def plot_missing_data():
d = open("attr_counter")
profile_counter = pickle.load(d)
attr_counter = pickle.load(d)
missing = {}
for key in attr_counter:
missing[key] = float(2985414 - attr_counter[key]) / 2985414
from collections import OrderedDict
from pylab import arange
ordered_missing = OrderedDict(sorted(missing.items(), key=lambda k: k[1]))
plt.bar(range(len(ordered_missing.values())), sorted(missing.values()))
plt.xticks(arange(len(missing)) + 0.4, ordered_missing.keys(), rotation="vertical")
plt.plot(missing)
plt.savefig("missing_data.png")
profile_missing = {}
for i in profile_counter.keys():
profile_missing[16 - i] = profile_counter[i]
plt.bar(profile_missing.keys(), profile_missing.values())
plt.xticks(arange(len(profile_missing)) + 0.4, profile_missing.keys())
m_dump = open("missing_data1")
p_dump = open("person_missing_data1")
missing_data = pickle.load(m_dump)
person_missing_data = pickle.load(p_dump)
def DirectSearch(Range1, Range2, f, N=10.0, tol=.00001, output=False):
PrevBest = None
for i in range(100):
if output: print Range1, Range2
Width1 = Range1[1] - Range1[0]
xs = m.arange(Range1[0], Range1[1] + Width1/N, Width1 / N)
Width2 = Range2[1] - Range2[0]
ys = m.arange(Range2[0], Range2[1] + Width2/N, Width2 / N)
if abs(Width1) < tol or abs(Width2) < tol: return PrevBest
Best, BestVal = (xs[0], ys[0]), f(xs[0], ys[0])
for x in xs:
for y in ys:
val = f(x,y)
if val < BestVal:
BestVal = val
Best = (x, y)
if Best == PrevBest or PrevBest!=None and abs(Best[0]-PrevBest[0])+abs(Best[1]-PrevBest[1])<.000001:
Range1 = (Best[0] - Width1 / 4, Best[0] + Width1 / 4)
Range2 = (Best[1] - Width2 / 4, Best[1] + Width2 / 4)
else:
Range1 = (Best[0] - Width1 / 2, Best[0] + Width1 / 2)
Range2 = (Best[1] - Width2 / 2, Best[1] + Width2 / 2)
PrevBest = Best
if output: print BestVal, Best
return Best
def plot_multiple_roc(rocList,title='',labels=None, include_baseline=False, equal_aspect=True):
""" Plots multiple ROC curves on the same chart.
Parameters:
rocList: the list of ROCData objects
title: The tile of the chart
labels: The labels of each ROC curve
include_baseline: if it's True include the random baseline
equal_aspect: keep equal aspect for all roc curves
"""
pylab.clf()
pylab.ylim((0,1))
pylab.xlim((0,1))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
if equal_aspect:
cax = pylab.gca()
cax.set_aspect('equal')
pylab.xlabel("1 - Specificity")
pylab.ylabel("Sensitivity")
pylab.title(title)
if not labels:
labels = [ '' for x in rocList]
_remove_duplicate_styles(rocList)
for ix, r in enumerate(rocList):
pylab.plot([x[0] for x in r.derived_points], [y[1] for y in r.derived_points], r.linestyle, linewidth=1, label=labels[ix])
if include_baseline:
pylab.plot([0.0,1.0], [0.0, 1.0], 'k-', label= 'random')
if labels:
pylab.legend(loc='lower right')
pylab.show()
def InvokeMap(coastfile='/media/sda4/map-data/aust-coast-noaa-2000000-1.dat',
lllon=80,
urlon=166,
lllat=-47,
urlat=-9,
draw_map=True):
global PYLIB_PATH
map = Basemap(projection='cyl',
llcrnrlon=lllon,
urcrnrlon=urlon,
llcrnrlat=lllat,
urcrnrlat=urlat,
#lat_ts=-35,
lat_0=-35,
lon_0=120,
resolution='l',
area_thresh=1000.)
try:
coast = p.load(coastfile)
coast = p.load(coastfile)
coast_x,coast_y = map(coast[:,0],coast[:,1])
p.plot(coast_x,coast_y,color='black')
except IOError:
map.drawcoastlines()
map.drawmapboundary()
map.drawmeridians(p.arange(0,360,10),labels=[0,0,1,0])
map.drawparallels(p.arange(-90,0,10),labels=[1,0,0,0])
return map
def plot(self,title='',include_baseline=False,equal_aspect=True):
""" Method that generates a plot of the ROC curve
Parameters:
title: Title of the chart
include_baseline: Add the baseline plot line if it's True
equal_aspect: Aspects to be equal for all plot
"""
pylab.clf()
pylab.plot([x[0] for x in self.derived_points], [y[1] for y in self.derived_points], self.linestyle)
if include_baseline:
pylab.plot([0.0,1.0], [0.0,1.0],'k-.')
pylab.ylim((0,1))
pylab.xlim((0,1))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
if equal_aspect:
cax = pylab.gca()
cax.set_aspect('equal')
pylab.xlabel('1 - Specificity')
pylab.ylabel('Sensitivity')
pylab.title(title)
pylab.show()
def get_minmax(data, deg=2):
"""
returns the interpolated extremum and position (in fractions of frames)
:args:
data (iterable): data to be fitted with a <deg> degree polynomial
deg (int): degree of polynomial to fit
:returns:
val, pos: a tuple of floats indicating the position
"""
x = arange(len(data))
p = polyfit(x, data, deg)
d = (arange(len(p))[::-1] * p)[:-1]
r = roots(d)
cnt = 0
idx = None
for nr, rx in enumerate(r):
if isreal(r):
idx = nr
cnt +=1
if cnt > 1:
raise ValueError("Too many real roots found." +
"Reduce order of polynomial!")
x0 = r[nr].real
return x0, polyval(p, x0)
def fBM_nd(dims, H, return_mat = False, use_eig_ev = True):
"""
creates fractional Brownian motion
parameters: dims is a tuple of the shape of the sample path (nxd);
H: Hurst exponent
this is the slow version of fBM. It might, however, be more precise than
fBM, however - sometimes, the matrix square root has a problem, which might
induce inaccuracy
use_eig_ev: use eigenvalue decomposition for matrix square root computation
(faster)
"""
n = dims[0]
d = dims[1]
Gamma = zeros((n,n))
print ('building ...\n')
for t in arange(n):
for s in arange(n):
Gamma[t,s] = .5*((s+1)**(2.*H) + (t+1)**(2.*H) - abs(t-s)**(2.*H))
print('rooting ...\n')
if use_eig_ev:
ev,ew = eig(Gamma.real)
Sigma = dot(ew, dot(diag(sqrt(ev)),ew.T) )
else:
Sigma = sqrtm(Gamma)
if return_mat:
return Sigma
v = randn(n,d)
return dot(Sigma,v)
def updateColorTable(self, cItem):
print "now viz!"+str(cItem.row())+","+str(cItem.column())
row = cItem.row()
col = cItem.column()
pl.clf()
#pl.ion()
x = pl.arange(self.dataDimen+1)
y = pl.arange(self.dataDimen+1)
X, Y = pl.meshgrid(x, y)
pl.subplot(1,2,1)
pl.pcolor(X, Y, self.mWx[row*self.dataMaxRange+col])
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.title("user 1")
pl.subplot(1,2,2)
pl.pcolor(X, Y, self.mWy[row*self.dataMaxRange+col])
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.title("user 2")
#pl.tight_layout()
pl.draw()
#pl.show()
pl.show(block=False)
def makecoords(expr, BL,UR,gridspacing=.1):
from pylab import arange, array
X=arange(BL[0],UR[0],gridspacing)
Y=arange(BL[1],UR[1],gridspacing)
fn=deriv(expr)
Z=array([[fn(x=x,y=y) for x in X] for y in Y])
return X,Y,Z
def surface(self) :
x = pl.arange(-self.dimension[0],self.dimension[0]+1e-8,self.dimension[0]/5)
y = pl.arange(-self.dimension[1],self.dimension[1]+1e-8,self.dimension[1]/5)
xx,yy = pl.meshgrid(x,y)
zz = 1/self.placement.orientation[2]*(pl.linalg.dot(self.placement.orientation,self.placement.location)-self.placement.orientation[0]*xx-self.placement.orientation[1]*yy)
return [xx,yy,zz]
def visualize_labeled_z():
x_batch, label_batch = sample_x_and_label_from_data_distribution(len(dataset), sequential=True)
z_batch = gen(x_batch, test=True)
z_batch = z_batch.data
# if z_batch[0].shape[0] != 2:
# raise Exception("Latent code vector dimension must be 2.")
fig = pylab.gcf()
fig.set_size_inches(20.0, 16.0)
pylab.clf()
colors = ["#2103c8", "#0e960e", "#e40402","#05aaa8","#ac02ab","#aba808","#151515","#94a169", "#bec9cd", "#6a6551"]
for n in xrange(z_batch.shape[0]):
result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[label_batch[n]], s=40, marker="o", edgecolors='none')
classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
recs = []
for i in range(0, len(colors)):
recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i]))
ax = pylab.subplot(111)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5))
pylab.xticks(pylab.arange(-4, 5))
pylab.yticks(pylab.arange(-4, 5))
pylab.xlabel("z1")
pylab.ylabel("z2")
pylab.savefig("%s/labeled_z.png" % args.visualization_dir)
def length_stats_chart(path, prefixes, sortby=1):
stats = []
for prefix in prefixes:
med, m,s = length_stats(prefix)
stats.append((prefix,med,m,s))
stats.sort(key=operator.itemgetter(sortby))
prefixes, med_list, mean_list, std_list = zip(*stats)
blockSize = 8
ind = p.arange(0, blockSize*len(prefixes), blockSize) # y location for groups
height = 3 # bar height
p3 = p.barh(ind, std_list, 2 * height, color = 'b', linewidth = 0)
p2 = p.barh(ind, med_list, height, color = 'g', linewidth = 0)
p1 = p.barh(ind+height, mean_list, height, color = 'r', linewidth = 0)
p.ylim(-height, len(prefixes) * blockSize)
yfontprop = FontProperties(size=4)
xfontprop = FontProperties(size='smaller')
p.xlabel('Unicode Codepoints')
p.ylabel('Language Code')
p.title('Descriptive Statistics for Document Lengths')
p.gca().yaxis.tick_left()
p.yticks(ind+height, prefixes, fontproperties = yfontprop)
xmin, xmax = p.xlim()
p.xticks( p.arange(xmin,xmax,1000),fontproperties = xfontprop)
p.gca().xaxis.grid(linestyle = '-', linewidth=0.15)
p.legend((p1[0], p2[0], p3[0]), ('Mean','Median','Standard Deviation'), prop = xfontprop, loc = 'lower right' )
p.savefig(path, dpi=300)
p.close()
p.clf()
def redshift():
"""
Evolution with redshift of matter power spectrum
"""
zs = M.arange(0.,5.,2.)
for z in zs:
print z
c = pt.Camb(hubble = 70., ombh2 = 0.05*(0.7)**2, omch2 = 0.25*(0.7)**2,transfer_redshift = [z])
c.run()
ps = pt.PowerSpectrum(c.cp)
c.kextend(-10,60) #To ensure accurate sigma(r) -- if it doesn't, a warning will ensue
pt.normalizePk(c,0.8*ps.d1(z)/ps.d1(0.)) #sigma_8 at redshift z
#Sheth-Tormen
h = halo.HaloModel(c,st_big_a = 0., st_little_a=0.707, stq = 0.3, k = 10**M.arange(-2,2.01,0.2),massdivsperdex=5)
h.pmm = halo.getHaloPknl(c,h)
M.loglog(h.k, h.pmm, label='z='+str(z))
M.loglog(h.k, h.pk,'k:',label='linear')
cp_halofit = c.cp
cp_halofit['do_nonlinear'] = 1 # Halofit (Smith et al) fit
chalofit = pt.Camb(cambParam=cp_halofit)
chalofit.run()
wheretoplot = N.where(chalofit.k > 1e-2)[0]
M.loglog(chalofit.k[wheretoplot[::10]],chalofit.pk[wheretoplot[::10]],'--',label='halofit')
M.legend()
M.show()
def _pvoc(self, X_hat, Phi_hat=None, R=None):
"""
::
a phase vocoder - time-stretch
inputs:
X_hat - estimate of signal magnitude
[Phi_hat] - estimate of signal phase
[R] - resynthesis hop ratio
output:
updates self.X_hat with modified complex spectrum
"""
N = self.nfft
W = self.wfft
H = self.nhop
R = 1.0 if R is None else R
dphi = (2*P.pi * H * P.arange(N/2+1)) / N
print "Phase Vocoder Resynthesis...", N, W, H, R
A = P.angle(self.STFT) if Phi_hat is None else Phi_hat
phs = A[:,0]
self.X_hat = []
n_cols = X_hat.shape[1]
t = 0
while P.floor(t) < n_cols:
tf = t - P.floor(t)
idx = P.arange(2)+int(P.floor(t))
idx[1] = n_cols-1 if t >= n_cols-1 else idx[1]
Xh = X_hat[:,idx]
Xh = (1-tf)*Xh[:,0] + tf*Xh[:,1]
self.X_hat.append(Xh*P.exp( 1j * phs))
U = A[:,idx[1]] - A[:,idx[0]] - dphi
U = U - P.np.round(U/(2*P.pi))*2*P.pi
phs += (U + dphi)
t += P.randn()*P.sqrt(PVOC_VAR*R) + R # 10% variance
self.X_hat = P.np.array(self.X_hat).T
def _pvoc2(self, X_hat, Phi_hat=None, R=None):
"""
::
alternate (batch) implementation of phase vocoder - time-stretch
inputs:
X_hat - estimate of signal magnitude
[Phi_hat] - estimate of signal phase
[R] - resynthesis hop ratio
output:
updates self.X_hat with modified complex spectrum
"""
N, W, H = self.nfft, self.wfft, self.nhop
R = 1.0 if R is None else R
dphi = P.atleast_2d((2*P.pi * H * P.arange(N/2+1)) / N).T
print "Phase Vocoder Resynthesis...", N, W, H, R
A = P.angle(self.STFT) if Phi_hat is None else Phi_hat
U = P.diff(A,1) - dphi
U = U - P.np.round(U/(2*P.pi))*2*P.pi
t = P.arange(0,n_cols,R)
tf = t - P.floor(t)
phs = P.c_[A[:,0], U]
phs += U[:,idx[1]] + dphi # Problem, what is idx ?
Xh = (1-tf)*Xh[:-1] + tf*Xh[1:]
Xh *= P.exp( 1j * phs)
self.X_hat = Xh
def makeGridPlots( histos, filename, ext="png" ) :
# old code : doesnt owrk
nplot = len( histos.keys() )
sh = sqrt( nplot )
fl = floor( sh )
ce = ceil( sh )
if sh - fl > 0.5 :
fl = ce
f = r2m.RootFile(filename)
hists = [ f.get(hist) for hist in sorted(histos.keys()) ]
opts = [ histos[key] for key in sorted(histos.keys()) ]
fig = plt.figure(figsize=[ce*(sml_plot_size[0]+fl),fl*(sml_plot_size[1]+fl)])
#fig.subplots_adjust(left=1, right=2, top=2, bottom=1)
ax_list = []
for h, (hist,opt) in enumerate(zip(hists,opts)) :
ax_list.append( fig.add_subplot(ce, fl, h ))
ax_list[-1].set_xlabel( hist.xlabel )
ax_list[-1].set_ylabel( hist.ylabel )
xmin, xmax = hist.xedges[0], hist.xedges[-1]
ymin, ymax = hist.yedges[0], hist.yedges[-1]
plt.axis([xmin, xmax, ymin, ymax])
hist.contour( levels=opt["contours"], colors = opt["colors"], linewidths=2 )
hist.colz()
plt.axis([xmin, xmax, ymin, ymax])
plt.clim(opt["zrange"][0],opt["zrange"][1])
pylab.yticks(pylab.arange(ymin, ymax, opt["yticks"]))
pylab.xticks(pylab.arange(xmin, xmax, opt["xticks"]))
ax_list[-1].set_title( opt["title"] )
#plt.show()
plt.savefig( grid_name( filename ) + ".%s" % ext )
def _make_log_freq_map(self):
"""
::
For the given ncoef (bands-per-octave) and nfft, calculate the center frequencies
and bandwidths of linear and log-scaled frequency axes for a constant-Q transform.
"""
fp = self.feature_params
bpo = float(self.nbpo) # Bands per octave
self._fftN = float(self.nfft)
hi_edge = float( self.hi )
lo_edge = float( self.lo )
f_ratio = 2.0**( 1.0 / bpo ) # Constant-Q bandwidth
self._cqtN = float( P.floor(P.log(hi_edge/lo_edge)/P.log(f_ratio)) )
self._dctN = self._cqtN
self._outN = float(self.nfft/2+1)
if self._cqtN<1: print "warning: cqtN not positive definite"
mxnorm = P.empty(self._cqtN) # Normalization coefficients
fftfrqs = self._fftfrqs #P.array([i * self.sample_rate / float(self._fftN) for i in P.arange(self._outN)])
logfrqs=P.array([lo_edge * P.exp(P.log(2.0)*i/bpo) for i in P.arange(self._cqtN)])
logfbws=P.array([max(logfrqs[i] * (f_ratio - 1.0), self.sample_rate / float(self._fftN))
for i in P.arange(self._cqtN)])
#self._fftfrqs = fftfrqs
self._logfrqs = logfrqs
self._logfbws = logfbws
self._make_cqt()
def initialise_axes(axes,hists,options,filename=None):
# hists is a list with potentially only one element
# try: hists[0]
# except:TypeError , hists=[hists]
xmins = [ hist.xedges[0] for hist in hists ]
ymins = [ hist.yedges[0] for hist in hists ]
xmaxs = [ hist.xedges[-1] for hist in hists ]
ymaxs = [ hist.yedges[-1] for hist in hists ]
xmin, xmax, ymin, ymax = min(xmins), min(xmaxs), min(ymins), max(ymaxs)
axes.set_xlabel( hists[0].xlabel )
axes.set_ylabel( hists[0].ylabel )
plt.axis( [xmin, xmax, ymin, ymax] )
if options.get("xlog") :
axes.set_xscale('log')
if options.get("ylog") :
axes.set_yscale('log')
if options.get('yticks') :
pylab.yticks(pylab.arange( ymin, ymax*1.001, options["yticks"] ) )
if options.get('xticks') :
pylab.xticks(pylab.arange( xmin, xmax*1.001, options["xticks"] ) )
if options.get('title') :
if filename:
# axes.set_title( file_dict.get(filename,{}).get('title'))
title=file_dict.get(filename,{}).get('title')
plt.text(0.5, 1.05, title, ha='center', fontsize=30,transform=axes.transAxes)
def observation_locns(spacestep,estimation_field_width,Delta_s): '''Define the center of sensors along x and y Atguments: ---------- spacestep: the spatial step in the simulated field observedwidthfield: the width of the observed field Delta_s: distance between sensors in mm Returns ------- observation_locns_mm: the observation location along x or y directions in mm''' steps_in_field = (2*estimation_field_width)/spacestep + 1; inv_spacestep = 1./spacestep; Nspacestep_in_observed_field = inv_spacestep*estimation_field_width+1 observation_offest = estimation_field_width/2; # mm observation_offset_units = observation_offest / spacestep -1; field_space=pb.arange(-estimation_field_width,estimation_field_width+spacestep,spacestep) spatial_location_num=(len(field_space))**2 Delta_s_units = Delta_s/spacestep nonsymmetric_obs_location_units = pb.arange(1,Nspacestep_in_observed_field,Delta_s_units) offset = ((Nspacestep_in_observed_field - nonsymmetric_obs_location_units[-1])/2.) symmetricobslocation_units = nonsymmetric_obs_location_units + offset + observation_offset_units observation_locs_mm = symmetricobslocation_units*spacestep - estimation_field_width return observation_locs_mm
def plot2d(self,key1='a',key2='b',scale1=0.1,scale2=0.1):
"""
under development
plot the least_squared around the minimum
assuming a quadratic approximation given by the hessian
"""
import pylab
key1,key2=key2,key1
keys=self.last_variables.keys()
keys.sort()
nv=len(self.last_variables)
for k in range(len(keys)):
if keys[k]==key1: i,vi=k, self.last_variables[key1]
if keys[k]==key2: j,vj=k, self.last_variables[key2]
ax=pylab.arange(vi*(1.0-10*scale1),vi*(1.0+12*scale1),vi*scale1)[:21]
ay=pylab.arange(vj*(1.0-10*scale2),vj*(1.0+12*scale2),vj*scale2)[:21]
dv=[0.0]*nv
z=[[0 for ik in ax] for jk in ay]
i0=0
for x in ax:
j0=0
for y in ay:
dv[i],dv[j]=x-vi,y-vj
least_squares=0.0
for ik in range(nv):
for jk in range(nv):
least_squares+=0.5*dv[ik]*dv[jk]*self.last_hessian[ik][jk]
z[i0][j0]=least_squares-self.last_least_squares
j0+=1
i0+=1
pylab.contour(z,extent=(min(ay),max(ay),min(ax),max(ax)))
pylab.show()
def figurepoimsimple_small(poim, l, start, savefile, show):
R = poim
py.figure(figsize=(14, 12))
motivelen = int(np.log(len(poim)) / np.log(4))
ylabel = []
for i in range(int(math.pow(4, motivelen))):
label = []
index = i
for j in range(motivelen):
label.append(index % 4)
index = int(index / 4)
label.reverse()
ylabel.append(veclisttodna(label))
py.pcolor(R[:, start:start + l])
cb=py.colorbar()
for t in cb.ax.get_yticklabels():
t.set_fontsize(40)
diff = int((l / 5)) - 1
x_places = py.arange(0.5, l, diff)
xa = np.arange(start, start + l, diff)
diff = int((l / 4))
x_places = py.arange(0.5, l , diff)
xa = np.arange(start + 1, start + 1 + l, diff)
py.xlabel("Position", fontsize=46)
py.ylabel("Motif", fontsize=46)
py.yticks(np.arange(math.pow(4, motivelen)) + 0.5, (ylabel),fontsize=40)
py.xticks(x_places, (xa.tolist()),fontsize=40)
if savefile != "":
py.savefig(savefile)
print "the poim should show up here"
if show:
py.show()
def plot_multiple_roc(rocList,title='',labels=None, include_baseline=False, equal_aspect=True,plot_average =True):
pylab.figure()
pylab.clf()
pylab.ylim((0,1))
pylab.xlim((0,1))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
if equal_aspect:
cax = pylab.gca()
cax.set_aspect('equal')
pylab.xlabel("1 - Specificity")
pylab.ylabel("Sensitivity")
pylab.title(title)
if not labels:
labels = [ '' for x in rocList]
_remove_duplicate_styles(rocList)
for ix, r in enumerate(rocList):
pylab.plot([x[0] for x in r.derived_points], [y[1] for y in r.derived_points], r.linestyle, linewidth=1, label=labels[ix])
if include_baseline:
pylab.plot([0.0,1.0], [0.0, 1.0], 'k-', label= 'random')
if labels:
pylab.legend(loc='lower right')
if plot_average:
stepsize=.1
av = multiple_roc_average(rocList,binstep=stepsize)
x=np.arange(stepsize/2,1,stepsize)
pylab.plot(x,[np.average(a) for a in av])
def adjuster(file_name, threshold=0.9):
record = collections.OrderedDict()
total = 0
for data in open(file_name).readlines():
curr = data.strip().split('\t')
record[curr[0]] = int(curr[1])
total += int(curr[1])
curr_count = 0
curr_list = list()
ratio_list = list()
for item in record.items():
curr_count += item[1]
curr_list.append(item[0])
ratio_list.append(curr_count / float(total))
if curr_count / float(total) >= threshold:
break
x = pylab.arange(1, len(ratio_list) + 1, 1)
plt.plot(x, ratio_list)
plt.show()
dx = 1
dy = diff(ratio_list) / dx
print len(dy)
print len(x)
x = pylab.arange(1, len(ratio_list), 1)
plt.plot(x, dy)
plt.show()
print dy
return curr_list
def test_expert_model_level_value():
d = data.ModelData()
ages=pl.arange(101)
# create model with no priors
vars = {}
vars.update(age_pattern.age_pattern('test', ages, knots=pl.arange(0,101,5), smoothing=.01))
vars.update(expert_prior_model.level_constraints('test', {}, vars['mu_age'], ages))
# fit model
m = mc.MCMC(vars)
m.sample(3)
# create model with expert priors
parameters = {}
parameters['level_value'] = dict(value=.1, age_below=15, age_above=95)
parameters['level_bound'] = dict(upper=.01, lower=.001)
vars = {}
vars.update(age_pattern.age_pattern('test', ages, knots=pl.arange(0,101,5), smoothing=.01))
vars.update(expert_prior_model.level_constraints('test', parameters, vars['mu_age'], ages))
# fit model
m = mc.MCMC(vars)
m.sample(3)
def drawROC(points,zeTitle,zeFilename,visible,show_fig,save_fig=True,
special_point=None,special_value=None,special_label=None):
AUC=computeAUC(points)
import pylab
pylab.clf()
pylab.grid(color='#aaaaaa', linestyle='-', linewidth=1,alpha=0.5)
pylab.plot([x[0] for x in points], [y[1] for y in points], '-', linewidth=3,color="#000088",zorder=3)
pylab.fill_between([x[0] for x in points], [y[1] for y in points],0,color='0.9')
pylab.plot([0.0,1.0], [0.0, 1.0], '-',color="#AAAAAA")
pylab.ylim((-0.01,1.01))
pylab.xlim((-0.01,1.01))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
ax=pylab.gca()
r = pylab.Rectangle((0,0), 1, 1, edgecolor='#444444', facecolor='none',zorder=1)
ax.add_patch(r)
[spine.set_visible(False) for spine in ax.spines.values()]
if len(points)<10:
for i in range(1,len(points)-1):
pylab.plot(points[i][0],points[i][1],'o',color="#000066",zorder=6)
pylab.xlabel('False positive rate')
pylab.ylabel('True positive rate')
if special_point is not None:
pylab.plot(special_point[0],special_point[1],'o',color="#DD9999",zorder=6)
if special_value is not None:
pylab.text(special_point[0]+0.01,special_point[1]-0.01, special_value,
{'color' : '#DD5555', 'fontsize' : 10},
horizontalalignment = 'left',
verticalalignment = 'top',
rotation = 0,
clip_on = False)
if special_label is not None:
if special_label!="":
labels=[special_label]
colors=['#DD9999']
circles=[pylab.Circle((0, 0), 1, fc=colors[0])]
legend_location = 'lower right'
pylab.legend(circles, labels, loc=legend_location)
pylab.text(0.5, 0.3,'AUC=%f'%AUC,
horizontalalignment='center',
verticalalignment='center',
fontsize=18)
pylab.title(zeTitle)
if save_fig:
pylab.savefig(zeFilename,dpi=300)
print("\n result in "+zeFilename)
if show_fig:
pylab.show()
def multi_plot_search_on_eval_metrics(roc_search_em, score_thresholds, labels, metrics, line_styles, query_id='Query'):
import pylab
pylab.clf()
pylab.xlim((0, 1))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.ylim((-0.5, 1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
pylab.xlabel('Score Thresholds')
for iix, score_key in enumerate(metrics):
for ix, eval_dict in enumerate(roc_search_em):
pylab.plot(score_thresholds, eval_dict[score_key],
linewidth=2, label=labels[ix] + '(%s)' % score_key,
color=METRIC_COLORS[ix], linestyle=line_styles[iix])
pylab.ylabel(' '.join(METRICS_DICT[score_key] for score_key in metrics))
pylab.title(' '.join(METRICS_DICT[score_key] for score_key in metrics))
if labels: pylab.legend(loc='lower left', prop={'size':9})
eval_file_name = '%s_%s.png' % (query_id, '_'.join(METRICS_DICT[score_key] for score_key in metrics))
pylab.savefig(eval_file_name, dpi=300, bbox_inches='tight', pad_inches=0.1)
print 'Saved figure: ', eval_file_name
print '----------------------------------------------------------------------------------'
def plotcp(nelem, chord, thick, u):
# Geometry
xnode = geomwing(nelem, chord, thick)
TEat1 = 1
dt = 1.0
# Boundary conditions
chisrf = bcondvel(xnode, u)
# Integral equation solution
B, C = srfmatbc(xnode)
phisrf = solvephi(B, C, chisrf)
cpoint = collocation(xnode)
# Pressure figure
spl = subplot(111)
spl.set_aspect("equal", "box")
plotgeom(xnode)
# Pressure calculation and plotting
U = u.reshape((1, 2))
cp = calccp(xnode, TEat1, dt, U, phisrf, chisrf)
cl = calchalfcl(xnode, TEat1, dt, U, phisrf, chisrf)
print cl
plot(cpoint[: nelem / 2 + 1, 0], cp[: nelem / 2 + 1, 0])
title(r"Stationary pressure coefficient, $c_L = %9.3f$" % cl[0])
ylabel(r"$c_p$", size=18)
xlabel(r"$x/c$", size=18)
xticks(arange(-0.2, 1.3, 0.2))
yticks(arange(-0.8, 1.3, 0.2))
def bars(data, figname, fig = None):
if fig == None:
P.ioff()
fig = P.figure()
ax = fig.add_subplot(111)
save = True
else:
ax = fig
fig = ax.get_figure()
save = False
ind = P.arange(len(data[0][0])) # the x locations for the groups
width = 0.20 # the width of the bars
#colors = ['r','b','g','y']
bar_groups = [ ax.bar( ind+width*i, grp[0], width,color=colors[i],\
yerr=grp[1], ecolor='k')\
for i,grp in enumerate(data)]
barsLegend = tuple([grp[0] for grp in bar_groups])
etiquetas = [str(i) for i in ctoa_list]
P.legend( barsLegend, ctoa_list, shadow=True)
#ax.set_title('Incremento de RT frente a CTD en distintos CTOAS', font, fontsize=12)
#ax.set_xlabel('CTD distance (cm)', font)
#ax.set_ylabel('RT increment (ms)', font)
ax.set_xticks(ind+width)
ax.set_xticklabels( ctd_names[:-1] )
ax.set_yticks(P.arange(-50,50,5))
ax.xaxis.set_ticks_position("bottom")
ax.yaxis.set_ticks_position("left")
ax.set_xlim(-width,len(ind))
ax.set_ylim(-60,60)
if save == True:
fig.savefig(figname+'bar'+graphext,dpi=dpi)
P.close(fig)
def fresnelConvolutionTransform(self,d) :
# make intensity distribution
i2 = Intensity2D(self.nx,self.startx,self.endx,
self.ny,self.starty,self.endy,
self.wl)
# FT on inital distribution
u1ft = pl.fft2(self.i)
# 2d convolution kernel
k = 2*pl.pi/i2.wl
# make spatial frequency matrix
maxsfx = 2*pl.pi/self.dx
maxsfy = 2*pl.pi/self.dy
dsfx = 2*maxsfx/(self.nx)
dsfy = 2*maxsfy/(self.ny)
self.sfx = pl.arange(-maxsfx/2,maxsfx/2+1e-15,dsfx/2)
self.sfy = pl.arange(-maxsfy/2,maxsfy/2+1e-15,dsfy/2)
[self.sfxgrid, self.sfygrid] = pl.fftshift(pl.meshgrid(self.sfx,self.sfy))
# make convolution kernel
kern = pl.exp(1j*d*(self.sfxgrid**2+self.sfygrid**2)/(2*k))
# apply convolution kernel and invert
i2.i = pl.ifft2(kern*u1ft)
return i2
def main():
# Create the grid
x = arange(-100, 101)
y = arange(-100, 101)
# Create the meshgrid
Y, X = meshgrid(x, y)
A = 1
B = 2
V = 6*pi / 201
W = 4*pi / 201
F = A*sin(V*X) + B*cos(W*Y)
Fx = V*A*cos(V*X)
Fy = W*B*-sin(W*Y)
# Show the images
show_image(F)
show_image(Fx)
show_image(Fy)
# Create the grid for the quivers
xs = arange(-100, 101, 10)
ys = arange(-100, 101, 10)
# Here we determine the direction of the quivers
Ys, Xs = meshgrid(ys, xs)
FFx = V*A*cos(V*Xs)
FFy = W*B*-sin(W*Ys)
# Draw the quivers and the image
clf()
imshow(F, cmap=cm.gray, extent=(-100, 100, -100, 100))
quiver(ys, xs, -FFy, FFx, color='red')
show()
def show_iq(self, out, pr, q=None):
"""
Display computed I(q)
"""
qtemp = pr.x
if q is not None:
qtemp = q
# Make a plot
maxq = -1
for q_i in qtemp:
if q_i > maxq:
maxq = q_i
minq = 0.001
# Check for user min/max
if pr.q_min is not None:
minq = pr.q_min
if pr.q_max is not None:
maxq = pr.q_max
x = pylab.arange(minq, maxq, maxq / 301.0)
y = np.zeros(len(x))
err = np.zeros(len(x))
for i in range(len(x)):
value = pr.iq(out, x[i])
y[i] = value
try:
err[i] = math.sqrt(math.fabs(value))
except:
err[i] = 1.0
print("Error getting error", value, x[i])
new_plot = Data1D(x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = IQ_FIT_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
title = "I(q)"
new_plot.title = title
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_FIT_LABEL
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
# If we have used slit smearing, plot the smeared I(q) too
if pr.slit_width > 0 or pr.slit_height > 0:
x = pylab.arange(minq, maxq, maxq / 301.0)
y = np.zeros(len(x))
err = np.zeros(len(x))
for i in range(len(x)):
value = pr.iq_smeared(out, x[i])
y[i] = value
try:
err[i] = math.sqrt(math.fabs(value))
except:
err[i] = 1.0
print("Error getting error", value, x[i])
new_plot = Data1D(x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = IQ_SMEARED_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_SMEARED_LABEL
new_plot.title = title
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
"""
Demonstrates the"reshape with non-integer intevals" and the optimization of
the reshape interval by minimizing the standard deviation of the overlap.
"""
import pylab as p
from segment_psp_trace import segment, optimize_segment
if __name__ == '__main__':
data = p.np.load("traces/spiketrace_1.npz")["arr_0"][:2400000]
dt = 1.
p.figure()
m = lambda interval: p.sum(p.var(segment(data, dt, interval), axis=0))
shift_values = p.arange(3841, 3842, .01)
p.plot(shift_values, map(m, shift_values), 'x')
p.show()
r = optimize_segment(data, 1., 3840)
print r
p.figure()
seg = segment(data, 1., r)
print len(seg)
mean = p.mean(seg, axis=0)
std = p.std(seg, axis=0)
dt = 1.
time = p.arange(len(std)) * dt
p.plot(time, mean, "r-")
p.fill_between(time, mean - std, mean + std, alpha=.2)
m = Basemap(resolution='c', projection='sinu', lon_0=0)
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7])
# make a filled contour plot.
x, y = m(lons, lats)
CS = m.contour(x, y, hgt, 15, linewidths=0.5, colors='k')
CS = m.contourf(x, y, hgt, 15, cmap=cm.jet)
l, b, w, h = ax.get_position()
cax = axes([l + w + 0.075, b, 0.05, h]) # setup colorbar axes
colorbar(drawedges=True, cax=cax) # draw colorbar
axes(ax) # make the original axes current again
# draw coastlines and political boundaries.
m.drawcoastlines()
m.drawmapboundary()
m.fillcontinents()
# draw parallels and meridians.
parallels = arange(-60., 90, 30.)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = arange(-360., 360., 30.)
m.drawmeridians(meridians)
title('Sinusoidal Filled Contour Demo')
print 'plotting with sinusoidal basemap ...'
# create new figure
fig = figure()
# setup of mollweide basemap
m = Basemap(resolution='c', projection='moll', lon_0=0)
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7])
# make a filled contour plot.
x, y = m(lons, lats)
CS = m.contour(x, y, hgt, 15, linewidths=0.5, colors='k')
CS = m.contourf(x, y, hgt, 15, cmap=cm.jet)
out(a1,a2)
endin
schedule(1,0,1,0dbfs*.9,60)
'''
cs = csound.Csound()
cs.setOption('-n')
cs.compileOrc(code)
cs.start()
spout = cs.spout()
sr = cs.sr()
N = int(sr) // 10
sig = pl.zeros(N)
n = 0
for i in range(0, int(len(sig) / (2 * cs.ksmps()))):
cs.performKsmps()
for i in spout:
sig[n] = i / cs.get0dBFS()
n += 1
pl.figure(figsize=(8, 3))
pl.title('PWM')
t = pl.arange(0, N // 2) / float(sr)
pl.xlim(0, max(t))
pl.ylim(-1.1, 1.1)
pl.xlabel("time (s)")
pl.plot(t, sig[1::2], 'k--', linewidth=2)
pl.plot(t, sig[0::2])
pl.show()
from scipy.stats import binom import pylab as plt n, p = 5, 0.4 x = plt.arange(6) y = binom.pmf(x, n, p) plt.subplot(121) plt.plot(x, y, 'ro') plt.vlines(x, 0, y, 'k', lw=3, alpha=0.5) plt.subplot(122) plt.stem(x, y, use_line_collection=True) plt.show()
#Py Matplotlib Test 3 from pylab import show,arange,sin, plot, pi t = arange(0,2,0.01) s = sin(2*pi*t) plot(t,s) show()
def plot(self, to_plot=None, do_save=None, fig_path=None, fig_args=None, plot_args=None,
scatter_args=None, axis_args=None, legend_args=None, as_dates=True, dateformat=None,
interval=None, n_cols=1, font_size=18, font_family=None, use_grid=True, use_commaticks=True,
log_scale=False, do_show=True, verbose=None):
'''
Plot the results -- can supply arguments for both the figure and the plots.
Args:
to_plot (dict): Nested dict of results to plot; see default_sim_plots for structure
do_save (bool or str): Whether or not to save the figure. If a string, save to that filename.
fig_path (str): Path to save the figure
fig_args (dict): Dictionary of kwargs to be passed to pl.figure()
plot_args (dict): Dictionary of kwargs to be passed to pl.plot()
scatter_args (dict): Dictionary of kwargs to be passed to pl.scatter()
axis_args (dict): Dictionary of kwargs to be passed to pl.subplots_adjust()
legend_args (dict): Dictionary of kwargs to be passed to pl.legend()
as_dates (bool): Whether to plot the x-axis as dates or time points
dateformat (str): Date string format, e.g. '%B %d'
interval (int): Interval between tick marks
n_cols (int): Number of columns of subpanels to use for subplot
font_size (int): Size of the font
font_family (str): Font face
use_grid (bool): Whether or not to plot gridlines
use_commaticks (bool): Plot y-axis with commas rather than scientific notation
log_scale (bool or list): Whether or not to plot the y-axis with a log scale; if a list, panels to show as log
do_show (bool): Whether or not to show the figure
verbose (bool): Display a bit of extra information
Returns:
fig: Figure handle
'''
if verbose is None:
verbose = self['verbose']
sc.printv('Plotting...', 1, verbose)
if to_plot is None:
to_plot = cvd.default_sim_plots
to_plot = sc.odict(to_plot) # In case it's supplied as a dict
# Handle input arguments -- merge user input with defaults
fig_args = sc.mergedicts({'figsize':(16,14)}, fig_args)
plot_args = sc.mergedicts({'lw':3, 'alpha':0.7}, plot_args)
scatter_args = sc.mergedicts({'s':70, 'marker':'s'}, scatter_args)
axis_args = sc.mergedicts({'left':0.1, 'bottom':0.05, 'right':0.9, 'top':0.97, 'wspace':0.2, 'hspace':0.25}, axis_args)
legend_args = sc.mergedicts({'loc': 'best'}, legend_args)
fig = pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.rcParams['font.size'] = font_size
if font_family:
pl.rcParams['font.family'] = font_family
res = self.results # Shorten since heavily used
# Plot everything
n_rows = np.ceil(len(to_plot)/n_cols) # Number of subplot rows to have
for p,title,keylabels in to_plot.enumitems():
if p == 0:
ax = pl.subplot(n_rows, n_cols, p+1)
else:
ax = pl.subplot(n_rows, n_cols, p + 1, sharex=ax)
if log_scale:
if isinstance(log_scale, list):
if title in log_scale:
ax.set_yscale('log')
else:
ax.set_yscale('log')
for key in keylabels:
label = res[key].name
this_color = res[key].color
y = res[key].values
pl.plot(res['t'], y, label=label, **plot_args, c=this_color)
if self.data is not None and key in self.data:
data_t = (self.data.index-self['start_day'])/np.timedelta64(1,'D') # Convert from data date to model output index based on model start date
pl.scatter(data_t, self.data[key], c=[this_color], **scatter_args)
if self.data is not None and len(self.data):
pl.scatter(pl.nan, pl.nan, c=[(0,0,0)], label='Data', **scatter_args)
pl.legend(**legend_args)
pl.grid(use_grid)
sc.setylim()
if use_commaticks:
sc.commaticks()
pl.title(title)
# Optionally reset tick marks (useful for e.g. plotting weeks/months)
if interval:
xmin,xmax = ax.get_xlim()
ax.set_xticks(pl.arange(xmin, xmax+1, interval))
# Set xticks as dates
if as_dates:
@ticker.FuncFormatter
def date_formatter(x, pos):
return (self['start_day'] + dt.timedelta(days=x)).strftime('%b-%d')
ax.xaxis.set_major_formatter(date_formatter)
if not interval:
ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
# Plot interventions
for intervention in self['interventions']:
intervention.plot(self, ax)
# Ensure the figure actually renders or saves
if do_save:
if fig_path is None: # No figpath provided - see whether do_save is a figpath
if isinstance(do_save, str) :
fig_path = do_save # It's a string, assume it's a filename
else:
fig_path = 'covasim.png' # Just give it a default name
fig_path = sc.makefilepath(fig_path) # Ensure it's valid, including creating the folder
pl.savefig(fig_path)
if do_show:
pl.show()
else:
pl.close(fig)
return fig
def fitnoise(pool):
allIntensities = 10 - pool['allIntensities']
allResponses = pool['allResponses'] # reverse the response
ntrial = pool['ntrial']
# curve fitting and plotting for each condition
res = {}
# print('label: ' + 'centre, ' + 'std, ' + 'ssq')
fig, axes = plt.subplots(1, len(pool['allIntensities']), figsize=(16, 6))
fig.suptitle(str(ntrial) + 'trials')
for idx, label in enumerate(allResponses.index):
res[label] = {}
res[label]['intensities'] = allIntensities[label]
res[label]['responses'] = allResponses[label]
# plt.scatter(res[label]['intensities'], res[label]['responses'])
res[label]['combinedInten'], res[label]['combinedResp'], res[label]['combinedN'] = \
data.functionFromStaircase(res[label]['intensities'],
res[label]['responses'],
bins=10) # bin data and fit to PF
# plt.scatter(res[label]['combinedInten'], res[label]['combinedResp'])
res[label]['sems'] = [
1.0 / (n / sum(res[label]['combinedN']))
for n in res[label]['combinedN']
] # sems is defined as 1/weight in Psychopy
guess = [5, 0.5]
# if label == 'hue_1p' or label == 'hue_2p':
# print('detected')
# guess = [8, 0.5]
res[label]['fit'] = FitCumNormal(
res[label]['combinedInten'],
res[label]['combinedResp'],
sems=res[label]['sems'],
guess=None,
expectedMin=0.5,
lapse=0.01) # customized cumulative Gaussian
# print(label + ':' + str(res[label]['fit'].params) + ', ' + str(
# res[label]['fit'].ssq)) # a list with [centre, sd] for the Gaussian distribution forming the cumulative
res[label]['thresh'] = res[label]['fit'].inverse(0.75) # threshold
this_res = res[label]
# print(label + ':' + str(this_res['fit'].params) + ', ' + str(
# this_res['fit'].ssq)) # a list with [centre, sd] for the Gaussian distribution forming the cumulative
ax = axes.flatten()[idx]
fontsize = 8
for inten, resp, se in zip(
this_res['combinedInten'], this_res['combinedResp'],
this_res['sems']): # plot combined data points
ax.plot(inten, resp, '.', alpha=0.5, markersize=100 / se)
smoothResp = pylab.arange(0.01, 0.96, .02)
smoothInt = this_res['fit'].inverse(smoothResp)
# smoothInt = pylab.arange(0, 6.0, 0.05)
# smoothResp = this_res['fit'].eval(smoothInt)
ax.plot(smoothInt, smoothResp, '-') # plot fitted curve
ax.plot([this_res['thresh'], this_res['thresh']], [0, 0.75],
'--',
color='grey')
ax.plot([0, this_res['thresh']], [0.75, 0.75], '--', color='grey')
ssq = np.round(this_res['fit'].ssq, decimals=3) # sum-squared error
ax.text(3.5, 0.55, 'ssq = ' + str(ssq), fontsize=fontsize)
ax.set_title(label + ' ' + 'threshold = %0.3f' % this_res['thresh'],
fontsize=fontsize)
ax.set_ylim([0.5, 1])
# ax.set_xlim([0, 6])
ax.tick_params(axis='both', which='major', labelsize=fontsize - 2)
ax.set_xlabel('level of consistence (10 - std)')
ax.set_ylabel('correctness')
# plt.setp(axes[:], xlabel='hue angle')
# plt.setp(axes[:, 0], ylabel='correctness')
plt.show()
def pyprops(datafile,
fluxfile,
assignfile,
root,
assignfile2=None,
montecarlo=0,
doplots=False):
import sys
gitpaths = ['/Users/remy/local/github/pyprops/']
for gitpath in gitpaths:
if not gitpath in sys.path:
sys.path.insert(0, gitpath)
print("importing modules and data")
import astropy.io.fits as fits
from astropy.table import Table
from measure_moments import measure_moments
from extrap import extrap
from deconvolve_gauss import deconvolve_gauss
from ellfit import ellfit
from add_noise_to_cube import add_noise_to_cube
import pylab as pl
import numpy as np
pl.ion()
datacube = fits.getdata(datafile)
s = datacube.shape
if len(s) == 4:
if s[0] == 1:
# cube has the trailing stokes axis which turns into a leading degenerate axis in python
datacube = datacube[0]
fluxmap = fits.getdata(fluxfile)
hdr = fits.getheader(datafile)
bmaj = hdr['bmaj'] # degrees
bmin = hdr['bmin'] # degrees
bpa = hdr['bpa'] * np.pi / 180 # ->rad
from astropy import wcs
w = wcs.WCS(hdr)
# pix=pl.sqrt(-pl.det(w.celestial.pixel_scale_matrix)) # degrees
pix = np.absolute(w.wcs.get_cdelt()[0]) # degrees
bmaj_pix = bmaj / pix
bmin_pix = bmin / pix
bm_pix = [bmaj_pix, bmin_pix, bpa]
import time
x = time.localtime()
datestr = str(x.tm_year) + ("%02i" % x.tm_mon) + ("%02i" % x.tm_mday)
import pickle
from cube_to_moments import cube_to_moments
assigncube = fits.getdata(assignfile)
moments, mcmoments = cube_to_moments(datacube,
assigncube,
montecarlo=montecarlo,
bm_pix=bm_pix,
fluxmap=fluxmap)
moments['posang'] = moments['posang'] % np.pi
moments['de_posang'] = moments['de_posang'] % np.pi
pickle.dump([moments, bm_pix, mcmoments], open(root + ".pyprops.pkl",
"wb"))
if assignfile2:
assigncube2 = fits.getdata(assignfile2)
moments2, mcmoments2 = cube_to_moments(datacube,
assigncube2,
montecarlo=montecarlo,
bm_pix=bm_pix,
fluxmap=fluxmap)
moments2['posang'] = moments2['posang'] % np.pi
moments2['de_posang'] = moments2['de_posang'] % np.pi
pickle.dump([moments2, bm_pix, mcmoments2],
open(root2 + "pyprops.pkl", "wb"))
#######################################################################
# diagnostic plots
if doplots:
pl.clf()
ellrad = np.sqrt(moments['halfmax_ell_maj'] *
moments['halfmax_ell_min'])
pl.plot(ellrad, moments['mom2v'], '.')
if assignfile2:
ellrad2 = np.sqrt(moments2['halfmax_ell_maj'] *
moments2['halfmax_ell_min'])
pl.plot(ellrad2, moments2['mom2v'], '.')
pl.xlabel("size")
pl.ylabel("linewidth")
pl.xscale("log")
pl.yscale("log")
pl.savefig(root + ".pyprops.sizeline.png")
if doplots:
pl.clf()
u = np.argsort(moments['flux'])[::-1]
pl.plot((moments['flux'][u]), pl.arange(len(u)))
if assignfile2:
u2 = np.argsort(moments2['flux'])[::-1]
pl.plot((moments2['flux'][u2]), np.arange(len(u2)))
pl.yscale("log")
pl.xscale("log")
pl.xlabel("flux")
pl.xlim(pl.xlim()[::-1])
pl.ylabel("cumulative number")
pl.savefig(root + ".pyprops.cumfluxdist.png")
if doplots and montecarlo > 0:
pl.clf()
fnu = 0.5 * (moments['fnu_maxintchan'] + moments['fnu_maxchan'])
delfnu = np.absolute(moments['fnu_maxintchan'] -
moments['fnu_maxchan'])
dfnu = 0.5 * np.sqrt(moments['dfnu_maxchan']**2 +
moments['dfnu_maxintchan']**2)
z = np.where(delfnu > dfnu)[0]
if len(z) > 0:
dfnu[z] = delfnu[z]
pl.errorbar(fnu,
moments['flux'],
xerr=dfnu,
yerr=moments['dflux'],
fmt='.')
pl.xlabel("I [Bunit * vpix]")
pl.ylabel("F [Bunit * pix^2 * vpix")
pl.xscale("log")
pl.yscale("log")
pl.xlim(.1, 100)
pl.ylim(.5, 500)
pl.plot(pl.xlim(), pl.array(pl.xlim()) * 3)
pl.savefig(root + ".pyprops.I_F.png")
# size comparisons
if doplots and montecarlo > 0:
pl.clf()
bmarea = bmaj_pix * bmin_pix * np.pi / 4 # not a beam "volume"
area1 = moments['de_mom2maj'] * moments[
'de_mom2min'] * 2.354**2 * np.pi / 4 / bmarea
darea1 = area1 * np.sqrt(
(moments['dde_mom2maj'] / moments['de_mom2maj'])**2 +
(moments['dde_mom2min'] / moments['de_mom2min'])**2)
area2 = moments['de_halfmax_ell_maj'] * moments[
'de_halfmax_ell_min'] * np.pi / 4 / bmarea
darea2 = area2 * np.sqrt((moments['dde_halfmax_ell_maj'] /
moments['de_halfmax_ell_maj'])**2 +
(moments['dde_halfmax_ell_min'] /
moments['de_halfmax_ell_min'])**2)
pl.errorbar(moments['flux'],
area1,
xerr=moments['dflux'],
yerr=darea1,
fmt='.',
label="mom2")
pl.errorbar(moments['flux'],
area2,
xerr=moments['dflux'],
yerr=darea2,
fmt='.',
label="ellfit")
pl.ylabel("area [beams]")
pl.xlabel("F [Bunit * pix^2 * vpix]")
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
pl.xscale("log")
pl.savefig(root + ".pyprops.F_area.png")
bmarea = bmaj_pix * bmin_pix * np.pi / 4 # not a beam "volume"
area1 = moments['mom2maj'] * moments[
'mom2min'] * 2.354**2 * np.pi / 4 / bmarea
darea1 = area1 * np.sqrt(
(moments['dmom2maj'] / moments['mom2maj'])**2 +
(moments['dmom2min'] / moments['mom2min'])**2)
de_area1 = moments['de_mom2maj'] * moments[
'de_mom2min'] * 2.354**2 * np.pi / 4 / bmarea
dde_area1 = de_area1 * np.sqrt(
(moments['dde_mom2maj'] / moments['de_mom2maj'])**2 +
(moments['dde_mom2min'] / moments['de_mom2min'])**2)
area2 = moments['halfmax_ell_maj'] * moments[
'halfmax_ell_min'] * np.pi / 4 / bmarea
darea2 = area2 * np.sqrt(
(moments['dhalfmax_ell_maj'] / moments['halfmax_ell_maj'])**2 +
(moments['dhalfmax_ell_min'] / moments['halfmax_ell_min'])**2)
de_area2 = moments['de_halfmax_ell_maj'] * moments[
'de_halfmax_ell_min'] * np.pi / 4 / bmarea
dde_area2 = de_area2 * np.sqrt((moments['dde_halfmax_ell_maj'] /
moments['de_halfmax_ell_maj'])**2 +
(moments['dde_halfmax_ell_min'] /
moments['de_halfmax_ell_min'])**2)
pl.clf()
pl.subplot(211)
pl.errorbar(area1,
de_area1,
xerr=darea1,
yerr=dde_area1,
fmt='.',
label="success")
z = np.where(np.isnan(de_area1))[0]
pl.errorbar(area1[z],
area1[z],
xerr=darea1[z],
fmt='.',
label="failed")
z = np.where(de_area1 == 0)[0]
pl.errorbar(area1[z], area1[z], xerr=darea1[z], fmt='.', label="ptsrc")
pl.ylabel("area [beams, deconv]")
pl.xlabel("area [beams, meas]")
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
pl.subplot(212)
pl.errorbar(area2,
de_area2,
xerr=darea2,
yerr=dde_area2,
fmt='.',
label="success")
z = np.where(np.isnan(de_area2))[0]
pl.errorbar(area2[z],
area2[z],
xerr=darea2[z],
fmt='.',
label="failed")
z = np.where(de_area2 == 0)[0]
pl.errorbar(area2[z], area2[z], xerr=darea2[z], fmt='.', label="ptsrc")
pl.ylabel("area [beams, deconv]")
pl.xlabel("area [beams, meas]")
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
#pl.xscale("log")
pl.savefig(root + ".pyprops.area_dearea.png")
raterr = area2 / area1
draterr = raterr * np.sqrt((darea2 / area2)**2 + (darea1 / area1)**2)
pl.clf()
pl.errorbar(area2, raterr, xerr=darea2, yerr=draterr, fmt='.')
pl.xlabel("ell area")
pl.ylabel("ell area / mom2 area")
pl.savefig(root + ".pyprops.ellarea_momarea.png")
# fluxes and integrated spectra:
if doplots and montecarlo > 0:
ratiofnu = moments['fnu_maxintchan'] / moments['fnu_maxchan']
dratio = ratiofnu * np.sqrt(
(moments['dfnu_maxchan'] / moments['fnu_maxchan'])**2 +
(moments['dfnu_maxintchan'] / moments['fnu_maxintchan'])**2)
difffnu = moments['fnu_maxintchan'] - moments['fnu_maxchan']
ddiff = np.sqrt(moments['dfnu_maxchan']**2 +
moments['dfnu_maxintchan']**2)
fnu = 0.5 * (moments['fnu_maxintchan'] + moments['fnu_maxchan'])
pl.clf()
z = np.where(difffnu > 0)[0]
pl.errorbar(moments['fnu_maxchan'][z], (difffnu / fnu)[z],
xerr=moments['dfnu_maxchan'][z],
yerr=(ddiff / fnu)[z],
fmt='.')
pl.xlabel("Fnu @max [Bunit * pix^2]")
pl.ylabel("(Fnu @maxint - Fnu @max)/Fnu")
pl.xscale("log")
pl.xlim(.3, 40)
pl.ylim(-.3, 1.5)
pl.savefig("pyprops." + datestr + ".F_maxint_v_max.png")
# convergence of errors with MC iterations:
if doplots and montecarlo > 100:
pl.clf()
k = 'max'
k = 'de_mom2min'
if montecarlo > 100:
pl.plot(moments[k], moments['d10' + k], '.', label='10')
pl.plot(moments[k], moments['d30' + k], '.', label='30')
pl.plot(moments[k], moments['d100' + k], '.', label='100')
pl.plot(moments[k], moments['d' + k], '.', label=str(montecarlo))
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
pl.xlabel(k)
pl.ylabel("d" + k)
if k == 'max':
pl.plot(pl.xlim(), [0.003, 0.003], 'k')
pl.plot(pl.xlim(), [0.005, 0.005], 'k', linestyle="dashed")
pl.xlim(0, 0.35)
if montecarlo > 100:
pl.savefig(root + ".pyprops." + k + ".mciters.png")
else:
pl.savefig(root + ".pyprops." + k + ".png")
if doplots:
# previously, we did
# if maj<bmaj*1.1: set de_maj,de_min to bmaj/2, bmin/2
# elif min<bmin*1.1: de_maj=pl.sqrt(maj**2-bmaj**2), de_min=bmin/2
pl.clf()
pl.plot(moments['halfmax_ell_maj'], moments['halfmax_ell_min'], '.')
pl.xlabel("measured major ell @halfmax")
pl.ylabel("measured minor ell @halfmax")
pl.plot([bm_pix[0], pl.xlim()[1]], [bm_pix[1], bm_pix[1]],
'k',
linestyle='dotted')
pl.plot([bm_pix[0], bm_pix[0]], [bm_pix[1], pl.ylim()[1]],
'k',
linestyle='dotted')
# if its something half the beamsize, convolved with the beam,
# it'll end up with size=pl.sqrt(1+.5**2)=1.12*bm
pl.plot([bm_pix[0] * 1.12, pl.xlim()[1]],
[bm_pix[1] * 1.12, bm_pix[1] * 1.12],
'k',
linestyle='dotted')
pl.plot([bm_pix[0] * 1.12, bm_pix[0] * 1.12],
[bm_pix[1] * 1.12, pl.ylim()[1]],
'k',
linestyle='dotted')
z = np.where(np.isnan(moments['de_halfmax_ell_maj']))[0]
pl.plot(moments['halfmax_ell_maj'][z],
moments['halfmax_ell_min'][z],
's',
label='dec=nan')
z = np.where((moments['de_halfmax_ell_min'] == 0) *
(moments['de_halfmax_ell_maj'] > 0))[0]
pl.plot(moments['halfmax_ell_maj'][z],
moments['halfmax_ell_min'][z],
'cd',
label='dec min=0')
z = np.where((moments['de_halfmax_ell_min'] == 0) *
(moments['de_halfmax_ell_maj'] == 0))[0]
pl.plot(moments['halfmax_ell_maj'][z],
moments['halfmax_ell_min'][z],
'r*',
label='dec both=0')
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
pl.savefig(root + ".pyprops.measured_ellipses.png")
pl.clf()
pl.plot(moments['posang'], moments['halfmax_ell_min'], '.')
pl.xlabel("posang")
pl.ylabel("measured minor ell @halfmax")
pl.plot([bm_pix[2], bm_pix[2]], pl.ylim(), 'k', linestyle='dashed')
pl.plot(pl.xlim(), [bm_pix[1], bm_pix[1]], 'k', linestyle='dotted')
pl.plot(pl.xlim(), [bm_pix[1] * 1.12, bm_pix[1] * 1.12],
'k',
linestyle='dotted')
z = np.where(np.isnan(moments['de_halfmax_ell_maj']))[0]
pl.plot(moments['posang'][z],
moments['halfmax_ell_min'][z],
's',
label='dec=nan')
z = np.where((moments['de_halfmax_ell_min'] == 0) *
(moments['de_halfmax_ell_maj'] > 0))[0]
pl.plot(moments['posang'][z],
moments['halfmax_ell_min'][z],
'cd',
label='dec min=0')
z = np.where((moments['de_halfmax_ell_min'] == 0) *
(moments['de_halfmax_ell_maj'] == 0))[0]
pl.plot(moments['posang'][z],
moments['halfmax_ell_min'][z],
'r*',
label='dec both=0')
pl.legend(loc="best", prop={"size": 10}, numpoints=1)
pl.savefig(root + ".pyprops.measured_ellipses_angles.png")
p = moments['posang'].copy()
dp = moments['de_posang'].copy()
p = (p - bm_pix[2] + np.pi / 2) % np.pi + bm_pix[2] - np.pi / 2
dp = (dp - bm_pix[2] + np.pi / 2) % np.pi + bm_pix[2] - np.pi / 2
pl.clf()
pl.plot(p, dp, '.')
pl.plot([bm_pix[2], bm_pix[2]], pl.ylim(), 'k')
pl.plot(pl.xlim(), [bm_pix[2], bm_pix[2]], 'k')
pl.xlabel("meas posang")
pl.ylabel("deconv posang")
pl.plot(pl.xlim(), [bm_pix[2] + np.pi / 2, bm_pix[2] + np.pi / 2],
'k',
linestyle='dotted')
pl.savefig(root + ".pyprops.dec_ellipses_angles.png")
p.rc('ytick', labelsize=TickSize)
genMeans = p.genfromtxt("GenotypeEnvelMean.dat")
genSTD = p.genfromtxt("GenotypeEnvelSTD.dat")
GenerData = p.genfromtxt("GeneralData.dat")
l = re.split(" ", ln.getline("ModelParams.dat", 7))
xResolution = float(l[6])
l = re.split(" ", ln.getline("ModelParams.dat", 6))
T = float(l[6])
print "Turbulence level = ", T
xSize = 2.0 / xResolution
if (genMeans.shape[1] != xSize):
print "ERROR: Env space size does not match genMeans space size!",
print "Check them."
exit()
x = p.arange(-1.0, 1.0, xResolution)
# -- trimmig rows
fullPlot = GenerData[:, 0:2]
lastRow = genMeans[-1, :]
genMeans = genMeans[::everyOtherRow]
genMeans = p.vstack([genMeans, lastRow])
lastRow = genSTD[-1, :]
genSTD = genSTD[::everyOtherRow]
genSTD = p.vstack([genSTD, lastRow])
lastRow = GenerData[-1, :]
GenerData = GenerData[::everyOtherRow]
GenerData = p.vstack([GenerData, lastRow])
# -- trimmed
fig = p.figure(1, figsize=(15, 9))
sizes = pylab.array([vm.size for vm in vm_data.values()])
if not all(sizes == sizes[0]):
#if not pcsim_data.shape == nest_data.shape == neuron_data.shape:
errmsg = "Data has different lengths. " + ", ".join([
"%s: %s" % (simulator, vm.shape) for simulator, vm in vm_data.items()
])
errmsg += "Trimming to the length of the shortest."
warnings.warn(errmsg)
new_length = min([vm.shape[0] for vm in vm_data.values()])
for simulator in vm_data:
vm_data[simulator] = vm_data[simulator][:new_length, :]
for simulator in gsyn_data:
gsyn_data[simulator] = gsyn_data[simulator][:new_length, :]
t = dt * pylab.arange(vm_data[vm_data.keys()[0]].shape[0])
for label, vm in vm_data.items():
pylab.plot(t, vm[:, 0], label=label)
pylab.legend()
pylab.title(example)
pylab.xlabel("Time (ms)")
pylab.ylabel("Vm (mV)")
pylab.savefig("Results/%s.png" % example)
if gsyn_data:
pylab.figure(2)
for label, gsyn in gsyn_data.items():
pylab.plot(t, gsyn[:, 0], label="%s (exc)" % label)
pylab.plot(t, gsyn[:, 1], label="%s (inh)" % label)
# Get info about dataset from header of .v file
exec(
get_header("Results/VAbenchmark_%s_exc_%s_np%d.v" %
(benchmark, simulator, num_nodes)))
# Plot membrane potential trace
allvdata = pylab.load("Results/VAbenchmark_%s_exc_%s_np%d.v" %
(benchmark, simulator, num_nodes),
comments='#')
cell_ids = allvdata[:, 1].astype(int)
allvdata = allvdata[:, 0]
sortmap = pylab.argsort(cell_ids, kind='mergesort')
cell_ids = pylab.take(cell_ids, sortmap)
allvdata = pylab.take(allvdata, sortmap)
for i in 0, 1:
tdata = pylab.arange(0, (n + 1) * dt, dt)
vdata = allvdata.compress(cell_ids == i)
vdata = pylab.where(vdata >= v_thresh - 0.05, 0.0,
vdata) # add fake APs for plotting
if len(tdata) > len(vdata):
print "Warning. Shortening tdata from %d to %d elements (%s)" % (
len(tdata), len(vdata), simulator)
tdata = tdata[0:len(vdata)]
assert len(tdata) == len(
vdata), "%d != %d (%s)" % (len(tdata), len(vdata), simulator)
subplot.plot(tdata, vdata)
# Plot spike rasters
subplot = figure.add_axes([x, y0 + 2 * dy, w, h])
exc_spikedata = signals.loadSpikeList(
"Results/VAbenchmark_%s_exc_%s_np%d.ras" %
def solve(param):
jsp = JSP(param['data'])
lib = param['solver']
verb = param['verbose']
model = JSP_Model(jsp)
if lib == 'Mistral':
solver = model.load(lib, model.sequence)
else:
solver = model.load(lib)
solver.setHeuristic('Scheduling', 'Promise')
solver.setVerbosity(param['verbose'] - 1)
(lb, ub) = (jsp.lower_bound() - 1, jsp.upper_bound())
(lb, ub, best) = dichotomic_search(model, solver, lb, ub, verb,
param['tcutoff'])
if verb > 0:
print('start branch & bound in [' + str(lb) + '..' + str(ub) + ']')
if lb + 1 < ub:
(lb, ub, best) = branch_and_bound(model, lib, lb, ub, verb, best)
## finalize the solution (tasks)
solver.reset()
if lib == 'Mistral':
for disjunct in model.sequence:
solver.post(disjunct == best[disjunct])
solver.propagate()
for task in model.tasks:
solver.post(task == task.get_min())
solver.propagate()
best = solver.get_solution()
schedule = [[-1] * ub for job in jsp.job]
index = 0
for machine in jsp.machine:
index += 1
for m in machine:
start = model.Jobs[m].get_value()
for i in range(model.Jobs[m].duration):
schedule[m[0]][start + i] = index
out = ''
if solver.is_sat():
out = str(schedule)
out += ('\nNodes: ' + str(solver.getNodes()))
return out
if param['print'] == 'yes':
###############################################
############# Output (Matplotlib) #############
###############################################
print('\n display schedule')
width = 60
print_schedule = []
for row in schedule:
print_schedule.extend([row] * width)
import pylab
pylab.yticks(
pylab.arange(int(width / 2), width * (len(jsp.job) + 1), width),
['job' + str(i + 1) for i in range(len(jsp.job))])
cmap = pylab.cm.get_cmap('jet', len(jsp.machine) + 1)
cmap.set_under(color='w')
pylab.imshow(print_schedule,
cmap=cmap,
interpolation='nearest',
vmin=0)
#pylab.colorbar()
pylab.show()
import pylab as pl
sr = 44100.
f1 = 3000
f2 = 200.
N = 10
t = pl.arange(0, sr)
w = 2 * pl.pi * f1 * t / sr
o = 2 * pl.pi * f2 * t / sr
a = 0.5
sinw = pl.sin(w)
cosmo = pl.cos((N + 1) * o)
cosno = pl.cos(N * o)
den = 1. - 2 * a * pl.cos(o) + a * a
scal = pl.sqrt(1. - a * a / (1 + a * a * -2 * a**(2 * N + 2)))
s = sinw * (1 - a * a - (2 * a**(N + 1)) * (cosmo - a * cosno)) / den
s *= scal
pl.figure(figsize=(8, 5))
pl.subplot(211)
pl.plot(t[0:440] / sr, s[0:440] / max(abs(s)), 'k-')
pl.xlabel("time (s)")
sig = s
N = 32768
start = 0
x = pl.arange(0, N / 2)
bins = x * sr / N
def cf(d):
return pylab.arange(1.0, float(len(d)) + 1.0) / float(len(d))
GRC_norm_model = pandas.read_csv('/home/j/Project/dismod/gbd/data/applications-fruit_GRC_norm_model.csv')
ISL_model = pandas.read_csv('/home/j/Project/dismod/gbd/data/applications-fruit_ISL_model.csv')
ISL_log_model = pandas.read_csv('/home/j/Project/dismod/gbd/data/applications-fruit_ISL_log_model.csv')
ISL_norm_model = pandas.read_csv('/home/j/Project/dismod/gbd/data/applications-fruit_ISL_norm_model.csv')
GRC_data = pandas.DataFrame(pl.zeros((len(GRC_model.columns),3)), columns=['0','1','2'])
ISL_data = pandas.DataFrame(pl.zeros((len(ISL_model.columns),3)), columns=['0','1','2'])
# age standardizing
import scikits.statsmodels.lib.io as pd
aw_file = pandas.DataFrame(pd.genfromdta('/home/j/Project/COD/envelope/data/age_weights_final.dta'))
for a in range(len(aw_file['age'])): aw_file['age'][a] = round(aw_file['age'][a],3)
aw_file = pandas.DataFrame(aw_file['weight'], index=list(aw_file['age']))
age_weights = pl.vstack((pl.arange(101), pl.zeros(101))).transpose()
for a in range(101):
if a == 0: age_weights[a,1] = aw_file.ix[0.0] + aw_file.ix[0.01] + aw_file.ix[0.1]
elif (a>=1) & (a<5): age_weights[a,1] = aw_file.ix[1.0]/4
elif (a>=5) & (a<10): age_weights[a,1] = aw_file.ix[5.0]/5
elif (a>=10) & (a<15): age_weights[a,1] = aw_file.ix[10.0]/5
elif (a>=15) & (a<20): age_weights[a,1] = aw_file.ix[15.0]/5
elif (a>=20) & (a<25): age_weights[a,1] = aw_file.ix[20.0]/5
elif (a>=25) & (a<30): age_weights[a,1] = aw_file.ix[25.0]/5
elif (a>=30) & (a<35): age_weights[a,1] = aw_file.ix[30.0]/5
elif (a>=35) & (a<40): age_weights[a,1] = aw_file.ix[35.0]/5
elif (a>=40) & (a<45): age_weights[a,1] = aw_file.ix[40.0]/5
elif (a>=45) & (a<50): age_weights[a,1] = aw_file.ix[45.0]/5
elif (a>=50) & (a<55): age_weights[a,1] = aw_file.ix[50.0]/5
elif (a>=55) & (a<60): age_weights[a,1] = aw_file.ix[55.0]/5
elif (a>=60) & (a<65): age_weights[a,1] = aw_file.ix[60.0]/5
import pylab as pl pi = pl.pi T = 1000 t = pl.arange(0, T) x = 0.51 - 0.49 * pl.cos(2 * pi * t / T) y = pl.sin(2 * pi * 10 * t / T) X = pl.rfft(y * x) pl.plot(pl.irfft(X)) pl.show()
def main():
args = parseCMD()
fileNames = args.fileName
Order = args.order
Smoothness = args.smoothness
matplotlib.rcParams['text.usetex'] = True
linestyles = ['-', '--']
colors = [(1, 0, 0), (0, 1, 0), (1, 1, 0), (1, 0, 1), (0, 0, 0), (0, 0, 1),
(1, 0.5, 0.5), (0.5, 0.5, 0.5), (0.5, 0, 0), (1, 0.5, 0)]
# Set up plots with shared axes
fig1 = pl.figure(1, figsize=(12, 12))
pl.connect('key_press_event', kevent.press)
ax1 = pl.subplot(311)
pl.setp(ax1.get_xticklabels(), visible=False)
pl.ylabel(r'$\mathrm{E\ [K]}$', fontsize=20)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.tick_params(axis='both', which='minor', labelsize=20)
pl.grid()
ax2 = pl.subplot(312, sharex=ax1)
pl.setp(ax2.get_xticklabels(), visible=False)
pl.ylabel(r'$\mathrm{C_V}$', fontsize=20)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.tick_params(axis='both', which='minor', labelsize=20)
pl.grid()
ax3 = pl.subplot(313, sharex=ax1)
pl.xlabel(r'$\mathrm{T\ [K]}$', fontsize=20)
pl.ylabel(r'${\mathrm{Entropy}}$', fontsize=20)
ax3.tick_params(axis='both', which='major', labelsize=20)
ax3.tick_params(axis='both', which='minor', labelsize=20)
pl.grid()
# do spline fitting for each data file
for k, fileName in enumerate(fileNames):
data = np.loadtxt(fileName)
ATemps = data[:, 0]
#ATemps = data[:,0] # Bohdan's test data
AEners = data[:, 1]
#AEners = data[:,5]
AEnerE = data[:, 2]
#AEnerE = data[:,6]
weights = 1 / AEnerE
ax1.errorbar(ATemps,
AEners,
AEnerE,
linestyle='None',
linewidth=2,
marker='D',
markeredgewidth=0.8,
color=colors[k],
markeredgecolor=colors[k],
markerfacecolor='white',
markersize=6,
capsize=3)
lowTRegL = 0
lowTRegH = len(ATemps[ATemps < args.LT * 1.0001]) - 1
highTRegL = lowTRegH + 1
highTRegH = len(ATemps) - 1
# Spline fit to T>Tc
SEners, SdEners, HSpline = SplineFitToData(ATemps[highTRegL:highTRegH],
AEners[highTRegL:highTRegH],
AEnerE[highTRegL:highTRegH],
Smoothness)
# Spline fit to T<Tc
#SLE,SLcv,LSpline = SplineFitToData(ATemps[lowTRegL:lowTRegH],
# AEners[lowTRegL:lowTRegH],AEnerE[lowTRegL:lowTRegH],Smoothness)
# Polynomial fit to T<Tc
SegRange = range(lowTRegL, lowTRegH)
FitCoefs, DerCoefs = PolyFitToData(ATemps[SegRange], AEners[SegRange],
AEnerE[SegRange], [Order])
polyf = np.poly1d(FitCoefs[0])
Dpolyf = np.poly1d(DerCoefs[0])
interT = IntersectionPoint(HSpline, Dpolyf, ATemps[lowTRegH],
ATemps[highTRegL])
#interT2S = IntersectionPoint2S(LSpline,HSpline,ATemps[lowTRegH],
# ATemps[highTRegL])
stepT = 0.01
newLTs = pl.arange(ATemps[0], interT, stepT)
highLTs = pl.arange(interT, ATemps[highTRegH], stepT)
lowLTs = pl.arange(ATemps[0], interT, stepT)
ax1.plot(newLTs,
polyf(newLTs),
linestyle=linestyles[0],
linewidth=0.5,
marker='None',
color=colors[k],
markerfacecolor=colors[k],
label='Pol. %s order fit for T: %s - %s K' %
(Order, ATemps[0], ATemps[lowTRegH]))
ax1.plot(highLTs,
interpolate.splev(highLTs, HSpline, der=0),
linestyle=':',
linewidth=1.5,
color=colors[k],
markerfacecolor='white',
label='Spline fit for T: %s - %s K; smoothness: %s' %
(ATemps[highTRegL], ATemps[highTRegH], Smoothness))
#ax1.plot(lowLTs, interpolate.splev(lowLTs,LSpline,der=0),
# linestyle=linestyles[0],linewidth=0.5,
# marker='None',color=colors[k],markerfacecolor=colors[k],
# label='Spline fit' )
ax2.plot(newLTs,
Dpolyf(newLTs),
linestyle=linestyles[0],
linewidth=2,
marker='None',
color=colors[k],
markerfacecolor=colors[k])
ax2.plot(highLTs,
interpolate.splev(highLTs, HSpline, der=1),
linestyle='-',
linewidth=2,
color=colors[k],
markerfacecolor='white')
#ax2.plot(lowLTs, interpolate.splev(lowLTs,LSpline,der=1),
# linestyle=':', linewidth=1.5,
# color=colors[k],markerfacecolor='white')
ticks = ax2.xaxis.get_major_ticks()
np.insert(ticks, 3, 2)
zeroT = ZeroPoint(Dpolyf, interT)
ax2.xaxis.set_ticks(
np.insert(ax2.xaxis.get_majorticklocs(), 0, round(zeroT, 2)))
ax2.text(interT,
interpolate.splev(interT, HSpline, der=1),
r'${\mathrm{T_c = %s}} $' % (round(interT, 3)),
fontsize=18)
ax2.plot([interT, interT],
[0, interpolate.splev(interT, HSpline, der=1)],
color='black',
linestyle='--',
linewidth=0.3)
ax2.plot([zeroT, zeroT], [
ax2.yaxis.get_majorticklocs()[0],
ax2.yaxis.get_majorticklocs()[-1]
],
color='black',
linewidth=0.3,
linestyle='--')
ax2.plot([
ax2.xaxis.get_majorticklocs()[0],
ax2.xaxis.get_majorticklocs()[-1]
], [0, 0],
color='black',
linewidth=0.3,
linestyle='--')
#if zeroT < 1:
# newETs = pl.arange(1,ATemps[-1], stepT)
# Entrops= [Entropy(T,interT,1,Dpolyf,HSpline) for T in newETs]
#else:
newETs = pl.arange(zeroT, ATemps[highTRegH], stepT)
print 'Zero T:', zeroT
print 'Critical T', interT
Entrops = [Entropy(T, interT, zeroT, Dpolyf, HSpline) for T in newETs]
ax3.plot(newETs, Entrops, linestyle='-', linewidth=2, color=colors[k])
lgd1 = ax1.legend(loc='best')
lgd3 = ax3.legend(loc='best')
lgd1.draggable(state=True)
pl.tight_layout()
pl.savefig('Helium_Critical_E_Cv_S.pdf', format='pdf', bbox_inches='tight')
pl.show()
dt2 = 0.0005 # time step for the synthetics
dt3 = 0.0005 # time step for the synthetics
dt_ref = 0.0002 # time step for the DWT
x0 = 11000.0 # position of the first receiver
dx = 25.0 # spacing between two receivers
NL = 12 # number of levels for DWT
fv0 = numpy.fromfile(dirname0 + '/' + filename0, 'f4')
fv1 = numpy.fromfile(dirname1 + '/OUTPUT_FILES/' + filename1, 'f4')
fv2 = numpy.fromfile(dirname2 + '/OUTPUT_FILES/' + filename2, 'f4')
fv3 = numpy.fromfile(dirname3 + '/OUTPUT_FILES/' + filename3, 'f4')
v0 = numpy.reshape(fv0, (nr, nt0))
v1 = numpy.reshape(fv1, (nr, nt1))
v2 = numpy.reshape(fv2, (nr, nt2))
v3 = numpy.reshape(fv3, (nr, nt3))
t0 = pylab.arange(0.0, nt0 * dt0, dt0)
t1 = pylab.arange(0.0, nt1 * dt1, dt1)
t2 = pylab.arange(0.0, nt2 * dt2, dt2)
t3 = pylab.arange(0.0, nt3 * dt3, dt3)
t_ref = pylab.arange(0.0, nt_ref * dt_ref, dt_ref)
f_ref = pylab.arange(0.0, (0.5 * (nt_ref - 1) + 1) / ((nt_ref - 1) * dt_ref),
1.0 / ((nt_ref - 1) * dt_ref))
# Loop on receivers
for i in range(0, nr):
seism0 = v0[i, :]
seism1 = -50.0 * v1[i, :]
seism2 = -50.0 * v2[i, :]
seism3 = -50.0 * v3[i, :]
f0 = interpolate.interp1d(t0, seism0)
f1 = interpolate.interp1d(t1, seism1)
def feature_plot(self,
feature=None,
normalize=False,
dbscale=False,
norm=False,
interp='nearest',
labels=True,
nofig=False,
**kwargs):
"""
::
Plot the given feature, default is self.feature,
returns an error if feature not extracted
Inputs:
feature - the feature to plot self.feature
features are extracted in the following hierarchy:
stft->cqft->mfcc->[lcqft,hcqft]->chroma,
if a later feature was extracted, then an earlier feature can be plotted
normalize - column-wise normalization ['alse]
dbscale - transform linear power to decibels: 20*log10(X) [False]
norm - make columns unit norm [False]
interp - how to interpolate values in the plot ['nearest']
labels - whether to plot labels
nofig - whether to make new figure
**kwargs - keyword arguments to imshow or plot
"""
feature = self._check_feature_params(
)['feature'] if feature is None else feature
# check plots
if feature == 'stft':
if not self._have_stft:
print("Error: must extract STFT first")
else:
feature_plot(P.absolute(self.STFT),
normalize,
dbscale,
norm,
title_string="STFT",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
self._feature_plot_yticks(
float(self.sample_rate) / (self.nfft))
P.xlabel('Time (secs)')
P.ylabel('Frequency (Hz)')
elif feature == 'power':
if not self._have_power:
print("Error: must extract POWER first")
else:
if not nofig:
P.figure()
P.plot(feature_scale(self.POWER, normalize, dbscale) / 20.0)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
P.title("Power")
P.xlabel("Time (s)")
P.ylabel("Power (dB)")
elif feature == 'cqft':
if not self._have_cqft:
print("Error: must extract CQFT first")
else:
feature_plot(self.CQFT,
normalize,
dbscale,
norm,
title_string="CQFT",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
# self._feature_plot_yticks(1.)
P.yticks(P.arange(0, self._cqtN, self.nbpo),
(self.lo *
2**(P.arange(0, self._cqtN, self.nbpo) /
self.nbpo)).round(1))
P.xlabel('Time (secs)')
P.ylabel('Frequency (Hz)')
elif feature == 'mfcc':
if not self._have_mfcc:
print("Error: must extract MFCC first")
else:
fp = self._check_feature_params()
X = self.MFCC[self.lcoef:self.lcoef + self.ncoef, :]
feature_plot(X,
normalize,
dbscale,
norm,
title_string="MFCC",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
P.xlabel('Time (secs)')
P.ylabel('Cepstral coeffient')
elif feature == 'lcqft':
if not self._have_lcqft:
print("Error: must extract LCQFT first")
else:
feature_plot(self.LCQFT,
normalize,
dbscale,
norm,
title_string="LCQFT",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
P.yticks(P.arange(0, self._cqtN, self.nbpo),
(self.lo *
2**(P.arange(0, self._cqtN, self.nbpo) /
self.nbpo)).round(1))
elif feature == 'hcqft':
if not self._have_hcqft:
print("Error: must extract HCQFT first")
else:
feature_plot(self.HCQFT,
normalize,
dbscale,
norm,
title_string="HCQFT",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
P.yticks(P.arange(0, self._cqtN, self.nbpo),
(self.lo *
2**(P.arange(0, self._cqtN, self.nbpo) /
self.nbpo)).round(1))
P.xlabel('Time (secs)')
P.ylabel('Frequency (Hz)')
elif feature == 'chroma' or feature == 'hchroma':
if not self._have_chroma:
print("Error: must extract CHROMA first")
else:
feature_plot(self.CHROMA,
normalize,
dbscale,
norm,
title_string="CHROMA",
interp=interp,
nofig=nofig,
**kwargs)
if labels:
self._feature_plot_xticks(
float(self.nhop) / float(self.sample_rate))
P.yticks(P.arange(0, self.nbpo, self.nbpo / 12.), [
'C', 'C#', 'D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A',
'Bb', 'B'
])
P.xlabel('Time (secs)')
P.ylabel('Pitch Class')
else:
print("Unrecognized feature, skipping plot: ", feature)
def do_solve(maxIter, solver, display, test_interval, test_iters):
# SET PLOTS DATA
train_loss_C = zeros(maxIter / display)
train_top1 = zeros(maxIter / display)
# train_top5 = zeros(maxIter/display)
val_loss_C = zeros(maxIter / test_interval)
val_top1 = zeros(maxIter / test_interval)
# val_top5 = zeros(maxIter/test_interval)
it_axes = (arange(maxIter) * display) + display
it_val_axes = (arange(maxIter) * test_interval) + test_interval
_, ax1 = subplots()
ax2 = ax1.twinx()
ax1.set_xlabel('iteration')
ax1.set_ylabel('train loss C (r), val loss C (y)')
ax2.set_ylabel(
'train TOP1 (b), val TOP1 (g)') # train TOP-5 (c), val TOP-5 (k)')
ax2.set_autoscaley_on(False)
ax2.set_ylim([0, 1])
lossC = np.zeros(maxIter)
acc1 = np.zeros(maxIter)
acc5 = np.zeros(maxIter)
#RUN TRAINING
for it in range(niter):
#st = time.time()
solver.step(1) # run a single SGD step in Caffepy()
#en = time.time()
#print "Time step: " + str((en-st))
#PLOT
if it % display == 0 or it + 1 == niter:
lossC[it] = solver.net.blobs['loss3/loss3'].data.copy()
acc1[it] = solver.net.blobs['loss3/top-1'].data.copy()
# acc5[it] = solver.net.blobs['loss3/top-5'].data.copy()
loss_disp = 'loss3C= ' + str(lossC[it]) + ' top-1= ' + str(
acc1[it])
print '%3d) %s' % (it, loss_disp)
train_loss_C[it / display] = lossC[it]
train_top1[it / display] = acc1[it]
# train_top5[it / display] = acc5[it]
ax1.plot(it_axes[0:it / display], train_loss_C[0:it / display],
'r')
ax2.plot(it_axes[0:it / display], train_top1[0:it / display], 'b')
# ax2.plot(it_axes[0:it / display], train_top5[0:it / display], 'c')
#ax1.set_ylim([0, 10])
plt.title(training_id)
plt.ion()
plt.grid(True)
plt.show()
plt.pause(0.001)
#VALIDATE
if it % test_interval == 0 and it > 0:
loss_val_C = 0
top1_val = 0
# top5_val = 0
for i in range(test_iters):
solver.test_nets[0].forward()
loss_val_C += solver.test_nets[0].blobs['loss3/loss3'].data
top1_val += solver.test_nets[0].blobs['loss3/top-1'].data
# top5_val += solver.test_nets[0].blobs['loss3/top-5'].data
loss_val_C /= test_iters
top1_val /= test_iters
# top5_val /= test_iters
print("Val loss C: {:.3f}".format(loss_val_C))
val_loss_C[it / test_interval - 1] = loss_val_C
val_top1[it / test_interval - 1] = top1_val
# val_top5[it / test_interval - 1] = top5_val
ax1.plot(it_val_axes[0:it / test_interval],
val_loss_C[0:it / test_interval], 'y')
ax2.plot(it_val_axes[0:it / test_interval],
val_top1[0:it / test_interval], 'g')
# ax2.plot(it_val_axes[0:it / test_interval], val_top5[0:it / test_interval], 'k')
#ax1.set_ylim([0, 10])
plt.title(training_id)
plt.ion()
plt.grid(True)
plt.show()
plt.pause(0.001)
title = '../../../datasets/EmotionDataset/models/training/' + training_id + str(
it) + '.png' # Save graph to disk
savefig(title, bbox_inches='tight')
return
3,
4,
], [1, 2, 3, 4]),
axis=[60, 104, .3, 4.5]),
]
for i, params in enumerate(param_list):
ax = pl.subplot(2, 2, i + 1)
if params['type'] == 'pf':
dismod3.graphics.plot_data_bars(best_model.get_data('csmr'),
color='grey')
else:
dismod3.graphics.plot_data_bars(best_model.get_data(params['type']),
color='grey')
pl.plot(pl.arange(101),
pl.array(output['model_' + params['type']]),
'k-',
linewidth=2,
label='Posterior Mean')
pl.plot(pl.arange(101),
pl.array(output['model_' + params['type'] + 'l']),
'k-',
linewidth=1,
label='95% HPD interval')
pl.plot(pl.arange(101),
pl.array(output['model_' + params['type'] + 'u']),
'k-',
linewidth=1)
pl.xlabel('Age (years)')
exit
if __name__=="__main__":
"""
var and es granularity adjustment as a function of the correlation
"""
#percentile = 0.99
#num_of_credits = 40
#default_probability = 0.01
#loss_given_default = 1.0
if len(sys.argv) != 5 and len(sys.argv) != 8:
usage()
else:
if len(sys.argv) == 5:
percentile, num_of_credits, pd, lgd = sys.argv[1:5]
x_min, x_max, x_step = (0.01, 1.00, 0.01)
else:
percentile, num_of_credits, pd, lgd, x_min, x_max, x_step = sys.argv[1:8]
import pylab
x_list = pylab.arange(float(x_min), float(x_max), float(x_step))
var_values, es_values = main(x_list, float(percentile), int(num_of_credits),
float(pd), float(lgd))
pylab.suptitle('First Order Granularity Adjustment', fontsize=12)
pylab.xlabel("correlation")
pylab.ylabel("function values")
pylab.plot(x_list, var_values, 'b--', label='var adjustment')
pylab.plot(x_list, es_values, 'g-' , label='es adjustment')
pylab.legend()
pylab.show()
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 14 18:16:40 2020
@author: Marie
"""
from pylab import arange, sqrt
x = arange(1, 11)
y = sqrt(x)
print(x)
print(y)
def kvadrer(x):
return x**2
z = kvadrer(x)
print(z)
for y_verdi in y:
print(f"{y_verdi:.2f}")
print(y[0])
def plotContig(contig, tracks, options, plot_legend=False,
extra_features=None):
"""plot data for contig."""
if extra_features and "figure" in extra_features:
figure = pylab.figure(**enterParams(extra_features['figure'],
(("figsize", lambda x: list(map(int, x.split(",")))),
("dpi", int),
"facecolor",
"edgecolor")))
else:
figure = pylab.figure()
if plot_legend:
if extra_features and "legend" in extra_features:
legend = pylab.figure(**enterParams(extra_features['legend'],
(("figsize", lambda x: list(map(int, x.split(",")))),
("dpi", int),
"facecolor",
"edgecolor")))
else:
legend = pylab.figure()
lx = legend.add_axes((0.1, 0.1, 0.9, 0.9))
lx.set_title("Legend")
lx.set_axis_off()
else:
legend = None
axes = []
ywidth = 0.8 / float(len(tracks))
yoffset = 0.1
axprops = {}
ayprops = {}
min_x, max_x = 1000000000, 0
for track in tracks:
if track.mData:
min_x = min(min_x, min([(x[0]) for x in track.mData[contig]]))
max_x = max(max_x, max([(x[1]) for x in track.mData[contig]]))
# make sure that we use the same view for all axes
axprops['xlim'] = (min_x, max_x)
nplotted = 0
for track in tracks:
labels, plots = [], []
ax = figure.add_axes(
(0.1, track.mYOffset, 0.8, track.mYWidth), **axprops)
if 'sharex' not in axprops:
ax.xaxis.set_major_formatter(
matplotlib.ticker.FuncFormatter(formatGenomicCoordinate))
ax.set_xlabel("Genomic position / Mb")
axprops['sharex'] = ax
else:
pylab.setp(ax.get_xticklabels(), visible=False)
ax.set_ylabel(track.mTitle, **ayprops)
if track.mSubTracks:
# compute maximum extent of y-axis in all of subtracks
first = True
for tt in track.mSubTracks:
if first:
min_y = min([x[2] for x in tt.mData[contig]])
max_y = max([x[2] for x in tt.mData[contig]])
first = False
else:
min_y = min(
min_y, min([x[2] for x in tt.mData[contig]]))
max_y = max(
max_y, max([x[2] for x in tt.mData[contig]]))
nsubplotted = 0
for tt in track.mSubTracks:
plot = addPlot(ax, tt, contig, nplotted,
nsubplotted, len(track.mSubTracks),
min_y, max_y)
nsubplotted += 1
plots.append(plot)
if hasattr(tt, "legend"):
labels.append(tt.legend)
else:
labels.append(tt.mTitle)
else:
min_y = min([x[2] for x in track.mData[contig]])
max_y = max([x[2] for x in track.mData[contig]])
if options.global_colours:
n_for_colour = nplotted
else:
n_for_colour = 0
plot = addPlot(ax, track, contig, n_for_colour)
plots.append(plot)
if hasattr(track, "legend"):
lables.append(track.legend)
else:
labels.append(track.mTitle)
# reduce number of ticks by 2
old_ticks = ax.get_yticks()
step_size = (old_ticks[1] - old_ticks[0]) * 2
new_ticks = list(pylab.arange(old_ticks[0], old_ticks[-1], step_size))
ax.set_yticks(new_ticks)
if nplotted % 2 == 0:
ax.yaxis.set_ticks_position("right")
ax.yaxis.set_label_position("right")
else:
ax.yaxis.set_ticks_position("left")
ax.yaxis.set_label_position("left")
# deal with extra_features
if extra_features:
for key, config in list(extra_features.items()):
if key == "vlines":
if contig not in config.mData:
continue
lines = []
for start, end, value in config.mData[contig]:
lines.append(start)
lines.append(end)
ax.vlines(
lines, min_y, max_y, **enterParams(config, ("colour", "linewidth")))
nplotted += 1
if legend:
lx = legend.add_axes((0.1, track.mYOffset, 0.8, track.mYWidth))
pylab.setp(lx.get_xticklabels(), visible=False)
lx.set_xticks([])
lx.set_yticks([])
lx.text(0.4, 0.5, track.mTitle)
lx.legend(plots, labels, 'center left')
if hasattr(track, "text"):
lx.text(0.6, 0.2, track.text, size="smaller",
clip_on=True)
ax.set_title(contig)
# has to be set at the end, otherwise re-scaled?
ax.set_xlim(min_x, max_x)
return figure, legend
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from supreme.lib import pywt
import time
import pylab
#r = pywfdb.Record('d:/mitdb/101')
#data = r.read(0, 5050, 1024)
data1 = pylab.array(range(1, 400) + range(398, 600) + range(601, 1024)) / 1024.
data2 = pylab.arange(612 - 80, 20, -0.5) / 250.
data2 = pylab.sin(40 * pylab.log(data2)) * pylab.sign((pylab.log(data2)))
from sample_data import ecg as data3
mode = pywt.MODES.sp1
DWT = 1
def plot(data, w, title):
w = pywt.Wavelet(w)
a = data
ca = []
cd = []
if DWT:
for i in xrange(5):
(a, d) = pywt.dwt(a, w, mode)
ca.append(a)
cd.append(d)
else:
for a, d in pywt.swt(data, w, 5):
'kernel': 0.5,
'weights': {
'uniform': {
'min': 0.5,
'max': 2.0
}
},
'delays': 1.0
}
topo.ConnectLayers(a, b, conndict)
# first, clear existing figure, get current figure
pylab.clf()
fig = pylab.gcf()
# plot targets of two source neurons into same figure, with mask
for src_pos in [[15, 15], [0, 0]]:
# obtain node id for center
src = topo.GetElement(a, src_pos)
topo.PlotTargets(src, b, mask=conndict['mask'], fig=fig)
# beautify
pylab.axes().set_xticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.axes().set_yticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.grid(True)
pylab.axis([-2.0, 2.0, -2.0, 2.0])
pylab.axes().set_aspect('equal', 'box')
pylab.title('Connection targets')
# pylab.savefig('conncon_targets.pdf')
center_plot = 0, 0
for i in range(0, 100):
points_plot.append([0, 0])
# Get default camera window size
ret, frame = cap.read()
Height, Width = frame.shape[:2]
frame_count = 0
# Initialize time varaible
then = time.time()
#####################################Plot Start#######################################
# Initialize plot parameters
xAchse = pylab.arange(0, 100, 1)
yAchse = pylab.array([0] * 100)
fig = pylab.figure(1)
ax = fig.add_subplot(121)
ay = fig.add_subplot(122)
ax.grid(True)
ay.grid(True)
ax.set_title("X vs Time")
ay.set_title("Y vs Time")
ax.set_xlabel("Time")
ax.set_ylabel("X Value")
ay.set_xlabel("Time")
ay.set_ylabel("Y Value")
ax.axis([0, 100, -1000, 1000])
#Cs = m.arange(18.5, 19.15, .005)
#C2s = m.arange(-100.0, -103, -.2)
#def Fit(C, beta, C2):
#return C * m.exp(beta * t) + C2
#BEST 1430.84646506 (18.644999999999971, 6.8319999999999856, -102.0)
# Blowup model
#Cs = m.arange(193, 199, .125)
#betas = m.arange(5.5, 5.95, .0125)
#C2s = m.arange(1.32, 1.38, .00125)
#def Fit(C, beta, C2):
#return C / (C2 - t)**beta
#BEST 78624.1696635 (198.625, 5.8875000000000055, 1.3637499999999991)
# Exponential model
betas = m.arange(7.2, 8, .001)
Cs = m.arange(9, 11, .0025)
C2s = [0]
def Fit(C, beta, C2):
return C * m.exp(beta * t)
#BEST 16703.0662184 (10.000000000000313, 7.6779999999999031, 0)
def Error(C, beta, C2):
fit = Fit(C, beta, C2) #beta / (C - t)
error = (fit - sup)
return m.dot(error, error)
def createKerdenSOMPlots(self):
apFile.removeFilePattern(
os.path.join(self.params['rundir'],
self.spectraTemporalFilesMask + ".png"))
#logging.debug('Inside createKerdenSOMPlots')
apDisplay.printMsg("Create Plots")
codeVectorFileName = os.path.join(self.params['rundir'],
self.timestamp + '.cod')
f1 = open(codeVectorFileName, 'r')
#Read first line, I need number of harmonic plus size
line = f1.readline()
splitline = line.split()
numberHarmonic = int(splitline[0])
xx = int(splitline[2])
yy = int(splitline[3])
numberCodevectors = xx * yy
xmin = int(self.params['spectralowharmonic'])
xmax = int(self.params['spectrahighharmonic'])
#array with x and y values
xvalues = []
#fill x array with harmonic number
for colNo in pylab.arange(xmin, xmax + 1):
xvalues.append(colNo)
#figure size in inches
pylab.rcParams['figure.figsize'] = 1, 1
pylab.rc("lines", linewidth=1.5)
pylab.rc(('xtick', 'ytick', 'axes'), labelsize=4.0) #fontsize
#read code vector
#compute y maximum
ymax = 0.
ymin = 150.
for rowNo in range(numberCodevectors):
line = f1.readline()
splitLine = line.split()
for colNo in pylab.arange(numberHarmonic):
yval = float(splitLine[colNo])
if ymax < yval:
ymax = yval
if ymin > yval:
ymin = yval
f1.close()
ymax = math.ceil(ymax) + 1
print "ymax ", ymax
ymin = max(math.floor(ymin) - 1, 0)
print "ymin ", ymin
f1 = open(codeVectorFileName, 'r')
#skip first line
line = f1.readline()
for rowNo in range(numberCodevectors):
line = f1.readline()
splitLine = line.split()
#print line
data = []
for colNo in pylab.arange(numberHarmonic):
data.append(float(splitLine[colNo]))
print xvalues
print data
#clear previous plot
pylab.clf()
lines = pylab.plot(xvalues, data)
pylab.ylim(ymin, ymax)
pylab.xlim(xmin, xmax)
pylab.xlabel('fold symmetry')
pylab.ylabel('likelihood')
pylab.xticks(xvalues)
basefilename = os.path.join(
self.params['rundir'],
self.spectraTemporalFiles % (int(rowNo / yy), rowNo % xx))
pylab.savefig(basefilename + ".png", dpi=256, format='png')
