def plot_roc(self, roc=None):
"""Plot ROC curves
.. plot::
:include-source:
:width: 80%
from dreamtools import rocs
r = rocs.ROC()
r.scores = [.9,.5,.6,.7,.1,.2,.6,.4,.7,.9, .2]
r.classes = [1,0,1,0,0,1,1,0,0,1,1]
r.plot_roc()
"""
if roc == None:
roc = self.get_roc()
from pylab import plot, xlim, ylim ,grid, title, xlabel, ylabel
x = roc['fpr']
plot(x, roc['tpr'], '-o')
plot([0,1], [0,1],'r')
ylim([0, 1])
xlim([0, 1])
grid(True)
title("ROC curve (AUC=%s)" % self.compute_auc(roc))
xlabel("FPR")
ylabel("TPR")
def simulationWithoutDrugNick(numViruses, maxPop, maxBirthProb, clearProb,
numTrials):
"""
Run the simulation and plot the graph for problem 3 (no drugs are used,
viruses do not have any drug resistance).
For each of numTrials trial, instantiates a patient, runs a simulation
for 300 timesteps, and plots the average virus population size as a
function of time.
numViruses: number of SimpleVirus to create for patient (an integer)
maxPop: maximum virus population for patient (an integer)
maxBirthProb: Maximum reproduction probability (a float between 0-1)
clearProb: Maximum clearance probability (a float between 0-1)
numTrials: number of simulation runs to execute (an integer)
"""
#Instantiate the viruses first, the patient second
viruses= [ SimpleVirus(maxBirthProb, clearProb) for i in range(numViruses) ]
patient = Patient(viruses, maxPop)
#Execute the patient.update method 300 times for 100 trials
steps = 300
countList = [0 for i in range(300)]
for trial in range(numTrials):
for timeStep in range(steps):
countList[timeStep] += patient.update()
avgList = [ countList[i]/float(numTrials) for i in range(steps) ]
#Plot a diagram with xAxis=timeSteps, yAxis=average virus population
xAxis = [ x for x in range(steps) ]
pylab.figure(2)
pylab.plot(xAxis, avgList, 'ro', label='Simple Virus')
pylab.xlabel('Number of elapsed time steps')
pylab.ylabel('Average size of the virus population')
pylab.title('Virus growth in a patient without the aid of any drag')
pylab.legend()
pylab.show()
def check_vpd_ks2_astrometry():
"""
Check the VPD and quiver plots for our KS2-extracted, re-transformed astrometry.
"""
catFile = workDir + '20.KS2_PMA/wd1_catalog.fits'
tab = atpy.Table(catFile)
good = (tab.xe_160 < 0.05) & (tab.ye_160 < 0.05) & \
(tab.xe_814 < 0.05) & (tab.ye_814 < 0.05) & \
(tab.me_814 < 0.05) & (tab.me_160 < 0.05)
tab2 = tab.where(good)
dx = (tab2.x_160 - tab2.x_814) * ast.scale['WFC'] * 1e3
dy = (tab2.y_160 - tab2.y_814) * ast.scale['WFC'] * 1e3
py.clf()
q = py.quiver(tab2.x_814, tab2.y_814, dx, dy, scale=5e2)
py.quiverkey(q, 0.95, 0.85, 5, '5 mas', color='red', labelcolor='red')
py.savefig(workDir + '20.KS2_PMA/vec_diffs_ks2_all.png')
py.clf()
py.plot(dy, dx, 'k.', ms=2)
lim = 30
py.axis([-lim, lim, -lim, lim])
py.xlabel('Y Proper Motion (mas)')
py.ylabel('X Proper Motion (mas)')
py.savefig(workDir + '20.KS2_PMA/vpd_ks2_all.png')
idx = np.where((np.abs(dx) < 10) & (np.abs(dy) < 10))[0]
print('Cluster Members (within dx < 10 mas and dy < 10 mas)')
print((' dx = {dx:6.2f} +/- {dxe:6.2f} mas'.format(dx=dx[idx].mean(),
dxe=dx[idx].std())))
print((' dy = {dy:6.2f} +/- {dye:6.2f} mas'.format(dy=dy[idx].mean(),
dye=dy[idx].std())))
def testTelescope(self):
import matplotlib
matplotlib.use('AGG')
import matplotlib.mlab as ml
import pylab as pl
import time
w0 = 8.0
k = 2*np.pi/3.0
gb = GaussianBeam(w0, k)
lens = ThinLens(150, 150)
gb2 = lens*gb
self.assertAlmostEqual(gb2._z0, gb._z0 + 2*150.0)
lens2 = ThinLens(300, 600)
gb3 = lens2*gb2
self.assertAlmostEqual(gb3._z0, gb2._z0 + 2*300.0)
self.assertAlmostEqual(gb._w0, gb3._w0/2.0)
z = np.arange(0, 150)
z2 = np.arange(150, 600)
z3 = np.arange(600, 900)
pl.plot(z, gb.w(z, k), z2, gb2.w(z2, k), z3, gb3.w(z3, k))
pl.grid()
pl.xlabel('z')
pl.ylabel('w')
pl.savefig('testTelescope1.png')
time.sleep(0.1)
pl.close('all')
def plotear(xi,yi,zi):
# mask inner circle
interior1 = sqrt(((xi+1.5)**2) + (yi**2)) < 1.0
interior2 = sqrt(((xi-1.5)**2) + (yi**2)) < 1.0
zi[interior1] = ma.masked
zi[interior2] = ma.masked
p.figure(figsize=(16,10))
pyplot.jet()
max=2.8
min=0.4
steps = 50
levels=list()
labels=list()
for i in range(0,steps):
levels.append(int((max-min)/steps*100*i)*0.01+min)
for i in range(0,steps/2):
labels.append(levels[2*i])
CSF = p.contourf(xi,yi,zi,levels,norm=colors.LogNorm())
CS = p.contour(xi,yi,zi,levels, format='%.3f', labelsize='18')
p.clabel(CS,labels,inline=1,fontsize=9)
p.title('electrostatic potential of two spherical colloids, R=lambda/3',fontsize=24)
p.xlabel('z-coordinate (3*lambda)',fontsize=18)
p.ylabel('radial coordinate r (3*lambda)',fontsize=18)
# add a vertical bar with the color values
cbar = p.colorbar(CSF,ticks=labels,format='%.3f')
cbar.ax.set_ylabel('potential (reduced units)',fontsize=18)
cbar.add_lines(CS)
p.show()
def showHistory(self, figNum):
pylab.figure(figNum)
plot = pylab.plot(self.history, label = 'Test Stock')
plot
pylab.title('Closing Price, Test ' + str(figNum))
pylab.xlabel('Day')
pylab.ylabel('Price')
def plotLists(xList, xLabel=None, eListTitle=None, eList=None, eLabel=None, fListTitle=None, fList=None, fLabel=None):
if h2o.python_username!='kevin':
return
import pylab as plt
print "xList", xList
print "eList", eList
print "fList", fList
font = {'family' : 'normal',
'weight' : 'normal',
'size' : 26}
### plt.rc('font', **font)
plt.rcdefaults()
if eList:
if eListTitle:
plt.title(eListTitle)
plt.figure()
plt.plot (xList, eList)
plt.xlabel(xLabel)
plt.ylabel(eLabel)
plt.draw()
if fList:
if fListTitle:
plt.title(fListTitle)
plt.figure()
plt.plot (xList, fList)
plt.xlabel(xLabel)
plt.ylabel(fLabel)
plt.draw()
if eList or fList:
plt.show()
def srcdiff_plot(env, model, **kwargs):
obj_index = kwargs.pop('obj_index', 0)
src_index = kwargs.pop('src_index', 0)
with_colorbar = kwargs.pop('with_colorbar', False)
xlabel = kwargs.pop('xlabel', r'arcsec')
ylabel = kwargs.pop('ylabel', r'arcsec')
obj, data = model['obj,data'][obj_index]
S = obj.basis.subdivision
R = obj.basis.mapextent
g = obj.basis.srcdiff_grid(data)[src_index]
vmin = np.log10(np.amin(g[g>0]))
g = g.copy() + 1e-10
kw = default_kw(R, kwargs) #, vmin=vmin, vmax=vmin+2)
#loglev = logspace(1, log(amax(g)-amin(g)), 20, base=math.e) + amin(g)
pl.matshow(np.log10(g), **kw)
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
if with_colorbar: glspl.colorbar()
# pl.over(contour, g, 50, colors='w', linewidths=1,
# extent=[-R,R,-R,R], origin='upper', extend='both')
#pl.grid()
pl.xlabel(xlabel)
pl.ylabel(ylabel)
def H0inv_plot(env, **kwargs):
_hist(env, '1/H0', xlabel=r'$H_0^{-1}$ (Gyr)')
return
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', 0)
key = kwargs.pop('key', 'accepted')
xlabel = kwargs.pop('xlabel', r'$H_0^{-1}$ (Gyr)')
ylabel = kwargs.pop('ylabel', r'Count')
# select a list to append to based on the 'accepted' property.
l = [[], [], []]
for m in models:
obj, data = m['obj,data'][0] # For H0inv we only have to look at one model because the others are the same
l[m.get(key,2)].append(data['1/H0'])
#l[2].append(data['kappa'][1])
#print amin(l[2]), amax(l[2])
not_accepted, accepted, notag = l
#print 'H0inv_plot',H0s
for d,s in zip(l, _styles):
if d:
#print len(d), d
#pl.hist(d, bins=20, histtype='step', edgecolor=s['c'], zorder=s['z'], label=s['label'])
pl.hist(d, bins=np.ptp(d)//1+1, histtype='step', edgecolor=s['c'], zorder=s['z'], label=s['label'], **kwargs)
#if not_accepted or accepted:
#pl.legend()
pl.axvline(13.7, c='k', ls=':', zorder = 2)
pl.xlabel(xlabel)
pl.ylabel(ylabel)
if accepted or not not_accepted:
if accepted:
h = np.array(accepted)
else:
h = np.array(accepted + notag)
hs = np.sort(h)
l = len(hs)
m = hs[l * 0.50]
u = hs[l * (0.50 + 0.341)]
l = hs[l * (0.50 - 0.341)]
#u = hs[l * 0.68]
#l = hs[l * 0.32]
pl.axvline(m, c='r', ls='-', zorder = 2)
pl.axvline(u, c='g', ls='-', zorder = 2)
pl.axvline(l, c='g', ls='-', zorder = 2)
Log( 'H0inv_plot: ', m, u, l )
Log( 'H0inv_plot: ', m, (u-m), (m-l) )
else:
Log( "H0inv_plot: No H0inv values accepted" )
def chisq_plot(env, **kwargs):
_hist(env, 'sigp:chisq', xlabel=r'$\chi^2$')
return
models = kwargs.pop('models', env.models)
objects = kwargs.pop('objects', None)
key = kwargs.pop('key', 'accepted')
# select a list to append to based on the 'accepted' property.
l = [[], [], []]
for m in models:
# For H0 we only have to look at one model because the others are the same
obj, data = m['obj,data'][0]
l[m.get(key,2)].append(data['sigp:chisq'])
not_accepted, accepted, notag = l
for d,s in zip(l, _styles):
if d:
pl.hist(d, histtype='step', edgecolor=s['c'], zorder=s['z'], label=s['label'], log=False, **kwargs)
if not_accepted or accepted:
pl.legend()
pl.xlabel(_chisq_xlabel)
pl.ylabel(r'Count')
def grad_kappa_plot(env, model, obj_index, which='x', with_contours=False, only_contours=False, clevels=30, with_colorbar=True):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
grid = obj.basis.kappa_grid(data)
grid = grid.copy()
kw = default_kw(R)
kw['vmin'] = -1
kw['vmax'] = 2
if not only_contours:
print '!!!!!!', grid.shape
if which == 'x': grid = np.diff(grid, axis=1)
if which == 'y': grid = np.diff(grid, axis=0)
print '!!!!!!', grid.shape
pl.matshow(grid, **kw)
if with_colorbar:
glspl.colorbar()
if with_contours:
kw.pop('cmap')
pl.over(contour, grid, clevels, extend='both', colors='k', alpha=0.7, **kw)
pl.xlabel('arcsec')
pl.ylabel('arcsec')
def plot_stress(self, block_ids=None, fignum=0):
block_ids = self.check_block_ids_list(block_ids)
#
plt.figure(fignum)
ax1=plt.gca()
plt.clf()
plt.figure(fignum)
plt.clf()
ax0=plt.gca()
#
for block_id in block_ids:
rws = numpy.core.records.fromarrays(zip(*filter(lambda x: x['block_id']==block_id, self.shear_stress_sequences)), dtype=self.shear_stress_sequences.dtype)
stress_seq = []
for rw in rws:
stress_seq += [[rw['sweep_number'], rw['shear_init']]]
stress_seq += [[rw['sweep_number'], rw['shear_final']]]
X,Y = zip(*stress_seq)
#
ax0.plot(X,Y, '.-', label='block_id: %d' % block_id)
#
plt.figure(fignum+1)
plt.plot(rws['sweep_number'], rws['shear_init'], '.-', label='block_id: %d' % block_id)
plt.plot(rws['sweep_number'], rws['shear_final'], '.-', label='block_id: %d' % block_id)
plt.figure(fignum)
ax0.plot([min(self.shear_stress_sequences['sweep_number']), max(self.shear_stress_sequences['sweep_number'])], [0., 0.], 'k-')
ax0.legend(loc=0, numpoints=1)
plt.figure(fignum)
plt.title('Block shear_stress sequences')
plt.xlabel('sweep number')
plt.ylabel('shear stress')
def time_delays_plot(env, **kwargs):
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', 0)
src_index = kwargs.pop('src_index', 0)
key = kwargs.pop('key', 'accepted')
d = defaultdict(list)
for m in models:
obj,data = m['obj,data'][obj_index]
t0 = data['arrival times'][src_index][0]
for i,t in enumerate(data['arrival times'][src_index][1:]):
d[i].append( float('%0.6f'%convert('arcsec^2 to days', t-t0, obj.dL, obj.z, data['nu'])) )
t0 = t
s = product(range(1,1+len(d)), ['solid', 'dashed', 'dashdot', 'dotted'])
for k,v in d.iteritems():
#print 'td plot', k, len(v)
#print v
lw,ls = s.next()
pl.hist(v, bins=25, histtype='step', color='k', ls=ls, lw=lw, label='%s - %s' % (str(k+1),str(k+2)), **kwargs)
#pl.xlim(xmin=0)
pl.ylim(ymin=0)
pl.xlim(xmin=pl.xlim()[0] - 0.01*(pl.xlim()[1] - pl.xlim()[0]))
pl.legend()
pl.xlabel(_time_delays_xlabel)
pl.ylabel(r'Count')
def geweke_plot(data, name, format='png', suffix='-diagnostic', path='./', fontmap = None,
verbose=1):
# Generate Geweke (1992) diagnostic plots
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Generate new scatter plot
figure()
x, y = transpose(data)
scatter(x.tolist(), y.tolist())
# Plot options
xlabel('First iteration', fontsize='x-small')
ylabel('Z-score for %s' % name, fontsize='x-small')
# Plot lines at +/- 2 sd from zero
pyplot((nmin(x), nmax(x)), (2, 2), '--')
pyplot((nmin(x), nmax(x)), (-2, -2), '--')
# Set plot bound
ylim(min(-2.5, nmin(y)), max(2.5, nmax(y)))
xlim(0, nmax(x))
# Save to file
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
def param_set_averages_plot(results):
averages_ocr = [
a[1] for a in sorted(
param_set_averages(results, metric='ocr').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_q = [
a[1] for a in sorted(
param_set_averages(results, metric='q').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_mse = [
a[1] for a in sorted(
param_set_averages(results, metric='mse').items(),
key=lambda x: int(x[0].split('-')[1]))
]
fig = plt.figure(figsize=(6, 4))
# plt.tight_layout()
plt.plot(averages_ocr, label='OCR', linewidth=2.0)
plt.plot(averages_q, label='Q', linewidth=2.0)
plt.plot(averages_mse, label='MSE', linewidth=2.0)
plt.ylim([0, 1])
plt.xlabel(u'Paslėptų neuronų skaičius')
plt.ylabel(u'Vidurinė Q įverčio pokyčio reikšmė')
plt.grid(True)
plt.tight_layout()
plt.legend(loc='lower right')
plt.show()
def generateKineticsModel(reaction, tunneling='', plot=False):
logging.info('Calculating rate coefficient for {0}...'.format(reaction))
if len(reaction.reactants) == 1:
kunits = 's^-1'
elif len(reaction.reactants) == 2:
kunits = 'm^3/(mol*s)'
elif len(reaction.reactants) == 3:
kunits = 'm^6/(mol^2*s)'
else:
kunits = ''
Tlist = 1000.0/numpy.arange(0.4, 3.35, 0.05)
klist = reaction.calculateTSTRateCoefficients(Tlist, tunneling)
arrhenius = Arrhenius().fitToData(Tlist, klist, kunits)
klist2 = arrhenius.getRateCoefficients(Tlist)
reaction.kinetics = arrhenius
if plot:
logging.info('Plotting kinetics model for {0}...'.format(reaction))
import pylab
pylab.semilogy(1000.0 / Tlist, klist * reaction.degeneracy, 'ok')
pylab.semilogy(1000.0 / Tlist, klist2 * reaction.degeneracy, '-k')
pylab.xlabel('1000 / Temperature (1000/K)')
pylab.ylabel('Rate coefficient (SI units)')
pylab.show()
def plotHistogram(data, preTime):
pylab.figure(1)
pylab.hist(data, bins=10)
pylab.xlabel("Virus Population At End of Simulation")
pylab.ylabel("Number of Trials")
pylab.title("{0} Time Steps Before Treatment Simulation".format(preTime))
pylab.show()
def drawPr(tp,fp,tot,show=True):
"""
draw the precision recall curve
"""
det=numpy.array(sorted(tp+fp))
atp=numpy.array(tp)
afp=numpy.array(fp)
#pylab.figure()
#pylab.clf()
rc=numpy.zeros(len(det))
pr=numpy.zeros(len(det))
#prc=0
#ppr=1
for i,p in enumerate(det):
pr[i]=float(numpy.sum(atp>=p))/numpy.sum(det>=p)
rc[i]=float(numpy.sum(atp>=p))/tot
#print pr,rc,p
ap=0
for c in numpy.linspace(0,1,num=11):
if len(pr[rc>=c])>0:
p=numpy.max(pr[rc>=c])
else:
p=0
ap=ap+p/11
if show:
pylab.plot(rc,pr,'-g')
pylab.title("AP=%.3f"%(ap))
pylab.xlabel("Recall")
pylab.ylabel("Precision")
pylab.grid()
pylab.show()
pylab.draw()
return rc,pr,ap
def createPlot(dataY, dataX, ticksX, annotations, axisY, axisX, dostep, doannotate):
if not ticksX:
ticksX = dataX
if dostep:
py.step(dataX, dataY, where='post', linestyle='-', label=axisY) # where=post steps after point
else:
py.plot(dataX, dataY, marker='o', ms=5.0, linestyle='-', label=axisY)
if annotations and doannotate:
for note, x, y in zip(annotations, dataX, dataY):
py.annotate(note, (x, y), xytext=(2,2), xycoords='data', textcoords='offset points')
py.xticks(np.arange(1, len(dataX)+1), ticksX, horizontalalignment='left', rotation=30)
leg = py.legend()
leg.draggable()
py.xlabel(axisX)
py.ylabel('time (s)')
# Set X axis tick labels as rungs
#print zip(dataX, dataY)
py.draw()
py.show()
return
def simulation1(numTrials, numSteps, loc):
results = {'UsualDrunk': [], 'ColdDrunk': [], 'EDrunk': [], 'PhotoDrunk': [], 'DDrunk': []}
drunken_types = {'UsualDrunk': UsualDrunk, 'ColdDrunk': ColdDrunk, 'EDrunk': EDrunk, 'PhotoDrunk': PhotoDrunk,
'DDrunk': DDrunk}
for drunken in drunken_types.keys():
#Create field
initial_loc = Location(loc[0], loc[1])
field = Field()
print "Simu", drunken
drunk = Drunk(drunken)
drunk_man = drunken_types[drunken](drunk)
field.addDrunk(drunk_man, initial_loc)
#print drunk_man
for trial in range(numTrials):
distance = walkVector(field, drunk_man, numSteps)
results[drunken].append((round(distance[0], 1), round(distance[1], 1)))
print drunken, "=", results[drunken]
for result in results.keys():
# x, y = zip(*results[result])
# print "x", x
# print "y", y
pylab.plot(*zip(*results[result]), marker='o', color='r', ls='')
pylab.title(result)
pylab.xlabel('X coordinateds')
pylab.ylabel('Y coordinateds')
pylab.xlim(-100, 100)
pylab.ylim(-100, 100)
pylab.figure()
pylab.show
def drawPrfastscore(tp,fp,scr,tot,show=True):
tp=numpy.cumsum(tp)
fp=numpy.cumsum(fp)
rec=tp/tot
prec=tp/(fp+tp)
#dif=numpy.abs(prec[1:]-rec[1:])
dif=numpy.abs(prec[::-1]-rec[::-1])
pos=dif.argmin()
pos=len(dif)-pos-1
ap=0
for t in numpy.linspace(0,1,11):
pr=prec[rec>=t]
if pr.size==0:
pr=0
p=numpy.max(pr);
ap=ap+p/11;
if show:
pylab.plot(rec,prec,'-g')
pylab.title("AP=%.3f EPRthr=%.3f"%(ap,scr[pos]))
pylab.xlabel("Recall")
pylab.ylabel("Precision")
pylab.grid()
pylab.show()
pylab.draw()
return rec,prec,scr,ap,scr[pos]
def plotEventFlop(library, num, eventNames, sizes, times, events, filename = None):
from pylab import legend, plot, savefig, semilogy, show, title, xlabel, ylabel
import numpy as np
arches = sizes.keys()
bs = events[arches[0]].keys()[0]
data = []
names = []
for event, color in zip(eventNames, ['b', 'g', 'r', 'y']):
for arch, style in zip(arches, ['-', ':']):
if event in events[arch][bs]:
names.append(arch+'-'+str(bs)+' '+event)
data.append(sizes[arch][bs])
data.append(1e-3*np.array(events[arch][bs][event])[:,1])
data.append(color+style)
else:
print 'Could not find %s in %s-%d events' % (event, arch, bs)
semilogy(*data)
title('Performance on '+library+' Example '+str(num))
xlabel('Number of Dof')
ylabel('Computation Rate (GF/s)')
legend(names, 'upper left', shadow = True)
if filename is None:
show()
else:
savefig(filename)
return
def Doplots_monthly(mypathforResults,PlottingDF,variable_to_fill, Site_ID,units,item):
ANN_label=str(item+"_NN") #Do Monthly Plots
print "Doing MOnthly plot"
#t = arange(1, 54, 1)
NN_label='Fc'
Plottemp = PlottingDF[[NN_label,item]][PlottingDF['day_night']!=1]
#Plottemp = PlottingDF[[NN_label,item]].dropna(how='any')
figure(1)
pl.title('Nightime ANN v Tower by year-month for '+item+' at '+Site_ID)
try:
xdata1a=Plottemp[item].groupby([lambda x: x.year,lambda x: x.month]).mean()
plotxdata1a=True
except:
plotxdata1a=False
try:
xdata1b=Plottemp[NN_label].groupby([lambda x: x.year,lambda x: x.month]).mean()
plotxdata1b=True
except:
plotxdata1b=False
if plotxdata1a==True:
pl.plot(xdata1a,'r',label=item)
if plotxdata1b==True:
pl.plot(xdata1b,'b',label=NN_label)
pl.ylabel('Flux')
pl.xlabel('Year - Month')
pl.legend()
pl.savefig(mypathforResults+'/ANN and Tower plots by year and month for variable '+item+' at '+Site_ID)
#pl.show()
pl.close()
time.sleep(1)
def plot_heatingrate(data_dict, filename, do_show=True):
pl.figure(201)
color_list = ['b','r','g','k','y','r','g','b','k','y','r',]
fmtlist = ['s','d','o','s','d','o','s','d','o','s','d','o']
result_dict = {}
for key in data_dict.keys():
x = data_dict[key][0]
y = data_dict[key][1][:,0]
y_err = data_dict[key][1][:,1]
p0 = np.polyfit(x,y,1)
fit = LinFit(np.array([x,y,y_err]).transpose(), show_graph=False)
p1 = [0,0]
p1[0] = fit.param_dict[0]['Slope'][0]
p1[1] = fit.param_dict[0]['Offset'][0]
print fit
x0 = np.linspace(0,max(x))
cstr = color_list.pop(0)
fstr = fmtlist.pop(0)
lstr = key + " heating: {0:.2f} ph/ms".format((p1[0]*1e3))
pl.errorbar(x/1e3,y,y_err,fmt=fstr + cstr,label=lstr)
pl.plot(x0/1e3,np.polyval(p0,x0),cstr)
pl.plot(x0/1e3,np.polyval(p1,x0),cstr)
result_dict[key] = 1e3*np.array(fit.param_dict[0]['Slope'])
pl.xlabel('Heating time (ms)')
pl.ylabel('nbar')
if do_show:
pl.legend()
pl.show()
if filename != None:
pl.savefig(filename)
return result_dict
def plot_cost(self):
if self.show_cost not in self.train_outputs[0][0]:
raise ShowNetError("Cost function with name '%s' not defined by given convnet." % self.show_cost)
train_errors = [o[0][self.show_cost][self.cost_idx] for o in self.train_outputs]
test_errors = [o[0][self.show_cost][self.cost_idx] for o in self.test_outputs]
numbatches = len(self.train_batch_range)
test_errors = numpy.row_stack(test_errors)
test_errors = numpy.tile(test_errors, (1, self.testing_freq))
test_errors = list(test_errors.flatten())
test_errors += [test_errors[-1]] * max(0,len(train_errors) - len(test_errors))
test_errors = test_errors[:len(train_errors)]
numepochs = len(train_errors) / float(numbatches)
pl.figure(1)
x = range(0, len(train_errors))
pl.plot(x, train_errors, 'k-', label='Training set')
pl.plot(x, test_errors, 'r-', label='Test set')
pl.legend()
ticklocs = range(numbatches, len(train_errors) - len(train_errors) % numbatches + 1, numbatches)
epoch_label_gran = int(ceil(numepochs / 20.)) # aim for about 20 labels
epoch_label_gran = int(ceil(float(epoch_label_gran) / 10) * 10) # but round to nearest 10
ticklabels = map(lambda x: str((x[1] / numbatches)) if x[0] % epoch_label_gran == epoch_label_gran-1 else '', enumerate(ticklocs))
pl.xticks(ticklocs, ticklabels)
pl.xlabel('Epoch')
# pl.ylabel(self.show_cost)
pl.title(self.show_cost)
def plotB2reg(prefix=''):
w=loadStanFit(prefix+'revE2B2LHregCa.fit')
px=np.array(np.linspace(-0.5,0.5,101),ndmin=2)
a1=np.array(w['ma'][:,4],ndmin=2).T+1
a0=np.array(w['ma'][:,3],ndmin=2).T
printCI(w,'ma')
y=np.concatenate([sap(a0+a1*px,97.5,axis=0),sap(a0+a1*px[:,::-1],2.5,axis=0)])
x=np.squeeze(np.concatenate([px,px[:,::-1]],axis=1))
man=np.array([-0.4,-0.2,0,0.2,0.4])
plt.plot(px[0,:],np.median(a0)+np.median(a1)*px[0,:],'red')
#plt.plot([-1,1],[0.5,0.5],'grey')
ax=plt.gca()
ax.set_aspect(1)
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
y=np.concatenate([sap(a0+a1*px,75,axis=0),sap(a0+a1*px[:,::-1],25,axis=0)])
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
mus=[]
for m in range(len(man)):
mus.append(loadStanFit(prefix+'revE2B2LHC%d.fit'%m)['ma4']+man[m])
mus=np.array(mus).T
errorbar(mus,x=man)
ax.set_xticks(man)
plt.xlim([-0.5,0.5])
plt.ylim([-0.6,0.8])
plt.xlabel('Pivot Displacement')
plt.ylabel('Perceived Displacemet')
def plotForce():
figure(size=3,aspect=0.5)
subplot(1,2,1)
from EvalTraj import plotFF
plotFF(vp=351,t=28,f=900,cm=0.6,foffset=8)
subplot_annotate()
subplot(1,2,2)
for i in [1,2,3,4]:
R=np.squeeze(np.load('Rdpse%d.npy'%i))
R=stats.nanmedian(R,axis=2)[:,1:,:]
dps=np.linspace(-1,1,201)[1:]
plt.plot(dps,R[:,:,2].mean(0));
plt.legend([0,0.1,0.2,0.3],loc=3)
i=2
R=np.squeeze(np.load('Rdpse%d.npy'%i))
R=stats.nanmedian(R,axis=2)[:,1:,:]
mn=np.argmin(R,axis=1)
y=np.random.randn(mn.shape[0])*0.00002+0.0438
plt.plot(np.sort(dps[mn[:,2]]),y,'+',mew=1,ms=6,mec=[ 0.39 , 0.76, 0.64])
plt.xlabel('Displacement of Force Origin')
plt.ylabel('Average Net Force Magnitude')
hh=dps[mn[:,2]]
err=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.975,hh.shape[0])
err2=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.75,hh.shape[0])
m=np.mean(hh)
print m, m-err,m+err
np.save('force',[m, m-err,m+err,m-err2,m+err2])
plt.xlim([-0.5,0.5])
plt.ylim([0.0435,0.046])
plt.grid(b=True,axis='x')
subplot_annotate()
def plot_sphere_x( s, fname ):
""" put plot of ionization fractions from sphere `s` into fname """
plt.figure()
s.Edges.units = 'kpc'
s.r_c.units = 'kpc'
xx = s.r_c
L = s.Edges[-1]
plt.plot( xx, np.log10( s.xHe1 ),
color='green', ls='-', label = r'$x_{\rm HeI}$' )
plt.plot( xx, np.log10( s.xHe2 ),
color='green', ls='--', label = r'$x_{\rm HeII}$' )
plt.plot( xx, np.log10( s.xHe3 ),
color='green', ls=':', label = r'$x_{\rm HeIII}$' )
plt.plot( xx, np.log10( s.xH1 ),
color='red', ls='-', label = r'$x_{\rm HI}$' )
plt.plot( xx, np.log10( s.xH2 ),
color='red', ls='--', label = r'$x_{\rm HII}$' )
plt.xlim( -L/20, L+L/20 )
plt.xlabel( 'r_c [kpc]' )
plt.ylim( -4.5, 0.2 )
plt.ylabel( 'log 10 ( x )' )
plt.grid()
plt.legend(loc='best', ncol=2)
plt.tight_layout()
plt.savefig( 'doc/img/x_' + fname )
def plot_number_alteration_by_tissue(self, fontsize=10, width=0.9):
"""Plot number of alterations
.. plot::
:width: 100%
:include-source:
from gdsctools import *
data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
bem = OmniBEMBuilder(data)
bem.filter_by_gene_list(gdsctools_data("test_omnibem_genes.txt"))
bem.plot_number_alteration_by_tissue()
"""
count = self.unified.groupby(['TISSUE_TYPE'])['GENE'].count()
try:
count.sort_values(inplace=True, ascending=False)
except:
count.sort(inplace=True, ascending=False)
count.plot(kind="bar", width=width)
pylab.grid()
pylab.xlabel("Tissue Type", fontsize=fontsize)
pylab.ylabel("Total number of alterations in cell lines",
fontsize=fontsize)
try:pylab.tight_layout()
except:pass
def getOptCandGamma(cv_train, cv_label):
print "Finding optimal C and gamma for SVM with RBF Kernel"
C_range = 10.0 ** np.arange(-2, 9)
gamma_range = 10.0 ** np.arange(-5, 4)
param_grid = dict(gamma=gamma_range, C=C_range)
cv = StratifiedKFold(y=cv_label, n_folds=40)
# Use the svm.SVC() as the cost function to evaluate parameter choices
# NOTE: Perhaps we should run computations in parallel if needed. Does it
# do that already within the class?
grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)
grid.fit(cv_train, cv_label)
score_dict = grid.grid_scores_
scores = [x[1] for x in score_dict]
scores = np.array(scores).reshape(len(C_range), len(gamma_range))
pl.figure(figsize=(8,6))
pl.subplots_adjust(left=0.05, right=0.95, bottom=0.15, top=0.95)
pl.imshow(scores, interpolation='nearest', cmap=pl.cm.spectral)
pl.xlabel('gamma')
pl.ylabel('C')
pl.colorbar()
pl.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)
pl.yticks(np.arange(len(C_range)), C_range)
pl.show()
print "The best classifier is: ", grid.best_estimator_
import numpy
import pylab
select = 2
if select == 1:
x = [0.5, 1, 2, 3, 4, 6, 8, 10, 15]
y = [63.2, 58, 55.27, 53, 45.5, 41.7, 38.4, 29.8, 22]
xl = 'Maximum Allowed Interrupt Rate (in KHz)'
yl = 'Tx packet rate (in Kpps)'
t = 'Tx packet rate as a function of the MAIR'
elif select == 2:
x = [0.5, 1, 2, 3, 4, 6, 8, 9, 12, 15]
y = [150, 182, 205, 185, 156, 110, 77, 59, 31, 14]
xl = 'Maximum Allowed Interrupt Rate (in KHz)'
yl = 'Rx critical rate (in Kpps)'
t = 'Rx critical rate as a function of the MAIR'
pylab.plot(x, y)
pylab.xlabel(xl)
pylab.ylabel(yl)
pylab.title(t)
pylab.grid(True)
#pylab.savefig('simple_plot')
pylab.show()
def GasMassFunction(self, G):
print('Plotting the cold gas mass function')
plt.figure() # New figure
ax = plt.subplot(111) # 1 plot on the figure
binwidth = 0.1 # mass function histogram bin width
# calculate all
w = np.where(G.ColdGas > 0.0)[0]
mass = np.log10(G.ColdGas[w] * 1.0e10 / self.Hubble_h)
sSFR = (G.SfrDisk[w] + G.SfrBulge[w]) / (G.StellarMass[w] * 1.0e10 /
self.Hubble_h)
mi = np.floor(min(mass)) - 2
ma = np.floor(max(mass)) + 2
NB = (ma - mi) / binwidth
(counts, binedges) = np.histogram(mass, range=(mi, ma), bins=NB)
# Set the x-axis values to be the centre of the bins
xaxeshisto = binedges[:-1] + 0.5 * binwidth
# additionally calculate red
w = np.where(sSFR < 10.0**sSFRcut)[0]
massRED = mass[w]
(countsRED, binedges) = np.histogram(massRED, range=(mi, ma), bins=NB)
# additionally calculate blue
w = np.where(sSFR > 10.0**sSFRcut)[0]
massBLU = mass[w]
(countsBLU, binedges) = np.histogram(massBLU, range=(mi, ma), bins=NB)
# Baldry+ 2008 modified data used for the MCMC fitting
Zwaan = np.array([[6.933, -0.333], [7.057, -0.490], [7.209, -0.698],
[7.365, -0.667], [7.528, -0.823], [7.647, -0.958],
[7.809, -0.917], [7.971, -0.948], [8.112, -0.927],
[8.263, -0.917], [8.404, -1.062], [8.566, -1.177],
[8.707, -1.177], [8.853, -1.312], [9.010, -1.344],
[9.161, -1.448], [9.302, -1.604], [9.448, -1.792],
[9.599, -2.021], [9.740, -2.406], [9.897, -2.615],
[10.053, -3.031], [10.178, -3.677], [10.335, -4.448],
[10.492, -5.083]],
dtype=np.float32)
ObrRaw = np.array([[7.300, -1.104], [7.576, -1.302], [7.847, -1.250],
[8.133, -1.240], [8.409, -1.344], [8.691, -1.479],
[8.956, -1.792], [9.231, -2.271], [9.507, -3.198],
[9.788, -5.062]],
dtype=np.float32)
ObrCold = np.array([[8.009, -1.042], [8.215, -1.156], [8.409, -0.990],
[8.604, -1.156], [8.799, -1.208], [9.020, -1.333],
[9.194, -1.385], [9.404, -1.552], [9.599, -1.677],
[9.788, -1.812], [9.999, -2.312], [10.172, -2.656],
[10.362, -3.500], [10.551, -3.635],
[10.740, -5.010]],
dtype=np.float32)
ObrCold_xval = np.log10(10**(ObrCold[:, 0]) / self.Hubble_h /
self.Hubble_h)
ObrCold_yval = (10**(ObrCold[:, 1]) * self.Hubble_h * self.Hubble_h *
self.Hubble_h)
Zwaan_xval = np.log10(10**(Zwaan[:, 0]) / self.Hubble_h /
self.Hubble_h)
Zwaan_yval = (10**(Zwaan[:, 1]) * self.Hubble_h * self.Hubble_h *
self.Hubble_h)
ObrRaw_xval = np.log10(10**(ObrRaw[:, 0]) / self.Hubble_h /
self.Hubble_h)
ObrRaw_yval = (10**(ObrRaw[:, 1]) * self.Hubble_h * self.Hubble_h *
self.Hubble_h)
plt.plot(ObrCold_xval,
ObrCold_yval,
color='black',
lw=7,
alpha=0.25,
label='Obr. \& Raw. 2009 (Cold Gas)')
plt.plot(Zwaan_xval,
Zwaan_yval,
color='cyan',
lw=7,
alpha=0.25,
label='Zwaan et al. 2005 (HI)')
plt.plot(ObrRaw_xval,
ObrRaw_yval,
color='magenta',
lw=7,
alpha=0.25,
label='Obr. \& Raw. 2009 (H2)')
# Overplot the model histograms
plt.plot(xaxeshisto,
counts / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
'k-',
label='Model - Cold Gas')
plt.yscale('log', nonposy='clip')
plt.axis([8.0, 11.5, 1.0e-6, 1.0e-1])
# Set the x-axis minor ticks
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
plt.ylabel(
r'$\phi\ (\mathrm{Mpc}^{-3}\ \mathrm{dex}^{-1})$') # Set the y...
plt.xlabel(r'$\log_{10} M_{\mathrm{X}}\ (M_{\odot})$'
) # and the x-axis labels
leg = plt.legend(loc='lower left', numpoints=1, labelspacing=0.1)
leg.draw_frame(False) # Don't want a box frame
for t in leg.get_texts(): # Reduce the size of the text
t.set_fontsize('medium')
outputFile = OutputDir + '3.GasMassFunction' + OutputFormat
plt.savefig(outputFile) # Save the figure
print('Saved file to', outputFile)
plt.close()
# Add this plot to our output list
OutputList.append(outputFile)
def BaryonicMassFunction(self, G):
print('Plotting the baryonic mass function')
plt.figure() # New figure
ax = plt.subplot(111) # 1 plot on the figure
binwidth = 0.1 # mass function histogram bin width
# calculate BMF
w = np.where(G.StellarMass + G.ColdGas > 0.0)[0]
mass = np.log10(
(G.StellarMass[w] + G.ColdGas[w]) * 1.0e10 / self.Hubble_h)
mi = np.floor(min(mass)) - 2
ma = np.floor(max(mass)) + 2
NB = (ma - mi) / binwidth
(counts, binedges) = np.histogram(mass, range=(mi, ma), bins=NB)
# Set the x-axis values to be the centre of the bins
xaxeshisto = binedges[:-1] + 0.5 * binwidth
# Bell et al. 2003 BMF (h=1.0 converted to h=0.73)
M = np.arange(7.0, 13.0, 0.01)
Mstar = np.log10(5.3 * 1.0e10 / self.Hubble_h / self.Hubble_h)
alpha = -1.21
phistar = 0.0108 * self.Hubble_h * self.Hubble_h * self.Hubble_h
xval = 10.0**(M - Mstar)
yval = np.log(10.) * phistar * xval**(alpha + 1) * np.exp(-xval)
if (whichimf == 0):
# converted diet Salpeter IMF to Salpeter IMF
plt.plot(np.log10(10.0**M / 0.7),
yval,
'b-',
lw=2.0,
label='Bell et al. 2003') # Plot the SMF
elif (whichimf == 1):
# converted diet Salpeter IMF to Salpeter IMF, then to Chabrier IMF
plt.plot(np.log10(10.0**M / 0.7 / 1.8),
yval,
'g--',
lw=1.5,
label='Bell et al. 2003') # Plot the SMF
# Overplot the model histograms
plt.plot(xaxeshisto,
counts / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
'k-',
label='Model')
plt.yscale('log', nonposy='clip')
plt.axis([8.0, 12.5, 1.0e-6, 1.0e-1])
# Set the x-axis minor ticks
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
plt.ylabel(
r'$\phi\ (\mathrm{Mpc}^{-3}\ \mathrm{dex}^{-1})$') # Set the y...
plt.xlabel(r'$\log_{10}\ M_{\mathrm{bar}}\ (M_{\odot})$'
) # and the x-axis labels
leg = plt.legend(loc='lower left', numpoints=1, labelspacing=0.1)
leg.draw_frame(False) # Don't want a box frame
for t in leg.get_texts(): # Reduce the size of the text
t.set_fontsize('medium')
outputFile = OutputDir + '2.BaryonicMassFunction' + OutputFormat
plt.savefig(outputFile) # Save the figure
print('Saved file to', outputFile)
plt.close()
# Add this plot to our output list
OutputList.append(outputFile)
def StellarMassFunction(self, G):
print('Plotting the stellar mass function')
plt.figure() # New figure
ax = plt.subplot(111) # 1 plot on the figure
binwidth = 0.1 # mass function histogram bin width
# calculate all
w = np.where(G.StellarMass > 0.0)[0]
mass = np.log10(G.StellarMass[w] * 1.0e10 / self.Hubble_h)
sSFR = (G.SfrDisk[w] + G.SfrBulge[w]) / (G.StellarMass[w] * 1.0e10 /
self.Hubble_h)
mi = np.floor(min(mass)) - 2
ma = np.floor(max(mass)) + 2
NB = (ma - mi) / binwidth
(counts, binedges) = np.histogram(mass, range=(mi, ma), bins=NB)
# Set the x-axis values to be the centre of the bins
xaxeshisto = binedges[:-1] + 0.5 * binwidth
# additionally calculate red
w = np.where(sSFR < 10.0**sSFRcut)[0]
massRED = mass[w]
(countsRED, binedges) = np.histogram(massRED, range=(mi, ma), bins=NB)
# additionally calculate blue
w = np.where(sSFR > 10.0**sSFRcut)[0]
massBLU = mass[w]
(countsBLU, binedges) = np.histogram(massBLU, range=(mi, ma), bins=NB)
# Baldry+ 2008 modified data used for the MCMC fitting
Baldry = np.array([
[7.05, 1.3531e-01, 6.0741e-02],
[7.15, 1.3474e-01, 6.0109e-02],
[7.25, 2.0971e-01, 7.7965e-02],
[7.35, 1.7161e-01, 3.1841e-02],
[7.45, 2.1648e-01, 5.7832e-02],
[7.55, 2.1645e-01, 3.9988e-02],
[7.65, 2.0837e-01, 4.8713e-02],
[7.75, 2.0402e-01, 7.0061e-02],
[7.85, 1.5536e-01, 3.9182e-02],
[7.95, 1.5232e-01, 2.6824e-02],
[8.05, 1.5067e-01, 4.8824e-02],
[8.15, 1.3032e-01, 2.1892e-02],
[8.25, 1.2545e-01, 3.5526e-02],
[8.35, 9.8472e-02, 2.7181e-02],
[8.45, 8.7194e-02, 2.8345e-02],
[8.55, 7.0758e-02, 2.0808e-02],
[8.65, 5.8190e-02, 1.3359e-02],
[8.75, 5.6057e-02, 1.3512e-02],
[8.85, 5.1380e-02, 1.2815e-02],
[8.95, 4.4206e-02, 9.6866e-03],
[9.05, 4.1149e-02, 1.0169e-02],
[9.15, 3.4959e-02, 6.7898e-03],
[9.25, 3.3111e-02, 8.3704e-03],
[9.35, 3.0138e-02, 4.7741e-03],
[9.45, 2.6692e-02, 5.5029e-03],
[9.55, 2.4656e-02, 4.4359e-03],
[9.65, 2.2885e-02, 3.7915e-03],
[9.75, 2.1849e-02, 3.9812e-03],
[9.85, 2.0383e-02, 3.2930e-03],
[9.95, 1.9929e-02, 2.9370e-03],
[10.05, 1.8865e-02, 2.4624e-03],
[10.15, 1.8136e-02, 2.5208e-03],
[10.25, 1.7657e-02, 2.4217e-03],
[10.35, 1.6616e-02, 2.2784e-03],
[10.45, 1.6114e-02, 2.1783e-03],
[10.55, 1.4366e-02, 1.8819e-03],
[10.65, 1.2588e-02, 1.8249e-03],
[10.75, 1.1372e-02, 1.4436e-03],
[10.85, 9.1213e-03, 1.5816e-03],
[10.95, 6.1125e-03, 9.6735e-04],
[11.05, 4.3923e-03, 9.6254e-04],
[11.15, 2.5463e-03, 5.0038e-04],
[11.25, 1.4298e-03, 4.2816e-04],
[11.35, 6.4867e-04, 1.6439e-04],
[11.45, 2.8294e-04, 9.9799e-05],
[11.55, 1.0617e-04, 4.9085e-05],
[11.65, 3.2702e-05, 2.4546e-05],
[11.75, 1.2571e-05, 1.2571e-05],
[11.85, 8.4589e-06, 8.4589e-06],
[11.95, 7.4764e-06, 7.4764e-06],
],
dtype=np.float32)
# Finally plot the data
# plt.errorbar(
# Baldry[:, 0],
# Baldry[:, 1],
# yerr=Baldry[:, 2],
# color='g',
# linestyle=':',
# lw = 1.5,
# label='Baldry et al. 2008',
# )
Baldry_xval = np.log10(10**Baldry[:, 0] / self.Hubble_h /
self.Hubble_h)
if (whichimf == 1):
Baldry_xval = Baldry_xval - 0.26 # convert back to Chabrier IMF
Baldry_yvalU = (Baldry[:, 1] + Baldry[:, 2]
) * self.Hubble_h * self.Hubble_h * self.Hubble_h
Baldry_yvalL = (Baldry[:, 1] - Baldry[:, 2]
) * self.Hubble_h * self.Hubble_h * self.Hubble_h
plt.fill_between(Baldry_xval,
Baldry_yvalU,
Baldry_yvalL,
facecolor='purple',
alpha=0.25,
label='Baldry et al. 2008 (z=0.1)')
# This next line is just to get the shaded region to appear correctly in the legend
plt.plot(xaxeshisto,
counts / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
label='Baldry et al. 2008',
color='purple',
alpha=0.3)
# # Cole et al. 2001 SMF (h=1.0 converted to h=0.73)
# M = np.arange(7.0, 13.0, 0.01)
# Mstar = np.log10(7.07*1.0e10 /self.Hubble_h/self.Hubble_h)
# alpha = -1.18
# phistar = 0.009 *self.Hubble_h*self.Hubble_h*self.Hubble_h
# xval = 10.0 ** (M-Mstar)
# yval = np.log(10.) * phistar * xval ** (alpha+1) * np.exp(-xval)
# plt.plot(M, yval, 'g--', lw=1.5, label='Cole et al. 2001') # Plot the SMF
# Overplot the model histograms
plt.plot(xaxeshisto,
counts / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
'k-',
label='Model - All')
plt.plot(xaxeshisto,
countsRED / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
'r:',
lw=2,
label='Model - Red')
plt.plot(xaxeshisto,
countsBLU / self.volume * self.Hubble_h * self.Hubble_h *
self.Hubble_h / binwidth,
'b:',
lw=2,
label='Model - Blue')
plt.yscale('log', nonposy='clip')
plt.axis([8.0, 12.5, 1.0e-6, 1.0e-1])
# Set the x-axis minor ticks
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
plt.ylabel(
r'$\phi\ (\mathrm{Mpc}^{-3}\ \mathrm{dex}^{-1})$') # Set the y...
plt.xlabel(r'$\log_{10} M_{\mathrm{stars}}\ (M_{\odot})$'
) # and the x-axis labels
plt.text(12.2, 0.03, whichsimulation, size='large')
leg = plt.legend(loc='lower left', numpoints=1, labelspacing=0.1)
leg.draw_frame(False) # Don't want a box frame
for t in leg.get_texts(): # Reduce the size of the text
t.set_fontsize('medium')
outputFile = OutputDir + '1.StellarMassFunction' + OutputFormat
plt.savefig(outputFile) # Save the figure
print('Saved file to', outputFile)
plt.close()
# Add this plot to our output list
OutputList.append(outputFile)
def VelocityDistribution(self, G):
print('Plotting the velocity distribution of all galaxies')
seed(2222)
mi = -40.0
ma = 40.0
binwidth = 0.5
NB = (ma - mi) / binwidth
# set up figure
plt.figure()
ax = plt.subplot(111)
pos_x = G.Pos[:, 0] / self.Hubble_h
pos_y = G.Pos[:, 1] / self.Hubble_h
pos_z = G.Pos[:, 2] / self.Hubble_h
vel_x = G.Vel[:, 0]
vel_y = G.Vel[:, 1]
vel_z = G.Vel[:, 2]
dist_los = np.sqrt(pos_x * pos_x + pos_y * pos_y + pos_z * pos_z)
vel_los = (pos_x / dist_los) * vel_x + (pos_y / dist_los) * vel_y + (
pos_z / dist_los) * vel_z
dist_red = dist_los + vel_los / (self.Hubble_h * 100.0)
tot_gals = len(pos_x)
(counts, binedges) = np.histogram(vel_los / (self.Hubble_h * 100.0),
range=(mi, ma),
bins=NB)
xaxeshisto = binedges[:-1] + 0.5 * binwidth
plt.plot(xaxeshisto,
counts / binwidth / tot_gals,
'k-',
label='los-velocity')
(counts, binedges) = np.histogram(vel_x / (self.Hubble_h * 100.0),
range=(mi, ma),
bins=NB)
xaxeshisto = binedges[:-1] + 0.5 * binwidth
plt.plot(xaxeshisto,
counts / binwidth / tot_gals,
'r-',
label='x-velocity')
(counts, binedges) = np.histogram(vel_y / (self.Hubble_h * 100.0),
range=(mi, ma),
bins=NB)
xaxeshisto = binedges[:-1] + 0.5 * binwidth
plt.plot(xaxeshisto,
counts / binwidth / tot_gals,
'g-',
label='y-velocity')
(counts, binedges) = np.histogram(vel_z / (self.Hubble_h * 100.0),
range=(mi, ma),
bins=NB)
xaxeshisto = binedges[:-1] + 0.5 * binwidth
plt.plot(xaxeshisto,
counts / binwidth / tot_gals,
'b-',
label='z-velocity')
plt.yscale('log', nonposy='clip')
plt.axis([mi, ma, 1e-5, 0.5])
# plt.axis([mi, ma, 0, 0.13])
plt.ylabel(r'$\mathrm{Box\ Normalised\ Count}$') # Set the y...
plt.xlabel(r'$\mathrm{Velocity / H}_{0}$') # and the x-axis labels
leg = plt.legend(loc='upper left', numpoints=1, labelspacing=0.1)
leg.draw_frame(False) # Don't want a box frame
for t in leg.get_texts(): # Reduce the size of the text
t.set_fontsize('medium')
outputFile = OutputDir + '11.VelocityDistribution' + OutputFormat
plt.savefig(outputFile) # Save the figure
print('Saved file to', outputFile)
plt.close()
# Add this plot to our output list
OutputList.append(outputFile)
def display_2nd_moments(data=None):
# remove the mean for all moments
data = subMeanAll(data)
pl.figure(figsize=(11, 5.5))
pl.subplot(1, 2, 1)
phi22 = 0.5 * np.arctan2(data[:, 3].imag, data[:, 3].real)
x = data[:, 0].real
y = data[:, 1].real
phi22[x < 0] = phi22 + np.deg2rad(180)
u = np.abs(data[:, 3]) * np.cos(phi22)
v = np.abs(data[:, 3]) * np.sin(phi22)
qvr = pl.quiver(x,
y,
u,
v,
width=0.004,
color='r',
pivot='middle',
headwidth=0.,
headlength=0.,
headaxislength=0.,
scale_units='width')
qk = pl.quiverkey(qvr,
-150,
-240,
np.max(np.sqrt(u**2 + v**2)),
str(round(np.max(np.sqrt(u**2 + v**2)), 3)) + ' pix^2',
coordinates='data',
color='blue')
pl.plot(x, y, 'b,')
pl.xlim(-250, 250)
pl.ylim(-250, 250)
pl.grid(color='g')
pl.xlabel('X [mm] (WEST)')
pl.ylabel('Y [mm] (NORTH)')
pl.title('M22')
pl.subplot(1, 2, 2)
m20sqr = np.sqrt(data[:, 2].real)
x = data[:, 0].real
y = data[:, 1].real
m20sqr_med = np.median(m20sqr)
m20sqr_diff = m20sqr - m20sqr_med
m20sqr_diff_absmed = np.median(np.abs(m20sqr_diff))
plotScale = 1. / m20sqr_diff_absmed * 100
pos = m20sqr_diff >= 0
neg = m20sqr_diff < 0
pl.scatter(x[pos],
y[pos],
s=m20sqr_diff[pos] * plotScale,
c='r',
alpha=0.5)
pl.scatter(x[neg],
y[neg],
s=-m20sqr_diff[neg] * plotScale,
c='b',
alpha=0.5)
pl.scatter(-230, -210, s=m20sqr_diff_absmed * plotScale, c='b', alpha=0.5)
pl.text(-200, -215, '-' + str(round(m20sqr_diff_absmed, 6)) + ' pix')
pl.scatter(-230, -230, s=m20sqr_diff_absmed * plotScale, c='r', alpha=0.5)
pl.text(-200, -235, str(round(m20sqr_diff_absmed, 6)) + ' pix')
pl.plot(x, y, 'y,')
pl.grid(color='g')
pl.xlim(-250, 250)
pl.ylim(-250, 250)
pl.xlabel('X [mm] (WEST)')
pl.ylabel('Y [mm] (NORTH)')
pl.title('median ' + r'$\sqrt{M20}$: ' +
str(round(scale * m20sqr_med, 3)) + ' [arcsec]')
return '---done!--'
pl.grid()
pl.title('mag: 15, CCD: S4, Truth')
pl.subplot(2, 1, 2)
pl.boxplot(momtsWTrue.imag)
pl.xticks(np.arange(1, 5),
['M20.imag', 'M22.imag', 'M31.imag', 'M33.imag'])
pl.grid()
#----distribution ----
pl.figure(figsize=(15, 9))
pl.subplot(2, 3, 1)
pl.hist(momtsW[:, 0].real, bins=10, normed=False)
pl.title(
str(round(np.mean(momtsW[:, 0].real), 6)) + r'$\pm$' +
str(round(np.std(momtsW[:, 0].real) / np.sqrt(momtsW.shape[0]), 6)))
pl.xlabel('Wmoment M20')
pl.subplot(2, 3, 2)
pl.hist(momtsW[:, 1].real, bins=10, normed=False)
pl.title(
str(round(np.mean(momtsW[:, 1].real), 6)) + r'$\pm$' +
str(round(np.std(momtsW[:, 1].real) / np.sqrt(momtsW.shape[0]), 6)))
pl.xlabel('Wmoment M22.real')
pl.subplot(2, 3, 3)
pl.hist(momtsW[:, 1].imag, bins=10, normed=False)
pl.title(
str(round(np.mean(momtsW[:, 1].imag), 6)) + r'$\pm$' +
str(round(np.std(momtsW[:, 1].imag) / np.sqrt(momtsW.shape[0]), 6)))
pl.xlabel('Wmoment M22.imag')
pl.subplot(2, 3, 4)
pl.hist(momtsA[:, 0].real, bins=10, normed=False)
pl.title(
if __name__ == '__main__':
# Simple convergence demo.
# A few modules we need
import pylab as P
import numpy as N
# Create a list of points 'nrange' where we'll compute Wallis' formula
nrange = N.linspace(10, 2000, 20).astype(int)
# Make an array of such values
wpi = N.array(map(pi, nrange))
# Compute the difference against the value of pi in numpy (standard
# 16-digit value)
diff = abs(wpi - N.pi)
# Make a new figure and build a semilog plot of the difference so we can
# see the quality of the convergence
P.figure()
# Line plot with red circles at the data points
P.semilogy(nrange, diff, '-o', mfc='red')
# A bit of labeling and a grid
P.title(r"Convergence of Wallis' product formula for $\pi$")
P.xlabel('Number of terms')
P.ylabel(r'Absolute Error')
P.grid()
# Display the actual plot
P.show()
Z = sum(rho[j, j] for j in range(nx + 1)) * dx
pi_of_x = [rho[j, j] / Z for j in range(nx + 1)]
# graphics
'''
y = [j * dtau for j in range(N)]
pylab.plot(x, y, 'b-')
pylab.xlabel('$x$', fontsize=18)
pylab.ylabel('$\\tau$', fontsize=18)
pylab.title('Levi harmonic path, N= %i, $\\beta$ = %.0f'%(N, beta))
pylab.xlim(-3.0, 3.0)
pylab.savefig('plot_B2_levi_path_beta%s.png' % beta)
pylab.show()
'''
pylab.hist(data, density=True, bins=200, label='Levi-path')
pylab.plot(x, pi_of_x, label='matrix-square')
list_x = [0.1 * a for a in range(-30, 31)]
list_y = [math.sqrt(math.tanh(beta / 2.0)) / math.sqrt(math.pi) * \
math.exp(-x ** 2 * math.tanh(beta / 2.0)) for x in list_x]
#pylab.plot(list_x, list_y, label='analytic')
pylab.legend()
pylab.xlabel('$x$')
pylab.ylabel('$\\pi(x)$ (normalized)')
pylab.title(
'metro_levi_free_anharmonic\n(beta=%s, N=%i, n_steps=%i, Ncut=%i)' %
(beta, N, n_steps, Ncut))
pylab.xlim(-2, 2)
pylab.savefig('plot_C1_metro_levi_free.png')
pylab.show()
seffta[i] = np.trapz(seffpa*np.sin(th*pi/180),th*pi/180)
sefftc[i] = np.trapz(seffpc*np.sin(th*pi/180),th*pi/180)
sefftopt[i] = np.trapz(seffpopt*np.sin(th*pi/180),th*pi/180)
## Compute event rate
print ''
ee = pow(10,eu)
print 'Energy:',ee,'eV'
print 'Apperture (agressive):',seffta,'km2.sr'
print 'Apperture (conservative):',sefftc,'km2.sr'
print 'Optimal apperture:',sefftopt[0],'km2.sr'
pl.figure(2)
pl.plot(eu,seffta,label='Agressive')
pl.plot(eu,sefftc,label='Conservative')
pl.plot(eu,sefftopt,label='Optimal')
pl.xlabel('Energy (eV)')
pl.ylabel('Apperture (km$^2$.sr)')
pl.grid(True)
pl.legend(loc='best')
pl.title("GRAND CR apperture")
#pl.show()
seffta = seffta*1e6 #m2
sefftc = sefftc*1e6 #m2
sefftopt = sefftopt*1e6 #m2
dt = 3600*24*ndays #1 day
evt1da = np.trapz(seffta*J1*pow(ee,-gamma1)*dt,ee)
evt1dc = np.trapz(sefftc*J1*pow(ee,-gamma1)*dt,ee)
evt1dopt = np.trapz(sefftopt*J1*pow(ee,-gamma1)*dt,ee)
print 'Expected daily event rate in 10^17-10^18eV (agressive):',evt1da
print 'Expected daily event rate in 10^17-10^18eV (conservative):',evt1dc
set_weights_bias2((100, 10), X.dtype, w4, b4)
for i in range(noIters):
trX, trY = shuffle_data(trX, trY)
for start, end in zip(range(0, len(trX), batch_size),
range(batch_size, len(trX), batch_size)):
cost = train3(trX[start:end], trY[start:end])
a3.append(np.mean(np.argmax(teY, axis=1) == predict(teX)))
trainCost3.append(cost / (len(trX) // batch_size))
print(a[i])
pylab.figure()
pylab.plot(range(noIters), a, label='SGD')
pylab.plot(range(noIters), a2, label='SGD with momentum')
pylab.plot(range(noIters), a3, label='RMSprop')
pylab.xlabel('epochs')
pylab.ylabel('test accuracy')
pylab.legend(loc='lower right')
pylab.title('test accuracy ')
pylab.savefig('testAccuracy')
pylab.figure()
pylab.plot(range(noIters), trainCost, label='SGD')
pylab.plot(range(noIters), trainCost2, label='SGD with momentum')
pylab.plot(range(noIters), trainCost3, label='RMSprop')
pylab.xlabel('epochs')
pylab.ylabel('training cost')
pylab.legend(loc='upper right')
pylab.title('training cost')
pylab.savefig('trainingCost')
residual3 = delta_tensor_norm(statistic_res, check_tensor)
x_values.append(train_percent)
y_values1.append(residual1)
y_values2.append(residual2)
y_values3.append(residual3)
train_percent += 0.2
pylab.plot(x_values,
y_values1,
'rs',
linewidth=1,
linestyle="-",
label=u"MTT")
pylab.plot(x_values,
y_values2,
'ks',
linewidth=1,
linestyle="-",
label=u"DTA")
pylab.plot(x_values,
y_values3,
'gs',
linewidth=1,
linestyle="-",
label=u"Baseline")
pylab.xlabel(u"训练集比重")
pylab.ylabel(u"平均误差")
pylab.title(u"训练集比重与平均误差的关系")
pylab.legend(loc='center right')
pylab.show()
def lin_fit(crvfile,
refcrvfile,
outname,
dump_frequency,
Er,
Ed,
recoil_relaxation_time=10000,
start_timeoffset=500):
crv = CRV.CRV(crvfile)[0]
eps = crv['lz']
x = crv['step']
pyy = crv['pyy']
n_atoms = crv['atoms'][0]
crv1 = CRV.CRV(refcrvfile)[0]
eps_ref = crv1['lz']
# eps = np.subtract(eps,eps_ref)
print eps
nrecoils = []
#pressure tensor component
pxx = []
ezz = []
ezz_ref = []
dump_factor = int(recoil_relaxation_time / dump_frequency)
pressure_conversion = 1e9 #GPa -> Pa
#reduce timeaxis to recoil axis
for i in range(len(x) / dump_factor):
nrecoils.append((x[i * dump_factor] - start_timeoffset) /
float(recoil_relaxation_time))
pxx.append(pyy[i * dump_factor] * pressure_conversion)
# ezz.append(((eps[i*dump_factor] - eps[0])/eps[0] - (eps_ref[i*dump_factor] - eps_ref[0])/eps_ref[0]))
# ezz.append(abs( (eps[i*dump_factor] -eps_ref[i*dump_factor])/ (eps[0] - eps_ref[0])))
ezz.append(((-eps[dump_factor] + eps[i * dump_factor])))
ezz_ref.append(((-eps_ref[dump_factor] + eps_ref[i * dump_factor])))
del nrecoils[0]
del pxx[0]
del ezz[0]
del ezz_ref[0]
#number of displacements per target atom
ndpa = np.multiply(nrecoils, (Er / (2.5 * Ed * n_atoms)))
fit_start = 0
fit_end = 20
dezz = np.multiply(np.subtract(ezz, ezz_ref), 1. / eps[dump_factor])
#fit1
popt1, pcov1 = opt.curve_fit(
lambda ndpa, eta, offset: eta_lin(ndpa, pxx[0], eta, offset),
ndpa[fit_start:fit_end], dezz[fit_start:fit_end])
#popt1 = [1,1]
#fit2
# popt2, pcov2 = opt.curve_fit( lambda ndpa,eta,offset: eta_lin(ndpa,pxx[0],eta,offset), ndpa[0:fit_start], ezz[0:fit_start])
#popt2 = [1,1]
#anotate to add value to plot
# print 'RIV (lin)= {:.4e}'.format(popt1[0])
# npa interpolated
ndpa_interp = np.linspace(0, ndpa[-1] * 1.5, 1000)
fig = plt.figure(1, figsize=fsize)
plt.xlim(0, ndpa_interp[-1])
# plt.ylim(ezz[-1]*0.8, ezz[-1]*1.1)
plt.grid()
plt.plot(ndpa[fit_start:fit_end],
dezz[fit_start:fit_end],
'bs',
markeredgecolor='blue',
markerfacecolor='None',
markeredgewidth=mew,
markersize=ms,
label='MD Simulation')
# plt.plot(ndpa, ezz_ref, 'bs', markeredgecolor = 'magenta', markerfacecolor= 'None', markeredgewidth=mew, markersize = ms, label = 'MD Simulation Reference')
plt.plot(ndpa_interp,
eta_lin(ndpa_interp, pxx[0], *popt1),
'r-',
linewidth=lw,
label='Fit')
# plt.plot(ndpa_interp, eta_lin(ndpa_interp,pxx[0],*popt2), '-', color = 'black', linewidth = lw, label='Fit2')
plt.xlabel('Number of displacements per atom')
plt.ylabel(r'$ \Delta \varepsilon_{zz} $')
# legtitle = r'$ \eta_{ri,1} = $' + '{:.4e}'.format(popt1[0]) + ' $ \mathrm{Pa \cdot dpa} $' + '\n' + r'$ \eta_{ri,2} = $' + '{:.4e}'.format(popt2[0]) + ' $ \mathrm{Pa \cdot dpa} $' + '\n'r'$\sigma_0 = $' + '{:.2e}'.format(abs(pxx[0])) + r'$\,\mathrm{Pa}$' + '\n' + r'$E_D = ' + '{:.1e}'.format(Ed) + 'eV $'+ '\n' + r'$ E_R = $' + '{:.1e}'.format(Er) + ' $ eV $'
legtitle = r'$ \eta^\prime = $' + '{:.4e}'.format(
popt1[0]
) + ' $ \mathrm{Pa \cdot dpa} $' + '\n' r'$\sigma_0 = $' + '{:.2e}'.format(
abs(pxx[0])
) + r'$\,\mathrm{Pa}$' + '\n' + r'$E_D = ' + '{:.1e}'.format(
Ed) + '\mathrm{eV} $' + '\n' + r'$ E_R = $' + '{:.1e}'.format(
Er) + ' $ \mathrm{eV} $'
plt.legend(loc='best',
shadow=False,
title=legtitle,
prop={'size': legpropsize},
numpoints=1)
#every other tick label
for label in plt.gca().xaxis.get_ticklabels()[::2]:
label.set_visible(False)
#plt.show()
fig.tight_layout()
fig.savefig(outname)
print "Png file written to " + outname
plt.close("all")
# extract the time course for different labels from the stc
stc_lh = stc.in_label(aud_lh)
stc_rh = stc.in_label(aud_rh)
stc_bh = stc.in_label(aud_lh + aud_rh)
# calculate center of mass and transform to mni coordinates
vtx, _, t_lh = stc_lh.center_of_mass('sample')
mni_lh = mne.vertex_to_mni(vtx, 0, 'sample')[0]
vtx, _, t_rh = stc_rh.center_of_mass('sample')
mni_rh = mne.vertex_to_mni(vtx, 1, 'sample')[0]
# plot the activation
pl.figure()
pl.axes([.1, .275, .85, .625])
hl = pl.plot(stc.times, stc_lh.data.mean(0), 'b')
hr = pl.plot(stc.times, stc_rh.data.mean(0), 'g')
hb = pl.plot(stc.times, stc_bh.data.mean(0), 'r')
pl.xlabel('Time (s)')
pl.ylabel('Source amplitude (dSPM)')
pl.xlim(stc.times[0], stc.times[-1])
# add a legend including center-of-mass mni coordinates to the plot
labels = [
'LH: center of mass = %s' % mni_lh.round(2),
'RH: center of mass = %s' % mni_rh.round(2), 'Combined LH & RH'
]
pl.figlegend([hl, hr, hb], labels, 'lower center')
pl.suptitle('Average activation in auditory cortex labels', fontsize=20)
pl.show()
U, S, D = HOSVD(numpy.array(tensor), 0.7)
A = reconstruct(S, U)
print "reconstruct tensor: ", A
print frobenius_norm(tensor-A)
avg_precision, avg_recall, avg_f1_score, availability = recommend(A, recommends, unknow_poi_set, time_slice, top_k, order)
print "avg_precision: ", avg_precision
print "avg_recall: ", avg_recall
print "avg_f1_score: ", avg_f1_score
print "availability: ", availability
y_values1.append(avg_precision)
y_values2.append(avg_recall)
y_values3.append(avg_f1_score)
y_values4.append(availability)
x_values.append(cluster_radius)
cluster_radius += 0.1
pylab.plot(x_values, y_values1, 'rs', linewidth=1, linestyle="-", label=u"准确率")
pylab.plot(x_values, y_values2, 'gs', linewidth=1, linestyle="-", label=u"召回率")
pylab.plot(x_values, y_values3, 'bs', linewidth=1, linestyle="-", label=u"f1值")
# pylab.plot(x_values, y_values4, 'ks', linewidth=1, linestyle="-", label=u"可用率")
pylab.xlabel(u"poi近邻集合的半径（km）")
pylab.ylabel(u"准确率-召回率")
pylab.title(u"poi近邻集合的半径与准确率-召回率之间的关系")
pylab.legend(loc='center right')
# pylab.xlim(1, 10)
# pylab.ylim(0, 1.)
pylab.show()
DATASET_INDEX,
dataset_prefix='large_kitchen_appliances',
batch_size=128)
print("--- Run Time = %s seconds ---" % ((time.time() - start_time)))
print("--- Run Time = %s minutes ---" %
((time.time() - start_time) / 60.0))
text_file = open("training_time.txt", "w")
text_file.write("--- Run Time =" + str(((time.time() - start_time))) +
" seconds ---" + "\n" + "--- Run Time = " +
str(((time.time() - start_time) / 60.0)) + " minutes ---" +
"\n")
print(history.history.keys())
plt.plot(history.history['loss'])
plt.xlabel('epoch', fontsize=16)
plt.ylabel('loss', fontsize=16)
plt.savefig("./resulted_plotes/train_loss.jpg")
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['train', 'validation'], loc='upper right', fontsize='large')
plt.ylabel('loss', fontsize=16)
plt.xlabel('epoch', fontsize=16)
plt.savefig("./resulted_plotes/all_loss.jpg")
plt.show()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
#plot initial wave for t=0
plt.figure
plt.plot(x, u[0], label='t=0.0')
#loop over time, excluding the initial condition
for j in range(1, Nt - 1):
#current time
t = j * dt
#loop over x, excluding the boundry
for i in range(1, Nx - 1):
#formula constant
beta = epsilon * dt / dx
#apply leapfrog step
u[j + 1,
i] = u[j - 1, i] - beta / 2 * ((u[j, i + 1])**2 - (u[j, i - 1])**2)
#plot the function at the given times
if abs(t - t2) < delta:
plt.plot(x, u[j], label='t=0.5')
elif abs(t - t3) < delta:
plt.plot(x, u[j], label='t=1.0')
elif abs(t - t4) < delta:
plt.plot(x, u[j], label='t=1.5')
#plotting values
plt.xlabel("Position, x")
plt.ylabel("Displacement, u(x,t)")
plt.title("Solutions to Burger's equation")
plt.grid()
plt.legend()
plt.savefig("../images/burgers.png", dpi=600)
def exp_fit(crvfile,
outname,
dump_frequency,
Er,
Ed,
Ebi,
recoil_relaxation_time=30000,
start_timeoffset=500):
## CRV File - renamed to .crv and manually changed header (crv format) from extracted data file
## PNG output
## timestep used in recoil.in during recoil insertion --> datapoint after each recoil
crv = CRV.CRV(crvfile)[0]
#extract mech. stress tensor in x or y
pyy = crv['pyy']
x = crv['step']
n_atoms = crv['atoms'][0]
#calc timeaxis out of pxx or pyy size
#x = np.arange(0,len(pyy)*timestep,timestep)
#number of recoils
nrecoils = []
#pressure tensor component
pxx = []
dump_factor = int(recoil_relaxation_time / dump_frequency)
print dump_factor
#reduce timeaxis to recoil axis
for i in range(len(x) / dump_factor):
nrecoils.append((x[i * dump_factor] - start_timeoffset) /
float(recoil_relaxation_time))
pxx.append(abs(pyy[i * dump_factor] / pyy[0]))
#number of displacements per target atom
ndpa = np.multiply(nrecoils, (Er / (2.5 * Ed * n_atoms)))
#fit
popt, pcov = opt.curve_fit(lambda ndpa, eta: eta_exp(ndpa, Ebi, eta),
ndpa,
pxx,
p0=2e8)
#anotate to add value to plot
print 'RIV (exp)= {:.4e}'.format(popt[0])
# npa interpolated
ndpa_interp = np.linspace(0, ndpa[-1] * 5, 1000)
fig = plt.figure(1, figsize=fsize)
plt.xlim(0, ndpa_interp[-1])
#plt.ylim(0.95*pxx[-1] , 1)
plt.grid()
plt.plot(ndpa,
pxx,
'bs',
markeredgecolor='blue',
markerfacecolor='None',
markeredgewidth=mew,
markersize=ms,
label='MD Simulation')
plt.plot(ndpa_interp,
eta_exp(ndpa_interp, Ebi, *popt),
'r-',
linewidth=lw,
label='Fit')
plt.xlabel('Number of displacements per atom')
plt.ylabel(r'$ \frac{\sigma}{\vert \sigma_0 \vert} $')
legtitle = r'$ \eta_{ri} = $' + '{:.4e}'.format(
popt[0]
) + ' $ Pa \cdot dpa $' + '\n' + r'$ E_{bi} = $' + '{:.3e}'.format(
Ebi) + ' $ Pa $' + '\n' + r'$ E_D = $' + '{:.1e}'.format(
Ed) + ' $ \mathrm{eV} $' + '\n' + r'$ E_R = $' + '{:.1e}'.format(
Er) + ' $ \mathrm{eV} $'
plt.legend(loc='best',
shadow=False,
title=legtitle,
prop={'size': legpropsize},
numpoints=1)
#every other tick label
for label in plt.gca().xaxis.get_ticklabels()[::2]:
label.set_visible(False)
#plt.show()
fig.savefig(outname)
print "Png file written to " + outname
plt.close("all")
return popt[0]
# Plots the drag coefficient against the particle Reynolds number.
from math import sqrt, pi
from numpy import *
import pylab
from fluidity_tools import stat_parser
C_wen_yu = zeros(200000)
C_stokes = zeros(200000)
particle_Re = arange(0.001, 1000, 0.005)
for i in range(0, len(particle_Re)):
# Drag coefficients for the Wen & Yu and Stokes drag correlations respectively.
C_wen_yu[i] = (24.0 / particle_Re[i]) * (1.0 +
0.15 * particle_Re[i]**0.687)
C_stokes[i] = (24.0 / particle_Re[i])
s = stat_parser("./mphase_wen_yu_drag_correlation.stat")
numerical_particle_Re_wen_yu = s["Tephra"]["ParticleReynoldsNumber"]["max"][-1]
numerical_C_wen_yu = s["Tephra"]["DragCoefficient"]["max"][-1]
pylab.loglog(particle_Re, C_stokes, "-r", label="Stokes drag correlation")
pylab.loglog(particle_Re, C_wen_yu, "-g", label="Wen & Yu drag correlation")
pylab.loglog(numerical_particle_Re_wen_yu,
numerical_C_wen_yu,
"*b",
label="Numerical result")
pylab.legend(loc=1)
pylab.xlabel("ParticleReynoldsNumber")
pylab.ylabel("DragCoefficient")
pylab.show()
s = init.create_state_random_packing(imsize=36,
radius=5.0,
sigma=0.05,
seed=10)
bl_pos = s.explode(s.create_block('pos'))
bl_rad = s.explode(s.create_block('rad'))
f = s.fisher_information(bl_pos + bl_rad)
inv = np.linalg.inv(f)
crb = np.sqrt(np.diag(inv))
bp = 3 * s.N
br = 4 * s.N
pl.figure()
pl.imshow(inv)
pl.colorbar()
pl.hlines(bp, 0, br, lw=1)
pl.vlines(bp, 0, br, lw=1)
pl.xlim(0, br - 1)
pl.ylim(0, br - 1)
pl.title("Dense suspension CRB")
pl.figure()
pl.semilogy(crb, 'o')
pl.xlabel("Parameter index")
pl.ylabel("CRB")
pl.title("Dense suspension CRB")
pl.xlim(0, br)
pl.show()
pl.figure(figsize=(1.7, 1.7))
pl.subplots_adjust(left=0.4,
right=0.9,
top=0.85,
bottom=0.3,
wspace=0.2,
hspace=0.6)
pl.subplot(1, 1, 1, aspect='equal')
pl.plot(batch_phases[batch_idx][:, 0],
batch_phases[batch_idx][:, 1],
'.k',
ms=7)
pp.custom_axes()
pl.xlabel('Grid phase-x [m]')
pl.ylabel('Grid phase-y [m]')
pl.xlim(-0.3, 0.3)
pl.ylim(-0.3, 0.3)
pl.xticks([-0.3, 0., 0.3])
pl.yticks([-0.3, 0., 0.3])
pl.savefig(figures_path + '/fig8' + suffix[batch_idx] + '1.eps',
dpi=300,
transparent=True)
# angles
pl.figure(figsize=(4., 1.5))
pl.subplots_adjust(left=0.3, right=1, top=0.85, bottom=0.3, wspace=0.2)
pl.subplot(1, 3, 1)
bins, edges = np.histogram(np.array(batch_angles[batch_idx]) * 180 / np.pi,
line = xy.readline()
#split into two separate lists: x and y
x_and_y = line.split()
# If we do not have 2 words then it must be blank lines at the end of the file.
if len(x_and_y) != 2:
break #exits loop
except:
# If we failed to read a line then we must have got to the end.
break #exits loop
#append x and y where x value is the first column and y is second column
#save these values as floats- if this is not possible it will go to except
x.append(float(x_and_y[0]))
y.append(float(x_and_y[1]))
#convert x and y lists to numpy arrays
x = np.array(x)
y = np.array(y)
pylab.plot(x, y)
pylab.xlabel("x")
pylab.ylabel("y")
pylab.show()
print "Maximum x: ", max(x)
print "Maximum y: ", max(y)
def plotReport( self, summ={} ,fignum=1 ):
if not ( summ.has_key('summaryminor') and summ.has_key('summarymajor') and summ.has_key('threshold') and summ['summaryminor'].shape[0]==6 ):
print 'Cannot make summary plot. Please check contents of the output dictionary from tclean.'
return summ
import pylab as pl
from numpy import max as amax
# 0 : iteration number (within deconvolver, per cycle)
# 1 : peak residual
# 2 : model flux
# 3 : cyclethreshold
# 4 : deconvolver id
# 5 : subimage id (channel, stokes..)
pl.ioff()
pl.figure(fignum)
pl.clf();
minarr = summ['summaryminor']
if minarr.size==0:
casalog.post("Zero iteration: no summary plot is generated.", "WARN")
else:
iterlist = minarr[0,:]
eps=0.0
peakresstart=[]
peakresend=[]
modfluxstart=[]
modfluxend=[]
itercountstart=[]
itercountend=[]
peakresstart.append( minarr[1,:][0] )
modfluxstart.append( minarr[2,:][0] )
itercountstart.append( minarr[0,:][0] + eps )
peakresend.append( minarr[1,:][0] )
modfluxend.append( minarr[2,:][0] )
itercountend.append( minarr[0,:][0] + eps )
for ii in range(0,len(iterlist)-1):
if iterlist[ii]==iterlist[ii+1]:
peakresend.append( minarr[1,:][ii] )
peakresstart.append( minarr[1,:][ii+1] )
modfluxend.append( minarr[2,:][ii] )
modfluxstart.append( minarr[2,:][ii+1] )
itercountend.append( iterlist[ii]-eps )
itercountstart.append( iterlist[ii]+eps )
peakresend.append( minarr[1,:][len(iterlist)-1] )
modfluxend.append( minarr[2,:][len(iterlist)-1] )
itercountend.append( minarr[0,:][len(iterlist)-1] + eps )
# pl.plot( iterlist , minarr[1,:] , 'r.-' , label='peak residual' , linewidth=1.5, markersize=8.0)
# pl.plot( iterlist , minarr[2,:] , 'b.-' , label='model flux' )
# pl.plot( iterlist , minarr[3,:] , 'g--' , label='cycle threshold' )
pl.plot( itercountstart , peakresstart , 'r.--' , label='peak residual (start)')
pl.plot( itercountend , peakresend , 'r.-' , label='peak residual (end)',linewidth=2.5)
pl.plot( itercountstart , modfluxstart , 'b.--' , label='model flux (start)' )
pl.plot( itercountend , modfluxend , 'b.-' , label='model flux (end)',linewidth=2.5 )
pl.plot( iterlist , minarr[3,:] , 'g--' , label='cycle threshold', linewidth=2.5 )
maxval = amax( minarr[1,:] )
maxval = max( maxval, amax( minarr[2,:] ) )
bcols = ['b','g','r','y','c']
minv=1
niterdone = len(minarr[4,:])
if len(summ['summarymajor'].shape)==1 and summ['summarymajor'].shape[0]>0 :
pl.vlines(summ['summarymajor'],0,maxval, label='major cycles', linewidth=2.0)
pl.hlines( summ['threshold'], 0, summ['iterdone'] , linestyle='dashed' ,label='threshold')
pl.xlabel( 'Iteration Count' )
pl.ylabel( 'Peak Residual (red), Model Flux (blue)' )
ax = pl.axes()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width, box.height*0.8])
pl.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05),
ncol=3, fancybox=True, shadow=True)
pl.savefig('summaryplot_'+str(fignum)+'.png')
pl.ion()
return summ;
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record()
p.run(runtime)
v = populations[0].get_v(compatible_output=True)
gsyn = populations[0].get_gsyn(compatible_output=True)
spikes = populations[0].getSpikes(compatible_output=True)
if spikes is not None:
print spikes
pylab.figure()
pylab.plot([i[1] for i in spikes], [i[0] for i in spikes], ".")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title('spikes')
pylab.show()
else:
print "No spikes received"
# Make some graphs
if v is not None:
ticks = len(v) / nNeurons
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('v')
pylab.title('v')
for pos in range(0, nNeurons, 20):
# along x
pylab.subplot(3, 3, 7)
pylab.xticks([])
pylab.yticks([])
f = open("map_x_amr.dat", 'rb')
s = f.read(2 * RM + struct.calcsize("2i"))
par, nx, ny, par = struct.unpack(SRM + "i" + "i" + SRM, s)
for i in arange(Ntick):
xval = 1000 * (yc + (i - Nspace / 2) * BL / Nspace - yc)
yval = 1000 * (zc + (i - Nspace / 2) * BL / Nspace - zc)
xt.append("%.0f" % xval)
yt.append("%.0f" % yval)
pylab.xticks(nx / Nspace * arange(Ntick), xt, fontsize=fs)
pylab.yticks(ny / Nspace * arange(Ntick), yt, fontsize=fs)
pylab.xlabel(r"$r\quad[{\rm kpc}/h]$", fontsize=fs)
pylab.ylabel(r"$r\quad[{\rm kpc}/h]$", fontsize=fs)
s = f.read(RM)
binvalues = ar.array(IMGSIZE)
binvalues.read(f, nx * ny)
data = numpy.array(binvalues, 'float')
data = numpy.reshape(data, (ny, nx))
data2 = numpy.zeros((ny, nx), 'float')
f.close()
for i in xrange(nx):
for j in xrange(ny):
data2[j, i] = data[ny - j - 1, i]
pylab.imshow(data2, aspect=1, cmap=cm.hsv)
def fe55_gain_fitter(signals,
ccdtemp=-95,
make_plot=False,
xrange=None,
bins=100,
hist_nsig=10,
title='',
plot_filename=None,
interactive=True,
ylog=True):
"""
Function to fit the distribution of charge cluster DN values from
a Fe55 dataset. A two Gaussian model of Mn K-alpha and K-beta
lines is assumed with the ratio between the K-alpha and K-beta
energies fixed at 5.889/6.49 and the the Gaussian width of the
lines set equal.
The gain (Ne/DN), location and sigma of the K-alpha peak (in units
of DN) are returned as a tuple.
If make_plot=True, then a matplotlib plot of the distribution and
fit is displayed.
If xrange is not None, then that 2-element tuple is used as the
histogram x-range.
If xrange is None, then the histogram x-range is set to
+/- hist_nsig*clipped_stdev about the median of the signal
distribution.
"""
flags = afwMath.MEDIAN | afwMath.STDEVCLIP
try:
stats = afwMath.makeStatistics(signals.tolist(), flags)
except:
print signals
raise
median = stats.getValue(afwMath.MEDIAN)
stdev = stats.getValue(afwMath.STDEVCLIP)
if xrange is None:
# Set range of histogram to include both Kalpha and Kbeta peaks.
xmin = max(median - hist_nsig * stdev, 200)
xmax = min(median * 1785. / 1620. + hist_nsig * stdev, 1000)
xrange = xmin, xmax
# Save pylab interactive state.
pylab_interactive_state = pylab.isinteractive()
# Determine distribution mode and take that as the location of the
# Kalpha peak
hist = np.histogram(signals, bins=bins, range=xrange)
xpeak = hist[1][np.where(hist[0] == max(hist[0]))][0]
xrange = max(0, xpeak - 200), xpeak * 1785. / 1620. + 200
hist = np.histogram(signals, bins=bins, range=xrange)
yrange = 1, max(hist[0]) * 1.5
if make_plot:
if interactive:
pylab.ion()
else:
pylab.ioff()
# fig = pylab.figure()
# axes = fig.add_subplot(111)
win = plot.Window()
hist = pylab.hist(signals,
bins=bins,
range=xrange,
histtype='bar',
color='b',
log=ylog)
if ylog:
plot.setAxis(xrange, yrange)
else:
pylab.ioff()
# hist = np.histogram(signals, bins=bins, range=xrange)
x = (hist[1][1:] + hist[1][:-1]) / 2.
y = hist[0]
ntot = sum(y)
#
# Starting values for two Gaussian fit. The relative
# normalizations are initially set at the expected line ratio
# of K-alpha/K-beta = 0.88/0.12. The relative peak locations
# and relative widths are fixed in fe55_lines(...) above.
#
p0 = (ntot * 0.88, median, stdev / 2., ntot * 0.12)
pars, _ = scipy.optimize.curve_fit(fe55_lines, x, y, p0=p0)
kalpha_peak, kalpha_sigma = pars[1], pars[2]
fe55_yield = Fe55Yield(ccdtemp)
gain = fe55_yield.alpha()[0] / kalpha_peak
if make_plot:
pylab.xlabel('Bias Corrected Event Signal (DN)')
pylab.ylabel('Entries / bin')
xx = np.linspace(x[0], x[-1], 1000)
pylab.plot(xx, fe55_lines(xx, *pars), 'r--', markersize=3, linewidth=1)
pylab.annotate(("K-alpha peak = %i DN\n\n" + "Gain = %.2f e-/DN\n\n") %
(kalpha_peak, gain), (0.5, 0.7),
xycoords='axes fraction')
win.set_title(title)
if plot_filename is not None:
pylab.savefig(plot_filename)
# Restore pylab interactive state.
pylab.interactive(pylab_interactive_state)
return gain, kalpha_peak, kalpha_sigma
idx1 = 10000
idx2 = idx1 + frameSize
index1 = idx1 * 1.0 / framerate
index2 = idx2 * 1.0 / framerate
acf = pt.ACF(waveData[idx1:idx2])
acf[0:10] = -acf[0]
acfmax = np.argmax(acf)
print(acfmax)
print(framerate * 1.0 / acfmax)
pl.subplot(411)
pl.title("pitchTrack")
pl.plot(time, waveData)
pl.plot([index1, index1], [-1, 1], 'r')
pl.plot([index2, index2], [-1, 1], 'r')
pl.xlabel("time (seconds)")
pl.ylabel("Amplitude")
pl.subplot(412)
pl.plot(np.arange(frameSize), waveData[idx1:idx2], 'r')
pl.xlabel("index in 1 frame")
pl.ylabel("Amplitude")
pl.subplot(413)
pl.plot(np.arange(frameSize), acf, 'g')
pl.xlabel("index in 1 frame")
pl.ylabel("ACF")
# pitch tracking
acfmethod = pt.ACF
pitchtrack = pt.PitchTrack(waveData, framerate, frameSize, overLap, acfmethod)
def compareAbsorption(recipe, curvNo, noIndicators):
worker = recipe.getWorker("AddColumn")
table = worker.plugCompute.getResult(
subscriber=TextSubscriber("Result Functional"))
xPos = table[u"x-position"]
yPos = table[u"y-position"]
index = curvNo2Index(table[u"pixel"], curvNo)
title_template = "$%%s_{%s}$(%s %s,%s %s)=%%s %%s" % (
curvNo, xPos.data[index], xPos.unit.unit.name(), yPos.data[index],
yPos.unit.unit.name())
worker = recipe.getWorker("ThicknessModeller")
simulation = worker.plugCalcAbsorption.getResult()
thickness = table[u"thickness"]
residuum = (simulation.dimensions[0].data - thickness.data[index])**2
absorption = simulation.data[residuum.argmin(), :]
pylab.plot(simulation.dimensions[1].data,
absorption,
label="$%s$ functional" % simulation.shortname)
title = "Functional based: " + title_template % (
thickness[index].shortname, thickness.data[index],
thickness.unit.unit.name())
try:
worker = recipe.getWorker("Res Direct")
table = worker.plugCompute.getResult(
subscriber=TextSubscriber("Result without Functional"))
thickness = table[u"thickness"]
residuum = (simulation.dimensions[0].data - thickness.data[index])**2
absorption = simulation.data[residuum.argmin(), :]
pylab.plot(simulation.dimensions[1].data,
absorption,
label="$%s$ immediate" % simulation.shortname)
title += "\nImmediate: " + title_template % (
thickness[index].shortname, thickness.data[index],
thickness.unit.unit.name())
except:
pass
worker = recipe.getWorker("Slicing")
noisyAbsorption = worker.plugExtract.getResult()
worker = recipe.getWorker("MRA Exp")
minimaPos = worker.plugMra.getResult()[r'\lambda_{min}'].inUnitsOf(
simulation.dimensions[1])
maximaPos = worker.plugMra.getResult()[r'\lambda_{max}'].inUnitsOf(
simulation.dimensions[1])
pylab.plot(noisyAbsorption.dimensions[1].inUnitsOf(
simulation.dimensions[1]).data,
noisyAbsorption.data[index, :],
label="$%s$" % noisyAbsorption.shortname)
if not noIndicators:
pylab.vlines(minimaPos.data[:, index],
0.1,
1.0,
label="$%s$" % minimaPos.shortname)
pylab.vlines(minimaPos.data[:, index] + minimaPos.error[:, index],
0.1,
1.0,
label="$\\Delta%s$" % minimaPos.shortname,
linestyle='dashed')
pylab.vlines(minimaPos.data[:, index] - minimaPos.error[:, index],
0.1,
1.0,
label="$\\Delta%s$" % minimaPos.shortname,
linestyle='dashed')
pylab.vlines(maximaPos.data[:, index],
0.1,
1.0,
label="$%s$" % maximaPos.shortname)
pylab.vlines(maximaPos.data[:, index] + maximaPos.error[:, index],
0.1,
1.0,
label="$\\Delta%s$" % maximaPos.shortname,
linestyle='dashed')
pylab.vlines(maximaPos.data[:, index] - maximaPos.error[:, index],
0.1,
1.0,
label="$\\Delta%s$" % maximaPos.shortname,
linestyle='dashed')
pylab.title(title)
pylab.xlabel(simulation.dimensions[1].label)
pylab.legend(loc="lower left")
# plot analytic solution
tt = numpy.linspace(0.0, 1.0, 1000)
pylab.plot(tt, analytic(tt), label="analytic solution")
# plot RK4 solution -- we need a step of 1.e-3 or smaller to get this
# right, because that is the fastest timescale. If you go above 2.e-3,
# it blows up severely
tRK4, yRK4 = rk4(y0, 1.e-3, 1.0)
pylab.plot(tRK4, yRK4, label=r"R-K 4, $\tau = 10^{-3}$")
pylab.xlim(0.0, 0.1)
pylab.xlabel("t")
pylab.ylabel("y")
leg = pylab.legend()
ltext = leg.get_texts()
pylab.setp(ltext, fontsize='small')
leg.draw_frame(0)
pylab.savefig("stiff-rk4-dt-1e-3.png")
# now do dt = 2.5e-3
pylab.clf()
pylab.plot(tt, analytic(tt), label="analytic solution")
tRK4, yRK4 = rk4(y0, 2.5e-3, 1.0)
t_final = 0.1
time = np.arange(dt, t_final + dt, dt)
N_q1 = domain.N_q1
N_q2 = domain.N_q2
h5f = h5py.File('dump/0000.h5', 'r')
q1 = h5f['q1'][:].reshape(N_q1, N_q2)
q2 = h5f['q2'][:].reshape(N_q1, N_q2)
n = h5f['n'][:].reshape(N_q1, N_q2)
h5f.close()
pl.plot(q1[:, 0], n[:, 0])
pl.title('Time = 0')
pl.ylim([0, 65])
pl.xlabel(r'$x$')
pl.ylabel(r'$n$')
pl.savefig('images/0000.png')
pl.clf()
for time_index, t0 in enumerate(time):
h5f = h5py.File('dump/%04d' % (time_index + 1) + '.h5', 'r')
n = h5f['n'][:].reshape(N_q1, N_q2)
h5f.close()
if ((time_index + 1) % 20 == 0):
pl.plot(q1[:, 0], n[:, 0])
pl.title('Time =' + str(t0))
pl.xlabel(r'$x$')
pl.ylabel(r'$n$')
