def make_plotI(self):
# retrieve data
D=self.D
kmap={}
kmap['AV18'] = {'c':'r','ls':'-'}
kmap['CDBONN'] = {'c':'g','ls':'--'}
kmap['WJC1'] = {'c':'k','ls':'-.'}
kmap['WJC2'] = {'c':'b','ls':':'}
ax=py.subplot(111)
for k in ['AV18','CDBONN','WJC1','WJC2']:
DF=D[k]
DF=DF[DF.Q2==10]
if k=='CDBONN':
label='CDBonn'
else:
label=k
cls=kmap[k]['c']+kmap[k]['ls']
ax.plot(DF.X,DF.THEORY,cls,lw=2.0,label=tex(label))
ax.set_xlabel('$x$',size=25)
ax.set_ylabel(r'$F_2^d\, /\, F_2^N$',size=25)
ax.set_ylim(0.97,1.08)
ax.axhline(1,color='k',ls='-',alpha=0.2)
ax.legend(frameon=0,loc=2,fontsize=22)
py.tick_params(axis='both',labelsize=22)
py.tight_layout()
py.savefig('gallery/F2d_F2_I.pdf')
py.close()
def plotBar(data=None,color_id=None,figure_id=None,name=None,flag=False):
ax = pl.subplot(figure_id)
width = 0.8
x=sp.arange(7)
if not (name=="VaribenchSelected"):
pl.bar(x-0.4,data,width=width,color=color_t[color_id],hatch="/o/o/")
else:
pl.bar(x-0.4,data,width=width,color=color_t[color_id],hatch="ooo")
tmp = data.copy()
tmp[1::] = 0
pl.xticks(x,['All','Pure',']0.0,1.0[','[0.1,0.9]','[0.2,0.8]','[0.3,0.7]','[0.4,0.6]'],fontsize=font_size,rotation=90)
ln = sp.log10(len(name))
pl.text(3.5-ln,0.95,name)
if flag:
remove_border(left=False)
pl.yticks([0.5,0.6,0.7,0.8,0.9,1.0])
pl.grid(axis='y')
pl.tick_params(axis='y',which="both",labelleft='off',left='off')
else:
pl.ylabel("AUC")
remove_border()
pl.yticks([0.5,0.6,0.7,0.8,0.9,1.0])
pl.grid(axis='y')
pl.ylim(0.5,1)
pl.xlim(-0.5,7.5)
return ax
def fixup_innerprod(current_data):
import pylab
size = 28
addgauges(current_data)
pylab.title('Inner Product', fontsize=size)
pylab.xticks(fontsize=size)
pylab.tick_params(axis='y', labelleft='off')
def make_plotII(self):
# retrieve data
D=self.D
kmap={}
kmap['Q2 = 2'] = {'c':'r','ls':'-'}
kmap['Q2 = 5'] = {'c':'g','ls':'--'}
kmap['Q2 = 10'] = {'c':'b','ls':'-.'}
kmap['Q2 = 100'] = {'c':'k','ls':':'}
ax=py.subplot(111)
DF=D['AV18']
for Q2 in [2,5,10,100]:
k='Q2 = %d'%Q2
Q2=float(k.split('=')[1])
DF=D['AV18'][D['AV18'].Q2==Q2]
cls=kmap[k]['c']+kmap[k]['ls']
ax.plot(DF.X,DF.THEORY,cls,lw=2.0,label=r'$Q^2=%0.0f~{\rm GeV}^2$'%Q2)
ax.set_xlabel('$x$',size=25)
ax.set_ylabel(r'$F_2^d\, /\, F_2^N$',size=25)
ax.set_ylim(0.97,1.08)
ax.axhline(1,color='k',ls='-',alpha=0.2)
ax.legend(frameon=0,loc=2,fontsize=22)
py.tick_params(axis='both',labelsize=22)
py.tight_layout()
py.savefig('gallery/F2d_F2_II.pdf')
def chart(idx, a, b, label, FILE):
pylab.ioff()
fig_width_pt = 350 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = ((5**0.5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width*0.42,fig_height]
params = { 'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True,
'figure.figsize': fig_size }
pylab.rcParams.update(params)
home = '/home/nealbob'
folder = '/Dropbox/Thesis/IMG/chapter3/'
img_ext = '.pdf'
pylab.figure()
pylab.boxplot(idx, whis=100)
pylab.ylim(a, b)
#pylab.ylabel(label)
pylab.tick_params(axis='x', which = 'both', labelbottom='off')
pylab.savefig(home + folder + FILE + img_ext)
pylab.show()
def plot_data(comp, c='b'):
"""utility function to make the Kantrowitz Limit Plot"""
MN = []
W_tube = []
W_kant = []
for m in np.arange(.1,1.1,.1):
comp.Mach_pod = m
comp.run()
#print comp.radius_tube, comp.Mach_pod, comp.W_tube, comp.W_kant, comp.W_excess
MN.append(m)
W_kant.append(comp.W_kant)
W_tube.append(comp.W_tube)
fig = p.plot(MN,W_tube, '-', label="%3.1f Req."%(comp._tube_area/comp._inlet_area), lw=3, c=c)
p.plot(MN,W_kant, '--', label="%3.1f Limit"%(comp._tube_area/comp._inlet_area), lw=3, c=c)
#p.legend(loc="best")
p.tick_params(axis='both', which='major', labelsize=15)
p.xlabel('Pod Mach Number', fontsize=18)
p.ylabel('Flow Rate (kg/sec)', fontsize=18)
p.title('Tube Flow Limits for Three Area Ratios', fontsize=20)
return fig
def plot(self, ylog10scale = False, timescale = "years", year = 25):
"""
Generate figure and axis for the population structure
timescale choose from "2N0", "4N0", "generation" or "years"
"""
time = self.Time
pop = self.pop
for i in range(1,len(self.pop)):
if type(pop[i]) == type(""):
# ignore migration commands, and replace by (unchanged) pop size
pop[i] = pop[i-1]
if time[0] != 0 :
time.insert(0, float(0))
pop.insert(0, float(1))
if timescale == "years":
time = [ti * 4 * self.scaling_N0 * year for ti in time ]
pl.xlabel("Time (years, "+`year`+" years per generation)", fontsize=20)
#pl.xlabel("Years")
elif timescale == "generation":
time = [ti * 4 * self.scaling_N0 for ti in time ]
pl.xlabel("Generations)")
elif timescale == "4N0":
time = [ti*1 for ti in time ]
pl.xlabel("Time (4N generations)")
elif timescale == "2N0":
time = [ti*2 for ti in time ]
pl.xlabel("Time (2N generations)")
else:
print "timescale must be one of \"4N0\", \"generation\", or \"years\""
return
time[0] = time[1] / float(20)
time.append(time[-1] * 2)
yaxis_scaler = 10000
pop = [popi * self.scaling_N0 / float(yaxis_scaler) for popi in pop ]
pop.insert(0, pop[0])
pl.xscale ('log', basex = 10)
#pl.xlim(min(time), max(time))
pl.xlim(1e3, 1e7)
if ylog10scale:
pl.ylim(0.06, 10000)
pl.yscale ('log', basey = 10)
else:
pl.ylim(0, max(pop)+2)
pl.ylim(0,5)
pl.tick_params(labelsize=20)
#pl.step(time, pop , color = "blue", linewidth=5.0)
pl.step(time, pop , color = "red", linewidth=5.0)
pl.grid()
#pl.step(time, pop , color = "black", linewidth=5.0)
#pl.title ( self.case + " population structure" )
#pl.ylabel("Pop size ($*$ "+`yaxis_scaler` +")")
pl.ylabel("Effective population size",fontsize=20 )
def createBasisGraph(self, data):
plt.figure(self.nFig)
plt.suptitle('Basis')
nBase = data.shape[1]
# The cols number of subplot is
nSubCols = nBase / 10
if nSubCols > 0:
if nBase % 2 == 0:
nSubRows = nBase / nSubCols
else:
nSubRows = nBase / nSubCols + 1
else:
nSubRows = nBase
nSubCols = 1
# freqList = np.fft.fftfreq(513, d = 1.0 / 44100)
for i in range(nBase):
nowFig = self.nFig + (i / nSubRows) + 1
# Because Index of graph is start by 1, The Graph index start from i + 1.
plt.subplot(nSubRows, nSubCols, i + 1)
plt.tick_params(labelleft='off', labelbottom='off')
# FIXME
#plt.ylabel(self.st5[i%12] + str(i/12 + 1))
plt.ylabel(str(i))
plt.plot(data[:,i])
# Beacuse I want to add lable in bottom, xlabel is declaration after loop.
plt.tick_params(labelleft='off', labelbottom='on')
plt.xlabel('frequency [Hz]')
#self.nFig += nowFig
self.nFig += 1
def GraphMaker(self,name,c,d,e):
"""Produces graphs based on the user entered equations in the Result Window"""
model = name.rstrip("']")
model = model.lstrip("'[")
model = str(model)
name = ''
for i in xrange(len(model)):
if model[i] == '_':
name += ' '
else:
name += model[i]
__location__ = os.path.dirname(sys.argv[0])
loc = os.path.join(__location__, 'Data/')
Params = ["w_0", "w_a", "w_p", "w_DE", "Omega_M", "Omega_DE", "x1", "x2", "y1", "y2", "c1", "c2", "u", "Lambda_1", "Lambda_2", "n"]
for entry in xrange(len(Params)):
exec( Params[entry] + " = np.genfromtxt(loc+str(model)+'.dat', usecols = "+str(entry)+", skip_header = 1)" )
A = eval(c)
B = eval(d)
py.figure(int(e))
py.plot(A, B, 'b.')
py.xlabel(c, fontsize = 55)
py.ylabel(d, fontsize = 55)
py.title('Results from '+str(name), fontsize = 55)
py.tick_params(labelsize = 35, size = 15, width = 5, top = 0, right = 0)
py.show()
def histplot(self, extradataA = [], extradataG = [], intensity = []):
pylab.figure(figsize = (25,8))
cat = ['NT, 500ng/mL DOX', 'DLG siRNA, 500ng/mL DOX', 'NuMA siRNA, 500ng/mL DOX', 'NT, 1ug/mL DOX']
pops = []
for i in xrange(3):
pylab.subplot(1,3,i+1)
pop = self.angles[(self.categories == i)]# & (self.GFP > -np.log(12.5))]# & (intensity == 'r')]
print "cat {0}, pop {1}, pop + GFP {2}".format(i, len(self.angles[self.categories == i]), len(pop))
pops.append(pop)
hist, binedges = np.histogram(pop, bins = 18)
pylab.tick_params(axis='both', which='major', labelsize=25)
pylab.plot(binedges[:-1], np.cumsum(hist)/1./len(pop), data.colors[i], label = data.cat[i], linewidth = 4)
if len(extradataA) > i:
print extradataA[i]
h, bins = np.histogram(extradataA[i], bins= 18)
hbis = h/1./len(extradataA[i])
x, y = [], []
for index in xrange(len(hbis)):
x.extend([bins[index], bins[index+1]])
y.extend([hbis[index], hbis[index]])
print x, y, len(x)
pylab.tick_params(axis='both', which='major', labelsize=25)
pylab.plot(bins[:-1], np.cumsum(h)/1./len(extradataA[i]), 'k', linewidth = 4)
pylab.xlabel("Angle (degre)", fontsize = 25)
#pylab.title(cat[i])
pylab.ylim([0., 1.2])
pylab.legend(loc = 2, prop = {'size' : 20})
for ip, p in enumerate(pops):
for ip2, p2 in enumerate(pops):
ksstat, kspval = scipy.stats.ks_2samp(p2, p)
print "#### cat{0} & cat{3} : ks Stat {1}, pvalue {2}".format(ip, ksstat, kspval, ip2)
pylab.show()
def plotAgainstGFP(self, extradataA = [], extradataG = [], intensity = [], seq = []):
fig1 = pylab.figure(figsize = (25, 10))
print len(self.GFP)
for i in xrange(min(len(data.cat), 3)):
print len(self.GFP[self.categories == i])
vect = []
pylab.subplot(1,3,i+1)
#pylab.hist(self.GFP[self.categories == i], bins = 20, color = data.colors[i])
pop = self.GFP[self.categories == i]
pylab.plot(self.GFP[self.categories == i], self.angles[self.categories == i], data.colors[i]+'o', markersize = 8)#, label = data.cat[i])
print "cat", i, "n pop", len(self.GFP[(self.categories == i) & (self.GFP > -np.log(12.5))])
x = np.linspace(np.min(self.GFP[self.categories == i]), np.percentile(self.GFP[self.categories == i], 80),40)
#fig1.canvas.mpl_connect('pick_event', onpick)
for j in x:
vect.append(np.median(self.angles[(self.GFP > j) & (self.categories == i)]))
pylab.plot([-4.5, -0.5], [vect[0], vect[0]], data.colors[i], label = "mediane de la population entiere", linewidth = 5)
print vect[0], vect[np.argmax(x > -np.log(12.5))]
pylab.plot([-np.log(12.5), -0.5], [vect[np.argmax(x > -np.log(12.5))] for k in [0,1]], data.colors[i], label = "mediane de la population de droite", linewidth = 5, ls = '--')
pylab.axvline(x = -np.log(12.5), color = 'm', ls = '--', linewidth = 3)
pylab.xlim([-4.5, -0.5])
pylab.legend(loc = 2, prop = {'size':17})
pylab.title(data.cat[i].split(',')[0], fontsize = 24)
pylab.xlabel('score GFP', fontsize = 20)
pylab.ylabel('Angle (degre)', fontsize = 20)
pylab.tick_params(axis='both', which='major', labelsize=20)
pylab.ylim([-5, 105])
##pylab.xscale('log')
pylab.show()
def chart(SW, a, b, label, folder, FILE):
pylab.ioff()
fig_width_pt = 350 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = ((5**0.5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = { 'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True,
'figure.figsize': fig_size }
pylab.rcParams.update(params)
home = '/home/nealbob'
img_ext = '.pdf'
pylab.figure()
pylab.boxplot([SW['SWA'], SW['OA'], SW['NS']], whis=5)
pylab.axhline(y=1.0, color='0.5', linewidth=0.5, alpha=0.75, linestyle=':')
pylab.ylim(a, b)
pylab.ylabel(label)
pylab.tick_params(axis='x', which = 'both', labelbottom='off')
pylab.figtext(0.225, 0.06, 'SWA', fontsize = 10)
pylab.figtext(0.495, 0.06, 'OA', fontsize = 10)
pylab.figtext(0.76, 0.06, 'NS', fontsize = 10)
pylab.savefig(home + folder + FILE + img_ext)
pylab.show()
def createCoefGraph(data, nFig, lim, ymin):
plt.figure(nFig)
plt.suptitle('Coef')
nBase = data.shape[0]
nSubCols = nBase / 10
if nSubCols > 0:
nSubRows = nBase / nSubCols
else:
nSubRows = nBase
nSubCols = 1
# print data.shape
# サンプリング周波数とシフト幅によって式を変える必要あり
timeLine = [i * 1024 / 8000.0 for i in range(data.shape[1])]
# print len(timeLine)
for i in range(nBase):
plt.subplot(nSubRows, nSubCols, i + 1)
plt.tick_params(labelleft='off', labelbottom='off')
# FIXME: Arguments of X
# plt.plot(timeLine, data[i,:])
if lim:
plt.ylim(ymin=ymin)
plt.plot(timeLine, data[i,:])
# Beacuse I want to add lable in bottom, xlabel is declaration after loop.
plt.tick_params(labelleft='off', labelbottom="on")
plt.xlabel('time [ms]')
def plot_data(p, c='b'):
'''utility function to make the Kantrowitz Limit Plot'''
Machs = []
W_tube = []
W_kant = []
for Mach in np.arange(.2, 1.1, .1):
p['comp.Mach'] = Mach
p.run()
Machs.append(Mach)
W_kant.append(p['comp.W_kant'])
W_tube.append(p['comp.W_tube'])
print('Area in:', p['comp.inlet.area_out'])
fig = pylab.plot(Machs,
W_tube,
'-',
label="%3.1f Req." %
(p['comp.tube_area'] / p['comp.inlet.area_out']),
lw=3,
c=c)
pylab.plot(Machs,
W_kant,
'--',
label="%3.1f Limit" %
(p['comp.tube_area'] / p['comp.inlet.area_out']),
lw=3,
c=c)
pylab.tick_params(axis='both', which='major', labelsize=15)
pylab.xlabel('Pod Mach Number', fontsize=18)
pylab.ylabel('Flow Rate (kg/sec)', fontsize=18)
pylab.title('Tube Flow Limits for Three Area Ratios', fontsize=20)
return fig
def plots_of_lp_event_exchanges():
pylab.title('Remote Events Sent Between LPs')
data = np.loadtxt("analysisData/eventsExchanged-remote.csv", dtype=np.float_, delimiter = ",", skiprows=2, usecols=(2,3,4,5))
outFile = outDir + 'countsOfLpToLpEventExchanges'
pylab.plot(data[data[:,0].argsort()][:,0].astype(np.intc))
# pylab.xlabel('Number of Events')
pylab.tick_params(axis='x',labelbottom='off')
pylab.ylabel('Number of Events Sent')
display_graph(outFile)
pylab.title('Timestamp Deltas of Remote Events')
outFile = outDir + 'timeStampDeltasOfRemoteEvents'
stride = max(int(max(len(data[:,1]),len(data[:,2]),len(data[:,3]))/20),1)
pylab.plot(data[data[:,1].argsort()][:,1], color=colors[0], label="Minimum", marker='o', markevery=stride)
pylab.plot(data[data[:,3].argsort()][:,3], color=colors[1], label="Average", marker='x', markevery=stride)
# pylab.plot(data[data[:,2].argsort()][:,2], color=colors[2], label="Maximum", marker='*', markevery=stride)
pylab.tick_params(axis='x',labelbottom='off')
pylab.ylabel('Timestamp Delta (ReceiveTime - SendTime)')
pylab.ylim([-.1,np.amax(data[:,3].astype(np.intc))+1])
# pylab.yscale('log')
pylab.legend(loc='best')
display_graph(outFile)
pylab.title('Histogram of Timestamp Deltas of Remote Events')
outFile = outDir + 'timeStampDeltasOfRemoteEvents-hist'
pylab.hist((data[:,1],data[:,3],data[:,2]), label=('Minimum', 'Average', 'Maximum'), color=(colors[0], colors[1], colors[2]), bins=10)
pylab.xlabel('Timestamp Delta (ReceiveTime - SendTime)')
pylab.ylabel('Number of LPs')
pylab.legend(loc='best')
display_graph(outFile)
return
def profile_of_local_events_exec_by_lp():
pylab.title('Locally Generated Events')
outFile = outDir + 'percentOfExecutedEventsThatAreLocal'
data = np.loadtxt("analysisData/eventsExecutedByLP.csv", dtype=np.intc, delimiter = ",", skiprows=2, usecols=(1,2,3))
x_index = np.arange(len(data))
pylab.plot(x_index, sorted(percent_of_LP_events_that_are_local(data)))
pylab.xlabel('LPs (sorted by percent local)')
pylab.tick_params(axis='x',labelbottom='off')
pylab.ylabel('Percent of Total Executed (Ave=%.2f%%)' % np.mean(percent_of_LP_events_that_are_local(data)))
pylab.ylim((0,100))
# fill the area below the line
ax = pylab.gca()
# ax.fill_between(x_index, sorted(percent_of_LP_events_that_are_local(data)), 0, facecolor=colors[0])
ax.get_yaxis().set_major_formatter(mpl.ticker.FormatStrFormatter('%.1f%%'))
display_graph(outFile)
pylab.title('Locally Generated Events Executed')
outFile = outDir + 'percentOfExecutedEventsThatAreLocal-histogram'
pylab.hist(sorted(percent_of_LP_events_that_are_local(data)))
ax = pylab.gca()
ax.get_xaxis().set_major_formatter(mpl.ticker.FormatStrFormatter('%.1f%%'))
pylab.xlabel('Percent of Local Events Executed')
pylab.ylabel('Number of LPs (Total=%s)' % "{:,}".format(total_lps))
display_graph(outFile)
return
def plot_currents(self, plot_which):
global figures
if plot_which == "all":
plot_which = [i for i in range(1, self.instance["no_feeders"] + 1)]
time_scale = [datetime.datetime(2000, 1, 1, 0) + datetime.timedelta(minutes=i) for i in range(1440)]
plt.figure(figures)
figures += 1
lines_to_plot = len(plot_which)
for i in range(lines_to_plot):
CR = np.array(self.extract_from_csv(plot_which[i], self.instance["iteration"]))
CR = np.reshape(CR, (-1, 3))
plt.subplot(lines_to_plot, 1, 1 + i)
if (i + 1) != lines_to_plot:
plt.plot(time_scale, CR)
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
else:
plt.plot(time_scale, CR)
plt.subplot(lines_to_plot, 1, 1)
plt.title("Current at the head of the Feeders")
def embedSystemtsne(embedding):
pl.figure()
pl.scatter(embedding[:,0],embedding[:,1],c=range(len(embedding[:,0])),linewidths=0)
pl.title('t-distributed stochastic neighbor embedding of observed system states')
pl.tick_params(labelleft='off', labelbottom='off')
pl.colorbar().set_label('Time (ms)')
pl.show()
def embedIndividualstsne(embedding):
pl.figure()
pl.scatter(embedding[:,0],embedding[:,1],c=d,linewidths=0)
pl.title('t-distributed stochastic neighbor embedding of all individual neurons')
pl.tick_params(labelleft='off', labelbottom='off')
pl.colorbar().set_label('Parameter d (after-spike increment value of recovery variable u)')
pl.show()
def fixup_adjoint(current_data):
import pylab
size = 36
addgauges(current_data)
pylab.title('Adjoint Pressure', fontsize=size)
pylab.xticks([-2, 0, 2, 4, 6], fontsize=size)
pylab.tick_params(axis='y', labelleft='off')
def clusterHeatmap(df, title, row_label_map, col_label_map, colormap=my_cmap,
cluster_rows=False, cluster_columns=False, cluster_data=None,
row_dendrogram=False, column_dendrogram=False, width=30, height=20, vmin=-3, vmax=3, distmethod="correlation", colorbar=True, colorbar_shrink=0.2, label_values=False):
cm = pylab.get_cmap(colormap)
cm.set_bad("0.9")
# do clustering
if cluster_data is None:
cluster_data = df # cluster the same data that we are plotting
matplotlib.rcParams['figure.figsize'] = [width, height]
# pylab.figsize(20, 10)
pylab.title(title)
# pylab.text(0,-5,str(datetime.date.today()))
# ylabels = [genesym[geneid] for geneid in pt.axes[0][Z['leaves']]]
# xlabels = pt.axes[1][cZ['leaves']]
orderedVal = df
if cluster_rows:
distances = scipy.cluster.hierarchy.distance.pdist(cluster_data.values, distmethod)
rowY = fastcluster.linkage(distances)
rowZ = scipy.cluster.hierarchy.dendrogram(rowY, orientation='right', no_plot=True)
orderedVal = df.reindex(index=df.axes[0][rowZ['leaves']])
if cluster_columns:
coldist = scipy.cluster.hierarchy.distance.pdist(df.values.transpose(), distmethod)
cY = scipy.cluster.hierarchy.linkage(coldist)
cZ = scipy.cluster.hierarchy.dendrogram(cY, no_plot=True)
orderedVal = orderedVal.reindex(columns=df.axes[1][cZ['leaves']])
# row labels
if row_label_map is not None:
pylab.yticks(range(0, len(orderedVal.index)), [row_label_map[i] for i in orderedVal.index])
else:
pylab.yticks(range(0, len(orderedVal.index)), orderedVal.index)
pylab.xticks(range(0, len(orderedVal.columns)), orderedVal.columns, rotation=90)
if col_label_map is not None:
pylab.xticks(range(0, len(orderedVal.columns)), [col_label_map[i] for i in orderedVal.columns])
if label_values:
cmatrix = orderedVal.as_matrix()
for x in range(cmatrix.shape[0]):
for y in range(cmatrix.shape[1]):
if cmatrix[x, y] >= 0:
pylab.text(y, x, "%.1f" % cmatrix[x,y], horizontalalignment='center',
verticalalignment='center')
#orderedVal = orderedVal[:,]
pylab.tick_params(direction="out")
pylab.imshow(orderedVal, interpolation="nearest", cmap=cm, aspect='auto', norm=None, vmin=vmin, vmax=vmax)
if colorbar:
pylab.colorbar(shrink=colorbar_shrink)
def fixup_innerprod(current_data):
import pylab
size = 28
addgauges(current_data)
pylab.title("Inner Product", fontsize=size)
pylab.xticks(fontsize=size)
pylab.tick_params(axis="y", labelleft="off")
def aa_innerprod(current_data):
from pylab import ticklabel_format, xticks, gca, cos, pi, yticks
plotcc(current_data)
title_innerproduct(current_data)
ticklabel_format(format='plain',useOffset=False)
xticks([180, 200, 220, 240], rotation=20, fontsize = 28)
pylab.tick_params(axis='y', labelleft='off')
a = gca()
a.set_aspect(1./cos(41.75*pi/180.))
def plot(data_x,data_y,title = "", lim = True):
"Helper function to plot results of koch curve"
py.title(title)
py.plot(data_x,data_y,'r',lw=2)
py.tick_params(axis='both', which='both', bottom=0, top=0,
left=0, right=0, labelbottom=0, labelleft =0)
if lim:
py.xlim([0,3])
py.ylim([0,1])
def _setup_grid_and_axes(label_x, label_y):
grid(True)
# Set axes labels
xlabel(label_x, fontsize=AXIS_LABEL_SIZE)
ylabel(label_y, fontsize=AXIS_LABEL_SIZE)
# Set the axis ticks
tick_params(axis='both', which='major', labelsize=TICKS_LABEL_SIZE)
def include_png(in_file, title=None, figsize=(11.7,8.3)):
fig = plt.figure(figsize=figsize)
img = plt.imread(in_file)
plt.imshow(img)
plt.grid(False)
plt.tick_params(labelbottom='off', labeltop='off', labelleft='off', labelright='off')
plt.title(title, fontsize='10')
plt.close()
return fig
def theme(ax=None, minorticks=False):
""" update plot to make it nice and uniform """
from matplotlib.ticker import AutoMinorLocator
from pylab import rcParams, gca, tick_params
if minorticks:
if ax is None:
ax = gca()
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_minor_locator(AutoMinorLocator())
tick_params(which='both', width=rcParams['lines.linewidth'])
def embedSystem(embedding,explained_variance):
pl.figure(figsize=(fw,fh))
pl.scatter(embedding[:,0],embedding[:,1],c=spikes,linewidths=0)
pl.title('Two-dimensional embedding explains ' + str(round(explained_variance,3)*100) + '% of variance among observed system states')
pl.xlabel('Principal component 1')
pl.ylabel('Principal component 2')
pl.tick_params(labelleft='off', labelbottom='off')
pl.colorbar().set_label('Instantaneous spike rate (spikes/ms)')
#pl.show()
pl.savefig(str(N) + "--" + "2Dsystemembedding.pdf")
def embedIndividuals(embedding,explained_variance):
pl.figure(figsize=(fw,fh))
pl.scatter(embedding[:,0],embedding[:,1],c=d,linewidths=0)
pl.title('Two-dimensional embedding explains ' + str(round(explained_variance,3)*100) + '% of variance among individual neurons')
pl.xlabel('Principal component 1')
pl.ylabel('Principal component 2')
pl.tick_params(labelleft='off', labelbottom='off')
pl.colorbar().set_label('Parameter d (after-spike increment value of recovery variable u)')
#pl.show()
pl.savefig(str(N) + "--" + "2Dindividualembedding.pdf")
def plot_stc_time_point(stc, subject, limits=[5, 10, 15], time_index=0,
surf='inflated', measure='dSPM', subjects_dir=None):
"""Plot a time instant from a SourceEstimate using matplotlib
The same could be done with mayavi using proper 3D.
Parameters
----------
stc : instance of SourceEstimate
The SourceEstimate to plot.
subject : string
The subject name (only needed if surf is a string).
time_index : int
Time index to plot.
surf : str, or instance of surfaces
Surface to use (e.g., 'inflated' or 'white'), or pre-loaded surfaces.
measure : str
The label for the colorbar. None turns the colorbar off.
subjects_dir : str, or None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
"""
subjects_dir = get_subjects_dir(subjects_dir)
pl.figure(facecolor='k', figsize=(8, 5))
hemis = ['lh', 'rh']
if isinstance(surf, str):
surf = [read_surface(op.join(subjects_dir, subject, 'surf',
'%s.%s' % (h, surf))) for h in hemis]
my_cmap = mne_analyze_colormap(limits)
for hi, h in enumerate(hemis):
coords = surf[hi][0][stc.vertno[hi]]
if hi == 0:
vals = stc_all_cluster_vis.lh_data[:, time_index]
else:
vals = stc_all_cluster_vis.rh_data[:, time_index]
ax = pl.subplot(1, 2, 1 - hi, axis_bgcolor='none')
pl.tick_params(labelbottom='off', labelleft='off')
flipper = -1 if hi == 1 else 1
sc = ax.scatter(flipper * coords[:, 1], coords[:, 2], c=vals,
vmin=-limits[2], vmax=limits[2], cmap=my_cmap,
edgecolors='none', s=5)
ax.set_aspect('equal')
pl.axis('off')
try:
pl.tight_layout(0)
except:
pass
if measure is not None:
cax = pl.axes([0.85, 0.15, 0.025, 0.15], axisbg='k')
cb = pl.colorbar(sc, cax, ticks=[-limits[2], 0, limits[2]])
cb.set_label(measure, color='w')
pl.setp(pl.getp(cb.ax, 'yticklabels'), color='w')
pl.draw()
pl.show()
def plot_held_units(rec_dirs, held_df, save_dir, rec_names=None):
'''Plot waveforms of held units side-by-side
Parameters
----------
rec_dirs : list of str
full paths to recording directories
held_df : pandas.DataFrame
dataframe listing held units with columns matching the names of the
recording directories or the given rec_names. Also colulmns:
- unit : str, unit name
- single_unit : bool
- unit_type : str, unit_type
- electrode : int
- J3 : list of float, J3 values for the held unit
save_dir : str, directory to save plots in
rec_names : list of str (optional)
abbreviated rec_names if any were used for held_df creation
if not given, rec_names are assumed to be the basenames of rec_dirs
'''
if rec_names is None:
rec_names = [os.path.basename(x) for x in rec_dirs]
rec_labels = {x: y for x, y in zip(rec_names, rec_dirs)}
print('\n----------\nPlotting held units\n----------\n')
for idx, row in held_df.iterrows():
n_subplots = 0
units = {}
for rn in rec_names:
if not pd.isna(row.get(rn)):
n_subplots += 1
units[rn] = row.get(rn)
if n_subplots == 0:
continue
single_unit = row['single_unit']
if single_unit:
single_str = 'single-unit'
else:
single_str = 'multi-unit'
unit_type = row['unit_type']
unit_name = row['unit']
electrode = row['electrode']
area = row['area']
J3_vals = row['J3']
J3_str = np.array2string(np.array(J3_vals), precision=3)
print('Plotting Unit %s...' % unit_name)
title_str = 'Unit %s\nElectrode %i: %s %s\nJ3: %s' % (
unit_name, electrode, unit_type, single_str, J3_str)
fig, fig_ax = plt.subplots(ncols=n_subplots, figsize=(20, 10))
ylim = [0, 0]
row_ax = []
for ax, unit_info in zip(fig_ax, units.items()):
rl = unit_info[0]
u = unit_info[1]
rd = rec_labels.get(rl)
params = dio.params.load_params('clustering_params', rd)
if params is None:
raise FileNotFoundError('No dataset pickle file for %s' % rd)
#waves, descriptor, fs = get_unit_waveforms(rd, x[1])
waves, descriptor, fs = dio.h5io.get_raw_unit_waveforms(rd, u)
waves = waves[:, ::10]
fs = fs / 10
time = np.arange(0, waves.shape[1], 1) / (fs / 1000)
snapshot = params['spike_snapshot']
t_shift = snapshot['Time before spike (ms)']
time = time - t_shift
mean_wave = np.mean(waves, axis=0)
std_wave = np.std(waves, axis=0)
ax.plot(time, mean_wave, linewidth=5.0, color='black')
ax.plot(time,
mean_wave - std_wave,
linewidth=2.0,
color='black',
alpha=0.5)
ax.plot(time,
mean_wave + std_wave,
linewidth=2.0,
color='black',
alpha=0.5)
ax.set_xlabel('Time (ms)', fontsize=35)
ax.set_title('%s %s\ntotal waveforms = %i' %
(rl, u, waves.shape[0]),
fontsize=20)
ax.autoscale(axis='x', tight=True)
plt.tick_params(axis='both', which='major', labelsize=32)
if np.min(mean_wave - std_wave) - 20 < ylim[0]:
ylim[0] = np.min(mean_wave - std_wave) - 20
if np.max(mean_wave + std_wave) + 20 > ylim[1]:
ylim[1] = np.max(mean_wave + std_wave) + 20
for ax in row_ax:
ax.set_ylim(ylim)
fig_ax[0].set_ylabel('Voltage (microvolts)', fontsize=35)
plt.subplots_adjust(top=.75)
plt.suptitle(title_str)
fig.savefig(os.path.join(save_dir, 'Unit%s_waveforms.png' % unit_name),
bbox_inches='tight')
plt.close('all')
time_per_turn_seconds_nslice = computational_time_seconds_nslices / N_turns_done_nslices
# Figure parameters
fig_index = 0
fig_size = (12, 8)
axis_font = {'fontname': 'Arial', 'size': '24'}
axis_font_title = {'fontname': 'Arial', 'size': '20'}
labelsize_choice = 24
labelsize_legend = 24
line_width = 3.5
fig_index = fig_index + 1
fig = pl.figure(fig_index, figsize=fig_size)
pl.plot(n_slices_vect,
time_per_turn_seconds_nslice,
'o-',
linewidth=line_width)
pl.ylim(bottom=0)
pl.xlabel('Slices', **axis_font)
pl.ylabel('Computational Time [s/Turn]', **axis_font)
pl.title(
'ArcQuad %s Segments = %d MPs/Slice = %d e$^-$ MPs = %d' %
(PyPICmode_tag, n_segments, macroparticles_per_slice, eMPs),
**axis_font_title)
pl.tick_params(labelsize=labelsize_choice)
pl.grid(linestyle='dashed')
pl.savefig(folder_work + 'computational_time_nSlices.png', dpi=300)
pl.show()
def plot_held_units(rec_dirs, held_df, J3_df, save_dir):
'''Plot waveforms of held units side-by-side
Parameters
----------
rec_dirs : list of str
full paths to recording directories
held_df : pandas.DataFrame
dataframe listing held units with columns matching the names of the
recording directories and a unit column with the unit names
J3_df : pandas.DataFrame
dataframe with same rows and columns as held df except the values are
lists fo inter_J3 values for units that were found to be held
save_dir : str, directory to save plots in
'''
print('\n----------\nPlotting held units\n----------\n')
for idx, row in held_df.iterrows():
unit_name = row.pop('unit')
electrode = row.pop('electrode')
area = row.pop('area')
n_subplots = row.notnull().sum()
idx = np.where(row.notnull())[0]
cols = row.keys()[idx]
units = row.values[idx]
fig = plt.figure(figsize=(18, 6))
ylim = [0, 0]
row_ax = []
for i, x in enumerate(zip(cols, units)):
J3_vals = J3_df[x[0]][J3_df['unit'] == unit_name].values[0]
J3_str = np.array2string(np.array(J3_vals), precision=3)
ax = plt.subplot(1, n_subplots, i+1)
row_ax.append(ax)
rd = [y for y in rec_dirs if x[0] in y][0]
params = get_clustering_parameters(rd)
if params is None:
raise FileNotFoundError('No dataset pickle file for %s' % rd)
#waves, descriptor, fs = get_unit_waveforms(rd, x[1])
waves, descriptor, fs = get_raw_unit_waveforms(rd, x[1])
waves = waves[:, ::10]
fs = fs/10
time = np.arange(0, waves.shape[1], 1) / (fs/1000)
snapshot = params['spike_snapshot']
t_shift = snapshot['Time before spike (ms)']
time = time - t_shift
mean_wave = np.mean(waves, axis=0)
std_wave = np.std(waves, axis=0)
plt.plot(time, mean_wave,
linewidth=5.0, color='black')
plt.plot(time, mean_wave - std_wave,
linewidth=2.0, color='black',
alpha=0.5)
plt.plot(time, mean_wave + std_wave,
linewidth=2.0, color='black',
alpha=0.5)
plt.xlabel('Time (ms)',
fontsize=35)
if i==0:
plt.ylabel('Voltage (microvolts)', fontsize=35)
plt.title('%s %s\ntotal waveforms = %i, Electrode: %i\n'
'J3: %s, Single Unit: %i, RSU: %i, FS: %i'
% (x[0], x[1], waves.shape[0],
descriptor['electrode_number'],
J3_str,
descriptor['single_unit'],
descriptor['regular_spiking'],
descriptor['fast_spiking']),
fontsize = 20)
plt.tick_params(axis='both', which='major', labelsize=32)
if np.min(mean_wave - std_wave) - 20 < ylim[0]:
ylim[0] = np.min(mean_wave - std_wave) - 20
if np.max(mean_wave + std_wave) + 20 > ylim[1]:
ylim[1] = np.max(mean_wave + std_wave) + 20
for ax in row_ax:
ax.set_ylim(ylim)
plt.subplots_adjust(top=.7)
plt.suptitle('Unit %s' % unit_name)
fig.savefig(os.path.join(save_dir,
'Unit%s_waveforms.png' % unit_name),
bbox_inches='tight')
plt.close('all')
def plot_hams(self):
print("plotting normal hams")
fig, ax = pylab.subplots(figsize=(self.fig_size, self.fig_size))
box_font = self.box_font_size
# axes labels
xlabel = r"$d$"
ylabel = "f"
if self.protein == "Kinase":
start = 120
end = 230
x_tick_range = np.arange(start, end, 20)
pylab.xlim(start, end)
all_freqs = dict()
for label, seqs_file in self.vis_seqs.items():
if label in self.skip:
print("skipping ", label)
continue
if not self.which_models[label]: # model is 'false' in the which_models{}, then continue
continue
label = self.label_dict[label]
print("computing hams for:\t", label)
seqs = loadSeqs(self.msa_dir + "/" + seqs_file, names=self.ALPHA)[0][0:self.keep_hams]
h = histsim(seqs).astype(float)
h = h/np.sum(h)
all_freqs[label] = h
rev_h = h[::-1]
if label == "Target":
if "nat" in self.synth_nat:
target_label = "Nat-Target"
else:
target_label = "Synth-Target"
line_style = "dashed"
my_dashes = (1, 1)
ax.plot(rev_h, linestyle=line_style, linewidth=self.line_width, dashes=my_dashes,
alpha=self.line_alpha, color=self.color_set[label], label=target_label, zorder=self.z_order[label])
else:
line_style = "solid"
ax.plot(rev_h, linestyle=line_style, linewidth=self.line_width,
alpha=self.line_alpha, color=self.color_set[label], label=label, zorder=self.z_order[label])
tvds = dict()
print("all_freqs")
print(all_freqs.keys())
delete_key = ''
save_value = ''
for data_label, f in all_freqs.items():
if 'arget' in data_label:
save_value = f
delete_key = data_label
del all_freqs[delete_key]
all_freqs["Target"] = save_value
for data_label, f in all_freqs.items():
if data_label != 'Target':
tvds[data_label] = round(np.sum(np.abs(all_freqs['Target'] - f))/2, 4)
print(tvds)
y_tick_range = np.arange(0.0, 0.08, 0.02)
pylab.ylabel(ylabel, fontsize=self.label_size)
pylab.xlabel(xlabel, fontsize=self.label_size)
pylab.xticks(x_tick_range, rotation=45)
pylab.yticks(y_tick_range)
pylab.tick_params(direction='in',axis='both', which='major', labelsize=self.tick_size,
length=self.tick_length, width=self.tick_width)
#my_title = "Hamming Distance Distributions\n" + self.parent_dir_name
file_name = "ham_" + self.name + "_" + self.synth_nat + "_" + self.which_size + ".pdf"
#pylab.title(self.which_size, fontsize=self.title_size)
pylab.tight_layout()
pylab.legend(fontsize=self.tick_size-3, loc="upper left", frameon=False)
save_name = self.output_dir + "/" + file_name
print(save_name)
pylab.savefig(save_name, dpi=self.dpi, format='pdf')
pylab.close()
def parse_and_plot_ref(runfile, spectrum_file):
fields = [('wl', 'f8'), ('gf', 'f8'), ('z', 'i'), ('istg', 'i'),
('chi', 'f8')]
ref = N.loadtxt("ref.dat", dtype=fields)
model = N.loadtxt(spectrum_file)
mylist = parse_runsynow(runfile)
numref = mylist['parms']['numref']
an = []
ai = []
for x,y,z in zip(mylist['parms']['tau1'][:numref],\
mylist['parms']['an'][:numref],\
mylist['parms']['ai'][:numref]):
if x > 0.:
an.append(y)
ai.append(z)
ions_used = [z * 100 + istg for z, istg in zip(an, ai)]
ref_ions = []
for i in xrange(N.size(ref['wl'])):
ref_ions.append(ref['z'][i] * 100 + ref['istg'][i])
ref_index = []
for ion in ions_used:
ref_index.append(ref_ions.index(ion))
pylab.interactive(True)
# One can supply an argument to AutoMinorLocator to
# specify a fixed number of minor intervals per major interval, e.g.:
# minorLocator = AutoMinorLocator(2)
# would lead to a single minor tick between major ticks.
minorLocator = AutoMinorLocator()
golden = (pylab.sqrt(5) + 1.) / 2.
figprops = dict(figsize=(8., 8. / golden),
dpi=128) # Figure properties for single and stacked plots
# figprops = dict(figsize=(16., 8./golden), dpi=128) # Figure properties for side by sides
adjustprops = dict(left=0.15,
bottom=0.1,
right=0.90,
top=0.93,
wspace=0.2,
hspace=0.2) # Subp
fig = pylab.figure(1, **figprops) # New figure
fig.clf()
fig.subplots_adjust(**adjustprops) # Tunes the subplot layout
ax1 = fig.add_subplot(1, 1, 1)
my_funcs.bold_labels(ax1)
p1, = ax1.plot(model[:, 0], model[:, 1], linewidth=2.0)
ax1.set_ylabel(r'$F_\lambda$', fontsize=14)
ax1.set_xlabel(r'$\lambda\ (\AA)$', fontsize=14)
# ax1.set_xlim([0.,60.])
# ax1.set_ylim([10.**41.4,10.**43.5])
# ax1.set_yscale('log')
# ax1.legend([p1,p2,p3,p4],['Day 10','Day 15','Day 25','Day 50'],frameon=False)
ax1.xaxis.set_minor_locator(minorLocator)
pylab.tick_params(which='both', width=2)
pylab.tick_params(which='major', length=7)
pylab.tick_params(which='minor', length=4, color='r')
#ax1.xaxis.grid(True,which='minor')
ax1.xaxis.grid(True, which='both')
wl_ref = []
f_ref = []
ymin, ymax = ax1.get_ybound()
for i in ref_index:
wl_ref.append([10. * ref['wl'][i], 10. * ref['wl'][i]])
ihelp = N.abs(model[:, 0] - 10. * ref['wl'][i]).argmin()
yhelp = model[ihelp, 1]
f_ref.append([ymin, yhelp])
for x, y in zip(wl_ref, f_ref):
ax1.plot(x, y, lw=2)
fields = [('Z','i'),('A','f8'),('Name','S13'),('sym','S4'),('MP','f8'),\
("BP",'f8'),('rho','f8'),('crust','f8'),('year','i'),\
('group','i'), ('config','S23'), ('chiion',"f8")]
# labels = N.loadtxt("periodic_table.dat",skiprows=1,delimiter=',',dtype=fields)
labels = N.genfromtxt("periodic_table.dat",
skip_header=1,
delimiter=',',
dtype=None)
syms = []
for x in labels['f3']:
syms.append(x.replace(" ", ""))
ref_Zs = []
for z in labels['f0']:
ref_Zs.append(z)
sym_indices = []
for z in an:
sym_indices.append(ref_Zs.index(z))
spect_notation = [
"I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX", "X"
]
text_labels = []
for i, j in enumerate(sym_indices):
help = syms[j] + " " + spect_notation[ai[i]]
text_labels.append(help)
for x, y, l in zip(wl_ref, f_ref, text_labels):
ax1.text(x[0], min(y[1] * 1.08, ymax), l, fontsize=8)
def PLOTdetrendPOSITIONfluxFULL(time, flux, position, TCK, **kwargs):
"""
Routine going with detrendPOSITIONflux to visualize the detrending of the photometry for the instrumental correction between position and flux of BRITE photometry.
Returns: Figure with -hopefully- enough diagnostics to determine the strength / weaknessess of the detrending
@param flux: flux measurements [adu]
@type flux: numpy array of length N
@param position: CCD position measurements along a position axis [pixel]
@type position: numpy array of length N
@param TCK: spline tck for the favored fit
@type TCK: tuple
"""
# Calculating the corrections
# ---------------------------
fluxCORRECTION = scInterp.splev(position, TCK)
# Setting up the figure window
# ----------------------------
figPOScorr = pl.figure(figsize=(16, 16))
gsPOScorr = gridspec.GridSpec(2,
2,
height_ratios=[1, 1],
width_ratios=[3, 2])
axTIMEorig = figPOScorr.add_subplot(gsPOScorr[0,
0]) # Initial flux with time
axTIMEcorr = figPOScorr.add_subplot(
gsPOScorr[1, 0], sharex=axTIMEorig,
sharey=axTIMEorig) # Corrected flux with time
axPOSorig = figPOScorr.add_subplot(
gsPOScorr[0, 1], sharey=axTIMEorig) # Initial flux with position
axPOScorr = figPOScorr.add_subplot(
gsPOScorr[1, 1], sharex=axPOSorig,
sharey=axTIMEorig) # Corrected flux with position
# Panels related to time
# ----------------------
axTIMEorig.plot(time, flux, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axTIMEorig.set_title('Original')
axTIMEorig.set_ylabel('Flux [adu]')
axTIMEcorr.plot(time, flux - fluxCORRECTION, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axTIMEcorr.set_title('After correction')
axTIMEcorr.set_ylabel('Flux [adu]')
axTIMEcorr.set_ylabel('Time [d]')
# Panels related to position
# --------------------------------------
axPOSorig.plot(position, flux, 'k.', ms=6, alpha=.4)
axPOSorig.plot(position, fluxCORRECTION, 'r.', ms=6, alpha=.8)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axPOSorig.set_title('Correction')
axPOScorr.plot(position, flux - fluxCORRECTION, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axPOScorr.set_title('Residuals correction')
axPOScorr.set_ylabel('CCD position [pixel]')
# Settings
# --------
axTIMEorig.set_xlim([np.min(time), np.max(time)])
axTIMEorig.set_ylim([np.min(flux) * 1.2,
np.max(flux) * 1.2])
axPOSorig.set_xlim([np.min(position) + 0.1, np.max(position) + 0.1])
axTIMEcorr.set_xlabel('Time [d]')
axTIMEorig.set_ylabel('Flux [adu]')
axTIMEcorr.set_ylabel('Flux [adu]')
axPOScorr.set_xlabel('Position [pixel]')
return
color=colours[k],
label="%s (%d deg^2)" % (labels[k], sarea[k]),
marker='.')
#line[0].set_dashes(linestyle[k])
for i in range(kc.size):
print "%03d -- %3.3e" % (i, kc[i])
# Title is k value of k bin
P.title("k = %3.3e Mpc$^{-1}$" % kc[kbin])
P.legend(loc='upper right', prop={'size': 'medium'}, frameon=False)
P.tick_params(axis='both',
which='major',
labelsize=20,
size=8.,
width=1.5,
pad=8.)
P.tick_params(axis='both', which='minor', labelsize=20, size=5., width=1.5)
P.xlabel(r"$z$", fontdict={'fontsize': 'x-large'})
P.ylabel(r"$\Delta P / P$", fontdict={'fontsize': 'x-large'})
P.ylim((5e-3, 5e-1))
P.yscale('log')
P.gcf().set_size_inches(8., 6.)
P.tight_layout()
P.savefig("pk_redshift_k%3.3e.pdf" % kc[kbin], transparent=True)
print "Output: pk_redshift_k%3.3e.pdf" % kc[kbin]
#P.show()
#loss = K.mean(layer_output[:, filter_index, :, :])
## compute the gradient of the input picture wrt this loss
#grads = K.gradients(loss, input_img)[0]
## normalization trick: we normalize the gradient
#grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)
## this function returns the loss and grads given the input picture
#iterate = K.function([input_img], [loss, grads])
## we start from a gray image with some noise
#input_img_data = np.random.random(((1,) + input_shape)) * 20 + 128.
## run gradient ascent for 20 steps
#for i in range(20):
# loss_value, grads_value = iterate([input_img_data])
# input_img_data += grads_value * step
# Visualize weights
W = DCNN_flatwindow.layers[0].W.get_value(borrow=True)
W = np.squeeze(W)
print("W shape : ", W.shape)
pl.figure(figsize=(15, 15))
pl.title('conv1 weights')
nice_imshow(pl.gca(), make_mosaic(W, 6, 3), cmap=cm.binary)
pl.tick_params(axis='y', labelleft='off')
pl.tight_layout()
pl.savefig('C://DATA//koumura birds//Bird1//bird1_conv1_1_kernel_weights.eps')
def find_time_points(t=0,
t_want=0,
i_plot=0,
i_test=0,
i_warn=1,
method='even'):
"""
Find time index for an array of time points on time array t
"""
if i_test == 1:
print('ueven test data used')
ind = numpy.linspace(-3, 2.2, 343)
t = 10**ind
t_want = numpy.array([9, 0.1, 0.05, 1.732, 12])
i_plot = 1
elif i_test == 2:
t = numpy.linspace(0, 5, 50000)
t_want = numpy.array([1.111, 3.222])
i_plot = 1
# convert a pure number t_want to a list with length and attribute
if not (type(t_want) == list):
t_want = [t_want]
if numpy.min(t_want) < t[0] or numpy.max(t_want) > t[-1]:
print('t[0]=', t[0])
print('t[len(t)-1]', t[len(t) - 1])
raise Exception('Error: t_want goes out the range of t')
# sum up the difference of time difference to judge whether it is even.
dt_sum = numpy.sum(numpy.diff(numpy.diff(t)))
if dt_sum < 10**-10 or method == 'even':
dt = (t[3] - t[0]) / 3.0
i_want = numpy.round((t_want - t[0]) / dt)
# convert i_want to python style index that start from 0
i_want = i_want - 1
elif dt_sum > 10**-10 or method == 'uneven':
if i_warn == 1 and method == 'even':
print('Sum of ddt: ', dt_sum)
print('Time array is not even, slow loop method used!')
# i_want = numpy.ones(len(t_want))*-1
i_want = numpy.zeros(len(t_want))
for i in range(0, len(t_want)):
for j in range(0, len(t)):
if t_want[i] >= t[j] and t_want[i] < t[j + 1]:
i_want[i] = j
# convert index i_want to integers
i_want = numpy.int_(i_want)
if i_plot == 1:
print('t_want: ', t_want)
print('i_want: ', i_want)
pylab.figure()
x = numpy.arange(0, t.shape[0], 1)
pylab.plot(x, t, '-o', color='blue', markersize=3)
pylab.hold('on')
pylab.plot(x[i_want], t[i_want], 'o', color='red')
pylab.xlabel('index')
pylab.ylabel('time (s)')
pylab.grid('on')
pylab.minorticks_on()
pylab.tick_params(which='major',
labelsize=10,
width=2,
length=10,
color='black')
pylab.tick_params(which='minor', width=1, length=5)
return i_want
def uniqHostsVsJobs(df):
'''
roi_df = df[['totalCores', 'Thrashing']]
roi_df = roi_df[(roi_df['Thrashing'] == True)]
totalCores = (df['totalCores'].values.tolist())
thrashingCores = roi_df['totalCores'].values.tolist()
ziplist = []
for coreNo in list(set(totalCores)):
totFltCorePcnt = (float(thrashingCores.count(coreNo))/float(totalCores.count(coreNo)))*100
totCorePcnt = (float(totalCores.count(coreNo))/float(len(totalCores)))*100
pcnt = (totCorePcnt * totFltCorePcnt)/100.00
ziplist.append((coreNo, totCorePcnt, pcnt, totCorePcnt-pcnt))
coreNo, totalpcnt, bottompcnt, topPcnt = zip(*sorted(ziplist, key=lambda x: x[1], reverse=True))
noOfBars = 30
filename = "500TTotalCoresVSJobs_Percentages.png"
fig, ax = mplt.subplots()
indices = np.arange(0, 0.3*noOfBars, 0.3)
bottomBar = ax.bar(indices, bottompcnt[:noOfBars], bar_width, color=blue, linewidth=outlinewgt[-1], hatch='////')
topBar = ax.bar(indices, topPcnt[:noOfBars], bar_width, bottom=bottompcnt[:noOfBars], color=lightblue, linewidth=outlinewgt[-1], hatch="////")
ax.set_xticks(indices + 0.5*(bar_width))
ax.set_xticklabels(coreNo[:noOfBars])
ax.set_xlabel('Total Cores', fontsize=labelFontSZ, fontweight=labelFontWT)
ax.set_ylabel('% of jobs', fontsize=labelFontSZ, fontweight=labelFontWT)
ax.text(ImgNoteX, ImgNoteY, 'Threshold: Peak major page fault > 500', \
horizontalalignment='center', \
verticalalignment='center', \
transform=ax.transAxes, fontsize=ticksFontSZ, fontweight=labelFontWT)
mplt.xticks(fontsize=ticksFontSZ, rotation='vertical')
mplt.yticks(fontsize=ticksFontSZ)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(ImgWidth, ImgHeight)
mplt.legend( (bottomBar[1], topBar[0]), ("% of Job > Threshold", "% of Jobs < Threshold"))
mplt.savefig(sys.argv[2]+"/"+ filename, format=ImgFormat, dpi=ImgDPI, bbox_inches=ImgProp)
'''
# Second plot
roi_df = df[['uniqHosts', 'Thrashing']]
totalCores = (df['uniqHosts'].values.tolist())
roi_df = roi_df[(roi_df['Thrashing'] == True)]
thrashingCores = roi_df['uniqHosts'].values.tolist()
ziplist = []
for coreNo in list(set(totalCores)):
totFltCoreCnt = thrashingCores.count(coreNo)
totCoreCnt = totalCores.count(coreNo)
ziplist.append(
(coreNo, totCoreCnt, totFltCoreCnt, totCoreCnt - totFltCoreCnt))
coreNo, totalCnt, bottomCnt, topCnt = zip(
*sorted(ziplist, key=lambda x: x[1], reverse=True))
noOfBars = 10
filename = "500TUniqHostsVSJobs_RawNumbers.png"
fig, ax = mplt.subplots()
indices = np.arange(0, 0.3 * noOfBars, 0.3)
topBar = ax.bar(indices, totalCnt[:noOfBars], bar_width, \
color=yellow, linewidth=outlinewgt[-1])#hatch="/")
bottomBar = ax.bar(indices, bottomCnt[:noOfBars], 0.75*bar_width, \
color=red, linewidth=outlinewgt[-1])#hatch='/')
for i in range(2):
labelBar.labelBar(ax, topBar[i], None, \
str(int(float(totalCnt[i])*100.00/len(totalCores)))+'%', 0.03, 0)
labelBar.labelBar(ax, bottomBar[i], None, \
str(int(float(bottomCnt[i])*100.00/totalCnt[i]))+'%', 0.0, 0)
ax.set_xticks(indices + 0.5 * (bar_width))
ax.set_xticklabels(coreNo[:noOfBars])
ax.set_xlabel('No. of nodes requested',
fontsize=labelFontSZ,
fontweight=labelFontWT)
ax.set_ylabel('No of jobs', fontsize=labelFontSZ, fontweight=labelFontWT)
plt.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
right='off', # ticks along the left edge are off
left='off') # ticks along the right edge are off
#ax.text(ImgNoteX, ImgNoteY, 'Threshold: Peak major page fault > 500', \
# horizontalalignment='center', \
# verticalalignment='center', \
# transform=ax.transAxes, fontsize=ticksFontSZ, fontweight=labelFontWT)
mplt.xticks(fontsize=ticksFontSZ)
mplt.yticks(fontsize=ticksFontSZ)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(ImgWidth, ImgHeight)
mplt.legend((bottomBar[0], topBar[0]),
("No of jobs > Threshold", "No of jobs"))
mplt.savefig(sys.argv[2] + "/" + filename,
format=ImgFormat,
dpi=ImgDPI,
bbox_inches=ImgProp)
'''
#Makes plots of S_lambda masked model
#print gc_result
print gc_result_masked_sl
for a in gc_result_masked_sl.median.keys():
setattr(model,a,gc_result_masked_sl.median[a])
sl_w,sl_f = model()
#for a in gc_result.median.keys():
# setattr(model,a,gc_result.median[a])
sl_unmasked_w,sl_unmasked_f = model()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.plot(w/(unmasked_median_fits['vrad_2']/3e5+0.0), starspectrum35.flux.value, label="Data")
'''
plt.plot(w/(unmasked_median_fits['vrad_2']/3e5+1.0),f,label="Unmasked model with best fit unmasked values")
plt.plot(sl_w/(gc_result_masked_sl.median['vrad_2']/3e5+1.0),sl_f-0.5,label="Masked model with best fit masked values")
plt.plot(sl_w/(gc_result_masked_sl.median['vrad_2']/3e5+1.0),masked_data_sl.flux.value-sl_f,label='Masked Model-Masked Data Residuals')
'''
plt.plot(w/(unmasked_median_fits['vrad_2']/3e5+1.0),f,label="Best Fit Model (No mask)")
#plt.plot(sl_w/(gc_result_masked_sl.median['vrad_2']/3e5+1.0),sl_f-0.5,label="Masked model with best fit masked values")
#plt.plot(w/(gc_result_masked_sl.median['vrad_2']/3e5+1.0),starspectrum35.flux.value-f,label='Masked Model-Masked Data Residuals')
#--setting y-axis range
pl.ylim(ylow, yhigh)
pl.yticks(np.linspace(ylow, tick_high, num_ticks, endpoint=True))
margin = bar_width + (1 - len(labels) * bar_width)
#-- setting x-axis
pl.xlim(side_margin - margin, len(data) - side_margin)
if len(xnote.strip()) != 0:
pl.xlabel(xnote)
#--adding minor ticks, one tick every 0.02 unit
#ml = MultipleLocator(10)
#pl.axes().yaxis.set_minor_locator(ml)
#--drawing lines for major and minor ticks
#pl.grid(True)
pl.axes().yaxis.grid(b=True, which='major', color='k', linestyle='--')
#pl.axes().yaxis.grid(b=True, which='minor', color=(0.5,0.5,0.5), linestyle=':')
#--setting the tick marks
pl.xticks(np.arange(len(data)) + bar_width * 0.5 * (NUM_BARS - 1),
data[headers[0]],
rotation=rotation_angle)
pl.tick_params(top="off")
pl.tick_params(bottom="off")
#pl.show()
#pl.savefig(datafile + ".eps", format='eps', dpi=1000, bbox_inches='tight')
pl.savefig(datafile + "." + figure_f,
format=figure_f,
dpi=1000,
bbox_inches='tight')
pl.close()
U[:, -1, 0],
label='Final Mass Density',
color=tableau20[0],
lw=3)
plt.plot(xPoints,
U[:, 0, 1],
label='Initial Momentum Density',
color=tableau20[3],
lw=3)
plt.plot(xPoints,
U[:, -1, 1],
label='Final Momentum Density',
color=tableau20[2],
lw=3)
plt.plot(xPoints,
U[:, 0, 2],
label='Initial Energy Density',
color=tableau20[5],
lw=3)
plt.plot(xPoints,
U[:, -1, 2],
label='Final Energy Density',
color=tableau20[4],
lw=3)
plt.title('Evolution of the Sod Tube at t=' + str(tMax), fontsize=24)
plt.xlabel('X Position', fontsize=18)
plt.ylabel('Conserved Quantity', fontsize=18)
plt.tick_params(labelsize=14)
plt.legend(loc='upper right')
plt.show()
def PLOTdetrendPOSITIONfluxDIAGinformCRIT(flux, position, AICtck, BICtck,
**kwargs):
"""
Routine going with detrendPOSITIONflux to visualize diagnostics in case the AIC and BIC do not favor the same fit.
Returns: Figure with -hopefully- enough diagnostics to determine the strength / weaknessess of the detrending
@param flux: flux measurements [adu]
@type flux: numpy array of length N
@param position: CCD position measurements along a position axis [pixel]
@type position: numpy array of length N
@param AICtck: spline tck for the favored fit by the AIC
@type AICtck: tuple
@param BICtck: spline tck for the favored fit by the BIC
@type BICtck: tuple
"""
# Calculating the corrections
fluxCORRECTIONaic = scInterp.splev(position, AICtck)
fluxCORRECTIONbic = scInterp.splev(position, BICtck)
# Setting up the figure window
# ----------------------------
figDIAGN = pl.figure(figsize=(16, 16))
axAIC = figDIAGN.add_subplot(221)
axBIC = figDIAGN.add_subplot(222, sharey=axAIC, sharex=axAIC)
axAICres = figDIAGN.add_subplot(223, sharey=axAIC, sharex=axAIC)
axBICres = figDIAGN.add_subplot(224, sharey=axAIC, sharex=axAIC)
# --
axAIC.plot(position, flux, 'k.', ms=6, alpha=.4)
axAIC.plot(position, fluxCORRECTIONaic, 'r.', ms=8, alpha=.6)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axAIC.set_title('best AIC fit')
axAIC.set_ylabel('Flux [adu]')
# --
axAICres.plot(position, flux - fluxCORRECTIONaic, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axAICres.set_ylabel('Res. Flux [adu]')
# --
axBIC.plot(position, flux, 'k.', ms=6, alpha=.4)
axBIC.plot(position, fluxCORRECTIONbic, 'r.', ms=8, alpha=.6)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axBIC.set_title('best BIC fit')
# --
axBICres.plot(position, flux - fluxCORRECTIONbic, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
# Settings
# --------
axAIC.set_xlim([np.min(position) - 0.1, np.max(position) + 0.1])
axAICres.set_xlabel('CCD position [pixel]')
axBICres.set_xlabel('CCD position [pixel]')
return
# plot the wave
time = np.arange(0, nframes) * (1.0 / framerate)
time2 = np.arange(0, len(volume11)) * (frameSize - overLap) * 1.0 / framerate
pl.figure(figsize=(6, 2.5))
pl.plot(time, waveData)
pl.ylabel("Amplitude", fontsize=11)
pl.xlabel('Time(s)', fontsize=11)
pl.ylim(-1, 1)
pl.xticks([
0.025, 0.100, 0.250, 0.365, 0.450, 0.540, 0.680, 0.939, 1.024, 1.150,
1.270, 1.350, 1.408, 1.507, 1.600
], [
0.025, '0.100', '0.250', 0.365, '0.450', '0.540', '0.680', 0.939, 1.024,
'1.150', '1.270', '1.350', 1.408, 1.507, '1.600'
])
pl.tick_params(axis='x', rotation=50)
pl.plot([0.025, 0.025], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.1, 0.1], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.25, 0.25], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.365, 0.365], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.45, 0.45], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.54, 0.54], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.68, 0.68], [-1, 1], linestyle='dashed', color='r')
pl.plot([0.939, 0.939], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.024, 1.024], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.15, 1.15], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.27, 1.27], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.35, 1.35], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.408, 1.408], [-1, 1], linestyle='dashed', color='r')
pl.plot([1.507, 1.507], [-1, 1], linestyle='dashed', color='r')
def imag_proc(file_name, num_of_tx, camera):
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
BLUE = (255, 0, 0)
GREEN = (0, 255, 0)
RED = (0, 0, 255)
YELLOW = (0, 255, 255)
TEAL = (255, 255, 0)
MAGENTA = (255, 0, 255)
if 'PICS' in os.environ:
debug = True
else:
debug = False
if 'DEBUG' in os.environ and int(os.environ['DEBUG']) >= 3:
logger.warn("DEBUG=3 doesn't save pictures any more")
logger.warn(
"I split saving pictures out to its own independent setting")
logger.warn("Use PICS=1 to save intermediate images")
if debug:
global dbg_step
dbg_step = 0
# Load image and convert to grayscale
logger.start_op("Loading image")
gray_image = cv2.imread(file_name, cv2.IMREAD_GRAYSCALE)
logger.debug('gray_image.shape = {}'.format(gray_image.shape))
if debug:
dbg_save('gray_image', gray_image)
logger.end_op()
# Handle orientation
logger.start_op("Normalizing image rotation")
if gray_image.shape[1] > gray_image.shape[0]:
logger.debug("Rotated image")
gray_image = numpy.rot90(gray_image, 3)
else:
logger.debug("No rotation")
if debug:
dbg_save('gray_image_rotated', gray_image)
logger.debug('gray_image.shape = {}'.format(gray_image.shape))
logger.end_op()
# Blur image
logger.start_op("Applying blur")
#m2 = cv2.GaussianBlur(gray_image, (31,31), 0)
m2 = cv2.blur(gray_image, (50, 50)) # faster and good enough
#m2 = cv2.blur(gray_image, (150,150)) # faster and good enough
if debug:
dbg_save('after_blur', m2)
logger.debug('m2.shape = {}'.format(m2.shape))
logger.end_op()
# Replace manual threshold with more efficient OTSU filter
logger.start_op("Threshold image")
#threshold, thresholded_img = cv2.threshold(m2, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
thresholded_img = cv2.adaptiveThreshold(m2, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 101, 2)
if debug:
dbg_save('thresholded_img', thresholded_img)
logger.end_op()
# Find and label disjoint sets of pixels (each transmitter blob)
logger.start_op("Locate transmitters")
# opencv3.0 has a connectedComponents API but that's sadly not released yet
#ret, markers = cv2.connectedComponents(thresholded_img)
# We solve this by drawing an outline ("contour") around each blob
contours, heirarchy = cv2.findContours(thresholded_img, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
if debug:
# drawContours draws contours on the supplied image, need a copy
contour_image = gray_image.copy()
cv2.drawContours(contour_image, contours, -1, 255, 3)
dbg_save('contours', contour_image)
#contours_kept_image = gray_image.copy()
contours_kept_image = cv2.imread(file_name, cv2.IMREAD_COLOR)
# Draw the center point; useful for eyeballing calibration
kept_center = (contours_kept_image.shape[1] / 2,
contours_kept_image.shape[0] / 2)
cv2.circle(
contours_kept_image,
kept_center,
5, # radius
RED, # color
-1 # fill circle
)
cv2.circle(
contours_kept_image,
(kept_center[0], kept_center[1] + 20),
5, # radius
RED, # color
-1 # fill circle
)
cv2.circle(
contours_kept_image,
(kept_center[0] + 20, kept_center[1]),
5, # radius
RED, # color
-1 # fill circle
)
cv2.circle(
contours_kept_image,
(kept_center[0] + 40, kept_center[1]),
5, # radius
RED, # color
-1 # fill circle
)
# And then fitting a circle to that contour
centers = []
radii = []
for contour in contours:
center, radius = cv2.minEnclosingCircle(contour)
center = map(int, center)
radius = int(radius)
if radius <= 33:
logger.debug(
'Skipping transmitter at {} with small radius ({} pixels)'.
format(center, radius))
continue
# For some reason minEnclosingCircle flips x and y?
center = (center[1], center[0])
#assert thresholded_img[center[0], center[1]] == 1, 'Center of blob is not lit?'
reject = False
for pt in contour:
# List of lists? Maybe some contour structure could have blob points?
assert len(pt) == 1
pt = pt[0]
# More x,y flip?
if \
pt[1] < 10 or \
pt[0] < 10 or \
pt[1] > (thresholded_img.shape[0]-10) or \
pt[0] > (thresholded_img.shape[1]-10):
reject = True
logger.debug("Bad edge point: {}".format(pt))
break
if reject:
logger.debug('Rejecting edge contour at {}'.format(center))
continue
contour_area = cv2.contourArea(contour)
circle_area = math.pi * radius**2
logger.debug(
'Transmitter area {:0.1f}. Radius {} px. Contour area {}. %age {:0.1f}'
.format(circle_area, radius, contour_area,
(contour_area / circle_area) * 100))
if (contour_area / circle_area) < .5:
logger.debug('Rejecting non-circular contour at {}'.format(center))
continue
centers.append(center)
radii.append(radius)
if debug:
cv2.drawContours(contours_kept_image, [
contour,
], -1, TEAL, 3)
if debug:
dbg_save('contours-kept', contours_kept_image)
number_of_transmitters = len(centers)
#assert number_of_transmitters >= 3, 'not enough transmitters'
logger.end_op()
# Compute transmitter frequencies
logger.start_op("Computing transmitter frequencies")
Fs = 1 / camera.rolling_shutter_r
T = 1 / Fs
# 2**14 good balance of speed / resolution [5~10 hz for small sample set]
NFFT = 2**14
gain = 5
estimated_frequencies = []
window_size = 100
average_window = 40
avg_threshold = 20
light_circles = gray_image.copy()
for i in xrange(number_of_transmitters):
try:
row_start = max(0, centers[i][0] - radii[i])
row_end = min(gray_image.shape[0] - 1, centers[i][0] + radii[i])
column_start = max(0, centers[i][1] - radii[i])
column_end = min(gray_image.shape[1] - 1, centers[i][1] + radii[i])
#Slice image around current center and sum across all rows
image_slice = gray_image[row_start:row_end,
column_start:column_end]
image_slice_mean = numpy.mean(image_slice)
image_row = numpy.sum(image_slice, axis=0)
#Remove any DC component
image_row = image_row - numpy.mean(image_row)
#Apply window
y = image_row * numpy.hamming(image_row.shape[0])
#Take FFT
L = len(y)
Y = numpy.fft.fft(y * gain, NFFT) / float(L)
f = Fs / 2 * numpy.linspace(0, 1, NFFT / 2.0 + 1)
Y_plot = 2 * abs(Y[0:NFFT / 2.0 + 1])
#TODO: Apply heuristic to determine SNR
if debug:
pylab.subplot(number_of_transmitters, 2, 2 * i + 1)
pylab.title(str(centers[i]), size='xx-small')
pylab.ylim([-13000, 13000])
pylab.yticks([-13000, 0, 13000])
pylab.tick_params(labelsize=4)
pylab.plot(y)
##Improve center by thresholding image and obtaining minimum enclosing circle
#_, image_slice_thresh = cv2.threshold(image_slice, image_slice_mean*1.5, 1, cv2.THRESH_BINARY)
#image_slice_thresh_contours, _ = cv2.findContours(image_slice_thresh, cv2.RETR_LIST,
# cv2.CHAIN_APPROX_SIMPLE)
#image_slice_thresh_contours = numpy.vstack(image_slice_thresh_contours)
#center, radius = cv2.minEnclosingCircle(image_slice_thresh_contours)
#center = map(int, center)
#radius = int(radius)
#center = (center[1] + row_start, center[0] + column_start)
#cv2.circle(light_circles, (center[1], center[0]), radius + 3, WHITE, 3)
#centers[i] = center
#radii[i] = radius
#Find the best fit for the largest circle
radius = int(image_slice.shape[0] / 2)
first_time = True
max_val = 0
circle_area = 0
max_loc = (0, 0)
while radius > 0:
last_radius = radius
last_max_loc = max_loc
last_max_val = max_val
last_circle_area = circle_area
circle_template = numpy.zeros((radius * 2 + 1, radius * 2 + 1),
type(image_slice[0][0]))
cv2.circle(circle_template, (radius, radius), radius, WHITE,
-1)
res = cv2.matchTemplate(image_slice, circle_template,
cv2.TM_CCORR)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
circle_area = math.pi * math.pow(radius, 2)
#Continue to decrease the circle size until more than 10% of the remaining pixels
#print('{} {}'.format(max_val, last_max_val))
if first_time or max_val > last_max_val * (
(.4 * circle_area + .6 * last_circle_area) /
last_circle_area):
first_time = False
radius = radius - 1
else:
#print('GOT HERE')
radii[i] = last_radius
centers[i] = (row_start + last_max_loc[1] + last_radius +
1, column_start + last_max_loc[0] +
last_radius + 1)
cv2.circle(light_circles, (centers[i][1], centers[i][0]),
radius + 5, WHITE, 3)
break
if radii[i] <= 33:
raise NotImplementedError("hack")
if debug:
pylab.subplot(number_of_transmitters, 2, 2 * i + 2)
pylab.plot(f, Y_plot)
pylab.title(str(centers[i]), size='xx-small')
#pylab.xlabel('Frequency (Hz)')
pylab.xlim([0, 16000])
pylab.tick_params(labelsize=4)
peaks = scipy.signal.argrelmax(Y_plot)[0]
logger.debug2('peaks =\n{}'.format(peaks))
logger.debug2('f[peaks] =\n{}'.format(f[peaks]))
logger.debug2('Y_plot[peaks] =\n{}'.format(Y_plot[peaks]))
idx = numpy.argmax(Y_plot[peaks])
peak_freq = f[peaks[idx]]
logger.debug('center {}\tradius {}\tpeak_freq = {}'.format(
centers[i], radii[i], peak_freq))
if debug:
cv2.circle(contours_kept_image, (centers[i][1], centers[i][0]),
5, GREEN, -1)
cv2.circle(contours_kept_image, (centers[i][1], centers[i][0]),
radius + 5, GREEN, 2)
cv2.putText(
contours_kept_image,
"({} {}) {} Hz".format(centers[i][1], centers[i][0],
int(peak_freq)),
(centers[i][1] + 100, centers[i][0]),
cv2.FONT_HERSHEY_TRIPLEX, 2, YELLOW)
estimated_frequencies.append(peak_freq)
except:
logger.debug("Dropped failed center at {}".format(centers[i]))
estimated_frequencies.append(10)
if debug:
dbg_plot_subplots('freq_fft_transmitters')
dbg_save('contours-kept-labeled', contours_kept_image)
dbg_save('circles', light_circles)
logger.end_op()
centers = numpy.array(centers)
radii = numpy.array(radii)
estimated_frequencies = numpy.array(estimated_frequencies)
return (centers, radii, estimated_frequencies, gray_image.shape)
def plot(self, dir1, dir2):
read = LIGGGHTSER.read.Read()
top = read.read_ave(dir1)
ke = read.read_ave(dir2)
plt.figure(figsize=(20, 15))
font1 = {
'weight': 'normal',
'size': 18,
}
fontlabel = {
'weight': 'normal',
'size': 18,
}
ax411 = plt.subplot(411)
x = top['v_xForce']
y = top['v_yForce']
x = np.array(x)
y = np.array(y)
friction = x / y
l11 = plt.plot(top['TimeStep'], friction, linewidth=5.0, linestyle='-')
# plt.xlim((3e7,6e7))
# plt.ylim((0.1,0.51))
# plt.xticks(np.arange(3e7,6e7,0.5e7))
# plt.yticks(np.arange(0.1,0.5,0.1))
plt.tick_params(labelsize=18, direction='in', pad=15)
ax411.spines['bottom'].set_linewidth(3)
ax411.spines['top'].set_linewidth(3)
ax411.spines['left'].set_linewidth(3)
ax411.spines['right'].set_linewidth(3)
plt.title('Friction', loc='right', fontsize=24, pad=10)
# ax411.set_title('Friction',fontsize=18)
# plt.xlabel('TimeStep')
plt.ylabel('Friciton ratio', fontlabel)
ax412 = plt.subplot(412)
l21 = plt.plot(top['TimeStep'],
top['v_yPos'],
linewidth=5.0,
linestyle='-')
# plt.xlim((3e7,6e7))
# plt.ylim((0.148,0.150))
# plt.xticks(np.arange(3e7,6e7,0.5e7))
# plt.yticks(np.arange(1.48e-1,1.5e-1,5e-4))
plt.tick_params(labelsize=18, direction='in', pad=15)
ax412.spines['bottom'].set_linewidth(3)
ax412.spines['top'].set_linewidth(3)
ax412.spines['left'].set_linewidth(3)
ax412.spines['right'].set_linewidth(3)
plt.title('Mesh Position', loc='right', fontsize=24, pad=10)
# plt.xlabel('TimeStep')
plt.ylabel('Topmesh Position', fontlabel)
ax413 = plt.subplot(413)
l31 = plt.plot(ke['TimeStep'],
ke['c_2'],
linewidth=5.0,
linestyle='-',
zorder=30)
# plt.xlim((3e7,6e7))
# plt.ylim((0,10000))
# plt.xticks(np.arange(3e7,6e7,0.5e7))
# plt.yticks(np.arange(0,10000,1000))
plt.tick_params(labelsize=18, direction='in', pad=15)
ax413.spines['bottom'].set_linewidth(3)
ax413.spines['top'].set_linewidth(3)
ax413.spines['left'].set_linewidth(3)
ax413.spines['right'].set_linewidth(3)
# ax413.set_yscale("log")
# plt.title('Energy',loc='right',fontsize=24,pad=10)
# plt.xlabel('TimeStep')
plt.ylabel('Kinetic Energy', fontlabel)
plt.subplots_adjust(left=0.1,
right=0.97,
bottom=0.05,
top=0.95,
wspace=0.1,
hspace=0.4)
plt.show()
times = evoked.times * 1000
sel = fiff.pick_types(evoked.info,
meg=False,
eeg=False,
include=channelList)
print sel
data = evoked.data[sel] * 1e13
square = np.power(data, 2)
meanSquare = np.mean(square, 0)
rms = np.power(meanSquare, .5)
pl.plot(times, rms, color=colorList[c], linewidth=lWidth)
pl.ylim([ymin, ymax])
pl.xlim([xmin, xmax])
pl.box('off') # turn off the box frame
pl.axhline(y=0, xmin=0, xmax=1, color='k',
linewidth=2) #draw a thicker horizontal line at 0
pl.axvline(
x=0, ymin=0, ymax=1, color='k', linewidth=2
) #draw a vertical line at 0 that goes 1/8 of the range in each direction from the middle (e.g., if the range is -8:8, =16, 1/8 of 16=2, so -2:2).
pl.tick_params(axis='both', right='off',
top='off') #turn off all the tick marks
pl.yticks(np.array([0., 4., 8., 12., 16.]))
pl.xticks(np.array([0, 200, 400, 600]))
#pl.title(hem + group)
#pl.show()
outFile = results_path + args.prefix + '-' + str(args.set1) + '-' + str(
args.set2) + '-' + group + '.png'
pl.savefig(outFile)
def PLOTdetrendORBITfluxFULL(time, flux, orbitalPHASE, TCK, **kwargs):
"""
Routine going with detrendORBITflux to visualize the detrending of the photometry for the instrumental correction between the satellite's orbital phase and BRITE flux.
Returns: Figure with -hopefully- enough diagnostics to determine the strength / weaknessess of the detrending
@param flux: flux measurements [adu]
@type flux: numpy array of length N
@param orbitalPHASE: orbital phase measurements []; ranges from 0 to 1
@type orbitalPHASE: numpy array of length N
@param TCK: spline tck for the favored fit
@type TCK: tuple
"""
# Calculating the corrections
# ---------------------------
fluxCORRECTION = scInterp.splev(orbitalPHASE, TCK)
# Setting up the figure window
# ----------------------------
figORBITcorr = pl.figure(figsize=(16, 16))
gsORBITcorr = gridspec.GridSpec(2,
2,
height_ratios=[1, 1],
width_ratios=[3, 2])
axTIMEorig = figORBITcorr.add_subplot(
gsORBITcorr[0, 0]) # Initial flux with time
axTIMEcorr = figORBITcorr.add_subplot(
gsORBITcorr[1, 0], sharex=axTIMEorig,
sharey=axTIMEorig) # Corrected flux with time
axORBITorig = figORBITcorr.add_subplot(
gsORBITcorr[0, 1], sharey=axTIMEorig) # Initial flux with orbitalPHASE
axORBITcorr = figORBITcorr.add_subplot(
gsORBITcorr[1, 1], sharex=axORBITorig,
sharey=axTIMEorig) # Corrected flux with orbitalPHASE
# Panels related to time
# ----------------------
axTIMEorig.plot(time, flux, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axTIMEorig.set_title('Original')
axTIMEorig.set_ylabel('Flux [adu]')
axTIMEcorr.plot(time, flux - fluxCORRECTION, 'k.', ms=6, alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axTIMEcorr.set_title('After correction')
axTIMEcorr.set_ylabel('Flux [adu]')
axTIMEcorr.set_ylabel('Time [d]')
# Panels related to orbitalPHASE
# --------------------------------------
axORBITorig.plot(orbitalPHASE, flux, 'k.', ms=6, alpha=.4)
axORBITorig.plot(orbitalPHASE + 1., flux, 'k.', ms=6, alpha=.4)
axORBITorig.plot(orbitalPHASE, fluxCORRECTION, 'r.', ms=6, alpha=.8)
axORBITorig.plot(orbitalPHASE + 1., fluxCORRECTION, 'r.', ms=6, alpha=.8)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axORBITorig.set_title('Correction')
axORBITcorr.plot(orbitalPHASE, flux - fluxCORRECTION, 'k.', ms=6, alpha=.4)
axORBITcorr.plot(orbitalPHASE + 1.,
flux - fluxCORRECTION,
'k.',
ms=6,
alpha=.4)
pl.tick_params('both', length=10, width=2, which='major')
pl.tick_params('both', length=10, width=1, which='minor')
pyplot.locator_params(axis='x', nbins=5)
pyplot.locator_params(axis='y', nbins=5)
axORBITcorr.set_title('Residuals correction')
axORBITcorr.set_ylabel('Orbital phase')
# Settings
# --------
axTIMEorig.set_xlim([np.min(time), np.max(time)])
axTIMEorig.set_ylim([np.min(flux) * 1.2,
np.max(flux) * 1.2])
axORBITorig.set_xlim(
[np.min(orbitalPHASE) - 0.1,
np.max(orbitalPHASE) + 1.1])
axTIMEcorr.set_xlabel('Time [d]')
axTIMEorig.set_ylabel('Flux [adu]')
axTIMEcorr.set_ylabel('Flux [adu]')
axORBITcorr.set_xlabel('Orbital phase')
return
def plot_numpoints(targname, filt, exp_length, flashlvl, aperture,
outloc, ylims):
""" Plots fraction of non-ctecorr to ctecorr sources recovered
vs fluxbin, for a subset of epochs.
"""
proposid_ls = ['12379', '12692', '13083', '13566', '14012']
color_ls = ['red', 'orange', 'lime', 'green', 'blue']
marker_ls = ['o', 'd', 's', '*', '^']
#color_ls = itertools.cycle(colors)
epoch_ls = []
#proposid_ls = []
for proposid in proposid_ls:
if exp_length == 'l':
if '104' in targname:
exptime = 348
elif '6791' in targname:
exptime = 420
elif exp_length == 's':
if '104' in targname:
exptime = 30
elif '6791' in targname:
exptime = 60
epochs = query_for_dateobss(targname, proposid, filt, exptime)
epochs = list(set(epochs))
epoch_ls.append(epochs[-1])
#for epoch in epochs:
# proposid_ls.append(proposid)
print("epoch_ls: {}".format(epoch_ls))
# Set the figure.
pylab.figure(figsize=(12.5,8.5))
for epoch, proposid, color, marker in zip(epoch_ls, proposid_ls, color_ls, marker_ls):
# ctecorr True
slopes_ctecorr, stderrs_ctecorr, mjds_ctecorr, fluxbins_ctecorr, numpoints_ctecorr = query_db_python(
targname, filt, exp_length, flashlvl, ctecorr=True, aperture=aperture,
epoch=epoch, proposid=proposid)
mjds_ctecorr_cut = np.array(mjds_ctecorr[np.where(mjds_ctecorr == epoch)])
fluxbins_ctecorr_cut = np.array(fluxbins_ctecorr[np.where(mjds_ctecorr == epoch)])
numpoints_ctecorr_cut = np.array(numpoints_ctecorr[np.where(mjds_ctecorr == epoch)])
# ctecorr False
slopes, stderrs, mjds, fluxbins, numpoints, = query_db_python(
targname, filt, exp_length, flashlvl, ctecorr=False, aperture=aperture,
epoch=epoch, proposid=proposid)
mjds_cut = np.array(mjds[np.where(mjds == epoch)])
fluxbins_cut = np.array(fluxbins[np.where(mjds == epoch)])
numpoints_cut = np.array(numpoints[np.where(mjds == epoch)])
print("fluxbins_cut: {}".format(fluxbins_cut))
print("numpoints_cut: {}".format(numpoints_cut))
print("numpoints_ctecorr_cut: {}".format(numpoints_ctecorr_cut))
if len(numpoints_cut) == len(numpoints_ctecorr_cut) and len(numpoints_cut) != 0:
fluxbins_log = []
for fluxbin in fluxbins_cut:
fluxlo = float(fluxbin.split('-')[0])
fluxhi = float(fluxbin.split('-')[1])
flux_av = (fluxlo + fluxhi)/2.0
fluxbins_log.append(np.log10(flux_av))
# Calculate fraction recovered
frac_recovered =(1.0 - (numpoints_ctecorr_cut.astype(float) - numpoints_cut.astype(float))/ numpoints_ctecorr_cut.astype(float))*100.
print("frac_recovered: {}".format(frac_recovered))
# Set the next color in sequence.
#color = next(color_ls)
pylab.scatter(fluxbins_log, frac_recovered,
s=120, marker=marker, color='grey', alpha=0.6, label='MJD={}'.format(epoch))
pylab.xlabel('LOG10 Flux [e-])', fontsize=22, weight='bold')
pylab.ylabel('% Sources Recovered w/ CTEcorr', fontsize=22, weight='bold')
pylab.axhline(y=100.0, linewidth=2, linestyle='--', color='grey')
pylab.tick_params(axis='both', which='major', labelsize=20)
title = "{} {} explen'{}' pf{} ap{}".format(targname, filt,
exp_length, flashlvl, aperture)
pylab.title(title, fontsize=16)
pylab.xlim([2.5, 4.5])
pylab.ylim([40.,120])
pylab.legend(scatterpoints=1, loc='lower right')
pylab.savefig(os.path.join(outloc, '{}_{}_{}_pf{}_r{}_fracrecoverd.png'.format(
targname, filt, exp_length, flashlvl, aperture)),
bbox_inches='tight')
value = 'Density' # Choose quantity to plot: density, pressure, velocity
colormap = cm.jet # Choose colormap to use
title = 'Colormap of ' + value + ' for Bessel Beam'
tickSize = 14
labelSize = 18
titleSize = 24
## Plot colormap at some time
fig = plt.figure()
ax = fig.gca()
if value == 'Density':
data = rawData[:, :, 0, t]
plt.imshow(data, origin='lower', cmap=colormap)
plt.tick_params(labelsize=tickSize)
plt.title(title, fontsize=titleSize)
plt.xlabel('X Cell Number', fontsize=labelSize)
plt.ylabel('Y Cell Number', fontsize=labelSize)
plt.xlim([3, Nx - 2])
plt.ylim([3, Ny - 2])
m = cm.ScalarMappable(cmap=colormap)
m.set_array(data)
plt.colorbar(m)
dataNameShort = dataName[0:-4]
plt.savefig(dataNameShort + 't' + str(t) + value + '.png')
#plt.show()
def labelPlot(xlab, ylab, title,textsize):
plt.tick_params(labelsize=textsize)
plt.xlabel(xlab,fontsize=textsize)
plt.ylabel(ylab,fontsize=textsize)
plt.title(title,fontsize=textsize)
def cnn_visualization(folder_name=None, h5name=None, num=None):
"""
NAME: cnn_visualization
PURPOSE: To visualize CNN model
INPUT:
folder_name = parent folder name
data =
OUTPUT: plots
HISTORY:
2017-Nov-02 Henry Leung
"""
# Set number of spectra for CNN visualization
if num is None:
num = 20
random.seed(3)
data = h5name + '_train.h5'
currentdir = os.getcwd()
fullfolderpath = currentdir + '/' + folder_name
vis_parent_path = os.path.join(fullfolderpath, 'cnn_visual')
# load model
modelname = '/model_{}.h5'.format(folder_name[-11:])
model = load_model(os.path.normpath(fullfolderpath + modelname))
layer_1 = K.function([model.layers[0].input, K.learning_phase()], [model.layers[1].output])
layer_2 = K.function([model.layers[0].input, K.learning_phase()], [model.layers[2].output])
target = np.load(fullfolderpath + '/targetname.npy')
spec_meanstd = np.load(fullfolderpath + '/spectra_meanstd.npy')
with h5py.File(data) as F: # ensure the file will be cleaned up
i = 0
index_not9999 = []
for tg in target:
temp = np.array(F['{}'.format(tg)])
temp_index = np.where(temp != -9999)
if i == 0:
index_not9999 = temp_index
i += 1
else:
index_not9999 = reduce(np.intersect1d, (index_not9999, temp_index))
spectra = np.array(F['spectra'])
rel_index = np.array(F['index'])
spectra = spectra[index_not9999]
spectra -= spec_meanstd[0]
spectra /= spec_meanstd[1]
random_number = num
ran = random.sample(range(0, spectra.shape[0], 1), random_number)
spectra = spectra[ran]
rel_index = rel_index[ran]
num_label = spectra.shape[1]
for i in range(random_number):
temp_path = os.path.join(vis_parent_path, str(i))
if not os.path.exists(temp_path):
os.makedirs(temp_path)
reshaped = spectra[i].reshape((1, num_label, 1))
layer_1_output = layer_1([reshaped, 0])[0]
layer_2_output = layer_2([reshaped, 0])[0]
apogee_id = astroNN.NN.train_tools.apogee_id_fetch(relative_index=rel_index, dr=14)
plt.figure(figsize=(30, 15), dpi=200)
plt.rcParams['axes.grid'] = False
plt.plot(spectra[i] * spec_meanstd[1] + spec_meanstd[0], alpha=0.8, linewidth=0.7, label='APOGEE Spectra')
plt.xlabel('Pixel', fontsize=25)
plt.ylabel('Flux ', fontsize=25)
plt.title(apogee_id[i], fontsize=30)
plt.xlim((0, num_label))
plt.ylim((0.5, 1.5))
plt.tick_params(labelsize=20, width=1, length=10)
leg = plt.legend(loc='best', fontsize=20)
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
plt.tight_layout()
plt.savefig(temp_path + '/spectra_{}.png'.format(apogee_id[i]))
plt.close('all')
plt.clf()
plt.figure(figsize=(25, 20), dpi=200)
plt.ylabel('Pixel', fontsize=35)
plt.xlabel('CNN Filter number', fontsize=35)
plt.title(apogee_id[i], fontsize=30)
plt.imshow(layer_1_output[0, :, :], aspect='auto', norm=colors.PowerNorm(gamma=1. / 2.), cmap='gray')
plt.tick_params(labelsize=25, width=1, length=10)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=25, width=1, length=10)
plt.tight_layout()
plt.savefig(temp_path + '/cnn_layer1.png')
plt.close('all')
plt.clf()
plt.figure(figsize=(25, 20), dpi=200)
plt.ylabel('Pixel', fontsize=35)
plt.xlabel('CNN Filter number', fontsize=35)
plt.title(apogee_id[i], fontsize=30)
plt.imshow(layer_2_output[0, :, :], aspect='auto', norm=colors.PowerNorm(gamma=1. / 2.), cmap='gray')
plt.tick_params(labelsize=25, width=1, length=10)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=25, width=1, length=10)
plt.tight_layout()
plt.savefig(temp_path + '/cnn_layer2.png')
plt.close('all')
plt.clf()
burn = 3500
print '# mean, st. dev., best $\chi^2$'
plottinghist('CC.chain', 'r', 900)
plottinghist('DC.chain', '--r', burn)
plottinghist('GC.chain', ':r', burn)
plottinghist('CD.chain', 'b', 700)
plottinghist('DD.chain', '--b', 700)
plottinghist('GD.chain', ':b', 900)
plottinghist('CG.chain', 'g', 8500)
plottinghist('DG.chain', '--g', burn)
plottinghist('GG.chain', ':g', 500)
#pylab.axvline(x=Rhalf(50.0,30.0,15.0,10.0),color='r',ls='-.',lw=2)
#pylab.axvline(x=50.0/2.0**0.5,color='b',ls='-.',lw=2)
#pylab.axvline(x=numpy.log(4.0)**0.5*50.0/3.0,color='g',ls='-.',lw=2)
pylab.legend(loc=9, fontsize=12)
pylab.xlim(25, 102)
#pylab.xlim(15,50)
pylab.xlabel('$\mathrm{R_{p}}$', fontsize=20)
pylab.ylabel('$\mathrm{Probability\ density\ function}$', fontsize=20)
pylab.ylim(ymax=1.0)
pylab.axvline(x=50, color='k', ls='-.', lw=2)
pylab.savefig('Rp4all.eps')
pylab.tick_params(axis='both', which='major', labelsize=20)
pylab.tick_params(axis='both', which='minor', labelsize=20)
pylab.show()
l11 = plt.plot(top1['TimeStep'],
friction,
linewidth=linewidth_set,
linestyle='-',
label='Unbreakable')
x = top2['v_xForce']
y = top2['v_yForce']
x = np.array(x)
y = np.array(y)
friction = x / y
l12 = plt.plot(top2['TimeStep'],
friction,
linewidth=linewidth_set,
linestyle='-',
label='breakable s=120MPa')
plt.tick_params(labelsize=18, direction='in', pad=15)
plt.xlim((xlow, xhigh))
plt.ylim((0.2, 0.51))
plt.yticks(np.arange(0.2, 0.51, 0.1))
ax411.spines['bottom'].set_linewidth(3)
ax411.spines['top'].set_linewidth(3)
ax411.spines['left'].set_linewidth(3)
ax411.spines['right'].set_linewidth(3)
plt.ylabel('Friction', fontlabel)
# plt.legend([l11,l12],labels=['Unbreakable','breakable s=120MPa'],loc='lower right',prop=font1)
ax411y2 = ax411.twinx()
l13 = ax411y2.plot(timestep2,
break_atom2,
linewidth=linewidth_set,
color='blue',
linestyle='-',
def embed(words, matrix, classes, usermodel, fname):
perplexity = int(len(
words)**0.5) # We set perplexity to a square root of the words number
embedding = TSNE(n_components=2,
perplexity=perplexity,
metric="cosine",
n_iter=500,
init="pca")
y = embedding.fit_transform(matrix)
print("2-d embedding finished", file=sys.stderr)
class_set = [c for c in set(classes)]
colors = plot.cm.rainbow(np.linspace(0, 1, len(class_set)))
class2color = [colors[class_set.index(w)] for w in classes]
xpositions = y[:, 0]
ypositions = y[:, 1]
seen = set()
plot.clf()
for color, word, class_label, x, y in zip(class2color, words, classes,
xpositions, ypositions):
plot.scatter(
x,
y,
20,
marker=".",
color=color,
label=class_label if class_label not in seen else "",
)
seen.add(class_label)
lemma = word.split("_")[0].replace("::", " ")
mid = len(lemma) / 2
mid *= 4 # TODO Should really think about how to adapt this variable to the real plot size
plot.annotate(
lemma,
xy=(x - mid, y),
size="x-large",
weight="bold",
fontproperties=font,
color=color,
)
plot.tick_params(axis="x",
which="both",
bottom=False,
top=False,
labelbottom=False)
plot.tick_params(axis="y",
which="both",
left=False,
right=False,
labelleft=False)
plot.legend(loc="best")
plot.savefig(
root + "data/images/tsneplots/" + usermodel + "_" + fname + ".png",
dpi=150,
bbox_inches="tight",
)
plot.close()
plot.clf()
def BenchmarkSummaryFigure(models,
variables,
data,
figname,
vcolor=None,
rel_only=False):
"""Creates a summary figure for the benchmark results contained in the
data array.
Parameters
----------
models : list
a list of the model names
variables : list
a list of the variable names
data : numpy.ndarray or numpy.ma.ndarray
data scores whose shape is ( len(variables), len(models) )
figname : str
the full path of the output file to write
vcolor : list, optional
an array parallel to the variables array containing background
colors for the labels to be displayed on the y-axis.
"""
from mpl_toolkits.axes_grid1 import make_axes_locatable
# data checks
assert type(models) is type(list())
assert type(variables) is type(list())
assert (type(data) is type(np.empty(1))
or type(data) is type(np.ma.empty(1)))
assert data.shape[0] == len(variables)
assert data.shape[1] == len(models)
assert type(figname) is type("")
if vcolor is not None:
assert type(vcolor) is type(list())
assert len(vcolor) == len(variables)
# define some parameters
nmodels = len(models)
nvariables = len(variables)
maxV = max([len(v) for v in variables])
maxM = max([len(m) for m in models])
wpchar = 0.1
wpcell = 0.19
hpcell = 0.25
w = maxV * wpchar + max(4, nmodels) * wpcell
if not rel_only: w += (max(4, nmodels) + 1) * wpcell
h = maxM * wpchar + nvariables * hpcell + 1.0
bad = 0.5
if "stoplight" not in plt.colormaps(): RegisterCustomColormaps()
# plot the variable scores
if rel_only:
fig, ax = plt.subplots(figsize=(w, h), ncols=1, tight_layout=True)
ax = [ax]
else:
fig, ax = plt.subplots(figsize=(w, h), ncols=2, tight_layout=True)
# absolute score
if not rel_only:
cmap = plt.get_cmap('stoplight')
cmap.set_bad('k', bad)
qc = ax[0].pcolormesh(np.ma.masked_invalid(data[::-1, :]),
cmap=cmap,
vmin=0,
vmax=1,
linewidth=0)
div = make_axes_locatable(ax[0])
fig.colorbar(qc,
ticks=(0, 0.25, 0.5, 0.75, 1.0),
format="%g",
cax=div.append_axes("bottom", size="5%", pad=0.05),
orientation="horizontal",
label="Absolute Score")
plt.tick_params(which='both', length=0)
ax[0].xaxis.tick_top()
ax[0].set_xticks(np.arange(nmodels) + 0.5)
ax[0].set_xticklabels(models, rotation=90)
ax[0].set_yticks(np.arange(nvariables) + 0.5)
ax[0].set_yticklabels(variables[::-1])
ax[0].tick_params('both', length=0, width=0, which='major')
ax[0].tick_params(axis='y', pad=10)
if vcolor is not None:
for i, t in enumerate(ax[0].yaxis.get_ticklabels()):
t.set_backgroundcolor(vcolor[::-1][i])
# relative score
i = 0 if rel_only else 1
np.seterr(invalid='ignore', under='ignore')
data = np.ma.masked_invalid(data)
data.data[data.mask] = 1.
data = np.ma.masked_values(data, 1.)
mean = data.mean(axis=1)
std = data.std(axis=1).clip(0.02)
np.seterr(invalid='ignore', under='ignore')
Z = (data - mean[:, np.newaxis]) / std[:, np.newaxis]
Z = np.ma.masked_invalid(Z)
np.seterr(invalid='warn', under='raise')
cmap = plt.get_cmap('RdGn')
cmap.set_bad('k', bad)
qc = ax[i].pcolormesh(Z[::-1], cmap=cmap, vmin=-2, vmax=2, linewidth=0)
div = make_axes_locatable(ax[i])
fig.colorbar(qc,
ticks=(-2, -1, 0, 1, 2),
format="%+d",
cax=div.append_axes("bottom", size="5%", pad=0.05),
orientation="horizontal",
label="Relative Score")
plt.tick_params(which='both', length=0)
ax[i].xaxis.tick_top()
ax[i].set_xticks(np.arange(nmodels) + 0.5)
ax[i].set_xticklabels(models, rotation=90)
ax[i].tick_params('both', length=0, width=0, which='major')
ax[i].set_yticks([])
ax[i].set_ylim(0, nvariables)
if rel_only:
ax[i].set_yticks(np.arange(nvariables) + 0.5)
ax[i].set_yticklabels(variables[::-1])
if vcolor is not None:
for i, t in enumerate(ax[i].yaxis.get_ticklabels()):
t.set_backgroundcolor(vcolor[::-1][i])
# save figure
fig.savefig(figname)
#r' $\epsilon$ = ' +str(params['eps']) ,
#fontsize=14, fontweight='bold')
## [GHO08a], [VUK13]
#fig.suptitle('FHN - Local Dynamics : '+r'$\alpha$ = ' +str(params['alpha'])+
#r' $ \gamma$ = '+str(params['gamma']) + ' $ b$ = '+
#str(params['b']) + r' $\tau$ = '+str(params['TAU']) +
#' D = ' + str(params['D']) ,
#fontsize=25)
pl.subplot(121)
pl.xlabel('t', fontsize=30)
pl.ylabel('x(t) , y(t)', fontsize=30)
pl.plot(t, x, 'r', label='$x(t)$')
pl.plot(t, y, 'b', label='$y(t)$')
pl.tick_params(labelsize=30)
#pl.plot(sol_adap['t'],sol_adap['x'],'.r',linewidth=0.2)
#pl.plot(sol_adap['t'],sol_adap['y'],'.b',linewidth=0.2)
pl.axis([0, tfinal, -3, 3])
lg = legend(prop={'size': 30})
lg.draw_frame(False)
pl.subplot(122)
pl.xlabel('x', fontsize=30)
pl.ylabel('y', fontsize=30)
pl.plot(x, y, 'k', label='$x(t),y(t)$')
pl.tick_params(labelsize=30)
pl.plot(x[0], y[0], '.r', markersize=25)
#pl.plot(x_, y_ , 'b',label='$x_{nullcline}$')
#pl.plot(xx,yy,'k',label='$y_{nullcline}$')
#pl.plot(-params['a'],(-params['a']+pow(params['a'],3)/3),'ok')
#pl.xlim(xmin=1)
pl.ylim(ymin=0)
if int(args.matrixsize) != 1:
ax.legend((methodsReal), 'lower left', shadow=True, fancybox=True)
else:
ax.legend((methodsReal), 'upper left', shadow=True, fancybox=True)
# take real time of sequential computation to figure out the
# granularity of the yaxis
tmp_ticks = ax.yaxis.get_majorticklocs()
granu = tmp_ticks[len(tmp_ticks) - 1] // (len(tmp_ticks) - 1) // 5
if granu == 0.0:
granu = 1.0
ax.yaxis.set_minor_locator(MultipleLocator(granu))
pl.tick_params(axis='both', which='major', labelsize=6)
pl.tick_params(axis='both', which='minor', labelsize=6)
#pl.savefig('timings.pdf',format='pdf',papertype='a4',orientation='landscape')
pl.savefig(pp, format='pdf', papertype='a4', orientation='landscape')
fig = pl.figure()
ax = fig.add_subplot(111)
fig.suptitle('GFLOPS/sec: CALU fully-dynamic scheduling w/ MKL',
fontsize=10)
if int(args.matrixsize) != 1:
pl.title('Matrix dimensions: ' + dimensions[0] + ' x ' + dimensions[1],
fontsize=8)
else:
pl.title('Number of threads: ' + str(args.threads), fontsize=8)
if int(args.matrixsize) != 1:
marker='D',
label=r"WFIRST")
# Plot n(z) for Euclid for comparison
zmin, zmax, _a, nz_euclid, _b = np.genfromtxt("nz_euclid.dat").T
P.plot(0.5 * (zmin + zmax),
nz_euclid * 0.67**3.,
'm-',
lw=1.5,
marker='D',
label=r"Euclid")
P.legend(loc='upper right', ncol=1, frameon=False, prop={'size': 'medium'})
P.xlim((0., 5.5))
P.ylim((1e-7, 1e-3))
P.tick_params(axis='both',
which='major',
labelsize=18,
width=1.5,
size=8.,
pad=10)
P.tick_params(axis='both', which='minor', labelsize=18, width=1.5, size=8.)
P.xlabel("z", fontsize='x-large')
P.ylabel("n(z) [Mpc$^{-3}$]", fontsize='x-large')
P.yscale('log')
P.tight_layout()
P.show()
