # Radial mesh
Ra = R0[::-1] # Inverse radial mesh
Veff = -ones(len(Ra), dtype=float) / Ra
catm = [c + 1 for c in core
] # We add one more state to core to get atomic states
Etot_old = 0
# Finds bound states
(coreRho, coreE, coreZ, states) = FindCoreStates(catm, Ra, Veff, Z)
# Sorts them according to energy
states.sort(Atom_cmpb)
# Computes charge
(rho, Ebs) = Atom_ChargeDensity(states, Ra, Veff, Z)
rho = rho[::-1]
for itt in range(Nitt):
# Here we have increasing R ->
# Hartree potential
UHartree = SolvePoisson(Z, R0, rho)
# Adding exchange-correlation part
Vxc = [XC.Vx(rsi) + XC.Vc(rsi) for rsi in rs(rho)]
ExcVxc = [XC.EcVc(rsi) + XC.ExVx(rsi) for rsi in rs(rho)]
Veff = (UHartree - Z) / R0 + Vxc
Veff = Veff[::-1]
# Here we have decreasing R <-
# Finds bound states
(coreRho, coreE, coreZ, states) = FindCoreStates(catm, Ra, Veff, Z)
# Sorts them according to energy
states.sort(Atom_cmpb)
# Computes charge
(nrho, Ebs) = Atom_ChargeDensity(states, Ra, Veff, Z)
# Total energy
#pot = (ExcVxc*R0**2-0.5*UHartree*R0)*nrho[::-1]*4*pi
pot = (ExcVxc * pow(R0, 2) - 0.5 * UHartree * R0) * nrho[::-1] * 4 * pi
Etot = integrate.simps(pot, R0) + Ebs
Ediff = abs(Etot - Etot_old)
print RED, ' %d), Etot = %f, Eband = %f, Ediff = %f' % (
itt, Etot, Ebs, Ediff), DEFAULT_COLOR
#print ' %d), Etot = %f, Eband = %f, Ediff = %f' % (itt, Etot, Ebs, Ediff)
# Mixing
rho = mix * nrho[::-1] + (1 - mix) * rho
Etot_old = Etot
if Ediff < precision:
break
return (R0, rho)
pass
return my_fun2
"""
Pylab
-----------------
it can also capture matplotlib figures on the fly, maintaining all the
configurazione in the appropriate way"""
import pylab
from pylab import show
fig, ax = pylab.subplots(1, 1, figsize=(8, 4))
x = pylab.linspace(0, 10, 101)
ax.plot(x, x**2)
show()
"""to show the plot it is necessary to explicitly call the show method,
no shortcut available!
The show function show all the figure that has not already been shown,
so calling it twice in a row will do nothing.
"""
pylab.show()
"""if you want to show a figure for the second time, you will have to call
a specifi :code:`figure.show`.
"""
fig.show()
"""if external libraries are used, they interact in the expected way
"""
def plot_box(self):
"""
Plots the fraction of nodes that received a particular packet from the source
as a box-and-whisker with the probability p on the x-axis.
"""
logging.debug('')
configurations_per_variant = self.configurations_per_variant
gossip_variants_count = len(self.configurations['gossip'])
colors = self.options['color'](pylab.linspace(
0, 0.8, configurations_per_variant))
labels = []
for li_of_frac in self.label_info:
s = str()
for i, (param, value) in enumerate(li_of_frac):
if i > 0:
s += '\n'
s += '%s=%s' % (_cname_to_latex(param), value)
labels.append(s)
labels *= len(self.configurations['p'])
ps = list(
pylab.flatten(self.configurations_per_p * [p]
for p in self.configurations['p']))
#################################################################
# box plot
#################################################################
array = numpy.zeros([len(self.fraction_of_nodes[0]), self.length()])
for i, fracs in enumerate(self.fraction_of_nodes):
array[:, i] = fracs
fig = MyFig(self.options,
rect=[0.1, 0.2, 0.8, 0.75],
figsize=(max(self.length(), 10), 10),
xlabel='Probability p',
ylabel='Fraction of Nodes',
aspect='auto')
fig.ax.set_ylim(0, 1)
box_dict = fig.ax.boxplot(array, notch=1, sym='rx', vert=1)
#box_dict = fig.ax.boxplot(array, notch=1, sym='rx', vert=1, patch_artist=False)
for j, box in enumerate(box_dict['boxes']):
j = (j % self.configurations_per_p)
box.set_color(colors[j])
for _flier in box_dict['fliers']:
_flier.set_color('lightgrey')
fig.ax.set_xticklabels(ps, fontsize=self.options['fontsize'] * 0.6)
# draw vertical line to visually mark different probabilities
for x in range(0, self.length(), self.configurations_per_p):
fig.ax.plot([x + 0.5, x + 0.5], [0.0, 1.0],
linestyle='dotted',
color='red',
alpha=0.8)
#################################################################
# create some dummy elements for the legend
#################################################################
if configurations_per_variant > 1:
proxies = []
for i in range(0, configurations_per_variant):
r = Rectangle((0, 0),
1,
1,
edgecolor=colors[i % configurations_per_variant],
facecolor='white')
proxies.append((r, labels[i]))
fig.ax.legend([proxy for proxy, label in proxies],
[label for proxy, label in proxies],
loc='lower right')
self.figures['boxplot'] = fig.save('boxplot_' + str(self.data_filter))
def main():
import optparse
from numpy import sum
# Parse command line
parser = optparse.OptionParser(usage=USAGE)
parser.add_option("-p", "--plot", action="store_true",
help="Generate pdf with IR-spectrum")
parser.add_option("-i", "--info", action="store_true",
help="Set up/ Calculate vibrations & quit")
parser.add_option("-s", "--suffix", action="store",
help="Call suffix for binary e.g. 'mpirun -n 4 '",
default='')
parser.add_option("-r", "--run", action="store",
help="path to FHI-aims binary",default='')
parser.add_option("-x", "--relax", action="store_true",
help="Relax initial geometry")
parser.add_option("-m", "--molden", action="store_true",
help="Output in molden format")
parser.add_option("-w", "--distort", action="store_true",
help="Output geometry distorted along imaginary modes")
parser.add_option("-t", "--submit", action="store",
help="""\
Path to submission script, string <jobname>
will be replaced by name + counter, string
<outfile> will be replaced by filename""")
parser.add_option("-d", "--delta", action="store", type="float",
help="Displacement", default=0.0025)
options, args = parser.parse_args()
if options.info:
print __doc__
sys.exit(0)
if len(args) != 2:
parser.error("Need exactly two arguments")
AIMS_CALL=options.suffix+' '+options.run
hessian_thresh = -1
name=args[0]
mode=args[1]
delta=options.delta
run_aims=False
if options.run!='': run_aims=True
submit_script = options.submit is not None
if options.plot:
import matplotlib as mpl
mpl.use('Agg')
from pylab import figure
if options.plot or mode=='1':
from pylab import savetxt, transpose, eig, argsort, sort,\
sign, pi, dot, sum, linspace, argmin, r_, convolve
# Constant from scipy.constants
bohr=constants.value('Bohr radius')*1.e10
hartree=constants.value('Hartree energy in eV')
at_u=constants.value('atomic mass unit-kilogram relationship')
eV=constants.value('electron volt-joule relationship')
c=constants.value('speed of light in vacuum')
Ang=1.0e-10
hbar=constants.value('Planck constant over 2 pi')
Avo=constants.value('Avogadro constant')
kb=constants.value('Boltzmann constant in eV/K')
hessian_factor = eV/(at_u*Ang*Ang)
grad_dipole_factor=(eV/(1./(10*c)))/Ang #(eV/Ang -> D/Ang)
ir_factor = 1
# Asign all filenames
inputgeomerty = 'geometry.in.'+name
inputcontrol = 'control.in.'+name
atomicmasses = 'masses.'+name+'.dat';
xyzfile = name+'.xyz';
moldenname =name+'.molden';
hessianname = 'hessian.'+name+'.dat';
graddipolename = 'grad_dipole.'+name+'.dat';
irname = 'ir.'+name+'.dat';
deltas=array([-delta,delta])
coeff=array([-1,1])
c_zero = - 1. / (2. * delta)
f=open('control.in','r') # read control.in template
template_control=f.read()
f.close
if submit_script:
f=open(options.submit,'r') # read submission script template
template_job=f.read()
f.close
folder='' # Dummy
########### Central Point ##################################################
if options.relax and mode=='0':
# First relax input geometry
filename=name+'.out'
folder=name+'_relaxation'
if not os.path.exists(folder): os.mkdir(folder) # Create folder
shutil.copy('geometry.in', folder+'/geometry.in') # Copy geometry
new_control=open(folder+'/control.in','w')
new_control.write(template_control+'relax_geometry trm 1E-3\n') # Relax!
new_control.close()
os.chdir(folder) # Change directoy
print 'Central Point'
if run_aims:
os.system(AIMS_CALL+' > '+filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, 0, filename)
os.chdir('..')
############################################################################
# Check for relaxed geometry
if os.path.exists(folder+'/geometry.in.next_step'):
geometry=open(folder+'/geometry.in.next_step','r')
else:
geometry=open('geometry.in','r')
# Read input geometry
n_line=0
struc=structure()
lines=geometry.readlines()
for line in lines:
n_line= n_line+1
if line.rfind('set_vacuum_level')!=-1: # Vacuum Level
struc.vacuum_level=float(split_line(line)[-1])
if line.rfind('lattice_vector')!=-1: # Lattice vectors and periodic
lat=split_line(line)[1:]
struc.lattic_vector=append(struc.lattic_vector,float64(array(lat))
[newaxis,:],axis=0)
struc.periodic=True
if line.rfind('atom')!=-1: # Set atoms
line_vals=split_line(line)
at=Atom(line_vals[-1],line_vals[1:-1])
if n_line<len(lines):
nextline=lines[n_line]
if nextline.rfind('constrain_relaxation')!=-1: # constrained?
at=Atom(line_vals[-1],line_vals[1:-1],True)
else:
at=Atom(line_vals[-1],line_vals[1:-1])
struc.join(at)
geometry.close()
n_atoms= struc.n()
n_constrained=n_atoms-sum(struc.constrained)
# Atomic mass file
mass_file=open(atomicmasses,'w')
mass_vector=zeros([0])
for at_unconstrained in struc.atoms[struc.constrained==False]:
mass_vector=append(mass_vector,ones(3)*1./sqrt(at_unconstrained.mass()))
line='{0:10.5f}'.format(at_unconstrained.mass())
for i in range(3):
line=line+'{0:11.4f}'.format(at_unconstrained.coord[i])
line=line+'{0:}\n'.format(at_unconstrained.kind)
mass_file.writelines(line)
mass_file.close()
# Init
dip = zeros([n_constrained*3,3])
hessian = zeros([n_constrained*3,n_constrained*3])
index=0
counter=1
# Set up / Read folders for displaced atoms
for atom in arange(n_atoms)[struc.constrained==False]:
for coord in arange(3):
for delta in deltas:
filename=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)+'.out'
folder=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)
if mode=='0': # Put new geometry and control.in into folder
struc_new=copy.deepcopy(struc)
struc_new.atoms[atom].coord[coord]=\
struc_new.atoms[atom].coord[coord]+delta
geoname='geometry.i_atom_'+str(atom)+'.i_coord_'+str(coord)+\
'.displ_'+str(delta)+'.in'
if not os.path.exists(folder): os.mkdir(folder)
new_geo=open(folder+'/geometry.in','w')
newline='#\n# temporary structure-file for finite-difference '+\
'calculation of forces\n'
newline=newline+'# displacement {0:8.4f} of \# atom '.format(delta)+\
'{0:5} direction {1:5}\n#\n'.format(atom,coord)
new_geo.writelines(newline+struc_new.to_str())
new_geo.close()
#########################
# Editing starts
# Copying restart files for occupation calculation
shutil.copy("path_to_restart_files"+folder+"/restart", folder)
# Editing ends
#########################
new_control=open(folder+'/control.in','w')
template_control=template_control.replace('relax_geometry',
'#relax_geometry')
new_control.write(template_control+'compute_forces .true. \n'+\
'final_forces_cleaned '+\
'.true. \noutput dipole \n')
new_control.close()
os.chdir(folder) # Change directoy
print 'Processing atom: '+str(atom+1)+'/'+str(n_atoms)+', coord.: '+\
str(coord+1)+'/'+str(3)+', delta: '+str(delta)
if run_aims:
os.system(AIMS_CALL+' > '+filename)# Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, counter,
filename)
# os.system('qsub job.sh') # Mind the environment variables
os.chdir('..')
if mode=='1': # Read output
forces_reached=False
atom_count=0
data=open(folder+'/'+filename)
for line in data.readlines():
if line.rfind('Dipole correction potential jump')!=-1:
dip_jump = float(split_line(line)[-2]) # Periodic
if line.rfind('| Total dipole moment [eAng]')!=-1:
dip_jump = float64(split_line(line)[-3:]) # Cluster
if forces_reached and atom_count<n_atoms: # Read Forces
struc.atoms[atom_count].force=float64(split_line(line)[2:])
atom_count=atom_count+1
if atom_count==n_atoms:
forces_reached=False
if line.rfind('Total atomic forces')!=-1:
forces_reached=True
data.close()
if struc.periodic:
dip[index,2]=dip[index,2]+dip_jump*coeff[deltas==delta]*c_zero
else:
dip[index,:]=dip[index,:]+dip_jump*coeff[deltas==delta]*c_zero
forces=array([])
for at_unconstrained in struc.atoms[struc.constrained==False]:
forces=append(forces,coeff[deltas==delta]*at_unconstrained.force)
hessian[index,:]=hessian[index,:]+forces*c_zero
counter=counter+1
index=index+1
if mode=='1': # Calculate vibrations
print 'Entering hessian diagonalization'
print 'Number of atoms = '+str(n_atoms)
print 'Name of Hessian input file = '+hessianname
print 'Name of grad dipole input file = '+graddipolename
print 'Name of Masses input file = '+atomicmasses
print 'Name of XYZ output file = '+xyzfile
print 'Threshold for Matrix elements = '+str(hessian_thresh)
if (hessian_thresh < 0.0): print ' All matrix elements are taken'+\
' into account by default\n'
savetxt(hessianname,hessian)
savetxt(graddipolename,dip)
mass_mat=mass_vector[:,newaxis]*mass_vector[newaxis,:]
hessian[abs(hessian)<hessian_thresh]=0.0
hessian=hessian*mass_mat*hessian_factor
hessian=(hessian+transpose(hessian))/2.
# Diagonalize hessian (scipy)
print 'Solving eigenvalue system for Hessian Matrix'
freq, eig_vec = eig(hessian)
print 'Done ... '
eig_vec=eig_vec[:,argsort(freq)]
freq=sort(sign(freq)*sqrt(abs(freq)))
ZPE=hbar*(freq)/(2.0*eV)
freq = (freq)/(200.*pi*c)
grad_dipole = dip * grad_dipole_factor
eig_vec = eig_vec*mass_vector[:,newaxis]*ones(len(mass_vector))[newaxis,:]
infrared_intensity = sum(dot(transpose(grad_dipole),eig_vec)**2,axis=0)*\
ir_factor
reduced_mass=sum(eig_vec**2,axis=0)
norm = sqrt(reduced_mass)
eig_vec = eig_vec/norm
# The rest is output, xyz, IR,...
print 'Results\n'
print 'List of all frequencies found:'
print 'Mode number Frequency [cm^(-1)] Zero point energy [eV] '+\
'IR-intensity [D^2/Ang^2]'
for i in range(len(freq)):
print '{0:11}{1:25.8f}{2:25.8f}{3:25.8f}'.format(i+1,freq[i],ZPE[i],
infrared_intensity[i])
print '\n'
print 'Summary of zero point energy for entire system:'
print '| Cumulative ZPE = {0:15.8f} eV'.format(sum(ZPE))
print '| without first six eigenmodes = {0:15.8f} eV\n'.format(sum(ZPE)-
sum(ZPE[:6]))
print 'Stability checking - eigenvalues should all be positive for a '+\
'stable structure. '
print 'The six smallest frequencies should be (almost) zero:'
string=''
for zz in ZPE[:6]: string=string+'{0:25.8f}'.format(zz)
print string
print 'Compare this with the largest eigenvalue, '
print '{0:25.8f}'.format(freq[-1])
nums=arange(n_atoms)[struc.constrained==False]
nums2=arange(n_atoms)[struc.constrained]
newline=''
newline_ir='[INT]\n'
if options.molden:
newline_molden='[Molden Format]\n[GEOMETRIES] XYZ\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FREQ]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:10.3f}\n'.format(freq[i])
newline_molden=newline_molden+'[INT]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:17.6e}\n'.format(
infrared_intensity[i])
newline_molden=newline_molden+'[FR-COORD]\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord]/bohr)
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FR-NORM-COORD]\n'
for i in range(len(freq)):
newline=newline+'{0:6}\n'.format(n_atoms)
if freq[i]>0:
newline=newline+'stable frequency at '
elif freq[i]<0:
newline=newline+'unstable frequency at '
if options.distort and freq[i]<-50:
struc_new=copy.deepcopy(struc)
for i_atoms in range(n_constrained):
for i_coord in range(3):
struc_new.atoms[i_atoms].coord[i_coord]=\
struc_new.atoms[i_atoms].coord[i_coord]+\
eig_vec[(i_atoms)*3+i_coord,i]
geoname=name+'.distorted.vibration_'+str(i+1)+'.geometry.in'
new_geo=open(geoname,'w')
newline_geo='#\n# distorted structure-file for based on eigenmodes\n'
newline_geo=newline_geo+\
'# vibration {0:5} :{1:10.3f} 1/cm\n#\n'.format(i+1,freq[i])
new_geo.writelines(newline_geo+struc_new.to_str())
new_geo.close()
elif freq[i]==0:
newline=newline+'translation or rotation '
newline=newline+'{0:10.3f} 1/cm IR int. is '.format(freq[i])
newline=newline+'{0:10.4e} D^2/Ang^2; red. mass is '.format(
infrared_intensity[i])
newline=newline+'{0:5.3f} a.m.u.; force const. is '.format(
1.0/reduced_mass[i])
newline=newline+'{0:5.3f} mDyne/Ang.\n'.format(((freq[i]*(200*pi*c))**2)*
(1.0/reduced_mass[i])*at_u*1.e-2)
if options.molden: newline_molden=newline_molden+\
'vibration {0:6}\n'.format(i+1)
for i_atoms in range(n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(eig_vec[(i_atoms)*3+i_coord,i])
if options.molden: newline_molden=newline_molden+'{0:10.4f}'.format(
eig_vec[(i_atoms)*3+i_coord,i]/bohr)
newline=newline+'\n'
if options.molden: newline_molden=newline_molden+'\n'
for i_atoms in range(n_atoms-n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums2[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums2[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(0.0)
newline=newline+'\n'
newline_ir=newline_ir+'{0:10.4e}\n'.format(infrared_intensity[i])
xyz=open(xyzfile,'w')
xyz.writelines(newline)
xyz.close()
ir=open(irname,'w')
ir.writelines(newline_ir)
ir.close()
if options.molden:
molden=open(moldenname,'w')
molden.writelines(newline_molden)
molden.close()
if mode=='1' and options.plot:
x=linspace(freq.min()-500,freq.max()+500,1000)
z=zeros(len(x))
for i in range(len(freq)):
z[argmin(abs(x-freq[i]))]=infrared_intensity[i]
window_len=150
gauss=signal.gaussian(window_len,10)
s=r_[z[window_len-1:0:-1],z,z[-1:-window_len:-1]]
z_convolve=convolve(gauss/gauss.sum(),s,mode='same')[
window_len-1:-window_len+1]
fig=figure(0)
ax=fig.add_subplot(111)
ax.plot(x,z_convolve,'r',lw=2)
ax.set_xlim([freq.min()-500,freq.max()+500])
ax.set_ylim([-0.01,ax.get_ylim()[1]])
ax.set_yticks([])
ax.set_xlabel('Frequency [1/cm]',size=20)
ax.set_ylabel('Intensity [a.u.]',size=20)
fig.savefig(name+'_IR_spectrum.pdf')
print '\n Done. '
import pylab
data = pylab.loadtxt('thermo_volts.txt')
T = data[:, 0]
V = 1.0e-3 * data[:, 1]
fit = pylab.polyfit(V, T, 1)
V_fit = pylab.linspace(V.min(), V.max(), 500)
T_fit = pylab.polyval(fit, V_fit)
print 'fit poly: T*%f + %f' % (fit[0], fit[1])
pylab.plot(V, T, 'bo')
pylab.plot(V_fit, T_fit, 'r')
pylab.xlabel('(V)')
pylab.ylabel('(T)')
pylab.title('thermocouple calibration -- data sheet')
pylab.show()
