def __call__(self, **params):
p = ParamOverrides(self, params)
fig = plt.figure(figsize=(5, 5))
# This one-liner works in Octave, but in matplotlib it
# results in lines that are all connected across rows and columns,
# so here we plot each line separately:
# plt.plot(x,y,"k-",transpose(x),transpose(y),"k-")
# Here, the "k-" means plot in black using solid lines;
# see matplotlib for more info.
isint = plt.isinteractive() # Temporarily make non-interactive for
# plotting
plt.ioff()
for r, c in zip(p.y[::p.skip], p.x[::p.skip]):
plt.plot(c, r, "k-")
for r, c in zip(np.transpose(p.y)[::p.skip],np.transpose(p.x)[::p.skip]):
plt.plot(c, r, "k-")
# Force last line avoid leaving cells open
if p.skip != 1:
plt.plot(p.x[-1], p.y[-1], "k-")
plt.plot(np.transpose(p.x)[-1], np.transpose(p.y)[-1], "k-")
plt.xlabel('x')
plt.ylabel('y')
# Currently sets the input range arbitrarily; should presumably figure out
# what the actual possible range is for this simulation (which would presumably
# be the maximum size of any GeneratorSheet?).
plt.axis(p.axis)
if isint: plt.ion()
self._generate_figure(p)
return fig
def plot_movie(x,t,u,NN,**kwargs):
"""
x | Spatial coordinate vector, len (Nx)
t | Temporal coordinate vector, len (Nt)
data | The data in matrix form, len (Nt,Nx)
NN | integer giving interval of plotting.
"""
N,M = shape(u)
clf()
ion()
line, = plot(x,u[0,:],'k-',label=parse_kwargs('label','$Wave$ $equation:$ $BW$',**kwargs),
linewidth=2)
plot(x,u[0,:],color='gray',alpha=0.75)
line.axes.set_ylim(parse_kwargs('miny',-1,**kwargs),parse_kwargs('maxy',1,**kwargs))
legend(loc=0)
xlabel(parse_kwargs('xlabel','$x$',**kwargs))
ylabel(parse_kwargs('ylabel','$u$',**kwargs))
grid(True)
for i in range(0,N,N/NN):
title('$t={}$'.format(t[i]))
line.set_ydata(u[i,:])
plot(x,u[i,:],color='gray',alpha=0.2)
xlim([min(x),max(x)])
draw()
line.set_ydata(u[-1,:])
title('$t={}$'.format(t[-1]))
def main():
# u_t = u_xx
dx = .1
dt = .5
timesteps = 100000
x = np.arange(-10,10,dx)
m = len(x)
kappa = 50
# u''(x) = (u(x + dx) - 2u(x) + u(x - dx)) / dx^2
ones = lambda x: np.ones(x)
A = np.diag(ones(m-1),k=-1) + -2*np.diag(ones(m)) + np.diag(ones(m-1),k=1)
A *= kappa*(dx**2)
U = 0*ones(m)
for i in xrange(0,m):
if x[i] > -2 and x[i] < 2:
U[i] = 1
p.ion()
lines, = p.plot(x,U)
for n in xrange(0,timesteps):
U = U + dt*dudt(U,A)
if n % 100 == 0:
lines.set_ydata(U)
p.draw()
p.show()
def plot_average(filenames, save_plot=True, show_plot=False, dpi=100):
''' Plot Signal average from a list of averaged files. '''
fname = get_files_from_list(filenames)
# plot averages
pl.ioff() # switch off (interactive) plot visualisation
factor = 1e15
for fnavg in fname:
name = fnavg[0:len(fnavg) - 4]
basename = os.path.splitext(os.path.basename(name))[0]
print fnavg
# mne.read_evokeds provides a list or a single evoked based on condition.
# here we assume only one evoked is returned (requires further handling)
avg = mne.read_evokeds(fnavg)[0]
ymin, ymax = avg.data.min(), avg.data.max()
ymin *= factor * 1.1
ymax *= factor * 1.1
fig = pl.figure(basename, figsize=(10, 8), dpi=100)
pl.clf()
pl.ylim([ymin, ymax])
pl.xlim([avg.times.min(), avg.times.max()])
pl.plot(avg.times, avg.data.T * factor, color='black')
pl.title(basename)
# save figure
fnfig = os.path.splitext(fnavg)[0] + '.png'
pl.savefig(fnfig, dpi=dpi)
pl.ion() # switch on (interactive) plot visualisation
def count_barcodes(dataset, VERBOSE=0):
'''Count the abundance of each barcode'''
# Get the read filenames
data_filenames = get_raw_read_files(dataset)
datafile = data_filenames['adapter']
# Count the abundance of each barcode
bc_counts = defaultdict(int)
rc = 0
with open(datafile, 'r') as infile:
for read in SeqIO.parse(infile, 'fastq'):
bc_counts[read.seq.tostring()] += 1
rc += 1
if rc == maxreads:
break
print sorted(bc_counts.items(), key=lambda x:x[1], reverse=True)[:20]
# Plot results
plt.figure()
ax=plt.subplot(111)
plt.plot(range(1,len(bc_counts)+1), sorted(bc_counts.values(), reverse=True))
ax.set_yscale('log')
ax.set_xscale('log')
plt.xlabel('barcode rank')
plt.ylabel('abundance')
plt.ion()
plt.show()
def label_data(prefix, size=100, savename=None):
from glob import glob
from os.path import basename
from PIL import Image
from os.path import isfile
if savename==None: savename=labelpath+'label_'+prefix+'.txt'
# We want to avoid labeling an image twice, so keep track
# of what we've labeled in previous labeling sessions.
if isfile(savename):
fileout = open(savename,'r')
already_seen = [line.split(',')[0] for line in fileout]
fileout.close()
else: already_seen = []
# Now reopen the file for appending.
fileout = open(savename,'a')
pl.ion()
pl.figure(1,figsize=(9,9))
files = glob(imgpath+prefix+'*.png')
for file in np.random.choice(files, size=size, replace=False):
if basename(file) in already_seen: continue
pl.clf()
pl.subplot(1,1,1)
pl.imshow(np.array(Image.open(file)))
pl.title(file)
pl.axis('off')
pl.draw()
label = get_one_char()
if label=='q': break
fileout.write(basename(file)+','+label+'\n')
print file,label
fileout.close()
return
def kmr_test_plot(data, k, end_thresh):
from matplotlib.pylab import ion, figure, draw, ioff, show, plot, cla
ion()
fig = figure()
ax = fig.add_subplot(111)
ax.grid(True)
# get k centroids
kmr = kmeans.kmeans_runner(k, end_thresh)
kmr.init_data(data)
print kmr.centroids
plot(data[:,0], data[:,1], 'o')
i = 0
while kmr.stop_flag is False:
kmr.iterate()
#print kmr.centroids, kmr.itr_count
plot(kmr.centroids[:, 0], kmr.centroids[:, 1], 'sr')
time.sleep(.2)
draw()
i += 1
print "N Iterations: %d" % (i)
plot(kmr.centroids[:, 0], kmr.centroids[:, 1], 'g^', linewidth=3)
ioff()
show()
print kmr.itr_count, kmr.centroids
def plot(y, function):
""" Show an animation of Poincare plot.
--- arguments ---
y: A list of initial values
function: function which is argument of Runge-Kutta solver
"""
h = dt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)
plt.ion()
for i in range(nmax + 1):
for j in range(nstep):
rk4 = RK.RK4(function)
y = rk4.solve(y, j * h, h)
# -pi <= theta <= pi
while y[0] > pi:
y[0] = y[0] - 2 * pi
while y[0] < -pi:
y[0] = y[0] + 2 * pi
if ntransient <= i < nmax: # <-- draw the poincare plots
plt.scatter(y[0], y[1], s=2.0, marker='o', color='blue')
time_text.set_text('n = %d' % i)
plt.draw()
if i == nmax: # <-- to stop the interactive mode
plt.ioff()
plt.scatter(y[0], y[1], s=2.0, marker='o', color='blue')
time_text.set_text('n = %d' % i)
plt.show()
def rf_sub_size(Input, net_size, resolution):
size = np.zeros((net_size*net_size,))
coms = np.zeros((net_size*net_size, 2))
R = np.zeros((net_size*resolution, net_size*resolution))
Z = np.zeros((resolution, resolution))
scale = 1.0/(resolution**2)
X, Y = np.meshgrid(np.arange(Z.shape[0]), np.arange(Z.shape[1]))
count_roi, count_nroi, count_tot = 0, 0, 0
plt.ion()
for i in range(net_size):
for j in range(net_size):
Z = np.abs(Input[i, j, ...] * (Input[i, j, ...] > 0) +
0.0 * (Input[i, j, ...] < 0))
R[i*resolution:(i+1)*resolution, j*resolution:(j+1)*resolution] = Z
size[i*net_size+j] = area_of_activation(Z) * scale
d = np.unravel_index(Z.argmax(), Z.shape)
Z = np.roll(Z, Z.shape[0]/2-d[0], axis=0)
Z = np.roll(Z, Z.shape[1]/2-d[1], axis=1)
xc = ((Z*Y).sum()/Z.sum() - Z.shape[0]/2 + d[0])/float(Z.shape[0])
yc = ((Z*X).sum()/Z.sum() - Z.shape[1]/2 + d[1])/float(Z.shape[1])
coms[i*net_size+j, 0] = (xc+1.0) % 1
coms[i*net_size+j, 1] = (yc+1.0) % 1
return coms, R, size
def do_fit_trans(self):
f = self.fit_trans
p0 = self.p0.copy()
# p0[:-3]=0.0
#print p0,"call d0"
#d0 = f(p0)
if 0:
for i in range(len(p0)):
pt = p0.copy()
pt[i] = p0[i]+0.001
from matplotlib.pylab import clf, ion, title, plot, show
print pt - p0, pt
ion()
clf()
title("%d"%(i))
plot(d0, f(pt) - d0, ",")
show()
if raw_input()[0] != " ":
break
res = scipy.optimize.leastsq( f, p0, full_output=1)
pfit, pcov, info, errmsg, ier = res
if ier not in [1,2,3,4]:
print s_sq, ier, errmsg
else:
residu = f(pfit)
s_sq = (residu**2).sum()/(len(residu)-len(p0))
ubi = pfit[:9].reshape(3,3)
print ("%.6f "*6)%(indexing.ubitocellpars(ubi))
print pfit[9:12]
self.g = grain( ubi, pfit[9:12].copy())
def example():
# pl.ioff()
pl.ion()
import pandas
from numpy.random import uniform
n = 25
m = pandas.DataFrame({
'x': uniform(-1, 1, size=n),
'y': uniform(-1, 1, size=n),
'size': uniform(3, 10, size=n) ** 2,
'color': uniform(0, 1, size=n),
})
# test using a custom index
m['silly_index'] = ['%sth' % x for x in range(n)]
m.set_index('silly_index', drop=True, inplace=True, verify_integrity=True)
print m
ax = pl.subplot(111)
plt = ax.scatter(m['x'], m['y'])
b = LassoBrowser(m, ax)
print b.idxs
#from viz.interact.pointbrowser import PointBrowser
#pb = PointBrowser(m, plot=plt)
pl.show()
ip()
def demo():
'''
Load and plot a few CIB spectra.
'''
# define ell array.
l = np.arange(100,4000)
# get dictionary of CIBxCIB spectra.
cl_cibcib = get_cl_cibcib(l)
# plot
import matplotlib.pylab as pl
pl.ion()
lw=2
fs=18
leg = []
pl.clf()
for band in ['857','545','353']:
pl.semilogy(l, cl_cibcib['545',band],linewidth=lw)
leg.append('545 x '+band)
pl.xlabel(r'$\ell$',fontsize=fs)
pl.ylabel(r'$C_\ell^{TT, CIB} [\mu K^2]$',fontsize=fs)
pl.ylim(5e-2,6e3)
pl.legend(leg, fontsize=fs)
def plot_diagnostics(self):
if 0:
from matplotlib import pylab as plt
plt.ion()
plt.figure()
_phot = phot.read_fits(self.lcfn,'optimum')
with self.FigureManager('_0-aperture'):
plotting.phot.aperture(_phot)
with self.FigureManager('_1-background'):
plotting.phot.background(_phot)
if len(self.dfaper.npix.drop_duplicates()) > 1:
with self.FigureManager('_2-noise_vs_aperture_size'):
plotting.pipeline.noise_vs_aperture_size(self)
with self.FigureManager("_3-fdt_t_roll_2D"):
plotting.phot.detrend_t_roll_2D(_phot)
with self.FigureManager("_4-fdt_t_roll_2D_zoom"):
plotting.phot.detrend_t_roll_2D(_phot,zoom=True)
with self.FigureManager("_5-fdt_t_rollmed"):
plotting.phot.detrend_t_rollmed(_phot)
def eqDistribution(self, plot=True):
""" Obtain and plot the equilibrium probabilities of each macrostate
Parameters
----------
plot : bool, optional, default=True
Disable plotting of the probabilities by setting it to False
Returns
-------
eq : ndarray
An array of equilibrium probabilities of the macrostates
Examples
--------
>>> model = Model(data)
>>> model.markovModel(100, 5)
>>> model.eqDistribution()
"""
self._integrityCheck(postmsm=True)
macroeq = np.ones(self.macronum) * -1
for i in range(self.macronum):
macroeq[i] = np.sum(self.msm.stationary_distribution[self.macro_ofmicro == i])
if plot:
from matplotlib import pylab as plt
plt.ion()
plt.figure()
plt.bar(range(self.macronum), macroeq)
plt.ylabel('Equilibrium probability')
plt.xlabel('Macrostates')
plt.xticks(np.arange(0.4, self.macronum+0.4, 1), range(self.macronum))
plt.show()
return macroeq
def matrix_plot(self, matrix, figure_name='matrix_plot.pdf'):
import numpy
from matplotlib import pylab
def _blob(x,y,area,colour):
hs = numpy.sqrt(area) / 2
xcorners = numpy.array([x - hs, x + hs, x + hs, x - hs])
ycorners = numpy.array([y - hs, y - hs, y + hs, y + hs])
pylab.fill(xcorners, ycorners, colour, edgecolor=colour)
reenable = False
if pylab.isinteractive():
pylab.ioff()
pylab.clf()
maxWeight = 2**numpy.ceil(numpy.log(numpy.max(numpy.abs(matrix)))/numpy.log(2))
height, width = matrix.shape
pylab.fill(numpy.array([0,width,width,0]),numpy.array([0,0,height,height]),'white')
pylab.axis('off')
pylab.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = matrix[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2,'#0099CC')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2,'#660000')
if reenable:
pylab.ion()
pylab.savefig(figure_name)
def integral_plots(vals, new_vals, n):
"""
Parameters
-------------------------------------------------------
vals: non-normalized values of the table
new_vals: normalized values of the table
such that the maximum of each vertical
column is 1.
n: number of points in theta and omega arrays
(so the size of the 2D table is n x n)
Return
-------------------------------------------------------
Plots a density plot of the table using imshow
of the normalized table and also plots the integral
of the table along each axis (these are the integrals
of the non-normalized values)
"""
import matplotlib.gridspec as gridspec
omega_par = np.linspace(0.1,0.4,n)
theta_par = np.linspace(0.01,1.4,n)
oo = []
tt = []
# integral in left side (sum of each row)
for i in range(n):
oo.append(sum(vals[i]))
# integral in bottom side (sum of columns)
for i in range(n):
tt.append(sum(vals.T[i]))
plt.ion()
plt.figure(figsize=(11,9))
gs = gridspec.GridSpec(2, 2, width_ratios=[1,4],height_ratios=[4,1])
ax1 = plt.subplot(gs[0])
ax1.plot(oo,omega_par, linewidth=2)
plt.ylabel(r"$\Omega_{||}$", fontsize=20)
ax2 = plt.subplot(gs[1])
plt.imshow(new_vals, interpolation='nearest', origin="lower", extent=(theta_par.min(), theta_par.max(), omega_par.min(), omega_par.max()),aspect='auto')
#plt.imshow(vals, interpolation='nearest', origin="lower", extent=(theta_par.min(), theta_par.max(), omega_par.min(), omega_par.max()),aspect='auto')
plt.colorbar()
plt.title("$P(t_s) \propto t_s $, maximum normalized to 1", fontsize=15)
ax4 = plt.subplot(gs[3])
ax4.plot(theta_par,tt, linewidth=2)
plt.xlabel(r"$\theta_{||}$",fontsize=20)
def plot_pol_info_pat(problem_id, pol, times):
fig, axes = plt.subplots(nrows=len(times), ncols=1)
for idx, time in enumerate(times):
df = pd.DataFrame(pol[:, :, time])
df.plot(title='{0:s}: Whether to sell, depending on current price; time = {1:d}'.format(problem_id, time),
ax=axes[idx])
axes[idx].set_xlabel('Number of stocks in inventory')
axes[idx].set_ylabel('1->sell; 0->hold')
plt.ion()
def assignpeaks( gr, pars, colfile, tol=None, omegatol=0.2 ):
labels = np.zeros( colfile.nrows )
cyf = cyfit.cyfit(pars)
cyf.setscfc( colfile.sc, colfile.fc )
hkl = np.zeros( cyf.XL.shape, np.float)
drlv = np.zeros( colfile.nrows, np.float)
kcalc = np.zeros( cyf.XL.shape, np.float)
r_omega = np.array(colfile.omega*np.pi/180.0,np.float)
cyf.hkl( colfile.sc, colfile.fc, r_omega, gr, hkl, kcalc )
# Make integer hkl
wripaca.ih_drlv( hkl, drlv )
# Now replace omegaobs by omegacalc where this is appropriate
pre = np.eye(3).ravel()
posti = np.dot(gv_general.wedgemat(pars.get('wedge')),
gv_general.chimat(pars.get('chi'))).T.ravel()
axis = np.array([0,0,-1],np.float)
ub = np.linalg.inv(gr.ubi)
gcalc = np.dot( ub, hkl.T ).T.copy()
#print hkl
romegacalc = np.zeros( r_omega.shape, np.float)
romegaerr = np.zeros( r_omega.shape, np.float)
wripaca.omegacalcclose(gcalc,
pre,
posti,
axis,
r_omega,
romegacalc,
romegaerr,
pars.get('wavelength'),
)
# OK, now we will accept anything within omegatol
if 0:
pylab.hist(romegaerr, bins=50)
pylab.figure()
pylab.plot(r_omega, romegaerr,",")
pylab.show()
r_omega = np.where( romegaerr < omegatol*np.pi/180., romegacalc, r_omega )
cyf.hkl( colfile.sc, colfile.fc, r_omega, gr, hkl, kcalc )
drlv_omegafree = drlv.copy()
wripaca.ih_drlv( hkl, drlv_omegafree )
m = drlv < tol
sc = colfile.sc[m]
fc = colfile.fc[m]
omegaobs = colfile.omega[m].astype(np.float)
omega = colfile.omega[m].astype(np.float)
hkl = hkl[m]
print "Got",m.sum(),"peaks",
if 0:
pylab.ion()
pylab.title("tol = %f"%(tol))
pylab.hist( drlv, bins=np.arange(0,0.05,0.001))
pylab.hist( drlv_omegafree, bins=np.arange(0,0.05,0.001))
pylab.show()
raw_input("OK?")
return dummycf( sc, fc, omega, omegaobs, hkl )
def begin_draw():
pl.ion()
pl.figure( 1, figsize = ( 20, 20 ), dpi = 50 )
pl.clf()
pl.axis( "scaled" )
pl.xlim( -4, 4 )
pl.ylim( -2, 6 )
pl.plot( [d[0] for d in data], [d[1] for d in data], "b." )
for m in xrange( clusters ):
plot_gaussian( mu[m], sigma[m], "r" )
def normalized_contour_max1(ptype, n):
"""
Parameters
-------------------------------------------------------
ptype: type of the probability. It can be either
"Gauss", "one_over_ts" or "ts"
n: number of points in theta and omega arrays
Return
-------------------------------------------------------
val: non-normalized values of the 2D table.
new_val: normalized values fo the 2D table
such that the maximum value of each
vertical column is 1.
It also plots the normalized density plot.
"""
omega_par = np.linspace(0.1,0.4,n)
theta_par = np.linspace(0.01,1.4,n)
vals = np.zeros((n,n))
Nlist = []
new_vals = []
# calculating the 2D table of distribution
for i in range(n):
for j in range(n):
vals[i][j] = prob_Oparapar(5.,i, j, n, ptype)
# calculating the normalization A
for i in range(n):
xx = (np.max(vals.T[i]))
Nlist.append(1./xx)
# calculating the normalized values
for i in range(n):
xx = vals.T[i] * Nlist[i]
new_vals.append(xx)
new_vals = np.array(new_vals)
plt.ion()
plt.imshow(new_vals.T, interpolation='nearest', origin="lower", extent=(theta_par.min(), theta_par.max(), omega_par.min(), omega_par.max()),aspect='auto')
plt.xlabel(r"$\theta_{||}$",fontsize=20)
plt.ylabel(r"$\Omega_{||}$", fontsize=20)
plt.title("$p(t_s) = {0}$.formar(ptype), max of each column is 1", fontsize=15)
return vals, new_vals
def debug_eep(track, inds=None, ax=None):
if inds is None:
inds = track.iptcri[track.iptcri > 0]
if ax is None:
plt.ion()
ax = hrd(track, ax=ax)
ax = hrd(track, inds=inds, ax=ax)
annotate_plot(track, ax, logT, logL)
plt.legend()
return ax
def plot3d(self):
fsize3d = 16
plt.ion()
fig1 = plt.figure('3D plot')
ax = p3.Axes3D(fig1)
ax.set_xlabel(self.xlab, fontsize=fsize3d)
ax.set_ylabel('Time [s]', fontsize=fsize3d)
ax.plot_wireframe(self.m2g.r_new, self.m2g.t_new, self.m2g.g, linewidth=.3, rstride=1, cstride=3)
plt.draw()
def plotTimescales(self, lags=None, units='frames', errors=None, nits=None, results=False, plot=True):
""" Plot the implied timescales of MSMs of various lag times
Parameters
----------
lags : list
The lag times at which to compute the timescales. By default it spreads out 25 lag times linearly from lag
10 until the mode length of the trajectories.
units : str
The units of lag. Can be 'frames' or any time unit given as a string.
errors : errors
Calculate errors using Bayes (Refer to pyEMMA documentation)
nits : int
Number of implied timescales to calculate. Default: all
results : bool
If the method should return the calculated implied timescales
plot : bool
If the method should display the plot of implied timescales
Returns
-------
If given `results`=True this method will return the following data
its : np.ndarray
The calculated implied timescales. 2D array with dimensions (len(`lags`), `nits`)
lags : np.ndarray
A list of the lag times that were used to calculate the implied timescales
Examples
--------
>>> model = Model(data)
>>> model.plotTimescales()
>>> model.plotTimescales(lags=list(range(1,100,5)))
"""
import pyemma.plots as mplt
import pyemma.msm as msm
self._integrityCheck()
if lags is None:
lags = self._defaultLags()
else:
lags = unitconvert(units, 'frames', lags, fstep=self.data.fstep).tolist()
if nits is None:
nits = np.min((self.data.K, 20))
from htmd.config import _config
its = msm.its(self.data.St.tolist(), lags=lags, errors=errors, nits=nits, n_jobs=_config['ncpus'])
if plot:
from matplotlib import pylab as plt
plt.ion()
plt.figure()
mplt.plot_implied_timescales(its, dt=self.data.fstep, units='ns')
plt.show()
if results:
return its.get_timescales(), its.lags
def draw_sinus_move(self):
#Interactive mode ON
pylab.ion()
for n in range(25):
#next frame:
self.ylist = [math.sin (x + n / 2.0) for x in self.xlist]
#clear screen
pylab.clf()
#plot new info
pylab.plot (self.xlist, self.ylist)
#draw
pylab.draw()
def __init__(self, lock, points):
threading.Thread.__init__(self)
self.stop = threading.Event()
plt.ion()
self.points = points
self.lock = lock
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.data, = self.ax.plot([0, 0], [0, 0], "o")
self.ax.axis([0, 1024, 0, 850])
self.sleepDelay = 0.03
def plot_init(data):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_aspect('equal')
X,Y=np.meshgrid(np.linspace(0., float(DIMENSION-1),DIMENSION), np.linspace(float(DIMENSION-1),0, DIMENSION))
ax.plot_surface(X, Y, data)
plt.title(0)
plt.ylabel("give")
plt.xlabel("accept")
plt.ion()
plt.show()
return fig ,ax
def plot_pitch_matrix(pitch_matrix, title=None, xlabel=None, ylabel=None): fig, ax = plt.subplots(figsize=(21,8)) heatmap = ax.pcolor(pitch_matrix, cmap=plt.cm.Blues) plt.title(title, fontsize = 24) plt.ylabel(ylabel, fontsize = 24) plt.xlabel(xlabel, fontsize = 24) ax = plt.gca() ax.set_xlim([0, pitch_matrix.shape[1]]) ax.set_ylim([0, pitch_matrix.shape[0]]) plt.ion() plt.show()
def pltciri(self,itx,irx):
"""
plot channel impulse response interactively.
display all nodes position of Tx and Rx and choose
for which link the CIR is displayed.
Once self.pltciri(node1,node2) is called :
1) Press 't' on the displayed graph to chose the Tx
2) Press 'x' on the displayed graph to chose the Rx
3) Press Enter to display CIR betwen Tx-Rx
Parameters
----------
itx : int
node number
irx : int
node number
usage
>>> W=W2()
>>> W.pltciri(6,1)
"""
plt.ion()
fig1 = plt.figure(1)
ax=fig1.add_subplot(111)
self.ax2 = fig1.add_subplot(111)
self.L.showG(fig=fig1,graph='')
self.S.tx = RadioNode(typ='tx',name=itx)
self.S.tx.loadini(str(itx)+'.ini',rep=pstruc['DIRNETSAVE'])
self.S.rx = RadioNode(typ='rx',name=irx)
self.S.rx.loadini(str(irx)+'.ini',rep=pstruc['DIRNETSAVE'])
ax.plot(self.S.tx.position[0,:],self.S.tx.position[1,:],'ob')
ax.plot(self.S.rx.position[0,:],self.S.rx.position[1,:],'or')
plt.show()
print( '1. Press \'t\' and click to select a Tx ')
print( '2. Press \'x\' and click to select a Rx ' )
print( '3. Press Enter to see the associated CIR ' )
self.key=''
self.x1=''
self.x2=''
self.y1=''
self.y2=''
self.n1=''
self.n2=''
self.pos1=''
self.pos2=''
self.c1=[]
self.c2=[]
cid=fig1.canvas.mpl_connect('button_press_event', self.onclick)
cid=fig1.canvas.mpl_connect('key_press_event', self.on_key)
def run(pixfn, lcfn, transfn, tlimits=[-np.inf,np.inf], tex=None,
debug=False, ap_select_tlimits=None):
"""
Run the pixel decorrelation on pixel file
"""
pipe = PipelinePixDecor(
pixfn, lcfn,transfn, tlimits=tlimits, tex=None
)
pipe.print_parameters()
pipe.set_lc0('circular',10)
pipe.set_hyperparameters()
pipe.reject_outliers()
dfaper = pipe.get_dfaper_default()
dfaper = pipe.detrend_dfaper(dfaper)
dfaper_optimize = pipe.optimize_aperture()
dfaper = dfaper + dfaper_optimize
dfaper = pd.DataFrame(dfaper)
row = dfaper.loc[dfaper.noise.idxmin()].copy()
row['to_fits'] = True
row['fits_group'] = 'optimum'
dfaper = dfaper.append(row, ignore_index=True)
pipe.dfaper = dfaper
print dfaper.sort('npix')['fits_group npix noise to_fits'.split()]
pipe.to_fits(pipe.lcfn)
if 0:
from matplotlib import pylab as plt
plt.ion()
plt.figure()
import pdb;pdb.set_trace()
_phot = phot.read_fits(lcfn,'optimum')
with pipe.FigureManager('_0-aperture.png'):
plotting.phot.aperture(_phot)
with pipe.FigureManager('_1-background.png'):
plotting.phot.background(_phot)
with pipe.FigureManager('_2-noise_vs_aperture_size.png'):
plotting.pipeline.noise_vs_aperture_size(pipe)
with pipe.FigureManager("_3-fdt_t_roll_2D.png"):
plotting.phot.detrend_t_roll_2D(_phot)
with pipe.FigureManager("_4-fdt_t_roll_2D_zoom.png"):
plotting.phot.detrend_t_roll_2D(_phot,zoom=True)
with pipe.FigureManager("_5-fdt_t_rollmed.png"):
plotting.phot.detrend_t_rollmed(_phot)
def plot_init(data):
"""Initialize plot.
Args:
data: team matrix
"""
# Set figure style
sns.set_style("dark")
# Create subplot grid
fig = gridspec.GridSpec(2, 1)
# Create subplots
axarr = [plt.subplot(fig[0, 0]), plt.subplot(fig[1, 0])]
# Plot data
img = axarr[0].imshow(data, interpolation = 'nearest', cmap = plt.cm.ocean,
extent = (0.5, np.shape(data)[0] + 0.5, 0.5,
np.shape(data)[1] + 0.5))
# Display round
plt.title("Current Round:" + str(0))
# Set labels
axarr[0].set_ylabel("Give")
axarr[0].set_xlabel("Accept")
axarr[0].set_title("Distribution of Teams")
# Plot average deal data
axarr[1].plot(avg_deal_data)
# Set labels
axarr[1].set_xlim(0,rounds)
axarr[1].set_ylabel("Average Cash per Deal")
axarr[1].set_xlabel("Round Number")
# Create colorbar for strategy distribution
plt.colorbar(img, ax=axarr[0], label= "Prevalence vs. Uniform")
# Interactive mode
plt.ion()
# Changed this to use 'normal' instead of 'zoomed' since it didn't work on
# my system
mng = plt.get_current_fig_manager()
mng.window.state('normal')
# Display everything
plt.show()
return fig, axarr
def __init__(self, cells, t, solute=None, cmap=default_color_map):
"""
Creates a new HillPlot on the active figure, showing the state of each layer
- cells: The a sequence of cmf cells to use in this hill_plot. You can
use the whole project if you like
- t: Current time step. Needed to retrieve the fluxes
- solute:The solute concentration to show. If None, the wetness of the
layer will be shown
- cmap: a matplotlib colormap (see module cm) for coloring
"""
was_interactive = pylab.isinteractive()
if was_interactive:
pylab.ioff()
self.cells = cells
self.layers = cmf.node_list(chain(*[c.layers for c in cells]))
self.surfacewater = cmf.node_list(c.surfacewater for c in cells)
x_pos = [self.__x(c.x, c.y) for c in cells]
self.topline = pylab.plot(x_pos, self.__get_snow_height(), 'k-',
lw=2)[0]
self.__cells_of_layer = {}
self.polys = {}
# Get standard evaluation functions
if isinstance(solute, cmf.solute):
self.evalfunction = lambda layer: layer.conc(solute)
else:
self.evalfunction = lambda layer: layer.wetness
self.cmap = cmap
self.figure = pylab.gcf()
for i, c in enumerate(cells):
c_left = cells[i - 1] if i else c
c_right = cells[i + 1] if i < len(cells) - 1 else c
for l in c.layers:
x, z = self.__get_layer_shape(l, c, c_left, c_right)
self.polys[l.node_id], = pylab.fill(x,
z,
fc=self.cmap(
self.evalfunction(l)),
ec='none',
zorder=0)
self.__cells_of_layer[l.node_id] = (c, c_left, c_right)
layer_pos = self.layers.get_positions()
surf_pos = self.surfacewater.get_positions()
layer_f = self.layers.get_fluxes3d(t)
surf_f = self.surfacewater.get_fluxes3d(t)
scale = max(numpy.linalg.norm(surf_f.X), numpy.linalg.norm(
layer_f.X)) * 10
layer_x = self.__x(numpy.asarray(layer_pos.X),
numpy.asarray(layer_pos.Y))
surf_x = self.__x(numpy.asarray(surf_pos.X), numpy.asarray(surf_pos.Y))
self.q_sub = pylab.quiver(layer_x,
layer_pos.Z,
layer_f.X + layer_f.Y,
layer_f.Z,
scale=scale,
minlength=0.1,
pivot='middle',
zorder=1)
self.q_surf = pylab.quiver(surf_x,
surf_pos.Z,
surf_f.X + surf_f.Y,
surf_f.Z,
color='b',
scale=scale,
minlength=0.1,
pivot='middle',
zorder=1)
self.title = pylab.title(t)
if was_interactive:
pylab.draw()
pylab.ion()
def main():
plt.ion()
running_reward = 10
env.reset()
c1, c2 = [1, 1, 1, 1], [1, 1, 1, 1]
for i_episode in count(1):
state = env.reset()
feedback1 = np.zeros(state.shape)
feedback2 = np.zeros(state.shape)
for t in range(10000): # Don't infinite loop while learning
# env.render()
# Begin source coding here
# encoder:
encoder_state1 = c1 * state - feedback1
encoder_state2 = c2 * state - feedback2
stategy1 = select_strategy1(encoder_state1) # learn f_encoder
quantized_state1 = uniform_midtread_quantizer(
encoder_state1, 1 / 2**stategy1)
stategy2 = select_strategy2(encoder_state2) # learn f_encoder
quantized_state2 = uniform_midtread_quantizer(
encoder_state2, 1 / 2**stategy2)
# feedback:
feedback1 = quantized_state1 + feedback1
feedback2 = quantized_state2 + feedback2
# print(feedback)
control_state = np.stack([feedback1, feedback2]).flatten()
control_state = np.stack([state, state]).flatten()
# Controller:
action = select_action(control_state) # learn f_decoder
state, reward, done, _ = env.step(action)
# reward = reward -(abs(state[2]))
# if done:
# print(reward)
# finish source coding here
if args.render:
env.render()
rr = reward #- stategy1*0.1-stategy2*0.1
policy.rewards.append(rr)
quantizer1.rewards.append(rr)
quantizer2.rewards.append(rr)
if done:
break
running_reward = running_reward * 0.99 + t * 0.01
finish_episode()
quantizer1_finish_episode()
quantizer2_finish_episode()
episode_durations.append(t + 1)
episode_strategies.append(stategy1 + stategy2)
plot_strategies()
plot_durations() # Plot the durations
if i_episode % args.log_interval == 0:
print('Episode {}\tLast length: {:5d}\tAverage length: {:.2f}'.
format(i_episode, t, running_reward))
if running_reward > env.spec.reward_threshold: #(195)
print("Solved! Running reward is now {} and "
"the last episode runs to {} time steps!".format(
running_reward, t))
break
if i_episode % 50 == 0:
folder_path = os.path.join(
'/home/liang/PycharmProjects/SourceCoding/', 'model')
if not os.path.exists(folder_path):
os.makedirs(folder_path)
torch.save(policy.state_dict(), folder_path + '/policy_state_dict')
torch.save(quantizer1.state_dict(),
folder_path + '/quantizer1_state_dict')
torch.save(quantizer2.state_dict(),
folder_path + '/quantizer2_state_dict')
torch.save(policy.state_dict(), folder_path + '/policy_state_dict')
torch.save(quantizer1.state_dict(), folder_path + '/quantizer1_state_dict')
torch.save(quantizer2.state_dict(), folder_path + '/quantizer2_state_dict')
print('Complete')
import pickle
duration = open('duration2.pickle', 'wb')
pickle.dump(episode_durations, duration)
duration.close()
strategies = open('strategies2.pickle', 'wb')
pickle.dump(episode_strategies, strategies)
strategies.close()
env.close()
plt.ioff()
plt.show()
def correlator_online_mp(fileinput='input.txt',
dark_file='default',
mask_file='default',
plot='yes'):
global nq, n, chn, chn2, rcr, index_in_q, lag, dt, norm, nc, I_avg, I_avg2, lm1, lm2, lm1b, lm2b, ax1, ax1b, ax2, ax2b, nq, detector, ccd_img, flat_field, tot_darks, totmask, ttdata, tcalc_cum, tplot_cum, tread_cum, tI_avg, static_corrected, firstfile, tolerance, I_avgs, xnq, Mythread, l, y, v, input_info, wtotmask, totsaxs, tI_avg, q_2tcf
time1 = time.time()
p.rc('image', origin='lower')
p.rc('image', interpolation='nearest')
p.close()
print 'multiprocessor'
print 'reading input...'
input_info = get_input(fileinput)
##processing input file#####
dir = input_info['dir']
dir_dark = input_info['dark dir']
if dir_dark == 'none':
dir_dark = dir
file_prefix = input_info['file_prefix']
ext = input_info['file_suffix']
# New version has capabilities of reading also .gz files
if ext == '.edf.gz':
dataread = EdfFile.EdfGzipFile
else:
dataread = EdfFile.EdfFile
firstfile = int(input_info['n_first_image'])
lastfile = int(input_info['n_last_image']) + 1
firstdark = input_info['n_first_dark']
if firstdark.lower() != 'none':
firstdark = int(input_info['n_first_dark'])
lastdark = int(input_info['n_last_dark']) + 1
geometry = input_info['geometry'].lower()
tolerance = float32(float(input_info['tolerance']))
avgt = input_info['lag time'].lower()
if avgt == 'auto':
lagt = []
lagt1 = 0
for k in xrange(firstfile + 40, firstfile + 100):
filename = file_name(dir + file_prefix, ext, k)
while os.path.exists(filename) is False:
sys.stdout.write(50 * '\x08')
sys.stdout.write('file ' + filename + 'still not ready')
sys.stdout.flush()
#rint 'file ' ,filename, 'still not ready'
time.sleep(10)
f = dataread(filename)
params = f.GetHeader(0)
if input_info['detector'] == 'medipix':
lagt2 = float32(float(params['time_of_frame']))
lagt.append(lagt2 - lagt1)
lagt1 = lagt2
# if (input_info['detector']=='princeton' or input_info['detector']=='andor'):
else:
counters = params['counter_mne'].split(' ')
lagt_ind = counters.index('ccdtavg')
values = params['counter_pos'].split(' ')
lagt.append(float32(float(values[lagt_ind])))
del lagt[0]
dt = average(array(lagt, dtype=float32))
print 'lag time =', dt
else:
dt = float32(float(input_info['lag time']))
q_2tcf = (input_info['q for TRC']).lower()
if q_2tcf != 'none':
q_2tcf = int(q_2tcf)
out_dir = get_dir(input_info['output directory'])
out_prefix = get_prefix(input_info['output filename prefix'])
out_tot = out_dir + out_prefix
##end processing input file#####
firstname = dir + file_name(file_prefix, ext, firstfile)
f = dataread(firstname)
ccd_info = f.GetStaticHeader(0)
ncol = int(ccd_info['Dim_1'])
nrows = int(ccd_info['Dim_2'])
static = out_tot + 'static.edf'
static = EdfFile.EdfFile(static)
static_data = asfarray(static.GetData(0), dtype=float32)
if input_info['n_first_dark'].lower() == 'none':
print 'not using darks'
tot_darks = 0 * static_data
else:
print 'using darks'
if dark_file == 'default':
dark_file = out_tot + 'dark.edf'
print 'using dark file:', dark_file
dark = EdfFile.EdfFile(dark_file)
tot_darks = asfarray(dark.GetData(0), dtype=float32)
toplot = static_data + .001 #to avoid zeros in plotting logarithm###
print '...done'
print '...reading q mask'
if mask_file == 'default':
mask_file = out_tot + 'mask.edf'
print 'using mask file:', mask_file
tot = EdfFile.EdfFile(mask_file)
totmask = float32(tot.GetData(0) + tot.GetData(1))
wtotmask = where(totmask == 0)
p.ion()
fileq = out_tot + 'qmask.edf'
file = EdfFile.EdfFile(fileq)
q = file.GetData(0)
maxval = int(amax(q) + 2)
detector = input_info['detector']
flatfield_file = input_info['flatfield file']
if detector == 'medipix':
flat_field = flatfield(detector, flatfield_file)
else:
flat_field = 1.0
print '...done'
if geometry == 'saxs':
print '...correcting static for baseline'
xbeam = int(input_info['x direct beam'])
ybeam = int(input_info['y direct beam'])
static_data = rad_average(static_data, totmask, xbeam, ybeam)
qaxis_list = []
npix_per_q = []
oneq = []
index_in_q = []
firstq = float32(float(input_info['first q']))
deltaq = float32(float(input_info['delta q']))
stepq = float32(float(input_info['step q']))
qvalue = firstq + deltaq / 2
static_corrected = ones(shape(static_data), dtype=float32)
q *= abs(totmask - 1)
total_pixels = 0
for i in range(2, maxval, 2):
indices = where(q == i)
index_in_q.append(
indices
) #gives the indices of pixels that are not masked at this q
if geometry == 'saxs':
static_corrected[indices] = mean(
static_data[indices]) / static_data[indices]
npixel = len(static_data[indices])
npix_per_q.append(npixel)
oneq.append(ones((1, npixel)))
qaxis_list.append(qvalue)
qvalue += deltaq + stepq
total_pixels += npixel
print '...done'
nq = len(npix_per_q)
xnq = xrange(nq)
ncores = 1
ncores = min(ncores, nq)
tmp_pix = 0
q_sec = []
if nq == 1:
q_sec.append(0)
elif ncores >= nq:
q_sec = range(1, nq)
else:
for ii in xnq:
if tmp_pix < total_pixels / (ncores):
tmp_pix += npix_per_q[ii]
if ii == nq - 1:
q_sec.append(ii)
else:
q_sec.append(ii)
tmp_pix = 0 + npix_per_q[ii]
ncores = len(q_sec)
tmpdat = loadtxt(out_tot + '1Dstatic.dat')
qaxis = tmpdat[:, 0]
I_q = tmpdat[:, 1]
del tmpdat
##FINISHED INITIALIZING PART OF THE CODE######
##START MAIN PART FOR CORRELATION#####
chn = 16.
chn2 = chn / 2
nfile = lastfile - firstfile
rch = int(ceil(log(nfile / chn) / log(2)) + 1)
###2time
if q_2tcf != 'none':
ttdata = zeros((nfile, npix_per_q[q_2tcf - 1]), dtype=float32)
###2time
rcr = chn + chn2 * ceil(log(nfile / chn) / log(2))
lag = zeros((1, rcr), dtype=float32)
data_shape = p.shape(toplot)
smatr = zeros(data_shape, dtype=float32)
matr = zeros(data_shape, dtype=float32)
norm = zeros((1, rcr), dtype=float32)
for ir in xrange(rch):
if ir == 0:
lag[0, :chn] = dt * arange(1, chn + 1, 1)
norm[0, :chn] = 1. / arange(nfile - 2, nfile - chn - 2, -1)
else:
lag[0, chn2 * (ir + 1):chn2 *
(ir + 2)] = (dt * 2**ir) * arange(1 + chn2, chn + 1)
norm[0, chn2 * (ir + 1):chn2 * (ir + 2)] = 1. / arange(
(nfile - 1) / (2**ir) - chn2 - 1,
(nfile - 1) / (2**ir) - chn - 1, -1)
#END of declaring and initializing variables####
#READING FILES
filenames = []
for k in xrange(firstfile, lastfile):
filenames.append(file_name(file_prefix, ext, k))
n = 0
if plot != 'no':
ax1 = p.axes([0.11, 0.08, 0.75, 0.57])
ax1.set_xlabel('t [sec]')
ax1.set_ylabel('g^2(q,t)')
ax1b = p.twinx(ax1)
ax1b.yaxis.tick_right()
ax2 = p.axes([0.11, 0.73, 0.75, 0.19])
ax2.xaxis.tick_bottom()
ax2.set_xlabel('t [sec]')
ax2.set_ylabel('I(q,t) [a.u.]')
ax2b = p.gcf().add_axes(ax2.get_position(), frameon=False)
ax2b.xaxis.tick_top()
ax2b.yaxis.tick_right()
ax2b.xaxis.set_label_position('top')
ax2b.set_xlabel('Image no.')
label1 = 'q= %2.1e 1/Ang' % qaxis_list[0]
label2 = 'q= %2.1e 1/Ang' % qaxis_list[nq / 2]
lm1, = ax1.semilogx((1, ), (1, ), 'ro-', label=label1)
lm1b, = ax1b.semilogx((1, ), (1, ), 'bo-', label=label2)
ax1.legend(loc='lower left')
ax1b.legend(loc=(0.02, 0.1))
lm2, = ax2.plot((1, ), (1, ), 'r-')
lm2b, = ax2b.plot((1, ), (1, ), 'b-')
p.setp(ax1.get_yticklabels(), color='r')
p.setp(ax1b.get_yticklabels(), color='b')
p.setp(ax2.get_yticklabels(), color='r')
p.setp(ax2b.get_yticklabels(), color='b')
tplot_cum = 0
tread_cum = 0
tcalc_cum = 0
tqueue_cum = 0
I_avg = zeros((1, nfile), float32)
I_avg2 = zeros((1, nfile), float32)
I_avgs = zeros((nfile, nq), float32)
tI_avg = zeros((1, nfile), float32)
mon = zeros((1, nfile), int16)
detector = input_info['detector'].lower()
Mythread = threading.Thread
checkfile = os.path.exists
n = 0
totsaxs = 0 * static_data
goodsize = os.path.getsize(dir + filenames[n])
nnfile = nfile - 1
#if plot!='no':
# tmpf=lambda x : True
# thplot=Process(target=tmpf,args=([0]))
# thplot.start()
######################multiprocessing#######################################################
qur = []
qure = []
pcorr = []
for i in xrange(ncores):
qur.append(Queue())
qure.append(Queue())
#qur.append(Queue())
quplot = Queue()
for i in xrange(ncores):
if i == 0:
q_beg = 0
else:
q_beg = q_sec[i - 1]
q_end = q_sec[i]
if i == ncores - 1:
q_end = nq
pcorr.append(
Process(target=mp_corr,
args=(i, nfile, chn, plot, npix_per_q[q_beg:q_end],
index_in_q[q_beg:q_end], qur[i], qure[i], quplot)))
for i in xrange(ncores):
pcorr[i].start()
n = 0
nc = 0
nnfile = nfile - 1
if input_info['normalize'].lower() != 'none':
normalize = input_info['normalize']
print "normalizing to ", input_info['normalize']
else:
print "not normalizing"
while n < nnfile:
tread = time.time()
nc = n + 1
file = filenames[n]
tmf = dir + file
wait = 0
t0 = time.time()
stop = 0
while checkfile(tmf) is False:
p.draw()
sys.stdout.write(50 * '\x08')
sys.stdout.write('waiting for file' + file + '...')
sys.stdout.flush()
t1 = time.time()
wait += t1 - t0
time.sleep(dt)
t0 = t1
if wait > 10 * dt:
print nfile
ans = raw_input('\n will this file ever arrive? (y/N)')
if ans.lower() == 'y':
print '\n keep waiting...\n'
time.sleep(3 * dt)
wait = 0
else:
stop = 1
nfile = n + 1
break
if stop == 1:
break
if ext == '.edf':
filesize = os.path.getsize(tmf)
while filesize != goodsize:
sys.stdout.write(50 * '\x08')
sys.stdout.write('file ' + file + 'still not ready...')
sys.stdout.flush()
time.sleep(dt)
filesize = os.path.getsize(tmf)
f = dataread(tmf)
dread(f, n, tot_darks, flat_field, static_corrected)
mon[0, n] = monitor
#for plot. TO be faster, I only updated plot each chn files.
jj = 0
tmp_put = []
tqueue = time.time()
for i in xnq:
if i < q_sec[jj]:
tmp_put.append(ccd_img[index_in_q[i]])
elif i == nq - 1:
tmp_put.append(ccd_img[index_in_q[i]])
qur[jj].put(tmp_put)
else:
qur[jj].put(tmp_put)
tmp_put = []
tmp_put.append(ccd_img[index_in_q[i]])
jj += 1
tqueue_cum += time.time() - tqueue
if nc % chn == 0:
pct = 100.0 * n / nfile
sys.stdout.write(50 * '\x08')
sys.stdout.write('read ' + str(int(pct)) + '% of files' + 32 * ' ')
sys.stdout.flush()
if plot != 'no':
#thplot.join()
xx = quplot.get()
ttplot(xx[0], xx[1], xx[2], n + 1, I_avg[0, :n + 1],
I_avg2[0, :n + 1])
#thplot=Process(target=ttplot,args=([xx[0],xx[1],xx[2],n+1,I_avg[0,:n+1],I_avg2[0,:n+1]]))
#thplot.start()
#thplot.join()
n += 1
#if plot!='no':
#thplot.join()
sys.stdout.write(50 * '\x08')
sys.stdout.flush()
print "read 100% of files"
###############################################################################################
from_proc = []
for i in xrange(ncores):
from_proc.append(qure[i].get())
pcorr[i].join()
qure[i].close
#############################################################################################
#END OF MAIN LOOP
#calculate 2 times correlation function
print "saving results..."
if stop == 1:
tI_avg = tI_avg[:, :nfile]
mon = mon[:, :nfile]
I_avgs = I_avgs[:nfile, :]
rch = int(ceil(log(nfile / nchannels) / log(2)) + 1)
for ir in xrange(rch):
if ir == 0:
norm[0, :nchannels] = 1. / arange(nfile - 2,
nfile - nchannels - 2, -1)
else:
norm[0, nchannels2 * (ir + 1):nchannels2 *
(ir + 2)] = 1. / arange(
(nfile - 1) / (2**ir) - nchannels2 - 1,
(nfile - 1) / (2**ir) - nchannels - 1, -1)
#calculate correlation functions
corf = from_proc[0][0]
sl = from_proc[0][1]
sr = from_proc[0][2]
tcalc_cum = from_proc[0][3]
for i in xrange(1, ncores):
corf = concatenate((corf, from_proc[i][0]), axis=0)
sl = concatenate((sl, from_proc[i][1]), axis=0)
sr = concatenate((sr, from_proc[i][2]), axis=0)
tcalc_cum = max(tcalc_cum, from_proc[i][3])
indt = int(chn + chn2 * log(nfile / chn) / log(2)) - 2
cc = zeros((indt, nq + 1), float32)
q_title = '#q values:'
trace_title = '#file_no. , time, monitor, q values:'
for cindex in xnq:
q_title = q_title + ' ' + str(qaxis_list[cindex])
trace_title = trace_title + ' ' + str(qaxis_list[cindex])
cc[:,cindex+1]=corf[cindex,:indt]/(sl[cindex,:indt]*sr[cindex,:indt])/\
norm[0,:indt]
cc[:, 0] = lag[0, :indt]
q_title = q_title + '\n'
trace_title = trace_title + '\n'
del indt
f = open(out_tot + 'cf.dat', 'w')
f.write(q_title)
savetxt(f, cc)
f.close()
del cc
f = open(out_tot + 'trace.dat', 'w')
f.write(trace_title)
traces = zeros((nfile, nq + 3), float32)
traces[:, 0] = tI_avg / dt + firstfile
traces[:, 1] = tI_avg
traces[:, 2] = mon
traces[:, 3:] = I_avgs
savetxt(f, traces)
f.close()
del traces
static = out_tot + 'static.edf'
static = EdfFile.EdfFile(static)
totsaxs = totsaxs / n - tot_darks
totsaxs[totsaxs <= 0] = 0
static.WriteImage({}, totsaxs, 0)
del static
print 'correlation functions are saved to ', out_tot + 'cf.dat'
print 'traces are saved to ', out_tot + 'trace.dat'
if plot != 'no':
p.hold(True)
p.close()
if q_2tcf != 'none':
print "calculating time resolved cf and chi4..."
if nfile > 6000: #this is for 4 GB RAM PC
nfile = 6000
n = 6000
lind2 = npix_per_q[q_2tcf - 1] / 16
l = arange(5) * 0
y = []
v = []
for i in range(5):
y.append([])
v.append([])
ib = 0
for i in xrange(16):
sys.stdout.write(50 * '\x08')
sys.stdout.write('done ' + str(int(i / 16. * 100)) + '% of data' +
32 * ' ')
sys.stdout.flush()
ie = ib + lind2
y[0].append(trc(ttdata[:n - 1, ib:ie]))
v[0].append(vartrc(y[0][-1]))
if l[0] == 1:
recurf(0)
else:
l[0] += 1
ib += lind2
vm = []
for i in range(4, -1, -1):
vm.append(mean(v[i], 0))
vm = array(vm)
del ttdata
del v
sys.stdout.write(50 * '\x08')
sys.stdout.flush()
file_2times = out_tot + '2times_q_' + str(q_2tcf) + '.edf'
ytrc.write(file_2times, y[4][0])
print 'Time resolved CF is saved to ' + out_tot + '2times_q_' + str(
q_2tcf) + '.edf'
N = array([[1], [2], [4], [8], [16]]) / float(npix_per_q[q_2tcf - 1])
data = concatenate((N, vm), 1).T
#print 'number of pixels ',lind[ttcf_par]
#print 'q value=', qv[ttcf_par]
p0 = [0.0, 1.0]
it = range(len(data[1:, 0]))
p1 = zeros((len(data[1:, 0]), len(p0) + 1))
p1[:, 0] = (asfarray(it) + 1.0) * dt
xdata = data[0, :]
for i in it:
ydata = data[i + 1, :]
p1[i, 1:], success = leastsq(errfunc, p0, args=(xdata, ydata))
outfile = out_tot + 'fitchi4_q_' + str(q_2tcf) + '.dat'
f = open(outfile, 'w')
f.write("#time chi4 error q value:" + str(qaxis_list[q_2tcf - 1]) +
"\n")
savetxt(f, p1)
f.close()
print 'file is saved to ' + outfile
print "saving results..."
time2 = time.time()
print 'elapsed time', time2 - time1
print 'elapsed time for plotting', tplot_cum
print 'elapsed time for reading', tread_cum
print 'elapsed time for correlating', tcalc_cum
print 'elapsed time for queueing', tqueue_cum
print 'used ncores=', ncores
def main(unused_args):
tdLoader = TrafficDataLoader(
'internet-data/data/internet-traffic-11-cities-5min.csv', max_norm=5.)
tdConfig = TrafficDataConfig()
tmConfig = TrafficRNNConfig(tdConfig)
batch_size = tmConfig.batch_size
seq_input, seq_target = tdLoader.get_rnn_input(tdConfig)
print seq_input.shape, seq_target.shape
data = dict()
data['seq_input'] = seq_input
data['seq_target'] = seq_target
data['early_stop'] = tdConfig.batch_size
is_training = True
save_graph = False
with tf.Graph().as_default(), tf.Session() as session:
model = TrafficRNN(is_training=True, config=tmConfig)
saver = tf.train.Saver()
merged = None
writer = None
if is_training and save_graph:
writer = tf.train.SummaryWriter('/tmp/rnn_logs', session.graph_def)
tf.initialize_all_variables().run()
decay = .8
if is_training:
lr_value = 1e-3
for epoch in range(tmConfig.max_epoch):
if epoch > 10:
lr_value = 1e-3
elif epoch > 75:
lr_value = 1e-4
elif epoch > 100:
lr_value = 1e-6
elif epoch > 200:
lr_value = 1e-7
elif epoch > 250:
lr_value = 1e-8
model.assign_lr(session, lr_value)
net_outs_all = np.array([])
error, net_outs_all = run_epoch(session, model, data,
model.train_op, tdConfig)
error, net_outs_all = run_epoch(session, model, data,
tf.no_op(), tdConfig, writer)
print net_outs_all.shape, seq_target.shape
print('Epoch %d: %s') % (epoch, error)
if epoch == 0:
plt.figure(1, figsize=(20, 10))
plt.ion()
plt.ylim([-1, 6])
plt.plot(xrange(tdConfig.n_steps), seq_target, 'b-',
xrange(tdConfig.n_steps), net_outs_all, 'r-')
plt.show()
time.sleep(20)
else:
plt.clf()
plt.ylim([-1, 6])
plt.plot(xrange(tdConfig.n_steps), seq_target, 'b-',
xrange(tdConfig.n_steps), net_outs_all, 'r-')
img_loc = 'out-img/epoch-%05d.png' % (epoch)
plt.savefig(img_loc)
plt.draw()
time.sleep(.1)
if epoch > 40 and epoch % 20 == 9:
outfile = 'internet-data/saved-models/traffic-rnn-hid-%d-batch-%d-window-%d-lag-%d.chkpnt' % (
tmConfig.num_hidden, tdConfig.batch_size,
tdConfig.window_size, tdConfig.lag)
saver.save(session, outfile, global_step=epoch)
else:
saved_vars = 'internet-data/saved-models/traffic-rnn-hid-%d-batch-%d-window-%d-lag-%d.chkpnt-%d' % (
tmConfig.num_hidden, tdConfig.batch_size, tdConfig.window_size,
tdConfig.lag, tmConfig.max_epoch - 1)
saver.restore(session, saved_vars)
train_error, train_outs_all = run_epoch(session, model, data,
tf.no_op(), tdConfig)
testDataConfig = TestConfig()
test_seq_input, test_seq_target = tdLoader.get_rnn_input(
testDataConfig)
test_data = dict()
test_outs_all = np.array([])
test_data['seq_input'] = test_seq_input
test_data['seq_target'] = test_seq_target
test_data['early_stop'] = testDataConfig.batch_size
test_error, test_outs_all = run_epoch(session, model, test_data,
tf.no_op(), testDataConfig)
upper_curve = test_outs_all + .1 * test_outs_all
lower_curve = test_outs_all - .1 * test_outs_all
shift_left = np.zeros(test_outs_all.shape)
shift_right = np.zeros(test_outs_all.shape)
shift_left[:-18] = test_outs_all[18:]
shift_right[18:] = test_outs_all[:-18]
curve1 = np.maximum(upper_curve, shift_left)
curve1 = np.maximum(curve1, shift_left)
curve1 = np.maximum(curve1, shift_right)
curve2 = np.minimum(lower_curve, shift_right)
curve2 = np.minimum(curve2, upper_curve)
curve2 = np.minimum(curve2, shift_left)
print test_outs_all.shape
x = xrange(len(test_outs_all))
plt.figure(3, figsize=(20, 10))
plt.ioff()
plt.plot(x, test_outs_all, 'b-', alpha=1)
plt.plot(x, test_seq_target, 'g-', alpha=1)
plt.plot(x, curve1, 'r-', alpha=.1)
plt.plot(x, curve2, 'r-', alpha=.1)
plt.fill_between(x, curve1, curve2, color='grey', alpha=.3)
plt.show()
print 'Test error: %s' % test_error
plt.figure(2, figsize=(20, 10))
plt.plot(xrange(tdConfig.n_steps), seq_target, 'b-',
xrange(tdConfig.n_steps), train_outs_all, 'g--')
plt.plot(
xrange(tdConfig.n_steps - 24,
tdConfig.n_steps + testDataConfig.n_steps - 24),
test_seq_target, 'b-')
plt.plot(
xrange(tdConfig.n_steps - 24,
tdConfig.n_steps + testDataConfig.n_steps - 24),
test_outs_all, 'r--')
plt.show()
time.sleep(1)
else:
Lite = False
try:
try:
import matplotlib.pylab as pylab
except:
import matplotlib.matlab as pylab
# catch old versions which use set instead of setp
if not hasattr(pylab, 'setp'): pylab.setp = pylab.set
# gives right aspect ratio in subplots
pylab.rcParams['image.aspect'] = 'auto'
# increase vertical gap between plots
pylab.rcParams['figure.subplot.hspace'] = 0.3
# turn on interactive mode
pylab.ion()
gotMatplotlib = True
except:
if not Lite: print '\n ++++ CliMT: WARNING: matplotlib.pylab ' \
+'could not be loaded, so no runtime monitoring !\n'
gotMatplotlib = False
from numpy import *
from utils import squeeze
from state import KnownFields
def _figureSetUp(FieldKeys, Component):
"""
Sets up a figure consisting of up to 4 panels (subplots).
"""
import MEArec as mr
import MEAutility as mu
import matplotlib.pylab as plt
import matplotlib.gridspec as gridspec
from plotting_conventions import *
import numpy as np
import os
save_fig = False
plt.ion()
plt.show()
template_file = 'data/templates/templates_30_tetrode.h5'
recording_file = 'data/recordings/recordings_6cells_tetrode_30.0_10.0uV.h5'
tempgen = mr.load_templates(template_file)
recgen = mr.load_recordings(recording_file)
mea = mu.return_mea(info=recgen.info['electrodes'])
fig = plt.figure(figsize=(12, 11))
gs = gridspec.GridSpec(20, 3)
ax_timeseries = fig.add_subplot(gs[:4, :])
ax_waveforms = fig.add_subplot(gs[5:8, :])
ax_pca = fig.add_subplot(gs[10:, :])
mr.plot_recordings(recgen,
ax=ax_timeseries,
overlay_templates=True,
def neu():
global wa, wb, wa1, wb1, wc1, wa2, wb2, wc2, wconst1, wconst2, wconst3, w13, w23
wa = random.random()
wb = random.random()
wa1 = random.random()
wb1 = random.random()
wc1 = random.random()
wa2 = random.random()
wb2 = random.random()
wc2 = random.random()
w13 = random.random()
w23 = random.random()
wconst1 = random.random()
wconst2 = random.random()
wconst3 = random.random()
dwa1 = 0
dwb1 = 0
dwc1 = 0
dwa2 = 0
dwb2 = 0
dwc2 = 0
dw13 = 0
dw23 = 0
dwconst1 = 0
dwconst2 = 0
dwconst3 = 0
#-----------------------------------------------
def neut1(a, b, c, dwa1, dwb1, dwc1, dwconst1):
global wa1, wb1, wc1, wconst1
wa1 = wa1 + dwa1
wb1 = wb1 + dwb1
wc1 = wc1 + dwc1
wconst1 = wconst1 + dwconst1
S = a * wa1 + b * wb1 + c * wc1 + 1 * wconst1
F = sigmoid(S)
print(F)
return F
def neut2(a, b, c, dwa2, dwb2, dwc2, dwconst2):
global wa2, wb2, wc2, wconst2
wa2 = wa2 + dwa2
wb2 = wb2 + dwb2
wc2 = wc2 + dwc2
wconst2 = wconst2 + dwconst2
S = a * wa2 + b * wb2 + c * wc2 + 1 * wconst2
F = sigmoid(S)
print(F)
return F
def neut3(f1, f2, dw13, dw23, dwconst3, X):
global w13, w23, wconst3
w13 = w13 + dw13
w23 = w23 + dw23
wconst3 = wconst3 + dwconst3
S = f1 * w13 + f2 * w23 + 1 * wconst3
F = sigmoid(S)
E = X - F
print(F)
return F, w13, w23, E
#---------------------------------------------------
def neu1(a, b, c):
global wa1, wb1, wc1, wconst1
F = sigmoid(a * wa1 + b * wb1 + c * wc1 + wconst1)
return F
def neu2(a, b, c):
global wa2, wb2, wc2, wconst2
F = sigmoid(a * wa2 + b * wb2 + c * wc2 + wconst2)
return F
def neu3(a, b):
global w13, w23, wconst3
f3 = sigmoid(a * w13 + b * w23 + wconst3)
return f3
print("Training area initialized. Print what u want to do:")
print("e=exit;l=learn")
s = input()
s = "l"
if s == "e":
print("See you later!")
if s == "l":
print("Lets train something")
print("Enter values")
i = 0
q = 0
j = 0
x1 = [1, -1]
y1 = [0, 0]
x2 = [0, 0]
y2 = [1, -1]
plt.plot(x1, y1)
plt.plot(x2, y2)
plt.grid()
plt.ion()
while i < 7000:
#-Тренировочные_данные-\/
a = [1, 1, 1, 1, 0, 0, 0, 0]
b = [1, 1, 0, 0, 1, 1, 0, 0]
c = [1, 0, 1, 0, 1, 0, 1, 0]
x = [1, 1, 1, 1, 0, 0, 1, 0]
#-Тренировочные_данные-/\
ai = a[q]
bi = b[q]
ci = c[q]
xi = x[q]
f1 = neut1(ai, bi, ci, dwa1, dwb1, dwc1, dwconst1)
f2 = neut2(ai, bi, ci, dwa2, dwb2, dwc2, dwconst2)
f3, w13, w23, E = neut3(f1, f2, dw13, dw23, dwconst3, xi)
E1 = backerror(E, w13)
E2 = backerror(E, w23)
dwa1 = E1 * (f1 * (1 - f1)) * ai
dwb1 = E1 * (f1 * (1 - f1)) * bi
dwc1 = E1 * (f1 * (1 - f1)) * ci
dwconst1 = E1 * (f1 * (1 - f1)) * 1
dwa2 = E2 * (f2 * (1 - f2)) * ai
dwb2 = E2 * (f2 * (1 - f2)) * bi
dwc2 = E2 * (f2 * (1 - f2)) * ci
dwconst2 = E2 * (f2 * (1 - f1)) * 1
dw13 = E * (f3 * (1 - f3)) * f1
dw23 = E * (f3 * (1 - f3)) * f2
dwconst3 = E * (f3 * (1 - f3)) * 1
print("------------------")
if q == 6:
q = 0
else:
q = q + 1
i = i + 1
try:
Ap.remove()
except:
print("Старт")
Ap = plt.scatter(E, E)
plt.draw()
plt.pause(0.0001)
plt.ioff()
plt.show()
print("Lets train my skills")
while i < 5:
i = i + 1
a = int(input())
b = int(input())
c = int(input())
f1 = neu1(a, b, c)
f2 = neu2(a, b, c)
f3 = neu3(f1, f2)
print(f3)
use and modify it however you like).
"""
# import future syntax as for Python version >= 3.0
from __future__ import division # such that 0 != 1/2 == 0.5
from __future__ import print_function # available since 2.6, not needed
from math import log, exp
from random import normalvariate as random_normalvariate
# see randn keyword argument to CMAES.__init__
# Optional imports, can be out-commented, if not available
import sys
try:
import matplotlib.pylab as pylab
pylab.ion() # prevents that execution stops after plotting
# for plotting, scitools.easyfiz might be an alternative
except ImportError:
pylab = None
print(' pylab could not be imported ')
__version__ = '1.10'
__author__ = 'Nikolaus Hansen'
__docformat__ = 'reStructuredText'
def fmin(objectivefct,
xstart,
sigma,
args=(),
max_eval='1e3*N**2',
def plotTimescales(self,
lags=None,
units='frames',
errors=None,
nits=None,
results=False,
plot=True):
""" Plot the implied timescales of MSMs of various lag times
Parameters
----------
lags : list
The lag times at which to compute the timescales. By default it spreads out 25 lag times linearly from lag
10 until the mode length of the trajectories.
units : str
The units of lag. Can be 'frames' or any time unit given as a string.
errors : errors
Calculate errors using Bayes (Refer to pyEMMA documentation)
nits : int
Number of implied timescales to calculate. Default: all
results : bool
If the method should return the calculated implied timescales
plot : bool
If the method should display the plot of implied timescales
Returns
-------
If given `results`=True this method will return the following data
its : np.ndarray
The calculated implied timescales. 2D array with dimensions (len(`lags`), `nits`)
lags : np.ndarray
A list of the lag times that were used to calculate the implied timescales
Examples
--------
>>> model = Model(data)
>>> model.plotTimescales()
>>> model.plotTimescales(lags=list(range(1,100,5)))
"""
import pyemma.plots as mplt
import pyemma.msm as msm
self._integrityCheck()
if lags is None:
lags = self._defaultLags()
else:
lags = unitconvert(units, 'frames', lags,
fstep=self.data.fstep).tolist()
if nits is None:
nits = np.min((self.data.K, 20))
from htmd.config import _config
its = msm.its(self.data.St.tolist(),
lags=lags,
errors=errors,
nits=nits,
n_jobs=_config['ncpus'])
if plot:
from matplotlib import pylab as plt
plt.ion()
plt.figure()
mplt.plot_implied_timescales(its, dt=self.data.fstep, units='ns')
plt.show()
if results:
return its.get_timescales(), its.lags
def process_data(self, **kwds):
"""Keywords are passed spike.plot_correlogram as style formatting.
"""
plt.ioff()
self.figure = {}
res = self.results
lags = res['lags']
C = res['C_overall']
C_rat = res['C_rat']
# Set an informative title
title = "Scan " + res['scan_time'] + ": " + res['query']
if res['shuffled']:
title += ' [shuffled]'
fmt = dict(lw=1)
fmt.update(kwds)
corr_args = dict(is_corr=True, norm=False, plot_type="lines", fmt=fmt)
# Plot the per-rat correlograms normalized in different ways
C_rat_norm = []
for norm in 0, 1, 2:
f = plt.figure(figsize=(8, 7))
if norm == 0:
self.figure['rat'] = f
rat_title = title + ' [per rat]'
elif norm == 1:
self.figure['rat_norm_max'] = f
rat_title = title + ' [per rat] [norm max]'
elif norm == 2:
self.figure['rat_norm_zero'] = f
rat_title = title + ' [per rat] [norm zero]'
ax = plt.axes()
for i, psth in enumerate(C_rat.values()):
if norm == 1:
psth /= psth.max()
C_rat_norm.append(psth)
elif norm == 2:
psth /= psth[lags==0]
plot_correlogram((psth, lags), **corr_args)
ax.set_xlabel('Spike Lag (%s)'%res['units'])
ax.set_ylabel('Correlation')
ax.set_title(rat_title)
# if len(C_rat_norm):
# valid = filter(lambda x: np.isfinite(x).all(), C_rat_norm)
# C_rat_norm = np.array(C_rat_norm)
# mu = C_rat_norm.mean(axis=0)
# std = C_rat_norm.std(axis=0)
# ci = 1.96*std/np.sqrt(C_rat_norm.shape[0])
# Plot overal correlogram with min/max per-rat envelope
self.figure['overall'] = plt.figure(figsize=(8, 7))
ax = plt.axes()
fmt['lw'] = 2
fmt['c'] = 'k'
corr_args['norm'] = True
h = plot_correlogram((C, lags), **corr_args)
h.set_label('Overall Spikes')
# if len(C_rat_norm):
# ax.plot(lags, mu, 'b-', zorder=-1, label='Rat Average')
# ax.plot(lags, mu+ci, 'r--', zorder=-2, label='Rat 95% CI')
# ax.plot(lags, mu-ci, 'r--', zorder=-2)
ax.set_xlabel('Spike Lag (%s)'%res['units'])
ax.set_ylabel('Correlation')
ax.set_title(title)
ax.legend(loc=4)
plt.ion()
plt.show()
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import time
TIME = 30
path = 'C:/Users/asus/Desktop/0022R.wav'
audio,fs = read_audio_soundfile(path)
# footstep 0007R range[250:280]0 ; gun 0022R range[0:30] vehicle 0018R range[1020:1050]
audio = audio[fs*0:fs*30]
time = np.arange(0, len(audio)) * (1.0 / fs)
split_len = int(30*fs/1000)
N = int(len(audio)/split_len)
predict_all = []
count = 0
plt.ion() #start interactive mode
# ax = plt.subplot(121)
m = []
acc = []
for i in range(N):
test_data = audio[i*split_len:(i+1)*split_len]
feature = mel_f.extract_logmel(test_data,fs)
feature = avg_frame(feature)
predict = svm_c.predict_svm([feature],[1])
if predict == 0:
count += 1
predict_point = np.ones(split_len)*predict
# print(predict_point)
predict_all.append(predict_point)
m.append(predict_point)
acc.append(count/(i+1))
def plot(self,
block=False,
period=False,
fap=None,
gls=True,
data=True,
residuals=True):
"""
Create a plot.
Parameters
----------
period : boolean
The periodogram is plotted against log(Period).
fap : float, list
Plots the FAP levels.
gls : boolean
Plots the GLS periodogram.
data : boolean
Plots the data.
residuals : boolean
Plots the residuals.
Returns
-------
fig : mpl.figure
A figure which can be modified.
"""
try:
import matplotlib
import matplotlib.pylab as mpl
except ImportError:
raise (ImportError("Could not import matplotlib.pylab."))
fbest, T0 = self.best["f"], self.best["T0"]
fig = mpl.figure()
fig.canvas.set_window_title('GLS periodogram')
fig.subplots_adjust(hspace=0.05,
wspace=0.04,
right=0.97,
bottom=0.09,
top=0.84)
fs = 10 # fontsize
nrow = gls + data + residuals
plt, plt1, plt2, plt3, plt4 = [None] * 5
if gls:
# Periodogram
plt = fig.add_subplot(nrow, 1, 1)
plt.tick_params(direction='in')
if period:
plt.set_xscale("log")
plt.set_xlabel("Period P")
else:
plt.set_xlabel("Frequency $f$")
plt.set_ylabel(self.label["ylabel"])
plt.plot(1 / self.f if period else self.f,
self.power,
'b-',
linewidth=.5)
# mark the highest peak
plt.plot(1 / fbest if period else fbest,
self.power[self.p.argmax()],
'r.',
label="$1/f = %f$" % (1 / fbest))
x2tics = 1 / np.array([0.5, 1, 2, 3, 5, 10, 20., 100])
mx2tics = 1 / np.array([0.75, 1.5, 2.5, 4, 15, 40, 60., 80, 100])
def tick_function(X):
return ["%g" % (1 / z) for z in X]
plt.tick_params(direction='in', which='both', top=True, right=True)
plt.minorticks_on()
plt.autoscale(enable=True, axis='x', tight=True)
if not period:
ax2 = plt.twiny()
ax2.tick_params(direction='in', which='both')
ax2.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, 1 /
x, y)
ax2.set_xticks(x2tics)
ax2.set_xticks(mx2tics, minor=True)
ax2.set_xticklabels(tick_function(x2tics))
ax2.set_xlim(plt.get_xlim())
ax2.set_xlabel("Period")
plt.tick_params(top=False)
if fap is not None:
if isinstance(fap, float):
fap = [fap]
n = max(1, len(fap) - 1) # number of dash types
for i, fapi in enumerate(fap):
plt.axhline(self.powerLevel(fapi),
linewidth=0.5,
color='r',
dashes=(8 + 32 * (n - i) / n, 8 + 32 * i / n),
label="FAP = %s%%" % (fapi * 100))
plt.legend(numpoints=1, fontsize=fs, frameon=False)
# Data and model
col = mpl.cm.rainbow(mpl.Normalize()(self.t))
def plot_ecol(plt, x, y):
# script for scatter plot with errorbars and time color-coded
datstyle = dict(color=col,
marker='.',
edgecolor='k',
linewidth=0.5,
zorder=2)
if self.e_y is not None:
errstyle = dict(yerr=self.e_y,
marker='',
ls='',
elinewidth=0.5)
if matplotlib.__version__ < '2.':
errstyle['capsize'] = 0.
datstyle['s'] = 8**2 # requires square size !?
else:
errstyle['ecolor'] = col
_, _, (c, ) = plt.errorbar(x, y, **errstyle)
if matplotlib.__version__ < '2.':
c.set_color(col)
plt.scatter(x, y, **datstyle)
def phase(t):
#return (t-T0)*fbest % 1
return (t - T0) % (1 / fbest)
if data:
# Time series
tt = arange(self.t.min(), self.t.max(), 0.01 / fbest)
ymod = self.sinmod(tt)
plt1 = fig.add_subplot(nrow, 2, 2 * gls + 1)
plt1.set_ylabel("Data")
if residuals:
mpl.setp(plt1.get_xticklabels(), visible=False)
else:
plt1.set_xlabel("Time")
plot_ecol(plt1, self.t, self.y)
plt1.plot(tt, ymod, 'k-', zorder=0)
# Phase folded data
tt = arange(T0, T0 + 1 / fbest, 0.01 / fbest)
yy = self.sinmod(tt)
plt2 = fig.add_subplot(nrow, 2, 2 * gls + 2, sharey=plt1)
mpl.setp(plt2.get_yticklabels(), visible=False)
if residuals:
mpl.setp(plt2.get_xticklabels(), visible=False)
else:
plt2.set_xlabel("Phase")
plot_ecol(plt2, phase(self.t), self.y)
xx = phase(tt)
ii = np.argsort(xx)
plt2.plot(xx[ii], yy[ii], 'k-')
plt2.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, x *
fbest, y)
if residuals:
# Time serie of residuals
yfit = self.sinmod()
yres = self.y - yfit
plt3 = fig.add_subplot(nrow, 2, 2 * (gls + data) + 1, sharex=plt1)
plt3.set_xlabel("Time")
plt3.set_ylabel("Residuals")
plot_ecol(plt3, self.t, yres)
plt3.plot([self.t.min(), self.t.max()], [0, 0], 'k-')
# Phase folded residuals
plt4 = fig.add_subplot(nrow,
2,
2 * (gls + data) + 2,
sharex=plt2,
sharey=plt3)
plt4.set_xlabel("Phase")
mpl.setp(plt4.get_yticklabels(), visible=False)
plot_ecol(plt4, phase(self.t), yres)
plt4.plot([0, 1 / fbest], [0, 0], 'k-')
plt4.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, x *
fbest, y)
for x in fig.get_axes()[2:]:
x.tick_params(direction='in', which='both', top=True, right=True)
x.minorticks_on()
x.autoscale(enable=True, tight=True)
if hasattr(mpl.get_current_fig_manager(), 'toolbar'):
# check seems not needed when "TkAgg" is set
mpl.get_current_fig_manager().toolbar.pan()
#t = fig.canvas.toolbar
#mpl.ToggleTool(mpl.wx_ids['Pan'], False)
fig.tight_layout() # to get the left margin
marleft = fig.subplotpars.left * fig.get_figwidth() * fig.dpi / fs
def tighter():
# keep margin tight when resizing
xdpi = fs / (fig.get_figwidth() * fig.dpi)
ydpi = fs / (fig.get_figheight() * fig.dpi)
fig.subplots_adjust(bottom=4. * ydpi,
top=1 - ydpi - 4 * gls * ydpi,
right=1 - 1 * xdpi,
wspace=4 * xdpi,
hspace=4 * ydpi,
left=marleft * xdpi)
if gls and (residuals or data):
# gls plot needs additional space for x2axis
fig.subplots_adjust(top=1 - 8 * ydpi)
if matplotlib.__version__ < '2.':
ax2.set_position(plt.get_position().translated(
0, 4 * ydpi))
plt.set_position(plt.get_position().translated(0, 4 * ydpi))
#fig.canvas.mpl_connect("resize_event", lambda _: (fig.tight_layout()))
fig.canvas.mpl_connect("resize_event", lambda _: (tighter()))
fig.show()
if block:
print("Close the plot to continue.")
# needed when called from shell
mpl.show()
else:
# avoids blocking when: import test_gls
mpl.ion()
# mpl.show(block=block) # unexpected keyword argument 'block' in older matplotlib
return fig
def test_weathering_model():
from weathering_model.utils import pack_values
from weathering_model.muscl import muscl, vanAlbada
import numpy as np
nx = 1000
dx = 0.05
Xo = 1
x0 = np.zeros((nx, 2))
x0[:, 0] = Xo
t = np.array([0, 2, 4, 6, 8, 10, 12])
vstar = 1
r = 0.25
Yostar = 1
def bc_function(x):
return (np.array([[1.0, 1], [0, 0]]), np.array([[1.0, yb], [0, 0]]))
def flux_function(x):
v = np.zeros_like(x)
v[:, 1] = vstar
return x * v
def source_function(x):
X = x[:, 0]
Y = (x[:, 1] > 0) * x[:, 1]
s = np.zeros_like(x)
s[:, 0] = -np.power(Y, r) * np.power(X, 2)
s[:, 1] = -r * np.power(X, 2) * np.power(Y, r) / Yostar
return s
def diffusion_function(x):
x_d = np.zeros((x0.shape[0] + 2, 2))
x_d[1:-1, :] = x
(topBC, bottomBC) = bc_function(x)
x_d[0, 1] = topBC[0, 1]
x_d[-1, 1] = bottomBC[0, 1]
q = np.diff(x_d[:, 1]) / dx
dxdt = np.zeros_like(x)
dxdt[:, 1] = vstar * np.diff(q) / dx
return dxdt
def prop_speed(x):
v = np.zeros_like(x)
v[:, 1] = vstar
return np.abs(v)
def to_integrate(t, x):
x_unpacked = pack_values(x, packing_geometry=x0.shape)
dxdt_flux = muscl(x_unpacked,
dx,
bc_function,
flux_function,
vanAlbada,
prop_speed=prop_speed,
reconstruction='parabolic')
dxdt_source = source_function(x_unpacked)
dxdt_diffusion = diffusion_function(x_unpacked)
dxdt = dxdt_flux + dxdt_source + dxdt_diffusion
print(t)
return pack_values(dxdt, packing_geometry=None)
from scipy.integrate import solve_ivp
out = solve_ivp(to_integrate, (np.min(t), np.max(t)),
pack_values(x0, packing_geometry=None),
method='RK45',
t_eval=t)
y = out.y.T
import matplotlib.pylab as plt
plt.ion()
x_a = np.arange(0, nx * dx, dx)
for i in range(len(t)):
this_y = pack_values(y[i], packing_geometry=x0.shape)
plt.figure(1)
plt.plot(x_a, this_y[:, 0], '.')
plt.figure(2)
plt.plot(x_a, this_y[:, 1], '.')
def setupAxes(self):
if self.showFigs:
# create figure with axes:
pylab.ion() # Force interactive
plt.close('all')
### for 'Qt4Agg' backend maximize figure
plt.switch_backend('QT5Agg')
# plt.rc('text', usetex=True)
plt.rc('font', family='serif')
self.fig = plt.figure()
# gs1 = gridspec.GridSpec(1, 2)
# fig.show()
# fig.set_tight_layout(True)
self.figManager = plt.get_current_fig_manager()
DPI = self.fig.get_dpi()
self.fig.set_size_inches(800.0 / DPI, 600.0 / DPI)
gs = gridspec.GridSpec(1, 2)
self.fig.clf()
self.FTR.axes = self.fig.add_subplot(gs[0, 0])
self.FTR.axes.invert_xaxis()
self.FTR.axes.set_title('$FT(r)$')
self.FTR.axes.grid(True)
self.Chi_k.axes = self.fig.add_subplot(gs[0, 1])
self.Chi_k.axes.invert_xaxis()
self.Chi_k.axes.set_title('$\chi(k)$')
self.Chi_k.axes.grid(True)
self.FTR.axes.set_ylabel('Reletive Intensity (a.u.)',
fontsize=16,
fontweight='bold')
self.FTR.axes.set_xlabel('$r$ $[\AA]$',
fontsize=16,
fontweight='bold')
self.Chi_k.axes.set_ylabel('Reletive Intensity (a.u.)',
fontsize=16,
fontweight='bold')
self.Chi_k.axes.set_xlabel('$k$ $[\AA^{-1}]$',
fontsize=16,
fontweight='bold')
# Change the axes border width
for axis in ['top', 'bottom', 'left', 'right']:
self.FTR.axes.spines[axis].set_linewidth(2)
self.Chi_k.axes.spines[axis].set_linewidth(2)
# plt.subplots_adjust(top=0.85)
# gs1.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
self.fig.tight_layout(rect=[0.03, 0.03, 1, 0.95], w_pad=1.1)
# put window to the second monitor
# figManager.window.setGeometry(1923, 23, 640, 529)
self.figManager.window.setGeometry(1920, 20, 1920, 1180)
plt.show()
self.fig.suptitle(self.graph_title_txt,
fontsize=self.suptitle_fontsize,
fontweight='normal')
# put window to the second monitor
# figManager.window.setGeometry(1923, 23, 640, 529)
# self.figManager.window.setGeometry(780, 20, 800, 600)
self.figManager.window.setWindowTitle('Compare xftf')
self.figManager.window.showMinimized()
def main(conf_file='config.cfg', logfile=None):
#%% parameters
print "reading config parameters..."
config, pars = front_end.parser(conf_file)
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
front_end.record_config_file(pars)
logfile = front_end.make_logfile_name(pars)
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = netio.load_network(pars)
# load existing learning curve
lc = zstatistics.CLearnCurve(pars['train_load_net'])
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = netio.seed_network(pars, is_seed=True)
else:
print "initializing network..."
net = netio.init_network(pars)
# initalize a learning curve
lc = zstatistics.CLearnCurve()
# show field of view
print "field of view: ", net.get_fov()
print "output volume info: ", net.get_outputs_setsz()
# set some parameters
print 'setting up the network...'
vn = utils.get_total_num(net.get_outputs_setsz())
eta = pars['eta'] #/ vn
net.set_eta(eta)
net.set_momentum(pars['momentum'])
net.set_weight_decay(pars['weight_decay'])
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = front_end.CSamples(config, pars, pars['train_range'], net, outsz,
logfile)
print "\n\ncreate test samples..."
smp_tst = front_end.CSamples(config, pars, pars['test_range'], net, outsz,
logfile)
# initialization
elapsed = 0
err = 0
cls = 0
# interactive visualization
plt.ion()
plt.show()
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
print "start from ", iter_last + 1
for i in xrange(iter_last + 1, pars['Max_iter'] + 1):
vol_ins, lbl_outs, msks = smp_trn.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward(vol_ins)
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn'](props, lbl_outs)
err = err + cerr
cls = cls + cost_fn.get_cls(props, lbl_outs)
# mask process the gradient
grdts = utils.dict_mul(grdts, msks)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward(grdts)
if pars['is_malis']:
malis_weights = cost_fn.malis_weight(props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
if i % pars['Num_iter_per_test'] == 0:
# test the net
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i % pars['Num_iter_per_show'] == 0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# normalize
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = time.time() - start
elapsed = elapsed / pars['Num_iter_per_show']
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
if pars['is_visual']:
# show results To-do: run in a separate thread
front_end.inter_show(start, lc, eta, vol_ins, props, lbl_outs,
grdts, pars)
if pars['is_rebalance'] and 'aff' not in pars['out_type']:
plt.subplot(247)
plt.imshow(msks.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('rebalance weight')
if pars['is_malis']:
plt.subplot(248)
plt.imshow(malis_weights.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('malis weight (log)')
plt.pause(2)
plt.show()
# reset err and cls
err = 0
cls = 0
# reset time
start = time.time()
if i % pars['Num_iter_per_save'] == 0:
# save network
netio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save(pars, elapsed)
plotOption = sys.argv[2]
except IndexError:
print "No plot option provided. Options are: plot and movie"
sys.exit(1)
if plotOption == "plot":
for instance in planets_resh:
try:
plot(instance[:, 0], instance[:, 1], '*')
hold('on')
except:
print "ValueError!"
# movie:
if plotOption == "movie":
ion()
figure()
colors = ['*r', '*b', '*m']
min_length = 0
min_x = 0
max_x = 0
min_y = 0
max_y = 0
for i in range(len(planets_resh)):
max_x_tmp = planets_resh[i].max()
if max_x_tmp > max_x:
max_x = max_x_tmp
min_x_tmp = planets_resh[i].min()
if min_x_tmp < min_x:
min_x = min_x_tmp
max_y_tmp = planets_resh[i].max()
def move(self):
""" Move the Person
"""
if self.pdshow:
fig = plt.gcf()
fig, ax = self.L.showG('w',
labels=False,
alphacy=0.,
edges=False,
fig=fig)
plt.draw()
plt.ion()
while True:
if self.moving:
if self.sim.verbose:
print('meca: updt ag ' + self.ID + ' @ ', self.sim.now())
# if np.allclose(conv_vecarr(self.destination)[:2],self.L.Gw.pos[47]):
# import ipdb
# ipdb.set_trace()
while self.cancelled:
yield passivate, self
print("Person.move: activated after being cancelled")
checked = []
for zone in self.world.zones(self):
if zone not in checked:
checked.append(zone)
zone(self)
# updating acceleration
acceleration = self.steering_mind(self)
acceleration = acceleration.truncate(self.max_acceleration)
self.acceleration = acceleration
# updating velocity
velocity = self.velocity + acceleration * self.interval
self.velocity = velocity.truncate(self.max_speed)
if velocity.length() > 0.2:
# record direction only when we've really had some
self.localy = velocity.normalize()
self.localx = vec3(self.localy.y, -self.localy.x)
# updating position
self.position = self.position + self.velocity * self.interval
# self.update()
self.position.z = 0
self.world.update_boid(self)
self.net.update_pos(self.ID, conv_vecarr(self.position),
self.sim.now())
p = conv_vecarr(self.position).reshape(3, 1)
v = conv_vecarr(self.velocity).reshape(3, 1)
a = conv_vecarr(self.acceleration).reshape(3, 1)
# fill panda dataframe 2D trajectory
self.df = self.df.append(
pd.DataFrame(
{
't': pd.Timestamp(self.sim.now(), unit='s'),
'x': p[0],
'y': p[1],
'vx': v[0],
'vy': v[1],
'ax': a[0],
'ay': a[1]
},
columns=['t', 'x', 'y', 'vx', 'vy', 'ax', 'ay']))
if self.pdshow:
ptmp = np.array([p[:2, 0], p[:2, 0] + v[:2, 0]])
if hasattr(self, 'pl'):
self.pl[0].set_data(self.df['x'].tail(1),
self.df['y'].tail(1))
self.pla[0].set_data(ptmp[:, 0], ptmp[:, 1])
circle = plt.Circle(
(self.df['x'].tail(1), self.df['y'].tail(1)),
radius=self.radius,
alpha=0.3)
ax.add_patch(circle)
else:
self.pl = ax.plot(self.df['x'].tail(1),
self.df['y'].tail(1),
'o',
color=self.color,
ms=self.radius * 10)
self.pla = ax.plot(ptmp[:, 0], ptmp[:, 1], 'r')
circle = plt.Circle(
(self.df['x'].tail(1), self.df['y'].tail(1)),
radius=self.radius,
alpha=0.3)
ax.add_patch(circle)
# try:
# fig,ax=plu.displot(p[:2],p[:2]+v[:2],'r')
# except:
# pass
# import ipdb
# ipdb.set_trace()
plt.draw()
plt.pause(0.0001)
if 'mysql' in self.save:
self.db.writemeca(self.ID, self.sim.now(), p, v, a)
if 'txt' in self.save:
pyu.writemeca(self.ID, self.sim.now(), p, v, a)
# new target when arrived in poi
if self.arrived and\
(self.L.pt2ro(self.position) ==\
self.L.Gw.node[self.rooms[1]]['room']):
self.arrived = False
if self.endpoint:
self.endpoint = False
self.roomId = self.nextroomId
# remove the remaining waypoint which correspond
# to current room position
del self.waypoints[0]
del self.rooms[0]
# del self.dlist[0]
#
# If door lets continue
#
#
# ig destination --> next room
#
#adjroom = self.L.Gr.neighbors(self.roomId)
#Nadjroom = len(adjroom)
if self.cdest == 'random':
# self.nextroomId = int(np.floor(random.uniform(0,self.L.Gr.size())))
self.nextroomId = random.sample(
self.L.Gr.nodes(), 1)[0]
# test 1 ) next != actualroom
# 2 ) nextroom != fordiden room
# 3 ) room not share without another agent
while self.nextroomId == self.roomId or (
self.nextroomId in self.forbidroomId
): # or (self.nextroomId in self.sim.roomlist):
# self.nextroomId = int(np.floor(random.uniform(0,self.L.Gr.size())))
self.nextroomId = random.sample(
self.L.Gr.nodes(), 1)[0]
elif self.cdest == 'file':
self.room_counter = self.room_counter + 1
if self.room_counter >= self.nb_room:
self.room_counter = 0
self.nextroomId = self.room_seq[self.room_counter]
self.wait = self.room_wait[self.room_counter]
#self.sim.roomlist.append(self.nextroomId) # list of all destiantion of all nodes in object sim
self.rooms, wp = self.L.waypointGw(
self.roomId, self.nextroomId)
# self.dlist = [i in self.L.Gw.ldo for i in self.rooms]
for tup in wp[1:]:
self.waypoints.append(vec3(tup))
#nextroom = adjroom[k]
# print "room : ",self.roomId
# print "nextroom : ",self.nextroomId
#p_nextroom = self.L.Gr.pos[self.nextroomId]
#setdoors1 = self.L.Gr.node[self.roomId]['doors']
#setdoors2 = self.L.Gr.node[nextroom]['doors']
#doorId = np.intersect1d(setdoors1,setdoors2)[0]
#
# coord door
#
#unode = self.L.Gs.neighbors(doorId)
#p1 = self.L.Gs.pos[unode[0]]
#p2 = self.L.Gs.pos[unode[1]]
#print p1
#print p2
#pdoor = (np.array(p1)+np.array(p2))/2
self.destination = self.waypoints[0]
if self.sim.verbose:
print('meca: ag ' + self.ID + ' wait ' +
str(self.wait))
yield hold, self, self.wait
else:
del self.waypoints[0]
del self.rooms[0]
# del self.dlist[0]
#print "wp : ", self.waypoints
if len(self.waypoints) == 1:
self.endpoint = True
self.destination = self.waypoints[0]
#print "dest : ", self.destination
else:
yield hold, self, self.interval
else:
# self.update()
self.world.update_boid(self)
self.net.update_pos(self.ID, conv_vecarr(self.position),
self.sim.now())
yield hold, self, self.interval
if matplotlib_backend in mpl_backends:
plt.switch_backend(matplotlib_backend)
else:
print "Warning: Matplotlib backend", matplotlib_backend, "not available"
print "Available backends:", mpl_backends
from matplotlib import pylab as pl
import matplotlib.ticker as ticker
from matplotlib import rc, colors
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.cm as cm
from matplotlib import lines
from mpl_toolkits.mplot3d import axes3d
from matplotlib import lines # for plotting lines in pendulum and PST
from matplotlib.patches import ConnectionStyle # for cartpole
pl.ion()
else:
print 'matplotlib is not available => No Graphics'
if module_exists('networkx'):
import networkx as nx
else:
'networkx is not available => No Graphics on SystemAdmin domain'
if module_exists('sklearn'):
from sklearn import svm
else:
'sklearn is not available => No BEBF representation available'
from scipy import stats
from scipy import misc
from scipy import linalg
def plot_rssi(self, port):
self.count = 0
self.dt = 0.5
self.tlen = 120
# Generate mesh for plotting
Y, X = np.mgrid[slice(0 - .5, 26 + 1.5, 1),
slice(0 - self.dt / 2, self.tlen + 1 -
self.dt / 2, self.dt)]
Z = np.zeros_like(X)
# X and Y are bounds, so Z should be the value *inside* those bounds.
# Therefore, remove the last value from the Z array.
Z = Z[:, :-1]
logging.debug("Creating figure")
fig = plt.figure()
pcm = plt.pcolormesh(X,
Y,
Z,
vmin=-128,
vmax=0,
cmap=plt.cm.get_cmap('jet'))
plt.xlim([0, self.tlen])
plt.ylim([0, 26])
plt.colorbar(label="Measured signal level [dB]")
plt.ylabel("Channel number")
plt.xlabel("Time [s]")
plt.ion()
logging.debug("Show plot window")
plt.show()
ch_min = 26
ch_max = 0
last_update = time.time()
logging.info("Begin collecting data from serial port")
while True:
line = port.readline().rstrip()
pkt_data = re.match(
r"\[([-+]?\d+),\s*([-+]?\d+),\s*([-+]?\d+)\]\s*(.*)",
line.decode(errors='replace'))
if pkt_data:
now = time.time()
try:
iface_id = int(pkt_data.group(1))
timestamp = int(pkt_data.group(2))
count = int(pkt_data.group(3))
tidx = int(timestamp / (self.dt * 1000000)) % (Z.shape[1])
except ValueError:
continue
logging.debug("data: tidx=%d if=%d t=%d", tidx, iface_id,
timestamp)
raw = pkt_data.group(4)
resize = False
for ch_ed in raw.split(","):
try:
pair = ch_ed.split(":")
ch = int(pair[0])
ed = float(pair[1])
if ch < ch_min:
ch_min = ch
resize = True
if ch > ch_max:
ch_max = ch
resize = True
Z[ch, tidx] = ed
except (ValueError, IndexError):
continue
#print("ch: %d ed: %d" % (ch, ed))
if resize:
logging.debug("resize: %d %d" % (ch_min, ch_max))
plt.ylim([ch_min - .5, ch_max + .5])
if now > last_update + 1:
last_update = now
#pcm = plt.pcolormesh(X, Y, Z)
pcm.set_array(Z.ravel())
pcm.autoscale()
pcm.changed()
plt.pause(0.01)
def make_waterfall_plots(filenames_list,
target,
f_start,
f_stop,
ion=False,
epoch=None,
local_host='',
plot_name='',
save_pdf_plot=False,
saving_fig=False,
**kwargs):
''' Makes waterfall plots per group of ON-OFF pairs (up to 6 plots.)
'''
matplotlib.rc('font', **font)
if ion:
plt.ion()
min_val = 0
max_val = 5.
factor = 1e6
units = 'Hz'
print(target)
n_plots = len(filenames_list)
fig = plt.subplots(n_plots,
sharex=True,
sharey=True,
figsize=(10, 2 * n_plots))
#finding plotting values range
fil = Filterbank(filenames_list[0], f_start=f_start, f_stop=f_stop)
plot_f, plot_data = fil.grab_data(f_start, f_stop, 0)
dec_fac_x, dec_fac_y = 1, 1
if plot_data.shape[0] > MAX_IMSHOW_POINTS[0]:
dec_fac_x = plot_data.shape[0] / MAX_IMSHOW_POINTS[0]
if plot_data.shape[1] > MAX_IMSHOW_POINTS[1]:
dec_fac_y = plot_data.shape[1] / MAX_IMSHOW_POINTS[1]
plot_data = rebin(plot_data, dec_fac_x, dec_fac_y)
A1_avg = np.median(plot_data)
A1_max = plot_data.max()
A1_std = np.std(plot_data)
if not epoch:
epoch = fil.header['tstart']
labeling = ['A', 'B', 'A', 'C', 'A', 'D']
# delta_f = ('%f0.6'%np.abs(f_start-f_stop))
delta_f = np.abs(f_start - f_stop)
mid_f = np.abs(f_start + f_stop) / 2.
for i, filename in enumerate(filenames_list):
print(filename)
plt.subplot(n_plots, 1, i + 1)
fil = Filterbank(filename, f_start=f_start, f_stop=f_stop)
this_plot = plot_waterfall(fil,
f_start=f_start,
f_stop=f_stop,
vmin=A1_avg - A1_std * min_val,
vmax=A1_avg + max_val * A1_std,
**kwargs)
if i == 0:
plt.title(target.replace('HIP', 'HIP '))
if i < len(filenames_list) - 1:
plt.xticks(np.arange(f_start, f_stop, delta_f / 4.),
['', '', '', ''])
#Some plot formatting.
ax = plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
print('delta_f', delta_f)
plt.xticks(np.arange(f_start, f_stop, delta_f / 4.), [
round(loc_freq) for loc_freq in np.arange(
(f_start - mid_f), (f_stop - mid_f), delta_f / 4.) * factor
])
plt.xlabel("Relative Frequency [%s] from %f MHz" % (units, mid_f),
fontdict=font)
#to plot color bar. for now.
cax = fig[0].add_axes([0.9, 0.11, 0.03, 0.77])
fig[0].colorbar(this_plot, cax=cax, label='Power [Arbitrary Units]')
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
plt.subplots_adjust(hspace=0, wspace=0)
if saving_fig:
if not plot_name:
plot_name = 'Candidate_waterfall_plots.%s.t%.0f.%s.f%.0f.png' % (
target, epoch, local_host, mid_f * 1e6)
print('Saving png figure.')
plt.savefig(plot_name, bbox_inches='tight')
if save_pdf_plot:
print('Saving pdf figure.')
plt.savefig(plot_name.replace('.png', '') + '.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
def plot_waveforms(waveforms,
sensors,
evt,
*,
wf_type="PMT",
range=(None, ),
overlay=False,
sum=False,
zoomx=False,
zoomy=False,
dual=False):
range = slice(*range)
wfsize = waveforms.shape[2]
time = np.arange(wfsize).astype(float)
if wf_type == "PMT": time /= 40
elif wf_type == "BLR": time /= 40
elif wf_type == "SiPM": pass
else:
raise ValueError("Unrecognized wf type {}. ".format(wf_type) +
"Valid options: are 'PMT', 'BLR' and 'SiPM'")
if sum: wf_type += " SUM"
gmin, gmax = float("inf"), -float("inf")
plt.ion()
ax1 = plt.gca()
if sum:
sum_wf = np.zeros(waveforms.shape[2])
if dual:
for wf, wf_dual, ID, color in zip(waveforms[0][range],
waveforms[1][range], sensors[range],
_colors):
ymin, ymax = min(wf_dual), max(wf_dual)
if ymin < gmin: gmin = ymin
if ymax > gmax: gmax = ymax
plt.plot(wf, drawstyle="steps", label=str(ID[0]), c=color)
plt.plot(wf_dual,
drawstyle="steps",
label=str(ID[0]),
c=next(_colors))
ylim = (0.99 * ymin, 1.01 * ymax)
customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt,
ID[0])
show_and_wait()
else:
for wf, ID, color in zip(waveforms[0][range], sensors[range], _colors):
ymin, ymax = min(wf), max(wf)
if ymin < gmin: gmin = ymin
if ymax > gmax: gmax = ymax
if sum:
bls_wf = wf - mode(wf)
sum_wf = sum_wf + bls_wf * (1 if "SiPM" in wf_type else -1)
else:
plt.plot(wf, drawstyle="steps", label=str(ID[0]), c=color)
if not overlay and not sum:
ylim = (0.99 * ymin, 1.01 * ymax)
customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt,
ID[0])
show_and_wait()
if overlay:
ylim = 0.99 * gmin, 1.01 * gmax
customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt)
show_and_wait()
if sum:
ylim = np.min(sum_wf) - 50, np.max(sum_wf) + 50
plt.plot(sum_wf, drawstyle="steps", c="k")
customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt)
show_and_wait()
def ratdisp(workdir='.', targetdir='.', cams=[0, 1, 2, 3], auto=False):
# check for non-list camera argument
if type(cams) is not list:
cams = [cams] # convert to list
pl.ion() # pylab in interactive mode
# move to working directory
start_dir = os.getcwd()
os.chdir(workdir)
d = os.getcwd().split('/')[-1] # name of current directory
if not auto:
print "\n* * * displaying science frames in {} for selection * * *".format(
d)
else:
print "\n* * * selecting all science frames in {} * * *".format(d)
# make target directory if it does not exist
if not os.path.exists(targetdir):
print "Creating target directory: ", targetdir
os.makedirs(targetdir)
# warn user if previous files may be overwritten
else:
print "Warning: Target directory exists. Existing files will be overwritten."
resp = raw_input("Proceed? (y/n): ")
if resp.lower() != 'y':
print "Exiting..."
os.chdir(start_dir) # move back to starting directory
return
# remove tailing / from target directory name if present
if targetdir[-1] == '/':
targetdir = targetdir[:-1]
# delete existing object and sky lists
fntemp = glob('{}/{}*.list'.format(targetdir, af.OBJ_NAME))
for fn in fntemp:
os.remove(fn)
fntemp = glob('{}/{}*.list'.format(targetdir, af.SKY_NAME))
for fn in fntemp:
os.remove(fn)
# work on FITs files for specified cameras
for cam_i in cams:
# print current camera number
print "\n* * * CAMERA {} * * *".format(cam_i)
# CCDs
if cam_i in [0, 1]:
fn_list = '{}_{}.list'.format(af.CAM_NAMES[cam_i], d)
try:
fin = open(fn_list, 'r') # open list of FITs files
except IOError:
print "Warning: {} not found. Skipping camera {}.".format(
fn_list, cam_i)
else:
# look at FITs data sequentially
for line in fin:
fits_fn = line.rstrip(
) # current fits file name with return removed
fits_id = fits_fn.split('.')[
0] # fits file name with extention removed
print '\n{}'.format(fits_fn)
# open data
hdulist = pf.open(fits_fn)
im = hdulist[0].data
h = hdulist[0].header
# check for required header keywords
if 'PRPSLID' in h:
prpslid = h['PRPSLID']
else:
"ERROR: ratdisp - PRPSLID not found in fits header."
os.chdir(start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
if 'VSTID' in h:
vstid = h['VSTID']
else:
"ERROR: ratdisp - VSTID keyword not found in fits header."
os.chdir(start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
targname = '{}-vis{}'.format(prpslid, vstid)
targname_sky = '{}-sky'.format(targname)
# rotate image or not
im = np.rot90(im, af.CAM_ROTAT[cam_i])
# get image statistics
m = np.median(im)
s = af.robust_sigma(im)
# display image and prompt user
if not auto:
implot = zoom(
im, ZOOM_LVL) # change image size for display
axim = pl.imshow(implot,
vmin=m - 5 * s,
vmax=m + 5 * s,
origin='lower',
cmap=pl.cm.gray)
axim.axes.set_xticks(axim.axes.get_xlim())
axim.axes.set_xticklabels(['E', 'W'])
axim.axes.set_yticks(axim.axes.get_ylim())
axim.axes.set_yticklabels(['S', 'N'])
pl.title(
r"{} band, Median = {}, $\sigma$ = {:.1f}".format(
h['FILTER'], int(m), s))
# print filter name
print '\t* Filter used: {}'.format(h['FILTER'])
# query user until valid response is provided
valid_entry = False
while not valid_entry:
# either select all if auto, or have user select
if auto:
direction = 'y'
else:
direction = raw_input(
"\nType Y for YES, N for NO, Q for QUIT: ")
if direction.lower() == 'y':
# set keyword values
h['PIXSCALE'] = af.CAM_PXSCALE[cam_i]
h['WAVELENG'] = 'OPT'
h['GAIN'] = (af.CAM_GAIN[cam_i](h['SOFTGAIN']),
'in electrons/DN')
h['SATURATE'] = (af.CAM_SATUR[cam_i](
h['SOFTGAIN']), 'in electrons/DN')
# object frame
imfits = '{}/{}_{}_{}.fits'.format(
targetdir, fits_id, af.OBJ_NAME, cam_i)
h['TARGNAME'] = targname
hdulist.writeto(imfits,
clobber=True) # save object frame
fout = open(
'{}/{}_{}.list'.format(targetdir, af.OBJ_NAME,
h['FILTER']),
'a') # append if this filter's list exists
fout.write(fits_id +
'\n') # write new file name to list
fout.close()
# sky frame
skyfits = '{}/{}_{}_{}.fits'.format(
targetdir, fits_id, af.SKY_NAME, cam_i)
h['TARGNAME'] = targname_sky
hdulist.writeto(skyfits,
clobber=True) # save sky frame
fout = open(
'{}/{}_{}.list'.format(targetdir, af.SKY_NAME,
h['FILTER']),
'a') # append if this filter's list exists
fout.write(fits_id +
'\n') # write new file name to list
fout.close()
valid_entry = True
elif direction.lower() == 'q': # exit function
print "Exiting..."
os.chdir(
start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
elif direction.lower() != 'n': # invalid case
print "'{}' is not a valid entry.".format(
direction)
else: # 'N' selected, skip
valid_entry = True
hdulist.close() # close FITs file
pl.close('all') # close image to free memory
# close files
fin.close()
# H2RGs
if cam_i in [2, 3]:
fn_list = '{}_{}.list'.format(af.CAM_NAMES[cam_i], d)
try:
fin = open(fn_list, 'r') # open list of FITs files
except IOError:
print "Warning: {} not found. Skipping camera {}.".format(
fn_list, cam_i)
else:
fout_img = [
open(
'{}/{}_{}.list'.format(targetdir, af.OBJ_NAME,
af.H2RG_FILTERS[cam_i - 2]),
'w'),
open(
'{}/{}_{}.list'.format(targetdir, af.OBJ_NAME,
af.H2RG_FILTERS[cam_i]), 'w')
] # create list files for new img FITs files (e, w)
fout_sky = [
open(
'{}/{}_{}.list'.format(targetdir, af.SKY_NAME,
af.H2RG_FILTERS[cam_i - 2]),
'w'),
open(
'{}/{}_{}.list'.format(targetdir, af.SKY_NAME,
af.H2RG_FILTERS[cam_i]), 'w')
] # create list files for new sky FITs files (e, w)
# look at FITs data sequentially
for line in fin:
fits_fn = line.rstrip(
) # current fits file name with return removed
fits_id = fits_fn.split('.')[
0] # fits file name with extention removed
print '\n{}'.format(fits_fn)
# open data
hdulist = pf.open(fits_fn)
im = hdulist[0].data
h = hdulist[0].header
# check for required header keywords
if 'PRPSLID' in h:
prpslid = h['PRPSLID']
else:
"ERROR: ratdisp - PRPSLID not found in fits header."
os.chdir(start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
if 'VSTID' in h:
vstid = h['VSTID']
else:
"ERROR: ratdisp - VSTID keyword not found in fits header."
os.chdir(start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
if af.CENTER_KEY in h:
center = h[af.CENTER_KEY].split('center')[0]
else:
"ERROR: ratdisp - {} keyword not found in fits header.".format(
af.CENTER_KEY)
os.chdir(start_dir) # move back to starting directory
pl.close('all')
return
targname = '{}-vis{}'.format(prpslid, vstid)
targname_sky = '{}-sky'.format(targname)
# rotate FITs data to align with filters (Z/J in E-left, Y/H in W-right)
im = np.rot90(im, af.CAM_ROTAT[cam_i])
if cam_i == 3:
im = np.flipud(im) # flip C3 about y axis
# get image statistics
imleft = im[af.H2RG_SLICES[cam_i - 2]]
mleft = np.median(imleft)
sleft = af.robust_sigma(imleft)
imright = im[af.H2RG_SLICES[cam_i]]
mright = np.median(imright)
sright = af.robust_sigma(imright)
# display image and prompt user
if not auto:
imleft = zoom(
imleft, ZOOM_LVL) # change image size for display
pl.subplot(121)
pl.subplots_adjust(left=0.125,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.0,
hspace=0.2)
axim = pl.imshow(imleft,
vmin=mleft - 5 * sleft,
vmax=mleft + 5 * sleft,
origin='lower',
cmap=pl.cm.gray)
axim.axes.set_xticks([axim.axes.get_xlim()[0]])
axim.axes.set_xticklabels(['E'])
axim.axes.set_yticks(axim.axes.get_ylim())
axim.axes.set_yticklabels(['S', 'N'])
pl.title(r"{} band".format(af.H2RG_FILTERS[cam_i -
2]) + '\n' +
"Median = {}, $\sigma$ = {:.1f}".format(
int(mleft), sleft))
imright = zoom(
imright, ZOOM_LVL) # change image size for display
pl.subplot(122)
axim = pl.imshow(imright,
vmin=mright - 5 * sright,
vmax=mright + 5 * sright,
origin='lower',
cmap=pl.cm.gray)
axim.axes.set_xticks([axim.axes.get_xlim()[1]])
axim.axes.set_xticklabels(['W'])
axim.axes.set_yticks([])
pl.title(r"{} band".format(af.H2RG_FILTERS[cam_i]) +
'\n' +
"Median = {}, $\sigma$ = {:.1f}".format(
int(mright), sright))
# print target center for user
if center.count(af.H2RG_FILTERS[cam_i]) != 0:
print "\t* The target is focused on the {} filter.".format(
af.H2RG_FILTERS[cam_i])
elif center.count(af.H2RG_FILTERS[cam_i - 2]) != 0:
print "\t* The target is focused on the {} filter.".format(
af.H2RG_FILTERS[cam_i - 2])
else:
print "\t* Warning: The target is NOT focused on an H2RG filter. The target is focused on the {} filter.".format(
center)
# query user until valid response is provided
valid_entry = False
while not valid_entry:
# either select based on center keyword if auto, or have user select
if auto:
if center.count(af.H2RG_FILTERS[cam_i]) != 0:
direction = 'w'
elif center.count(af.H2RG_FILTERS[cam_i - 2]) != 0:
direction = 'e'
else:
print "\t* Warning: Skipping frame not centered on H2RG filter."
direction = 'n'
else:
direction = raw_input(
"\nType E for EAST, W for WEST, N for NEXT, Q for QUIT: "
)
if direction.lower() == 'e' or direction.lower(
) == 'w': # selected
h['PIXSCALE'] = af.CAM_PXSCALE[cam_i]
h['WAVELENG'] = 'IR'
h['GAIN'] = (af.CAM_GAIN[cam_i](h['SOFTGAIN']),
'in electrons/DN')
h['SATURATE'] = (af.CAM_SATUR[cam_i](
h['SOFTGAIN']), 'in electrons/DN')
if direction.lower() == 'e':
f_img = cam_i - 2
f_sky = cam_i
else:
f_img = cam_i
f_sky = cam_i - 2
# filter side with object
imfits = '{}/{}_{}_{}.fits'.format(
targetdir, fits_id, af.OBJ_NAME,
af.H2RG_FILTERS[f_img])
im_img = im[af.H2RG_SLICES[f_img]]
h['NAXIS1'] = af.H2RG_SLICES[f_img][
0].stop - af.H2RG_SLICES[f_img][0].start
h['NAXIS2'] = af.H2RG_SLICES[f_img][
1].stop - af.H2RG_SLICES[f_img][1].start
h['FILTER'] = af.H2RG_FILTERS[f_img]
h['TARGNAME'] = targname
pf.writeto(imfits, im_img, header=h,
clobber=True) # save object frame
fout_img[0].write('{}_{}_{}\n'.format(
fits_id, af.OBJ_NAME, af.H2RG_FILTERS[f_img])
) # write new file name to list
# filter side with sky
skyfits = '{}/{}_{}_{}.fits'.format(
targetdir, fits_id, af.SKY_NAME,
af.H2RG_FILTERS[f_sky])
im_sky = im[af.H2RG_SLICES[f_sky]]
h['NAXIS1'] = af.H2RG_SLICES[f_sky][
0].stop - af.H2RG_SLICES[f_sky][
0].start # repeat incase filter sizes differ
h['NAXIS2'] = af.H2RG_SLICES[f_sky][
1].stop - af.H2RG_SLICES[f_sky][1].start
h['FILTER'] = af.H2RG_FILTERS[f_sky]
h['TARGNAME'] = targname_sky
pf.writeto(skyfits, im_sky, header=h,
clobber=True) # save sky frame
fout_sky[0].write('{}_{}_{}\n'.format(
fits_id, af.SKY_NAME, af.H2RG_FILTERS[f_sky])
) # write new file name to list
valid_entry = True
elif direction.lower() == 'q': # exit function
print "Exiting..."
os.chdir(
start_dir) # move back to starting directory
pl.close('all') # close image to free memory
return
elif direction.lower() != 'n': # invalid case
print "'{}' is not a valid entry.".format(
direction)
else: # 'N' selected, skip
valid_entry = True
hdulist.close() # close FITs file
pl.close('all') # close image to free memory
# close files
fin.close()
for f in fout_img:
f.close()
for f in fout_sky:
f.close()
# move back to starting directory
os.chdir(start_dir)
def run_detect(datasets_dir):
filenames = [f for f in sorted(os.listdir(datasets_dir)) if f.lower().endswith(".png")]
for i, filename in enumerate(filenames):
print i, filename
filename = os.path.join(datasets_dir, filename)
# filename = '../samples/aa_test.png'
image = cv2.imread(filename)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
show("gray", gray)
ret, thresholded = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY_INV)
show("thresholded", thresholded)
toContour = thresholded
# Added by Caryn to erase dots
# # kernel = np.ones((3,3),np.uint8)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
# opened = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel)
# show("opened", opened)
# toContour = opened
results = cv2.findContours(toContour, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
if len(results) == 2:
contours = results[0]
else:
contours = results[1]
draw_image = ~image.copy()
newimage = np.zeros(thresholded.shape, dtype=np.uint8)
boxes = [cv2.boundingRect(contour) for contour in contours]
widths = [box[2] for box in boxes]
typical_width = np.median(widths)
merge_width = int(typical_width)
# merge letters to form word blocks
for box in boxes:
if box[2] > 5 * typical_width or box[3] > 5 * typical_width:
continue
cv2.rectangle(newimage, (box[0] - merge_width, box[1]), (box[0] + box[2] + merge_width, box[1] + box[3]), 255, -1)
# refind contours in merged line image
boximage = newimage.copy()
results = cv2.findContours(newimage, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
if len(results) == 2:
contours = results[0]
else:
contours = results[1]
# make histogram of x coverage of word boxes.
hist_x1 = np.zeros(image.shape[1])
for contour in contours:
box = cv2.boundingRect(contour)
hist_x1[box[0]:box[0]+box[2]] += 1
max_x = np.max(hist_x1)
line_x = np.where(hist_x1 > max_x * 0.6)
maxtab, mintab = peakdetect(hist_x1, np.max(hist_x1) * 0.2)
for i, x in maxtab:
x = int(i)
cv2.line(draw_image, (x, 0), (x, 2000), (0, 0, 255), 2)
draw_image[boximage != 0, 0] = 255
cv2.imshow("process", draw_image)
pylab.clf()
pylab.plot(hist_x1)
pylab.plot(maxtab[:, 0], maxtab[:, 1], 'o')
pylab.ion()
pylab.show()
cv2.waitKey(0)
def ratdisp_calib(ftype=af.FLAT_NAME,
workdir='.',
cams=[0, 1, 2, 3],
auto=False,
amin=0.1,
amax=0.8):
# check for non-list camera argument
if type(cams) is not list:
cams = [cams] # convert to list
pl.ion() # pylab in interactive mode
# move to working directory
start_dir = os.getcwd()
os.chdir(workdir)
d = os.getcwd().split('/')[-1] # name of current directory
if not auto:
print "\n* * * displaying {} frames in {} for selection * * *".format(
ftype, d)
else:
print "\n* * * automatically selecting {} frames in {} * * *".format(
ftype, d)
# delete existing calibration lists
fntemp = glob(ftype + '*.list')
for fn in fntemp:
os.remove(fn)
# work on FITs files for specified cameras
for cam_i in cams:
# print current camera number
print "\n* * * CAMERA {} * * *".format(cam_i)
# CCDs
if cam_i in [0, 1]:
fn_list = '{}_{}.list'.format(af.CAM_NAMES[cam_i], d)
try:
fin = open(fn_list, 'r') # open list of FITs files
except IOError:
print "Warning: {} not found. Skipping camera {}.".format(
fn_list, cam_i)
else:
# look at FITs data sequentially
for line in fin:
fits_fn = line.rstrip(
) # current fits file name with return removed
fits_id = fits_fn.split('.')[
0] # fits file name with extention removed
print '{}'.format(fits_fn)
# open data
hdulist = pf.open(fits_fn)
im = hdulist[0].data
h = hdulist[0].header
# all bias frames are selected
if auto and ftype is af.BIAS_NAME:
fout = open('{}_{}.list'.format(ftype, cam_i),
'a') # append if this filter's list exists
fout.write(fits_id +
'\n') # write new file name to list
fout.close()
else:
# no rotation needed (why is C0 rotated by 270 deg in IDL code?)
im = np.rot90(im, af.CAM_ROTAT[cam_i])
# Let user determine if the image is good or not
implot = zoom(
im, ZOOM_LVL) # change image size for display
m = np.median(im)
s = af.robust_sigma(im)
print '\t* Median is {} counts.'.format(m)
# print filter name
print '\t* Filter used: {}'.format(h['FILTER'])
# select non-saturated flat frames with sufficient counts
if auto and ftype is af.FLAT_NAME:
# check whether median value is in specified range
sat_pt = af.CAM_SATUR[cam_i](h['SOFTGAIN'])
vmin = amin * sat_pt
vmax = amax * sat_pt
if m > vmin and m < vmax:
print "\t* Frame selected."
fout = open(
'{}_{}.list'.format(ftype, h['FILTER']),
'a') # append if this filter's list exists
fout.write(fits_id +
'\n') # write new file name to list
fout.close()
else:
if m < vmin:
print "\t* Frame rejected:\tUNDEREXPOSED."
else:
print "\t* Frame rejected:\tSATURATED."
# display image and prompt user
else:
axim = pl.imshow(implot,
vmin=m - 5 * s,
vmax=m + 5 * s,
origin='lower',
cmap=pl.cm.gray)
pl.title(r"Median = {}, $\sigma$ = {:.1f}".format(
int(m), s))
# query user until valid response is provided
valid_entry = False
while not valid_entry:
direction = raw_input(
"\nType Y for YES, N for NO, Q for QUIT: ")
if direction.lower() == 'y':
fout = open(
'{}_{}.list'.format(
ftype, h['FILTER']), 'a'
) # append if this filter's list exists
fout.write(
fits_id +
'\n') # write new file name to list
fout.close()
valid_entry = True
elif direction.lower() == 'q': # exit function
print "Exiting..."
os.chdir(
start_dir
) # move back to starting directory
pl.close(
'all') # close image to free memory
return
elif direction.lower() != 'n': # invalid case
print "'{}' is not a valid entry.".format(
direction)
else: # 'N' selected, skip
valid_entry = True
hdulist.close() # close FITs file
pl.close('all') # close image to free memory
# close files
fin.close()
# H2RGs
if cam_i in [2, 3]:
fn_list = '{}_{}.list'.format(af.CAM_NAMES[cam_i], d)
try:
fin = open(fn_list, 'r') # open list of FITs files
except IOError:
print "Warning: {} not found. Skipping camera {}.".format(
fn_list, cam_i)
else:
isfoutopen = False
# H2RGs do not have bias frames. inform user
if auto and ftype is af.BIAS_NAME:
print "\t* Warning: H2RG detectors do not have {} frames. Skipping...".format(
af.BIAS_NAME)
else:
fout = [
open(
'{}_{}.list'.format(ftype,
af.H2RG_FILTERS[cam_i - 2]),
'w'),
open(
'{}_{}.list'.format(ftype, af.H2RG_FILTERS[cam_i]),
'w')
] # create list files for new img FITs files (e, w)
isfoutopen = True
# look at FITs data sequentially
for line in fin:
fits_fn = line.rstrip(
) # current fits file name with return removed
fits_id = fits_fn.split('.')[
0] # fits file name with extention removed
print '{}'.format(fits_fn)
# open data
hdulist = pf.open(fits_fn)
im = hdulist[0].data
h = hdulist[0].header
# rotate FITs data to align with filters (Z/J in E-left, Y/H in W-right)
im = np.rot90(im, af.CAM_ROTAT[cam_i])
if cam_i == 3:
im = np.flipud(im) # flip C3 about y axis
# Let user determine if the image is good or not
implot = zoom(im,
ZOOM_LVL) # change image size for display
imleft = im[af.H2RG_SLICES[cam_i - 2]]
mleft = np.median(imleft)
sleft = af.robust_sigma(imleft)
imright = im[af.H2RG_SLICES[cam_i]]
mright = np.median(imright)
sright = af.robust_sigma(imright)
print '\t* Median of left side is {} counts'.format(mleft)
print '\t* Median of right side is {} counts'.format(
mright)
# select non-saturated flat frames with sufficient counts
if auto and ftype is af.FLAT_NAME:
# check whether median values are in specified range
sat_pt = af.CAM_SATUR[cam_i](h['SOFTGAIN'])
vmin = amin * sat_pt
vmax = amax * sat_pt
# left side
if mleft > vmin and mleft < vmax:
print "\t* Left side selected."
imfits_left = '{}_{}_{}.fits'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i - 2])
h['FILTER'] = af.H2RG_FILTERS[cam_i - 2]
pf.writeto(imfits_left,
imleft,
header=h,
clobber=True) # save left frame
fout[0].write('{}_{}_{}\n'.format(
fits_id, ftype, af.H2RG_FILTERS[
cam_i - 2])) # write new file name to list
else:
if mleft < vmin:
print "\t* Left side rejected:\tUNDEREXPOSED."
else:
print "\t* Left side rejected:\tSATURATED."
# right side
if mright > vmin and mright < vmax:
print "\t* Right side selected."
imfits_right = '{}_{}_{}.fits'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i])
h['FILTER'] = af.H2RG_FILTERS[cam_i]
pf.writeto(imfits_right,
imright,
header=h,
clobber=True) # save object frame
fout[1].write('{}_{}_{}\n'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i])
) # write new file name to list
else:
if mleft < vmin:
print "\t* Right side rejected:\tUNDEREXPOSED."
else:
print "\t* Right side rejected:\tSATURATED."
# display image and prompt user
else:
# display image and prompt user
pl.subplot(121)
pl.subplots_adjust(left=0.125,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.0,
hspace=0.2)
axim = pl.imshow(imleft,
vmin=mleft - 5 * sleft,
vmax=mleft + 5 * sleft,
origin='lower',
cmap=pl.cm.gray)
axim.axes.set_xticks([axim.axes.get_xlim()[0]])
axim.axes.set_xticklabels(['E'])
axim.axes.set_yticks(axim.axes.get_ylim())
axim.axes.set_yticklabels(['S', 'N'])
pl.title(r"Median = {}, $\sigma$ = {:.1f}".format(
int(mleft), sleft))
pl.subplot(122)
axim = pl.imshow(imright,
vmin=mright - 5 * sright,
vmax=mright + 5 * sright,
origin='lower',
cmap=pl.cm.gray)
axim.axes.set_xticks([axim.axes.get_xlim()[1]])
axim.axes.set_xticklabels(['W'])
axim.axes.set_yticks([])
pl.title(r"Median = {}, $\sigma$ = {:.1f}".format(
int(mright), sright))
# query user until valid response is provided, Z & J face the EASTERN part of the field
valid_entry = False
while not valid_entry:
direction = raw_input(
"\nType Y for YES, N for NO, Q for QUIT: ")
if direction.lower() == 'y':
imfits_left = '{}_{}_{}.fits'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i - 2])
h['FILTER'] = af.H2RG_FILTERS[cam_i - 2]
pf.writeto(imfits_left,
imleft,
header=h,
clobber=True) # save object frame
fout[0].write('{}_{}_{}\n'.format(
fits_id, ftype, af.H2RG_FILTERS[
cam_i -
2])) # write new file name to list
imfits_right = '{}_{}_{}.fits'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i])
h['FILTER'] = af.H2RG_FILTERS[cam_i]
pf.writeto(imfits_right,
imright,
header=h,
clobber=True) # save object frame
fout[1].write('{}_{}_{}\n'.format(
fits_id, ftype, af.H2RG_FILTERS[cam_i])
) # write new file name to list
valid_entry = True
elif direction.lower() == 'q': # exit function
print "Exiting..."
os.chdir(start_dir
) # move back to starting directory
pl.close('all') # close image to free memory
return
elif direction.lower() != 'n': # invalid case
print "'{}' is not a valid entry.".format(
direction)
else: # 'N' selected, skip
valid_entry = True
hdulist.close() # close FITs file
pl.close('all') # close image to free memory
# close files
fin.close()
if isfoutopen:
for f in fout:
f.close()
# move back to starting directory
os.chdir(start_dir)
def display(q):
'''
The worker thread sends audio data this display thread for real-time
oscilliscope display.
'''
pylab.ion()
fig = pylab.figure(figsize=(12, 6))
ax1 = fig.add_subplot(211)
ax1.grid(True)
ax1.set_xlabel('Time')
ax1.set_ylabel('Amplitude')
ax2 = fig.add_subplot(212)
ax2.grid(True)
ax2.set_xlabel('Frequency (Hz)')
ax2.set_ylabel('Amplitude')
#--------------------------------------------------------------------------
# Create oscilliscope data and plot axes
xdata = np.arange(int(DISP_WIDTH * SR + 0.5)) / float(SR)
n_samples = len(xdata)
ydata = ns.Buffer.zeros(n_samples)
ax1.axis([xdata[0], xdata[-1], -0.5, 0.5])
line1, = ax1.plot(xdata, ydata, rasterized=True)
# Create spectrogram data and plot axes
spec_args = (SR, SPEC_WINDOW, 0.010, ns.NUTTALL)
spec = ns.Spectrogram(ydata, *spec_args)
spec_xaxis = np.array(spec.getFrequencyAxis())
spec_yzeros = np.array(spec.computeMagnitude(ydata)[1:])
line2, = ax2.plot(spec_xaxis, spec_yzeros, rasterized=True)
ax2.set_xlim(20, SPEC_MAX_FREQ)
ax2.set_xscale('log')
ax2.set_ylim(-90, 60)
fig.canvas.draw()
# cache the backgrounds
bg1 = fig.canvas.copy_from_bbox(ax1.bbox)
bg2 = fig.canvas.copy_from_bbox(ax2.bbox)
cbuf = ns.CircularBuffer(2 * n_samples)
d0 = now()
dcount = 0
while True:
try:
d = q.get(False)
have_d = True
except queue.Empty:
have_d = False
if not have_d: continue
if d == "QUIT":
print "dcount = ", dcount
return
elif d == "RESET":
data = np.zeros(n_samples)
line1.set_ydata(data)
line2.set_ydata(spec_yzeros)
ax1.draw_artist(ax1.patch)
ax1.draw_artist(line1)
ax2.draw_artist(ax2.patch)
ax2.draw_artist(line2)
fig.canvas.draw()
fig.canvas.flush_events()
cbuf.write(ns.Buffer.zeros(2 * n_samples))
continue
cbuf.write(d)
d1 = now()
dt = d1 - d0
if dt.total_seconds() >= (1.0 / 30):
dcount += 1
data = cbuf.read()
#------------------------------------------------------------------
# oscilliscope
# Search the middle part of the data for the peak value
n = len(data)
i0 = n / 4
i1 = i0 + n_samples / 2
middle = data[i0:i1]
imax = i0 + middle.argmax()
# keep peak at 25 % into the window
i0 = imax - n_samples / 4
i1 = i0 + n_samples
ydata = data[i0:i1]
#------------------------------------------------------------------
# spectrogram
spec_ydata = spec.computeMagnitude(data).getdB()[1:]
#------------------------------------------------------------------
# Redraw plot
line1.set_ydata(ydata)
line2.set_ydata(spec_ydata)
# restore background
fig.canvas.restore_region(bg1)
fig.canvas.restore_region(bg2)
ax1.draw_artist(ax1.patch)
ax1.draw_artist(line1)
ax2.draw_artist(ax2.patch)
ax2.draw_artist(line2)
fig.canvas.blit(ax1.bbox)
fig.canvas.blit(ax2.bbox)
# cache the backgrounds
bg1 = fig.canvas.copy_from_bbox(ax1.bbox)
bg2 = fig.canvas.copy_from_bbox(ax2.bbox)
d0 = now()
def display_video(dataset):
"""Display 3D video."""
plt.ion()
for i in range(dataset.shape[2]):
plt.imshow(dataset[:, i])
plt.pause(0.05)
def plot(self, block=False, period=False):
"""
Create a plot.
"""
try:
import matplotlib
import matplotlib.pylab as mpl
except ImportError:
raise (ImportError("Could not import matplotlib.pylab."))
fbest, T0 = self.best["f"], self.best["T0"]
fig = mpl.figure()
fig.canvas.set_window_title('GLS periodogram')
fig.subplots_adjust(hspace=0.05,
wspace=0.04,
right=0.97,
bottom=0.09,
top=0.84)
fs = 10 # fontsize
# Periodogram
plt = fig.add_subplot(3, 1, 1)
plt.tick_params(direction='in')
if period:
plt.set_xscale("log")
plt.set_xlabel("Period P")
else:
plt.set_xlabel("Frequency f")
plt.set_ylabel(self.label["ylabel"])
plt.plot(1 / self.f if period else self.f,
self.power,
'b-',
linewidth=.5)
# mark the highest peak
plt.plot(1 / fbest if period else fbest,
self.power[self.p.argmax()],
'r.',
label="1/f = %f" % (1 / fbest))
plt.legend(numpoints=1, fontsize=fs, frameon=False)
x2tics = 1 / np.array([0.5, 1, 2, 3, 5, 10, 20., 100])
mx2tics = 1 / np.array([0.75, 1.5, 2.5, 4, 15, 40, 60., 80, 100])
def tick_function(X):
return ["%g" % (1 / z) for z in X]
plt.tick_params(direction='in', which='both', top=True, right=True)
plt.minorticks_on()
plt.autoscale(enable=True, axis='x', tight=True)
if not period:
ax2 = plt.twiny()
ax2.tick_params(direction='in', which='both')
ax2.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, 1 / x, y)
ax2.set_xticks(x2tics)
ax2.set_xticks(mx2tics, minor=True)
ax2.set_xticklabels(tick_function(x2tics))
ax2.set_xlim(plt.get_xlim())
ax2.set_xlabel("Period")
plt.tick_params(top=False)
# Data and model
col = mpl.cm.rainbow(mpl.Normalize()(self.t))
def plot_ecol(plt, x, y):
# script for scatter plot with errorbars and time color-coded
datstyle = dict(color=col,
marker='.',
edgecolor='k',
linewidth=0.5,
zorder=2)
if self.e_y is not None:
errstyle = dict(yerr=self.e_y,
marker='',
ls='',
elinewidth=0.5)
if matplotlib.__version__ < '2.':
errstyle['capsize'] = 0.
datstyle['s'] = 8**2 # requires square size !?
else:
errstyle['ecolor'] = col
_, _, (c, ) = plt.errorbar(x, y, **errstyle)
if matplotlib.__version__ < '2.':
c.set_color(col)
plt.scatter(x, y, **datstyle)
def phase(t):
#return (t-T0)*fbest % 1
return (t - T0) % (1 / fbest)
# Time series
tt = arange(self.t.min(), self.t.max(), 0.01 / fbest)
ymod = self.sinmod(tt)
yfit = self.sinmod()
plt1 = fig.add_subplot(3, 2, 3)
plt1.set_ylabel("Data")
mpl.setp(plt1.get_xticklabels(), visible=False)
plot_ecol(plt1, self.t, self.y)
plt1.plot(tt, ymod, 'k-', zorder=0)
# Phase folded data
tt = arange(T0, T0 + 1 / fbest, 0.01 / fbest)
yy = self.sinmod(tt)
plt2 = fig.add_subplot(3, 2, 4, sharey=plt1)
mpl.setp(plt2.get_xticklabels(), visible=False)
mpl.setp(plt2.get_yticklabels(), visible=False)
plot_ecol(plt2, phase(self.t), self.y)
xx = phase(tt)
ii = np.argsort(xx)
plt2.plot(xx[ii], yy[ii], 'k-')
plt2.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, x * fbest, y
)
# Time serie of residuals
yres = self.y - yfit
plt3 = fig.add_subplot(3, 2, 5, sharex=plt1)
plt3.set_xlabel("Time")
plt3.set_ylabel("Residuals")
plot_ecol(plt3, self.t, yres)
plt3.plot([self.t.min(), self.t.max()], [0, 0], 'k-')
# Phase folded residuals
plt4 = fig.add_subplot(3, 2, 6, sharex=plt2, sharey=plt3)
plt4.set_xlabel("Phase")
mpl.setp(plt4.get_yticklabels(), visible=False)
plot_ecol(plt4, phase(self.t), yres)
plt4.plot([0, 1 / fbest], [0, 0], 'k-')
plt4.format_coord = lambda x, y: "x=%g, x2=%g, y=%g" % (x, x * fbest, y
)
for x in [plt1, plt2, plt3, plt4]:
x.tick_params(direction='in', which='both', top=True, right=True)
x.minorticks_on()
x.autoscale(enable=True, tight=True)
if hasattr(mpl.get_current_fig_manager(), 'toolbar'):
# check seems not needed when "TkAgg" is set
mpl.get_current_fig_manager().toolbar.pan()
#t = fig.canvas.toolbar
#mpl.ToggleTool(mpl.wx_ids['Pan'], False)
if block:
print("Close the plot to continue.")
else:
mpl.ion()
fig.tight_layout() # to get the left margin
marleft = fig.subplotpars.left * fig.get_figwidth() * fig.dpi / fs
def tighter():
# keep margin tight when resizing
xdpi = fs / (fig.get_figwidth() * fig.dpi)
ydpi = fs / (fig.get_figheight() * fig.dpi)
fig.subplots_adjust(bottom=4. * ydpi,
top=1 - 8. * ydpi,
right=1 - 1 * xdpi,
wspace=4 * xdpi,
hspace=4 * ydpi,
left=marleft * xdpi)
if matplotlib.__version__ < '2.':
ax2.set_position(plt.get_position().translated(0, 4 * ydpi))
plt.set_position(plt.get_position().translated(0, 4 * ydpi))
#fig.canvas.mpl_connect("resize_event", lambda _: (fig.tight_layout()))
fig.canvas.mpl_connect("resize_event", lambda _: (tighter()))
mpl.show()
# mpl.show(block=block) # unexpected keyword argument 'block' in older matplotlib
return mpl
def test_fps(use_blit=True):
ax1.cla()
ax1.set_title('Sensor Input vs. Time -' +
'Blit [{0:3s}]'.format("On" if use_blit else "Off"))
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Sensor Input (mV)')
plt.ion(
) # Set interactive mode ON, so matplotlib will not be blocking the window
plt.show(False) # Set to false so that the code doesn't stop here
cur_time = time.time()
ax1.hold(True)
x, y = [], []
times = [time.time() - cur_time] # Create blank array to hold time values
y.append(0)
line1, = ax1.plot(times,
y,
'.-',
alpha=0.8,
color="gray",
markerfacecolor="red")
fig.show()
fig.canvas.draw()
if use_blit:
background = fig.canvas.copy_from_bbox(
ax1.bbox) # cache the background
tic = time.time()
niter = 200
i = 0
while i < niter:
fields = random.random() * 100
times.append(time.time() - cur_time)
y.append(fields)
# this removes the tail of the data so you can run for long hours. You can cache this
# and store it in a pickle variable in parallel.
if len(times) > 50:
del y[0]
del times[0]
xmin, xmax, ymin, ymax = [min(times) / 1.05, max(times) * 1.1, -5, 110]
# feed the new data to the plot and set the axis limits again
line1.set_xdata(times)
line1.set_ydata(y)
plt.axis([xmin, xmax, ymin, ymax])
if use_blit:
fig.canvas.restore_region(background) # restore background
ax1.draw_artist(line1) # redraw just the points
fig.canvas.blit(ax1.bbox) # fill in the axes rectangle
else:
fig.canvas.draw()
i += 1
fps = niter / (time.time() - tic)
print "Blit [{0:3s}] -- FPS: {1:.1f}, time resolution: {2:.4f}s".format(
"On" if use_blit else "Off", fps, 1 / fps)
return fps
def process_data(self):
"""Create figure plotting distributions of scan-firing measures and
compute various relevant statistics
"""
from scanr.tools.stats import smooth_pdf, t_one_tailed
from scanr.tools.string import snake2title
from scipy.stats import (ttest_ind as ttest, t as t_dist, ks_2samp as kstest)
os.chdir(self.datadir)
self.out.outfd = file('figure.log', 'w')
self.out.timestamp = False
self.figure = {}
self.figure['distros'] = f = plt.figure(figsize=(10, 16))
f.suptitle('Distributions of LEC/MEC Scan/Non-scan Firing')
LEC = self.results['LEC']
MEC = self.results['MEC']
data_types = LEC.dtype.names
N = len(data_types)
def mean_pm_sem(a):
return '%.4f +/- %.4f'%(a.mean(), a.std()/np.sqrt(a.size))
plt.ioff()
kw = dict(lw=2, aa=True)
for i, data in enumerate(data_types):
ax = plt.subplot(N, 1, i+1)
label = snake2title(data)
kw.update(label='LEC', c='b')
ax.plot(*smooth_pdf(LEC[data]), **kw)
kw.update(label='MEC', c='g')
ax.plot(*smooth_pdf(MEC[data]), **kw)
ax.axis('tight')
v = list(ax.axis())
v[3] *= 1.1
ax.axis(v)
med_LEC = np.median(LEC[data])
med_MEC = np.median(MEC[data])
ax.plot([med_LEC]*2, [v[2], v[3]], 'b--')
ax.plot([med_MEC]*2, [v[2], v[3]], 'g--')
self.out(label.center(50, '-'))
N_LEC = LEC[data].size
N_MEC = MEC[data].size
self.out('Median LEC(%s) = %.4f'%(label, med_LEC))
self.out('Mean/SEM LEC(%s) = %s'%(label, mean_pm_sem(LEC[data])))
if data.endswith('norm'):
self.out('T(LEC > 1) = %.4f, p = %.8f'%t_one_tailed(LEC[data]))
self.out('T_cstv(LEC > 1) = %.4f, p = %.8f'%t_one_tailed(
LEC[data], df=N_LEC/float(5)-1))
self.out('N LEC cell-sessions = %d'%N_LEC)
self.out('Median MEC(%s) = %.4f'%(label, med_MEC))
self.out('Mean/SEM MEC(%s) = %s'%(label, mean_pm_sem(MEC[data])))
if data.endswith('norm'):
self.out('T(MEC > 1) = %.4f, p = %.8f'%t_one_tailed(MEC[data]))
self.out('T_cstv(MEC > 1) = %.4f, p = %.8f'%t_one_tailed(
MEC[data], df=N_MEC/float(5)-1))
self.out('N MEC cell-sessions = %d'%N_MEC)
t = ttest(LEC[data], MEC[data])
k = kstest(LEC[data], MEC[data])
self.out('T-test(%s): t = %.4f, p = %.8f'%(label, t[0], t[1]))
self.out('KS-test(%s): D = %.4f, p = %.8f'%(label, k[0], k[1]))
if t[1] < 0.05:
ax.text(0.025, 0.8, '*t', size='x-large', transform=ax.transAxes)
if k[1] < 0.05:
ax.text(0.025, 0.6, '*KS', size='x-large', transform=ax.transAxes)
ax.set_ylabel('p[ %s ]'%label)
if i == 0:
ax.legend(loc=1)
plt.ion()
plt.show()
self.out.outfd.close()
def plot(self, fig_number=322):
"""plot the stored data in figure `fig_number`.
Dependencies: `matlabplotlib.pylab`
"""
from matplotlib import pylab
from matplotlib.pylab import (
gcf, gca, figure, plot, xlabel, grid, semilogy, text, draw, show, ion,
subplot as _subplot, tight_layout, rcParamsDefault, xlim, ylim
)
def title_(*args, **kwargs):
kwargs.setdefault('size', rcParamsDefault['axes.labelsize'])
pylab.title(*args, **kwargs)
def subtitle(*args, **kwargs):
kwargs.setdefault('horizontalalignment', 'center')
text(0.5 * (xlim()[1] - xlim()[0]), 0.9 * ylim()[1],
*args, **kwargs)
def legend_(*args, **kwargs):
kwargs.setdefault('framealpha', 0.3)
kwargs.setdefault('fancybox', True)
kwargs.setdefault('fontsize', rcParamsDefault['font.size'] - 2)
pylab.legend(*args, **kwargs)
def subplot(*args, **kwargs):
with _warnings.catch_warnings():
_warnings.simplefilter("ignore")
_subplot(*args, **kwargs) # catch unjustified deprecation warning because subplot and add_subplot are not separate
if isinstance(fig_number, int):
figure(fig_number)
dat = self._data # dictionary with entries as given in __init__
if not dat or not dat['eval'] or len(dat['eval']) <= 2:
return
try: # a hack to get the presumable population size lambda
strpopsize = ' (evaluations / %s)' % str(dat['eval'][-2] -
dat['eval'][-3])
except IndexError:
strpopsize = ''
# plot fit, Delta fit, sigma
subplot(221)
gca().clear()
if dat['fit'][0] is None: # plot is fine with None, but comput-
dat['fit'][0] = dat['fit'][1] # tations need numbers
# should be reverted later, but let's be lazy
assert dat['fit'].count(None) == 0
fmin = min(dat['fit'])
imin = dat['fit'].index(fmin)
dat['fit'][imin] = max(dat['fit']) + 1
fmin2 = min(dat['fit'])
dat['fit'][imin] = fmin
semilogy(dat['iter'], [f - fmin if f - fmin > 1e-19 else None
for f in dat['fit']],
'c', linewidth=1, label='f-min(f)')
semilogy(dat['iter'], [max((fmin2 - fmin, 1e-19)) if f - fmin <= 1e-19 else None
for f in dat['fit']], 'C1*')
semilogy(dat['iter'], [abs(f) for f in dat['fit']], 'b',
label='abs(f-value)')
semilogy(dat['iter'], dat['sigma'], 'g', label='sigma')
semilogy(dat['iter'][imin], abs(fmin), 'r*', label='abs(min(f))')
if dat['more_data']:
gca().twinx()
plot(dat['iter'], dat['more_data'])
grid(True)
legend_(*[[v[i] for i in [1, 0, 2, 3]] # just a reordering
for v in gca().get_legend_handles_labels()])
# plot xmean
subplot(222)
gca().clear()
plot(dat['iter'], dat['xmean'])
for i in range(len(dat['xmean'][-1])):
text(dat['iter'][0], dat['xmean'][0][i], str(i))
text(dat['iter'][-1], dat['xmean'][-1][i], str(i))
subtitle('mean solution')
grid(True)
# plot squareroot of eigenvalues
if dat['D'][-1][0] != dat['D'][-1][-1]:
subplot(223)
gca().clear()
semilogy(dat['iter'], dat['D'], 'm')
xlabel('iterations' + strpopsize)
title_('Axis lengths')
grid(True)
# plot stds
subplot(224)
# if len(gcf().axes) > 1:
# sca(pylab.gcf().axes[1])
# else:
# twinx()
gca().clear()
semilogy(dat['iter'], dat['stds'])
for i in range(len(dat['stds'][-1])):
text(dat['iter'][-1], dat['stds'][-1][i], str(i))
title_('Coordinate-wise STDs w/o sigma')
grid(True)
xlabel('iterations' + strpopsize)
_stdout.flush()
tight_layout() # avoid superfluous padding
# draw(), show() # canvas.draw seem to do the job better
ion() # may prevent that everything stops until figure is closed?
# todo: if in the same cell, the subplots are small until the cell is finished
gcf().canvas.draw()
CMAESDataLogger.plotted += 1