def _sp(data):
"""
Generate plots of convergence criteria, and energy vs. optimization cycles
:job: ccdata object, or file
:returns: TODO
"""
# TODO scfenergies, scfvalues, scftargets vs. scf cycles
print("\n\n")
#print("Optimization Converged: ", data.optdone)
criteria = [0, 0, 0]
criteria[0] = [x[0] for x in data.scfvalues]
criteria[1] = [x[1] for x in data.scfvalues]
criteria[2] = [x[2] for x in data.scfvalues]
idx = np.arange(len(criteria[0]))
# Plot Geometry Optimization Criteria for Convergence over opt cycles
plt.plot(idx, criteria[0], label='Criteria 1')
plt.plot(idx, criteria[1], label='Criteria 2')
plt.plot(idx, criteria[2], label='Criteria 3')
# Plot target criteria for convergence
plt.axhline(y=data.scftargets[0])
plt.yscale('log')
plt.title("SCF Convergence Analysis")
plt.xlabel("SCF Cycle")
plt.legend()
print(idx, criteria, data.scftargets)
plt.show()
def create_plots(iterations, data, M, step_size):
NN_b, NN_a, E = steepest_decent_training(iterations, data, M, step_size)
X = data[:,0]
X_cont = np.arange(-10,10,0.1)
t = data[:,1]
Y = [NN_a(np.array([[x],[1]]))[0] for x in X_cont]
f = np.vectorize(lambda(x): sin(x) / x)
plt.figure(1)
plt.plot(X_cont,Y,label='Neural network \n (M = %d)' % M)
plt.plot(X_cont,f(X_cont),label='sinc(x)')
plt.legend()
plt.savefig('images/nn_vs_real_%d_%d.%s' % (iterations,M,img_format), format=img_format)
plt.figure(2)
plt.plot(X_cont,Y,label='Neural network \n (M = %d)' % M)
plt.scatter(X,t,color='red',label='Training data')
plt.legend()
plt.savefig('images/nn_vs_training_%d_%d.%s' % (iterations,M,img_format), format=img_format)
plt.figure(3)
plt.plot(E, label='Error (M = %d)' % M)
plt.yscale('log')
plt.legend()
plt.savefig('images/error_%d_%d.%s' % (iterations,M,img_format), format=img_format)
plt.show()
def plot_figure_commute(file_name):
f = open(file_name)
lines = f.readlines()
lines[0] = lines[0][:-1]
f.close()
probabilities = lines[0].split(" ")
xx = xrange(len(probabilities))
probabilities = [float(x) for x in probabilities]
plt.figure(figsize=(12, 10))
plt.plot(xx, probabilities, label="D(degree > x)")
plt.ylabel('Percentage of vertices which degree> x')
plt.xlabel('Degree')
plt.title("Cumulative function for degrees")
plt.legend(loc='upper right')
name = "results_graphs/"
if file_name == "temp_degrees/cumulative_temp.txt":
name += "ful_cumulative"
else:
name += "good_cumulative"
plt.savefig(name + '.png')
plt.figure(figsize=(12, 10))
plt.plot(xx, probabilities, label="log(D(degree > x))")
plt.legend(loc='upper right')
plt.title("Cumulative function for degrees in log/log coords")
plt.ylabel('Log of percentage of vertices which degree> x')
plt.xlabel('Log of degrees')
plt.xscale('log')
plt.yscale('log')
plt.savefig(name + '_log' + '.png')
def setDiffsPlot(CF,d0,ySym = True):
CFtext = [str(j)+',' for j in CF]
bText = [CFtext[0],r'...,$a_i$+c,...',CFtext[-1]]
CFtext = ''.join(CFtext)
bText= ''.join(bText)
CFtext = '[' + CFtext[:-1] + ']'
bText = '[' + bText[:-1] + ']'
print(CFtext)
plt.ylabel(r'$d^{crit}_b - d^{crit}_a$',fontsize=20)
plt.xlabel(r'Element changed',fontsize=20)
xmin, xmax, ymin, ymax = plt.axis()
if ySym:
plt.yscale('symlog',linthreshy=1e-15)
yLoc = [y*ymax for y in [.1, .01, .001]]
else:
yLoc = [y*(ymax-ymin)+ymin for y in [0.95, 0.85, 0.75]]
plt.plot([0,xmax],[0,0],'k--',label='_')
plt.text((xmax-xmin)*0.15,yLoc[0],r'$a = [a_i] =$'+CFtext,fontsize=15)
plt.text((xmax-xmin)*0.15,yLoc[1],r'$b_i = $'+bText,fontsize=15)
plt.text((xmax-xmin)*0.15,yLoc[2],r'$d_a^{crit} = $'+str(float(d0)),fontsize=15)
plt.legend(loc='best')
plt.xscale('symlog',linthreshx=1e-14)
# plt.yscale('log')
plt.show()
def plot_max_avg_Rho_lev012(lev0,lev1,lev2, ic, spath):
'''lev -- TimeProfQs() object, whose lev.convert() attribute has been called'''
#avgRho
Tratio = lev0.t / ic.tCr
fig, ax = plt.subplots()
#initial
plt.hlines(ic.rho0, Tratio.min(),Tratio.max(), colors="magenta", linestyles='dashed',label="initial")
#lev0
plt.plot(Tratio,lev0.maxRho,ls="-",c="black",lw=2.,label="max Lev0")
plt.plot(Tratio,lev0.minRho,ls="-",c="green",lw=2.,label="min Lev0")
plt.plot(Tratio,lev0.avgRho,ls="-",c="blue",lw=2.,label="avg Lev0")
plt.plot(Tratio,lev0.avgRho_HiPa,ls="-",c="red",lw=2.,label=r"avg ($\rho > \rho_0$)")
#lev1
plt.plot(Tratio,lev1.maxRho,ls="--",c="black",lw=2.,label="max Lev1")
plt.plot(Tratio,lev1.minRho,ls="--",c="green",lw=2.,label="min Lev1")
plt.plot(Tratio,lev1.avgRho,ls="--",c="blue",lw=2.,label="avg Lev1")
plt.plot(Tratio,lev1.avgRho_HiPa,ls="--",c="red",lw=2.,label=r"avg Lev1 ($\rho > \rho_0$)")
#lev2
plt.plot(Tratio,lev2.maxRho,ls=":",c="black",lw=2.,label="max Lev2")
plt.plot(Tratio,lev2.minRho,ls=":",c="green",lw=2.,label="min Lev2")
plt.plot(Tratio,lev2.avgRho,ls=":",c="blue",lw=2.,label="avg Lev2")
plt.plot(Tratio,lev0.avgRho_HiPa,ls=":",c="red",lw=2.,label=r"avg Lev1 ($\rho > \rho_0$)")
#finish
plt.yscale("log")
plt.xlabel("t / t_cross")
plt.ylabel("Densities [g/cm^3]")
plt.title(r"Max & Avg Densities, Lev0")
py.legend(loc=4, fontsize="small")
name = spath+"max_avg_Rho_Lev012.pdf"
plt.savefig(name,format="pdf")
plt.close()
def _scatter(actual, prediction, args):
plt.figure()
plt.plot(actual, prediction, 'b'+args['plot_scatter_marker'])
xmin=min(actual)
xmax=max(actual)
ymin=min(prediction)
ymax=max(prediction)
diagxmin=min(math.fabs(x) for x in actual)
diagymin=min(math.fabs(y) for y in prediction)
diagpmin=min(diagxmin,diagymin)
pmin=min(xmin,ymin)
pmax=max(xmax,ymax)
plt.plot([diagpmin,pmax],[diagpmin,pmax],'k-')
if args['plot_identifier'] != 'NoName':
plt.title(args['plot_identifier'])
plt.xlabel('Observed')
plt.ylabel('Modeled')
if args['plot_performance_log'] == True:
plt.yscale('log')
plt.xscale('log')
if args['plot_scatter_free'] != True:
plt.axes().set_aspect('equal')
if args['plot_dump'] == True:
pfname=os.path.join(args['plot_dir'],args['plot_identifier']+'_eiger_scatter.pdf')
plt.savefig(pfname,format="pdf")
else:
plt.show()
def plot_probability_calibration_curves(self):
""" Compute true and predicted probabilities for a calibration plot
fraction_of_positives - The true probability in each bin (fraction of positives).
mean_predicted_value - The mean predicted probability in each bin.
"""
fig = plt.figure()
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0), rowspan=2)
ax1.set_ylabel("Fraction of positives")
ax1.set_ylim([-0.05, 1.05])
ax1.legend(loc="lower right")
ax1.set_title('Calibration plots (reliability curve) ' + self.description)
ax2.set_xlabel("Mean predicted value")
ax2.set_ylabel("Count")
ax2.legend(loc="upper center", ncol=2)
clf_score = brier_score_loss(self.y_true, self.y_pred, pos_label=1)
fraction_of_positives, mean_predicted_value = calibration_curve(self.y_true, self.y_pred, n_bins=50)
ax1.plot(mean_predicted_value, fraction_of_positives, "s-", color="#660066", alpha = 0.6, label="%s (%1.3f)" % (self.description, clf_score))
ax2.hist(self.y_pred, range=(0, 1), bins=50, color="#660066", linewidth=2.0 , alpha = 0.6, label="%s (%1.3f)" % (self.description, clf_score), histtype="step", lw=2)
plt.yscale('log')
return
def Config(**options):
"""Configures the plot.
Pulls options out of the option dictionary and passes them to
title, xlabel, ylabel, xscale, yscale, xticks, yticks, axis, legend,
and loc.
"""
title = options.get('title', '')
pyplot.title(title)
xlabel = options.get('xlabel', '')
pyplot.xlabel(xlabel)
ylabel = options.get('ylabel', '')
pyplot.ylabel(ylabel)
if 'xscale' in options:
pyplot.xscale(options['xscale'])
if 'xticks' in options:
pyplot.xticks(options['xticks'])
if 'yscale' in options:
pyplot.yscale(options['yscale'])
if 'yticks' in options:
pyplot.yticks(options['yticks'])
if 'axis' in options:
pyplot.axis(options['axis'])
loc = options.get('loc', 0)
legend = options.get('legend', True)
if legend:
pyplot.legend(loc=loc)
def main():
sample='q'
sm_bin='10.0_10.5'
catalogue = 'sm_9.5_s0.2_sfr_c-0.75_250'
#load in fiducial mock
filepath = './'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'_cov.npy'
cov = np.matrix(np.load(filepath+filename))
diag = np.diagonal(cov)
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu = np.array(data['wp'])
#load in comparison mock
plt.figure()
plt.errorbar(rbins, mu, yerr=np.sqrt(np.diagonal(cov)), color='black')
plt.plot(rbins, wp, color='red')
plt.xscale('log')
plt.yscale('log')
plt.show()
inv_cov = cov.I
Y = np.matrix((wp-mu))
X = Y*inv_cov*Y.T
print(X)
def pareto_graph(database, objectives=None):
'''Constructs a visualization of the movement of the best front over generations
The argument 'objectives' must be a list indicating the indices of the fitness values to be used.
The first two will be consumed. If the list has less than two elements, or if is not given, the
graph will be produced using the first two fitness values.
'''
if objectives is None or len(objectives) < 2:
objectives = [0, 1]
generations = []
if database.properties['highest_population'] < FRONT_COUNT:
generations = list(range(1, database.properties['highest_population'] + 1))
else:
step = database.properties['highest_population'] / FRONT_COUNT
generations = [round(i * step) for i in range(1, FRONT_COUNT + 1)]
for i, gen in enumerate(generations, start=1):
database.select(gen)
individual_data = [val for key, val in database.load_report().items()
if key.startswith('I') and val['rank'] == 1]
x_values = [val['fitness'][objectives[0]] for val in individual_data]
y_values = [val['fitness'][objectives[1]] for val in individual_data]
plt.plot(x_values, y_values,
color=str((FRONT_COUNT - i) / FRONT_COUNT),
linestyle='None',
marker='o',
markeredgecolor='white')
plt.title('Movement of best front')
plt.xscale('log')
plt.yscale('log')
plt.xlabel(database.properties['objective_names'][objectives[0]])
plt.ylabel(database.properties['objective_names'][objectives[1]])
plt.show()
def bar_graph_dict(dict_to_plot, plot_title="", xlab="", ylab="", log_scale=False, col="#71cce6", sort_key_list=None,
min_count=1):
"""
Plots a bar graph of the provided dictionary.
Params:
dict_to_plot (dict): should have the format {'label': count}
plot_title (str), xlab (str), ylab (str), log_scale (bool): fairly self-explanatory plot customization
col (str): colour for the bars
sort_key_list (list): the keys of the dictionary are assumed to match based its first item and reordered as such
min_count (int): do not plot items with less than this in the count
"""
# Sort dictionary & convert to list using custom keys if needed
if not sort_key_list:
list_to_plot = sorted(dict_to_plot.items())
else:
list_to_plot = sorted(dict_to_plot.items(), key=lambda x: sort_key_list.index(x[0]))
# Remove list items with less than min_count
if min_count != 1:
list_to_plot = [dd for dd in list_to_plot if dd[1] >= min_count]
# Bar plot of secondary structure regions containing mutants in each mutant Cas9
bar_width = 0.45
plt.bar(np.arange(len(list_to_plot)), [dd[1] for dd in list_to_plot], width=bar_width, align='center', color=col)
plt.xticks(range(len(list_to_plot)), [dd[0] for dd in list_to_plot], rotation=45, ha='right')
plt.title(plot_title)
plt.xlabel(xlab)
plt.ylabel(ylab)
if log_scale:
plt.yscale('log')
plt.ylim(min_count-0.1) # Show values with just the minimum count
plt.show()
def _init_ipython_plot(self,
plot_data,
legend_lst=[],
title_str=' ',
axis=None,
plot_relative=False):
""" Private member function that loads ipython plotting libraries
Parameters
----------
plot_data : lst of lst
A list of lists that stores the data series to be plotted.
legend : lst
A list of strings for the legend.
axis : bool
Indicates whether there is a user specified axis.
plot_relative : bool
Indicates whether the relative residuals should be plotted.
"""
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
for data_set in plot_data:
if plot_relative == True:
max_term = max(data_set)
for i,term in enumerate(data_set):
data_set[i] = data_set[i] / max_term
plt.plot(data_set)
plt.yscale("log")
plt.legend(legend_lst)
plt.title(title_str)
if axis is not None:
plt.xlim(axis[0],axis[1])
plt.show()
def plottamelo3():
a = np.arange(0,30)
nw = NWsc(matr1,True)
db = DBsc(matr1,True)
dbPR = productDBsc(matr1,True)
INsc = INsc1(matr1,True)
plt.yscale('log')
plt.plot(range(0,nw.iter),nw.res, color='blue', lw=2, label="Newton Sclaled")
#plt.plot(range(0,db.iter),db.res, color='red', lw=2, label="DB Sclaled")
#plt.plot(range(0,dbPR.iter),dbPR.res, color='green', lw=2, label="DB Product Sclaled")
#plt.plot(range(0,INsc.iter),INsc.res, color='orange', lw=2, label="IN Sclaled")
nw = newton(matr1,True)
db = DB(matr1,True)
dbPR = productDB(matr1,True)
IN = INiteration(matr1,True)
CR = CRiteration(matr1,True)
plt.yscale('log')
print len(range(0,nw.iter))
print len(nw.res)
plt.plot(range(0,nw.iter+1),nw.res, color='blue', lw=2, label="Newton")
#plt.plot(range(0,db.iter+1),db.res, color='red', lw=2, label="DB")
#plt.plot(range(0,dbPR.iter+1),dbPR.res, color='green', lw=2, label="DB Product")
#plt.plot(range(0,IN.iter+1),IN.res, color='orange', lw=2, label="IN")
#plt.plot(range(0,CR.iter+1),CR.res, color='brown', lw=2, label="CR")
plt.legend(loc='upper right')
plt.show()
def check_hod(self, z, prop):
data = np.genfromtxt(LOCATION + "/data/" + prop + "z" + str(z))
if prop == "ncen":
if PLOT:
plt.clf()
plt.plot(self.hod.hmf.M,
self.hod.n_cen,
label="mine")
plt.plot(data[:, 0] * self.hod.cosmo.h, data[:, 1], label="charles")
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.savefig(join(pref, "ncen" + prop + "z" + str(z) + ".pdf"))
assert max_diff_rel(self.hod.n_cen, data[:, 1], 0.01)
elif prop == "nsat":
if PLOT:
plt.clf()
plt.plot(self.hod.hmf.M,
self.hod.n_sat,
label="mine")
plt.plot(data[:, 0] * self.hod.cosmo.h, data[:, 1], label="charles")
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.savefig(join(pref, "nsat" + prop + "z" + str(z) + ".pdf"))
assert max_diff_rel(self.hod.n_sat, data[:, 1], 0.01)
def plot_samples(self):
""" Plot the samples requested for each arm """
plt.clf()
plt.scatter(range(0, self.K), self.sample_size)
plt.yscale('log')
plt.ioff()
plt.show()
def plot(objects, xscales={}, yscales={}, title=""):
from matplotlib.pyplot import plot, show, close, subplot,\
xscale, yscale, gcf
'''
Plots current state of objects in subplots.
Define xscales and yscales as dict of indexes.
'''
if not isinstance(objects, list):
objects = [objects]
l = len(objects)
first = round(l / 2.0) + 1
second = round(l / 2.0)
for i in range(0, l):
subplot(first, second, i + 1)
if i in xscales:
xscale(xscales[i])
if i in yscales:
yscale(yscales[i])
fig = gcf()
fig.suptitle(title, fontsize="x-large")
values = objects[i].get_y_values()
x, y = values.shape
for j in range(x):
plot(objects[i].get_t_values(), values[j, :])
show()
close()
def saskia_plot_pulseheight( data_file_name, station_number=501, detector=0, number_bins=200, range_start=0., range_end=4500 ):
# If the plot exist we skip the plotting
if os.path.isfile('./img/pulseheigt_histogram_%d_detector_%d.pdf' % (station_number, detector)):
# Say if the plot is present
print "Plot already present for station %d" % station_number
# If there is no plot we make it
else:
# Now transform the ROOT histogram to a python figure
rootpy.plotting.root2matplotlib.hist(ph_histo)
# Setting the limits on the axis
plt.ylim((pow(10,-1),pow(10,7)))
plt.xlim((range_start, range_end))
plt.yscale('log')
# Setting the plot labels and title
plt.xlabel("Pulseheight [ADC]")
plt.ylabel("Counts")
plt.title("Pulseheight histogram (log scale) for station (%d)" %station_number)
# Saving them Pica
plt.savefig(
'./img/pulseheigt_histogram_%d_detector_%d.pdf' % (station_number, detector) , # Name of the file
bbox_inches='tight') # Use less whitespace
def plot_roc_one_vs_rest(labels, predictions_dict, weights=None, physics_notion=False, predictions_dict_comparison=None, separate_particles=False, algorithms_name=('MVA', 'baseline')):
"""
Plot roc curves one versus rest.
:param array labels: labels form 0 to 5
:param dict(array) predictions_dict: dict of label/predictions
:param array weights: sample weights
"""
if separate_particles:
plt.figure(figsize=(22, 22))
else:
plt.figure(figsize=(10, 8))
for label, name in labels_names_correspondence.items():
if separate_particles:
plt.subplot(3, 2, label + 1)
for preds, prefix in zip([predictions_dict, predictions_dict_comparison], algorithms_name):
if preds is None:
continue
fpr, tpr, _ = roc_curve(labels == label, preds[label], sample_weight=weights)
auc = roc_auc_score(labels == label, preds[label], sample_weight=weights)
if physics_notion:
plt.plot(tpr * 100, fpr * 100, label='{}, {}, AUC={:1.5f}'.format(prefix, name, auc), linewidth=2)
plt.yscale('log', nonposy='clip')
else:
plt.plot(tpr, 1-fpr, label='{}, AUC={:1.5f}'.format(name, auc), linewidth=2)
if physics_notion:
plt.xlabel('Efficiency', fontsize=22)
plt.ylabel('Overall MisID Efficiency', fontsize=22)
else:
plt.xlabel('Signal efficiency', fontsize=22)
plt.ylabel('Background rejection', fontsize=22)
plt.legend(loc='best', fontsize=18)
def PlotEDepSummary(gFiles,nFiles,figureName='EDepSummary.png',tParse=GetThickness,
histKey='eDepHist'):
""" PlotEDepSummary
Plotss the energy deposition summary
"""
# Extrating the average values
gT = list()
gDep = list()
gDepError = list()
nT = list()
nDep = list()
nDepError = list()
for fname in gFiles:
f = TFile(fname,'r')
hist = f.Get(histKey)
gT.append(GetThickness(fname))
gDep.append(hist.GetMean())
gDepError.append(hist.GetMeanError())
for fname in nFiles:
f = TFile(fname,'r')
hist = f.Get(histKey)
nT.append(GetThickness(fname))
nDep.append(hist.GetMean())
nDepError.append(hist.GetMeanError())
# Plotting
plt.errorbar(gT,gDep,yerr=gDepError,fmt='r+')
plt.hold(True)
plt.errorbar(nT,nDep,yerr=nDepError,fmt='go')
plt.xlabel("Thickness (mm)")
plt.ylabel("Average Energy Deposition (MeV)")
plt.legend(["Co-60","Cf-252"])
plt.xscale("log")
plt.yscale("log")
plt.grid(True)
plt.savefig(figureName)
def Validation():
numSamples = 1000000
theta = np.random.rand(numSamples)*np.pi
ECo60 = np.array([1.117,1.332])
Ef0,Ee0 = Compton(ECo60[0],theta)
Ef1,Ee1 = Compton(ECo60[1],theta)
dSdE0 = diffXSElectrons(ECo60[0],theta)
dSdE1 = diffXSElectrons(ECo60[1],theta)
# Sampling Values
values = list()
piMax = np.max([dSdE0,dSdE1])
while (len(values) < numSamples):
values.append(SampleRejection(piMax,ComptonScattering))
# Binning the data
bins = np.logspace(-3,0.2,100)
counts = np.histogram(values,bins)
counts = counts[0]/float(len(values))
binCenters = 0.5*(bins[1:]+bins[:-1])
# Plotting
plt.figure()
plt.plot(binCenters,counts,ls='steps')
#plt.bar(binCenters,counts,align='center')
plt.grid(True)
plt.xlim((1E-3,1.4))
plt.xlabel('Electron Energy (MeV)')
plt.ylabel('Frequency per Photon')
plt.yscale('log')
plt.xscale('log')
plt.savefig('ValComptonScatteringXS.png')
def plot(self, debug = False):
"""plot figures for population, nuisance parameters"""
# first figure out what scheme is used
self.list_scheme()
# next get MABR sampling done
self.MBAR_analysis()
# load in precomputed P and dP from MBAR analysis
pops0, pops1 = self.P_dP[:,0], self.P_dP[:,self.K-1]
dpops0, dpops1 = self.P_dP[:,self.K], self.P_dP[:,2*self.K-1]
t0 = self.traj[0]
t1 = self.traj[self.K-1]
# Figure Plot SETTINGS
label_fontsize = 12
legend_fontsize = 10
fontfamily={'family':'sans-serif','sans-serif':['Arial']}
plt.rc('font', **fontfamily)
# determine number of row and column
if (len(self.scheme)+1)%2 != 0:
c,r = 2, (len(self.scheme)+2)/2
else:
c,r = 2, (len(self.scheme)+1)/2
plt.figure( figsize=(4*c,5*r) )
# Make a subplot in the upper left
plt.subplot(r,c,1)
plt.errorbar( pops0, pops1, xerr=dpops0, yerr=dpops1, fmt='k.')
plt.hold(True)
plt.plot([1e-6, 1], [1e-6, 1], color='k', linestyle='-', linewidth=2)
plt.xlim(1e-6, 1.)
plt.ylim(1e-6, 1.)
plt.xlabel('$p_i$ (exp)', fontsize=label_fontsize)
plt.ylabel('$p_i$ (sim+exp)', fontsize=label_fontsize)
plt.xscale('log')
plt.yscale('log')
# label key states
plt.hold(True)
for i in range(len(pops1)):
if (i==0) or (pops1[i] > 0.05):
plt.text( pops0[i], pops1[i], str(i), color='g' )
for k in range(len(self.scheme)):
plt.subplot(r,c,k+2)
plt.step(t0['allowed_'+self.scheme[k]], t0['sampled_'+self.scheme[k]], 'b-')
plt.hold(True)
plt.xlim(0,5)
plt.step(t1['allowed_'+self.scheme[k]], t1['sampled_'+self.scheme[k]], 'r-')
plt.legend(['exp', 'sim+exp'], fontsize=legend_fontsize)
if self.scheme[k].find('cs') == -1:
plt.xlabel("$\%s$"%self.scheme[k], fontsize=label_fontsize)
plt.ylabel("$P(\%s)$"%self.scheme[k], fontsize=label_fontsize)
plt.yticks([])
else:
plt.xlabel("$\sigma_{%s}$"%self.scheme[k][6:],fontsize=label_fontsize)
plt.ylabel("$P(\sigma_{%s})$"%self.scheme[k][6:],fontsize=label_fontsize)
plt.yticks([])
plt.tight_layout()
plt.savefig(self.picfile)
def avgDegree(G):
print "Nodes: ",
print G.number_of_nodes()
print "Edges: ",
print G.number_of_edges()
# avg degree
degrees = defaultdict(int)
total = 0
for node in G.nodes():
neighbors = G.neighbors(node)
degrees[len(neighbors)] += 1
total += len(neighbors)
max_degree = max(degrees.keys())
degrees_arr = (max_degree+1) * [0]
for index, count in degrees.iteritems():
degrees_arr[index] = count
plt.plot(range(max_degree+1), degrees_arr, '.')
plt.xscale('log', basex=2)
plt.xlabel('degree')
plt.yscale('log', basex=2)
plt.ylabel('# of people')
plt.savefig('degree_distribution.png')
plt.close()
def plot_gradient_over_time(points, get_grad_over_time):
fig = plt.figure(figsize=(6.5, 4))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
# ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
# ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
# ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
# ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
# ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
# ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
# ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
# ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
ax.grid(b=True, which='major', linewidth=0.75, linestyle=':', color='0.75')
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticks_position('none')
for wx, wRec, c in points:
grad_over_time = get_grad_over_time(wx, wRec)
x = np.arange(1, grad_over_time.shape[1]+1, 1)
plt.plot(x, np.sum(grad_over_time, axis=0), c+'.-', label='({0}, {1})'.format(wx, wRec), linewidth=1, markersize=8)
plt.xlim(1, grad_over_time.shape[1])
plt.xticks(x)
plt.gca().invert_xaxis()
plt.yscale('symlog')
plt.yticks([10**8, 10**6, 10**4, 10**2, 0, -10**2, -10**4, -10**6, -10**8])
plt.xlabel('time k')
plt.ylabel('gradient ')
plt.title('Unstability of gradient in backward propagation.\n(backpropagate from left to right)')
leg = plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False, numpoints=1)
leg_font = FontProperties()
leg_font.set_size('x-large')
leg.set_title('$(w_x, w_{rec})$', prop=leg_font)
def plot_citation_graph(citation_graph, filename, plot_title):
# find the indegree_distribution
indeg_dist = in_degree_distribution(citation_graph)
# sort freq by keys
number_citations = sorted(indeg_dist.keys())
indeg_freq = [indeg_dist[n] for n in number_citations]
# normalize
total = sum(indeg_freq)
indeg_freq_norm = [freq / float(total) for freq in indeg_freq]
# calculate log/log, except for the first one (0)
#log_number_citations = [math.log10(x) for x in number_citations[1:]]
#log_indeg_freq_norm = [math.log10(x) for x in indeg_freq_norm[1:]]
plot(number_citations[1:], indeg_freq_norm[1:], 'o')
xscale("log")
yscale("log")
xlabel("log10 #citations")
ylabel("log10 Norm.Freq.")
title(plot_title)
grid(True)
savefig(filename)
show()
def plotCurves(losses,rateOfExceedance,return_periods,lossLevels):
plt.figure(1, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
plt.scatter(losses,rateOfExceedance,s=20)
if len(return_periods) > 0:
annual_rate_exc = 1.0/np.array(return_periods)
for rate in annual_rate_exc:
if rate > min(rateOfExceedance):
plt.plot([min(losses),max(losses)],[rate,rate],color='red')
plt.annotate('%.6f' % rate,xy=(max(losses),rate),fontsize = 12)
plt.yscale('log')
plt.xscale('log')
plt.ylim([min(rateOfExceedance),1])
plt.xlim([min(losses),max(losses)])
plt.xlabel('Losses', fontsize = 16)
plt.ylabel('Annual rate of exceedance', fontsize = 16)
setReturnPeriods = 1/rateOfExceedance
plt.figure(2, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
plt.scatter(setReturnPeriods,losses,s=20)
if len(return_periods) > 0:
for period in return_periods:
if period < max(setReturnPeriods):
plt.plot([period,period],[min(losses),max(losses)],color='red')
plt.annotate(str(period),xy=(period,max(losses)),fontsize = 12)
plt.xscale('log')
plt.xlim([min(setReturnPeriods),max(setReturnPeriods)])
plt.ylim([min(losses),max(losses)])
plt.xlabel('Return period (years)', fontsize = 16)
plt.ylabel('Losses', fontsize = 16)
def __save(self, n, plot, sfile):
p.figure(figsize=sfile)
p.xlabel(plot.xlabel)
p.ylabel(plot.ylabel)
p.xscale(plot.xscale)
p.yscale(plot.yscale)
p.grid()
for curvetype, args, kwargs in plot.curves:
if curvetype == "plot":
p.plot(*args, **kwargs)
elif curvetype == "imshow":
p.imshow(*args, **kwargs)
elif curvetype == "hist":
p.hist(*args, **kwargs)
elif curvetype == "bar":
p.bar(*args, **kwargs)
p.axes().set_aspect(plot.aspect)
if plot.legend:
p.legend(shadow=0, loc=plot.loc)
if not os.path.isdir(plot.dir):
os.mkdir(plot.dir)
if plot.pgf:
p.savefig(plot.dir + plot.name + ".pgf")
print(plot.name + ".pgf")
if plot.pdf:
p.savefig(plot.dir + plot.name + ".pdf", bbox_inches="tight")
print(plot.name + ".pdf")
p.close()
def plot():
plt.plot(x_large, y_large, label=u'grafo de tamaño grande')
plt.plot(x_medium, y_medium, label=u'grafo de tamaño medio')
plt.yscale('log')
plt.legend()
plt.xlabel(u'iteración')
plt.ylabel(u'diferencia en la norma del vector resultado entre sucesivas iteraciones')
def create_time_plot(data, times, ylabel, title=None, filename=None,
xlims=None, ylims=None, log=False):
plt.close('all')
fig, ax = plt.subplots()
if type(data) == dict:
for lab in data.keys():
plt.plot(times[lab], data[lab], 'x', label=lab)
# plt.legend(loc='upper left')
plt.legend()
else:
plt.plot(times, data, 'x')
plt.grid()
plt.xlabel("Received Time")
plt.ylabel(ylabel)
if title is not None:
plt.title(title)
fig.autofmt_xdate()
if xlims is not None:
plt.xlim(xlims)
if ylims is not None:
plt.ylim(ylims)
if log:
plt.yscale('log')
if filename is not None:
plt.tight_layout()
plt.savefig(filename, fmt='pdf')
else:
plt.show()
def make_plot():
# Set up figure
fig = plot.figure(figsize = (700 / my_dpi, 600 / my_dpi), dpi = my_dpi)
### Plot ###
for i, mode in enumerate(default_modes):
alpha = 0.4
if mode == 1:
alpha = 1.0
if mode == 3 or mode == 5:
alpha = 0.7
plot.plot(frame_range, modes_over_time[i, :], linewidth = linewidth, alpha = alpha, label = "%d" % mode)
# Axis
plot.xlim(0, frame_range[-1])
plot.ylim(10**(-3.5), 10**(0.0))
plot.yscale("log")
# Annotate
this_title = readTitle()
plot.xlabel("Number of Planet Orbits", fontsize = fontsize)
plot.ylabel("Vortensity Mode Amplitudes", fontsize = fontsize)
plot.title("%s" % (this_title), fontsize = fontsize + 1)
plot.legend(loc = "upper right", bbox_to_anchor = (1.2, 1.0)) # outside of plot
# Save and Close
plot.savefig("fft_vortensity_modes.png", bbox_inches = 'tight', dpi = my_dpi)
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def make_plot(filename, title, arguments, methods, scale):
if not support_plots:
return
is_linear = (scale == 'linear')
plot_size = LINEAR_PLOT_SIZE if is_linear else OTHER_PLOT_SIZE
plt.figure(figsize=plot_size)
for name, func, measures in methods:
plt.plot(arguments, measures, 'o-', label=name, markersize=3)
if is_linear:
axis = plt.axis()
plt.axis((0, axis[1], 0, axis[3]))
plt.xscale(scale)
plt.yscale(scale)
plt.xticks(fontsize=NORMAL_FONT_SIZE)
plt.yticks(fontsize=NORMAL_FONT_SIZE)
plt.grid(True)
plt.title(title, fontsize=LABEL_FONT_SIZE)
plt.xlabel('Argument', fontsize=NORMAL_FONT_SIZE)
plt.ylabel('Time (seconds)', fontsize=NORMAL_FONT_SIZE)
plt.legend(loc='upper left', fontsize=NORMAL_FONT_SIZE)
plt.tight_layout(0.2)
path = os.path.join(PLOTS_DIR, filename)
plt.savefig(path)
print '[*] Saved plot "%s"' % path
def get_radial_profile(img, xoyo, nbin, disp=0):
''' Computes the mean radial profile of the image.
img:
2D image.
xoyo:
(xo,yo) center for the annuli.
nbin:
width of the annuli in pixels
disp:
optional key word for displaying the images.
Its value will serve as the window number that will be created.
'''
(xo, yo) = xoyo
(nx, ny) = img.shape
r = get_r_dist(nx, ny, xo, yo)
r_max = np.max(r) # radius of the image
r_max = np.max(r[xo, :])
npts = int(r_max / nbin)
O = np.ones((nx, ny))
Z = np.zeros((nx, ny))
Profile = np.zeros(npts)
if disp != 0:
plt.figure(disp)
plt.clf()
plt.subplot(121)
plt.plot(xo, yo, 'xw')
plt.title('PSF')
plt.title('Averaged radial profile')
plt.xlabel('Distance from center (pixels)')
plt.imshow(img, interpolation='none')
plt.colorbar()
val_min = np.min(img)
val_max = np.max(img)
for k in range(0, npts - 1, 1):
M = np.where(r > nbin * k, O, Z) * np.where(
r < nbin * (k + 1), O, Z)
Profile[k] = np.sum(img * M) / np.sum(M)
plt.figure(disp)
plt.subplot(121)
plt.imshow(img, interpolation='none')
plt.pause(.005)
plt.imshow(img * M,
interpolation='none',
vmin=val_min,
vmax=val_max)
plt.pause(.005)
plt.subplot(122)
plt.plot(Profile, 'rx')
plt.yscale('log')
plt.plot(Profile, 'r')
plt.subplot(121)
plt.imshow(img, interpolation='none', vmin=val_min, vmax=val_max)
for k in range(0, npts - 1, 1):
M = np.where(r > nbin * k, O, Z) * np.where(r < nbin * (k + 1), O, Z)
Profile[k] = np.sum(img * M) / np.sum(M)
return Profile
# Convert to numpy
np_wilayah = np.array(list_wilayah)
np_jokowi= np.array(jokowi)
np_prabowo= np.array(prabowo)
# plot data
fig,ax = plt.subplots(figsize=(10,5))
# fig,ax = plt.subplots()
# print(ax)
pos = list(range(len(np_jokowi)))
width = 0.25
# print(ind-width/2)
ax.bar(pos,np_jokowi,width,color='green',label='Jokowi')
ax.bar([p + width for p in pos],np_prabowo,width,color='red',label='Prabowo')
# ax.set_xticks(ind)
ax.set_xticks([p + 0.5 * width for p in pos])
ax.set_xticklabels(np_wilayah)
# # Naming label
plt.xlabel('provinsi')
plt.ylabel('perolehan suara')
# # styling x,y value
plt.yticks(np.arange(np_jokowi.min(),np_jokowi.max(),4000000))
plt.xticks(rotation='vertical',ha='right')
plt.legend(loc='upper right')
plt.yscale('linear')
plt.show()
h = scipy.optimize.curve_fit(fitfunc_gnfs,
np.array([i[0] for i in timestamps]),
np.array([i[1] for i in timestamps]),
p0=p0_gnfs)
plt.plot(range_gnfs,
fitfunc_gnfs(range_gnfs, *h[0]),
color='green',
linestyle='--')
elif enable_fits:
print("Too few points for fitting NFS")
except TypeError:
print("Error encountered when doing the curve fit")
plt.grid()
plt.yscale(
"log",
subsy=3600 *
np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]))
plt.yticks(YTICK_POSITION, YTICK_LABELS)
plt.xlabel("log10(n)")
if not ZOOM:
plt.xticks(XTICK_POSITION, XTICK_LABELS)
plt.xlim(80, None)
plt.ylim(60, None)
else:
plt.xlim(*ZOOM_X_LIMITS)
plt.ylim(*ZOOM_Y_LIMITS)
if SAVE_FIGURE:
plt.savefig(FILENAME, dpi=DPI)
]
#'darkolivegreen','darkmagenta','aquamarine','coral','burlywood',
#'beige','darkorange','crimson','darkcyan','bisque'
Ncolor = len(colo)
colo = colo * 10
styl = ['-'] * Ncolor + ['--'] * Ncolor + [':'] * Ncolor + ['-.'] * Ncolor * 7
widt = [2] * Ncolor * 10
#================== temperature-pressure structure ====================
fig, ax = plt.subplots()
plt.plot(Tg, press / bar, lw=4)
plt.xlabel(r'$T\ \mathrm{[K]}$', fontsize=20)
plt.ylabel(r'$p\ \mathrm{[bar]}$', fontsize=20)
plt.xlim(Tmin, Tmax)
plt.ylim(pmin, pmax)
if (pmax > pmin * 5): plt.yscale('log')
plt.tick_params(axis='both', labelsize=15)
plt.tick_params('both', length=6, width=1.5, which='major')
plt.tick_params('both', length=3, width=1, which='minor')
minorLocator = MultipleLocator(sep)
ax.xaxis.set_minor_locator(minorLocator)
cAl2O3 = [
14.1611, -2.8238E+04, -7.3843E-01, -3.7413E-07, 2.2086E-11, 2421, 3253
]
cSiO2 = [
-378.5210, 6.5473E+03, 1.3150E+02, -3.5774E-02, 3.4220E-06, 1883, 2503
]
cH2O = [29.8605, -3.1522E+03, -7.3037E+00, 2.4247E-09, 1.8090E-06, 273, 647]
cFe = [11.5549, -1.9538E+04, -6.2549E-01, -2.7182E-09, 1.9086E-13, 1808, 3008]
cNH3 = [37.1575, -2.0277E+03, -1.1601E+01, 7.4625E-03, -9.5811E-12, 195, 406]
cCO2 = [35.0187, -1.5119E+03, -1.1335E+01, 9.3383E-03, 7.7626E-10, 216, 305]
def process_input(para):
#dir_prefix = os.getcwd()
#os.chdir(dir_prefix + '/Box Sync/python_garage')
t_start_reading = os.times()[-1]
plt.close("all")
print('='*50)
plt.ioff()
# ==============================================================
# ============= parameter initialization =======================
# ==============================================================
Q1, Q2, bound, beta, J21, J12 = 100000.0, 100000.0, 2e-4, 5e-3, 0.0001, 0.001 # default values
gain, omega2 = 1e3, 1.1 # default values
Q1, Q2= 122680.0, 86505.0
omega2 = 1.0539 * 3 # here is to the 1:3 model
bound = 1e-4 # this is 'optimized' value, given Q1, Q2, and omega2
J21 = 7.6e-4 # this is 'optimized' value, given Q1, Q2, and omega2, bound
save_file = 0
save_file_FFT = 0
doRTSA = 0
doPlot = 1
#para_list = np.logspace(-4,-2,50)
#para_list = [0.0001]
reference = int(max(Q1, Q2))
time = np.linspace(0, 5*reference, 50*reference+1)
#time = np.linspace(0, 1000, 10000+1); # for debugging purpose
# ============ coupled oscillator case ============
# print('='*50)
print('Starting with parameter = {}'.format(para));
# ==============================================================
# ============= parameter setting! =============================
# ==============================================================
J12 = para
J21 = para
temp_folder = 'PythonParallelTemp_1_to_3_Js'
if not os.path.exists(temp_folder):
os.makedirs(temp_folder)
temp_file = os.path.join(os.getcwd(),temp_folder) + '/ss_Js_' + str(para) + '.png'
# ==============================================================
# ==============================================================
x1Int, v1Int, x2Int, v2Int = 0.1, 0, 0, 0
t_start_ss = os.times()[-1]
Assol = sp.integrate.odeint(solvr_nonlinear_osc_2modes, [x1Int, v1Int, x2Int, v2Int], time,
args = (Q1, Q2, omega2, beta, gain, bound, J21, J12)); # solve the equations!
AsFinal = Assol[-1]
if doPlot:
plt.figure(1)
plt.subplot(211)
plt.plot(time[::100], Assol[::100,0], 'b-')
plt.xlabel('time'); plt.ylabel('displacement'); plt.title('mode 1')
plt.subplot(212);
plt.plot(time[::100], Assol[::100,2], 'g-')
plt.xlabel('time'); plt.ylabel('displacement'); plt.title('mode 2')
#plt.show()
plt.savefig(temp_file)
plt.close(1)
freq1, fft_out1 = myFFT(time[-int(0.1*len(time)):], Assol[-int(0.4*len(time)):,0]);
# only last 10% of the time-domain data, which assuming to have reached steady-state
freq2, fft_out2 = myFFT(time[-int(0.1*len(time)):], Assol[-int(0.4*len(time)):,2]);
# only last 10% of the time-domain data, which assuming to have reached steady-state
if doPlot:
plt.figure(11)
#plt.plot(2*np.pi*freq1, abs(fft_out1),'b-')
plt.plot(2*np.pi*freq1, fft_out1.real,'bo-', 2*np.pi*freq2, fft_out2.real,'go-')
plt.xlim([1.0, 1.1])
plt.grid(b=True, which='both', color='0.65',linestyle='--')
plt.title('Steady-state')
plt.xlabel('Frequency (Hz)');
plt.ylabel('FFT amplitude (a.u.)'); plt.yscale('log')
#plt.show()
plt.savefig(os.path.join(os.getcwd(),temp_folder) + '/ss_FFT_Js_' + str(para) + '.png')
#print; print 'The FFT resolution for steady-state is {} Hz'.format(2*np.pi*mode(np.diff(freq2))[0][0])
#print; print 'The maximum in FFT for steady-state happens at {} Hz'.format(2*np.pi*freq2[fft_out2.argmax()]);
if save_file == 1:
np.savetxt(os.path.join(os.getcwd(),temp_folder) + '/ss_Js_' + str(para) + '.txt',
np.hstack([time[:,np.newaxis], Assol]))
#if save_file_FFT == 1:
# if not os.path.exists(temp_folder):
# os.makedirs(temp_folder)
# idx_low, idx_high = np.argmax(2*np.pi*freq1 > 0.5), np.argmin(2*np.pi*freq1 < 1.7)
# freq1, fft_out1, fft_out2 = freq1[idx_low:idx_high], fft_out1[idx_low:idx_high], fft_out2[idx_low:idx_high]
# to_save = np.vstack([2*np.pi*freq1, np.log(fft_out1.real), np.log(fft_out2.real), para*np.ones(freq1.shape[0])])
# to_save = np.transpose(to_save)
# with open(os.path.join(os.getcwd(),temp_folder) + '/' + str(para) + '.txt','w') as FFT_file:
# np.savetxt(FFT_file, to_save, fmt='%10.6f')
# print; print 'Saved data with parameter = {}'.format(para)
# #return to_save
print; print('elasped time for calculating single steady-state is {} seconds'.format(os.times()[-1] - t_start_ss));
# ===================================================================
# ================= Below I will start the ringdown =================
# ===================================================================
t_start_ss = os.times()[-1]
#time = np.linspace(0,5001,10*5001)
Ringdown = sp.integrate.odeint(solvr_nonlinear_osc_2modes, AsFinal, time,
args = (Q1, Q2, omega2, beta, gain, 0.0, J21, J12)); # solve the ringdown where bound is set to zero
if doPlot:
plt.figure(3)
plt.subplot(211)
plt.plot(time[::100], Ringdown[::100,0], 'b-', time[::100], max(Ringdown[:100,0])*np.exp(-time[::100]/2/Q1),'r--')
plt.xlabel('time'); plt.ylabel('displacement'); plt.title('Ringdown, mode 1')
#plt.yscale('log')
plt.subplot(212);
plt.plot(time[::100], Ringdown[::100,2], 'g-',time[::100], max(Ringdown[:100,2])*np.exp(-time[::100]/2/Q2),'r--')
plt.xlabel('time'); plt.ylabel('displacement'); plt.title('Ringdown, mode 2')
#plt.yscale('log')
#plt.show()
temp_file_2 = os.path.join(os.getcwd(),temp_folder) + '/rd_Js_' + str(para) + '.png'
plt.savefig(temp_file_2)
plt.close(3)
#idx_temp = np.argmax(time>5000)
##idx_temp = time.shape[0]
#freq1, fft_out1 = myFFT(time[:idx_temp], Ringdown[:idx_temp,0]);
## only the first 5000 seconds
#
#freq2, fft_out2 = myFFT(time[:idx_temp], Ringdown[:idx_temp,2])
## only the first 5000 seconds
#
#plt.figure(31)
##plt.plot(2*np.pi*freq1, abs(fft_out1),'b-')
#plt.plot(2*np.pi*freq1, fft_out1.real,'bo-', 2*np.pi*freq2, fft_out2.real,'go-')
#plt.xlim([0.8, 2])
#plt.grid(b=True, which='both', color='0.65',linestyle='--')
#plt.title('Ringdown...')
#plt.xlabel('Frequency (Hz)');
#plt.ylabel('FFT amplitude (a.u.)');plt.yscale('log')
#
#plt.show()
#print; print 'The FFT resolution for ringdown is {} Hz'.format(2*np.pi*mode(np.diff(freq2))[0][0])
#
#pass
#plt.figure(2)
#plt.plot(time, Assol[:,2])
#plt.xlabel('time'); plt.ylabel('displacement'); plt.title('mode 2');
#plt.show()
if save_file == 1:
np.savetxt(os.path.join(os.getcwd(),temp_folder) + '/rd_Js_' + str(para) + '.txt'
,np.hstack([time[:,np.newaxis], Ringdown]))
#print; print 'elasped time for total is {} seconds'.format(os.times()[-1] - t_start_reading);
if doRTSA:
RTSA(time, Ringdown[:,0], 100)
prettify_figure('Frequency', 'time', 'real-time spectrum, ringdown, mode 1')
plt.ylim([1.0, 1.1])
plt.yscale('linear')
plt.savefig(os.path.join(os.getcwd(),temp_folder) + '/RTSA_mode1_Js_' + str(para) + '.png', dpi = 400)
RTSA(time, Ringdown[:,2], 200)
prettify_figure('Frequency', 'time', 'real-time spectrum, ringdown, mode 2')
plt.ylim([1.0, 1.1])
plt.yscale('linear')
plt.savefig(os.path.join(os.getcwd(),temp_folder) + '/RTSA_mode2_Js_' + str(para) + '.png', dpi = 400)
print('elasped time for calculating single ringdown is {} seconds'.format(os.times()[-1] - t_start_ss));
def both_hazard(self, true, fitted, coefs):
"""
Plots the hazard function
:param true: pickle True hazard file (*.pickle)
:param fitted: pickle Fitted hazard file (*.pickle)
:param coefs: pickle Coefficients of fitted hazard
:return: figure object
"""
hazard_fit = fitted["hazard_fit"]
hazard_s = fitted["s"]
hazard_T = fitted["T"]
im, s, apoe = true
fig, ax = plt.subplots(figsize=(6, 4), dpi=100)
if self.period:
for i in range(len(self.period)):
# ID corresponding to the period
idx = int(self.period[i] * 10)
# Labels
labelAdd = f"PSHA, T={self.period[i]:.2f}s" if self.period[
i] > 0.0 else "PSHA, PGA"
labelAdd_2nd = r"Sa(%.2fs), $2^{nd}$ order fit" % self.period[i] if self.period[i] > 0.0 \
else r"PGA, $2^{nd}$ order fit"
labelAdd_1st = r"Sa(%.2fs), $1^{st}$ order fit" % self.period[i] if self.period[i] > 0.0 \
else r"PGA, $1^{st}$ order fit"
# Tag for the hazard to select
if self.period[i] > 0.0:
try:
hazardTag = f"SA({self.period[i]:.2f})"
apoeFit = np.array(hazard_fit[hazardTag])
coef = coefs[hazardTag]
except:
hazardTag = f"SA({self.period[i]:.1f})"
apoeFit = np.array(hazard_fit[hazardTag])
coef = coefs[hazardTag]
else:
hazardTag = "PGA"
apoeFit = np.array(hazard_fit[hazardTag])
coef = coefs[hazardTag]
# 1st order fits
h1, hx, saT1x = self.linearFit(coef, hazard_s)
# Plotting
plt.scatter(s[idx],
apoe[idx],
color=self.color_grid[i],
label=labelAdd,
marker=self.markers[i])
plt.loglog(hazard_s,
apoeFit,
color=self.color_grid[i],
label=labelAdd_2nd)
plt.loglog(hazard_s,
h1,
color=self.color_grid[i],
label=labelAdd_1st,
ls="--",
lw=0.8)
if i == 0:
labelScatter = "Fitting points"
else:
labelScatter = None
plt.scatter(saT1x,
hx,
marker='x',
c='k',
zorder=10,
s=40,
label=labelScatter)
if self.rp:
for i in self.rp:
plt.plot([0.01, 10.1], [1 / i, 1 / i],
ls='--',
color=self.gray)
self.add_text(ax,
0.011,
1.5 / i,
f'Return period: {i} years',
color=self.gray,
ha='left')
plt.ylabel('Annual probability of\n exceedance, ' + r'$H$',
fontsize=12)
plt.xlabel(r'Intensity, $s$ [g]', fontsize=12)
plt.ylim(10e-6, 1)
plt.xlim(0.01, 10.1)
plt.xticks([])
plt.xticks(np.array([0.01, 0.1, 1.0, 10]))
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.legend(frameon=False,
loc='upper right',
fontsize=12,
bbox_to_anchor=(1.55, 1))
plt.xscale("log")
plt.yscale("log")
plt.grid(True, which="major", ls="--", lw=0.4, dashes=(5, 10))
return fig
sig_ = -sigma_data[:, 1]
Vg_true.append(V_)
avg_tflux = numpy.array(avg_tflux)
fig = plt.figure(figsize=(4, 4))
plt.style.use("science")
for i, conc in enumerate(concentrations):
plt.plot(Vg_true[i],
avg_tflux[i, :],
"-o",
markersize=5,
label="{} mol/L$^3$".format(conc))
plt.yscale("log")
plt.xlabel("True $V_{g}$ (V)")
plt.xlim(0, 1.25)
plt.ylabel("Average total flux (mol/(m$^{-2}$*s))")
plt.legend()
plt.savefig(os.path.join(plot_path, "rect_Vg_no_correction.svg"))
# d_debye = Debye_length(numpy.array(concentrations)) / 1e-9
# rect = 1 - avg_tflux[:, 7] / avg_tflux[:, 0]
# plt.figure(figsize=(4, 4))
# plt.plot(d_debye, rect, "-s", markersize=5)
# plt.xlabel("Debye Length (nm)")
# plt.ylabel("Rectification ratio")
# plt.savefig(os.path.join(plot_path, "rect_debye_.pdf"))
w=[]
x=[]
with open(directory[0]+"/res/"+directory[1]) as f:
for j in range(30):
v=[]
for i in range(30):
v.append(float(f.readline().split()[3]))
f.readline()
w.append(v)
arr=np.array(w)
plt.plot(range(1000,31000,1000),
np.mean(arr,axis=1),
linewidth=2,color=trans[directory[0]][directory[1][-2:]])
labels.append(directory[0]+" d="+directory[1][-2:]+"%")
plt.yscale('log')
plt.xticks(range(0,31000,10000))
plt.xlabel('N')
plt.ylabel('time(s)')
plt.grid(False)
plt.subplots_adjust(bottom=0.15,right=0.65)
#plt.legend(labels,loc=4)
plt.legend(labels,loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
labels=[]
for directory in product(lista,files):
w=[]
x=[]
with open(directory[0]+"/res/"+directory[1]) as f:
for j in range(30):
#error_avg[r - loop_start + 1] = np.append(error_avg[r - loop_start + 1], sim_env.error / testing)
# error_avg[r - loop_start + 2] = [sim_env.error/testing] * episodes
# error_optimum_avg_all = np.sum(sim_env.loss) / testing_comps
# print(error_avg[r - loop_start +2])
for i in range(loop_end - loop_start + 1):
plt.figure(i)
#plt.plot(error_optimum[0], '-b.',error_optimum[1], '-r.', error_optimum[2], '-g.', markersize=1)
plt.plot(error, 'b.', markersize=1.5)
plt.xlabel('Cycles')
plt.ylabel('eps_RL')
plt.title('Absolute Error probability'.format(loop_start + i))
#plt.legend(['K=2', 'K=3', 'K=4'])
plt.axis([0, episodes * testing, 0, 10**2])
#ax1.set_yscale('symlog', nonposy='clip', linthreshy=0.01)
plt.yscale('symlog', nonposy='clip', linthreshy=10**-8)
plt.grid()
plt.figure(loop_end - loop_start + 2)
#plt.plot(error_optimum[0], '-b.',error_optimum[1], '-r.', error_optimum[2], '-g.', markersize=1)
plt.plot(loss_overall, '-b.', markersize=1.5)
plt.xlabel('Episodes')
plt.ylabel('Loss')
plt.title('Average Loss for 250 training cycles')
#plt.legend(['K=2', 'K=3', 'K=4'])
#plt.axis([0, episodes*testing, 0, 10**2])
#ax1.set_yscale('symlog', nonposy='clip', linthreshy=0.01)
#plt.yscale('symlog', nonposy='clip', linthreshy=10**-8)
plt.grid()
plt.figure(loop_end - loop_start + 4)
def fit_airy_2D(img, disp=0):
''' Fits a 2D Airy pattern on the image.
Returns the best fit parameters of the Airy pattern.
See twoD_Airy(xy, amplitude, xo, yo, sigma_x, sigma_y, theta, offset)
'''
nx = img.shape[1]
ny = img.shape[0]
x = np.linspace(0, nx - 1, nx)
y = np.linspace(0, ny - 1, ny)
x, y = np.meshgrid(x, y)
xy = (x, y)
init_xmax = np.unravel_index(img.argmax(), img.shape)[1]
init_ymax = np.unravel_index(img.argmax(), img.shape)[0]
initial_guess = (img.max(), init_xmax, init_ymax, .4)
#initial_guess = (img[init_xmax, init_ymax] , init_xmax, init_ymax, .6)
#plt.figure(27)
#plt.imshow(twoD_Airy(xy, img[init_xmax, init_ymax] , init_xmax, \
# init_ymax, .6).reshape(nx,ny))
popt, pcov = opt.curve_fit(twoD_Airy, xy, img.ravel(), p0=initial_guess)
if disp != 0:
data_fitted = twoD_Airy(xy, *popt)
#offset=np.min(img)
plt.figure(disp)
plt.clf()
plt.subplot(1, 3, 1)
plt.imshow(data_fitted.reshape(nx,ny), interpolation='none', \
cmap=cm.Greys_r)
plt.colorbar()
plt.subplot(1, 3, 2)
plt.plot(img[popt[1], :])
plt.plot(data_fitted.reshape(nx, ny)[popt[1], :], 'r--')
plt.yscale('log')
plt.subplot(1, 3, 3)
plt.plot(img[:, popt[2]])
plt.plot(data_fitted.reshape(nx, ny)[:, popt[2]], 'r--')
plt.yscale('log')
plt.figure(disp + 1)
plt.clf()
plt.subplot(121)
plt.imshow(data_fitted.reshape(nx,ny), interpolation='none', \
cmap=cm.Greys_r)
plt.colorbar()
P_fit=get_radial_profile(data_fitted.reshape(nx,ny), (popt[1], \
popt[2]), 1, disp=10)
P_mes = get_radial_profile(img, (popt[1], popt[2]), 1, disp=0)
plt.subplot(122)
plt.plot(P_mes, 'b')
plt.plot(P_fit, 'r--')
plt.yscale('log')
print('\n--- Airy disk fit results ---')
print('Amplitude =' + str(popt[0]))
print('Position of the maximum: \nxo=' + str(popt[1]) + ' nyo=' +
str(popt[2]))
print('F factor=' + str(popt[3]))
print('-----------------------------')
return popt
def modelling(datadir):
""" modelling """
edi_file = os.path.join(datadir, r"synth02.edi")
save_path = os.path.join(datadir, 'TM')
allfiles = glob.glob(save_path + '\\*.*')
for i in allfiles:
os.remove(i)
try:
os.remove(save_path + '\\Model1D')
os.remove(save_path + '\\OccamStartup1D')
except:
pass
d1 = occam1d.Data()
d1.write_data_file(edi_file=edi_file,
mode='TE',
save_path=save_path,
res_err='data',
phase_err='data',
res_errorfloor=4.,
phase_errorfloor=2.,
remove_outofquadrant=True)
m1 = occam1d.Model(target_depth=40000,
n_layers=100,
bottom_layer=100000,
z1_layer=10)
m1.write_model_file(save_path=d1.save_path)
#--> make a startup file
s1 = occam1d.Startup(data_fn=d1.data_fn,
model_fn=m1.model_fn,
max_iter=200,
target_rms=1.0)
s1.write_startup_file()
#--> run occam1d from python
occam_path = r"C:\Work\Programming\pygmi\pygmi\bin\occam1d.exe"
occam1d.Run(s1.startup_fn, occam_path, mode='TM')
#--plot the L2 curve
# l2 = occam1d.PlotL2(d1.save_path, m1.model_fn,
# fig_dpi=100,
# fig_size=(2,2)
# )
#--> see that iteration 7 is the optimum model to plot
iterfn = os.path.join(save_path, 'TM_005.iter')
respfn = os.path.join(save_path, 'TM_005.resp')
p1 = occam1d.Plot1DResponse(
data_te_fn=d1.data_fn,
model_fn=m1.model_fn,
iter_te_fn=iterfn,
resp_te_fn=respfn,
# depth_limits=(0,20),
fig_dpi=100,
fig_size=(2, 2))
oc1m = occam1d.Model(model_fn=m1.model_fn)
allfiles = glob.glob(save_path + '\*.iter')
roughness = []
rms = []
for iterfn in allfiles:
oc1m.read_iter_file(iterfn)
roughness.append(float(oc1m.itdict['Roughness Value']))
rms.append(float(oc1m.itdict['Misfit Value']))
roughness.pop(0)
rms.pop(0)
plt.plot(roughness, rms)
plt.xlabel('Roughness')
plt.ylabel('RMS')
plt.show()
plt.plot(rms)
plt.xlabel('Iteration')
plt.ylabel('RMS')
plt.show()
iterfn = os.path.join(save_path, 'TM_005.iter')
respfn = os.path.join(save_path, 'TM_005.resp')
oc1m.read_iter_file(iterfn)
oc1d = occam1d.Data(data_fn=d1.data_fn)
oc1d.read_resp_file(respfn)
depths = []
res = []
for i, val in enumerate(oc1m.model_res[:, 1]):
if i == 0:
continue
if i > 1:
depths.append(-oc1m.model_depth[i - 1])
res.append(val)
depths.append(-oc1m.model_depth[i])
res.append(val)
plt.plot(res, depths)
plt.xlabel('Res')
plt.ylabel('Depth')
plt.show()
plt.plot(1 / oc1d.freq, oc1d.data['resxy'][0], 'bs')
plt.plot(1 / oc1d.freq, oc1d.data['resxy'][2], 'r')
plt.xscale('log')
plt.yscale('log')
plt.grid(True)
plt.show()
plt.plot(1 / oc1d.freq, oc1d.data['phasexy'][0], 'bs')
plt.plot(1 / oc1d.freq, oc1d.data['phasexy'][2], 'r')
plt.xscale('log')
plt.yscale('log')
plt.grid(True)
plt.show()
breakpoint()
norm_labels = {'L2': '$L_2$', 'Linf': '$L_\infty$'}
for field_name in field_names:
for norm in norms:
differences = [
case.get_difference(exact, field_name, mask=mask, norm=norm)
for case in simulations
]
ax.plot(grid_spacings,
differences,
label='{} - {}-norm'.format(field_name, norm_labels[norm]),
marker='o',
zorder=10)
ax.legend()
ax.set_xlim(0.1 * min(grid_spacings), 10.0 * max(grid_spacings))
pyplot.xscale('log')
pyplot.yscale('log')
ax.axis('equal')
# save and display
if save_name:
print('[info] saving figure ...')
if not os.path.isdir(save_directory):
os.makedirs(save_directory)
time_step = simulations[0].fields[field_names[0]].time_step
pyplot.savefig(os.path.join(
save_directory, '{}{:0>7}.{}'.format(save_name, time_step, fmt)),
bbox_inches='tight',
dpi=dpi,
format=fmt)
if show:
print('[info] displaying figure ...')
pyplot.show()
def draw_dynamic_compare():
'''
draw the comparison of DLP and ICDE'14
'''
g_names = []
dlp_time = []
dlp_mem = []
icde14_time = []
icde14_mem = []
# fill the data
# load data
for data in datasets:
g_names.append(data_names[data])
dlp_data = parse_dynamic_local_pushfile(DYNAMIC_LOCAL_PUSH_DIR + data + ".txt")
icde_file = ICDE14_DIR + data + ".txt"
icde14_data = {"time": 0, "mem": 0}
if os.path.exists(icde_file):
icde14_data = parse_icde14_file(icde_file)
print(dlp_data, icde14_data)
dlp_time.append(dlp_data["time"])
dlp_mem.append(dlp_data["mem"])
icde14_time.append(icde14_data["time"])
icde14_mem.append(icde14_data["mem"])
# draw the cpu figures
size_of_fig = (FIG_SIZE_MULTIPLE)
fig, ax = plt.subplots()
N = len(g_names)
width = 0.35 # the width of the bars
ind = 1.2 * np.arange(N) # the x locations for the groups
dlp_cpu_bar = ax.bar(ind, dlp_time, width, hatch="\\", label="DLP", fill=False)
icde14_cpu_bar = ax.bar(ind + width, icde14_time, width, hatch="-", label="Inc-SR", fill=False)
plt.yscale('log') # log scale of y
# plt.ylim(ymin=-2) # this line
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(g_names, fontsize=LABEL_SIZE)
ax.set_ylabel("Avg Update Time(s)", fontsize=LABEL_SIZE)
plt.xticks(fontsize=TICK_SIZE)
plt.yticks(fontsize=TICK_SIZE)
fig.set_size_inches(*size_of_fig) # set ratio
plt.legend(prop={'size': LEGEND_SIZE, "weight": "bold"}, loc=2)
fig.savefig("./figures/" + 'dynamic_cpu_compare.pdf', bbox_inches='tight', dpi=300)
# draw the memory figure
fig, ax = plt.subplots()
N = len(g_names)
width = 0.35 # the width of the bars
ind = 1.2 * np.arange(N) # the x locations for the groups
dlp_mem_bar = ax.bar(ind, np.array(dlp_mem), width, \
hatch="\\", label="DLP", fill=False)
icde14_mem_bar = ax.bar(ind + width, np.array(icde14_mem), width, \
hatch="-", label="Inc-SR", fill=False)
plt.yscale('log') # log scale of y
# plt.ylim(ymin=-2) # this line
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(g_names, fontsize=LABEL_SIZE)
ax.set_ylabel("Memory Usage(MB)", fontsize=LABEL_SIZE)
plt.xticks(fontsize=TICK_SIZE)
plt.yticks(fontsize=TICK_SIZE)
fig.set_size_inches(*size_of_fig) # set ratio
plt.legend(prop={'size': LEGEND_SIZE, "weight": "bold"}, loc=2)
fig.savefig("./figures/" + 'dynamic_mem_compare.pdf', bbox_inches='tight', dpi=300)
return
p.figure(1, (6,6))
p.axes([0.2,0.18,0.75,0.75])
p.plot(xf,phi_h(10**xf,z_mean), c='cyan' , ls='dashed', lw=2, label='Ai15')#Aird 2-10 keV LADE')
#p.fill_between(xf, y1=(nall)*(1-(nallN)**(-0.5)), y2=(nall)*(1+(nallN)**(-0.5)), color='r', alpha=0.7, label='Soft mock')# 0.5-2 keV')
if len(Lx_c[ok_hasinger])>0:
p.fill_between(Lx_c[ok_hasinger], y1=phi[ok_hasinger]*(1-(Nobj[ok_hasinger])**(-0.5)), y2=phi[ok_hasinger]*(1+(Nobj[ok_hasinger])**(-0.5)), color='G', alpha=0.7, label='Ha05', lw=2)
if z_mean[0]>1 and z_mean[0]<1.5:
p.fill_between(log_LX_hard, y1=10**log_dndv_low, y2=10**log_dndv_up, color='g', alpha=0.7, label='Bu15', lw=2)
log_LX_hard, log_dndv_up, log_dndv_low
#nall, nallN, vol = get_nzs(LX_soft, redshift_R, fbins, zmin, zmax, simulation_area)
#p.fill_between(xf, y1=(nall)*(1-(nallN)**(-0.5)), y2=(nall)*(1+(nallN)**(-0.5)), color='g', alpha=0.7, label='Mock', lw=2)# 2-10 keV')
nall, nallN, vol = get_nzs(data_RXS.data['RXS_LOGLX'][data_RXS.observed], data_RXS.data['Z_BEST'][data_RXS.observed], fbins, zmin, zmax, area_eboss_dr16)
p.fill_between(xf, y1=(nall)*(1-(nallN)**(-0.5)), y2=(nall)*(1+(nallN)**(-0.5)), color='r', alpha=0.7, label='2RXS', lw=2)# 2-10 keV')
nall, nallN, vol = get_nzs(data_XMM.data['XMMSL2_LOGLX_FULL'][data_XMM.observed], data_XMM.data['Z_BEST'][data_XMM.observed], fbins, zmin, zmax, area_eboss_dr16)
p.fill_between(xf, y1=(nall)*(1-(nallN)**(-0.5)), y2=(nall)*(1+(nallN)**(-0.5)), color='b', alpha=0.7, label='XMMSL2', lw=2)# 2-10 keV')
p.xlabel(r'$\log_{10}(L_X/[erg/s])$')
p.ylabel(r'$\Phi$=dN/dlogL/dV [1/Mpc$^3$/dex]')
#if zmin<0.1:
p.legend(loc=3, fontsize=14)
p.yscale('log')
p.xlim((42., 47.5))
p.ylim((1/(2*vol),1e-3))
p.title(str(np.round(zmin,2))+"<z<"+str(np.round(zmax,2)))
p.grid()
p.savefig(os.path.join(figure_dir, "XLF_soft_"+str(np.round(zmin,2))+"_"+str(np.round(zmax,2))+".png"))
p.clf()
def plot_current_field(self,
xlim=None,
flim=None,
log=False,
wrap_order=0):
beam = self
# XXX Not sure why I have to copy, I suspect the fft
e = np.copy(beam.e[int(self.Nt / 2), :, :])
I = beam.intensity_from_field(e)
If = abs(fftshift(beam.fft(e)))**2
fx, fy = beam.get_f()
fx = fftshift(fx)
fy = fftshift(fy)
phase = np.angle(e)
# Images
X = beam.X
Y = beam.Y
ext = [-X / 2, X / 2, -Y / 2, Y / 2]
extf = [fx[0], fx[-1], fy[0], fy[-1]]
plt.figure(figsize=(16, 4), dpi=150)
plt.subplot(131)
plt.imshow(beam.prep_data(I),
aspect='auto',
extent=ext,
cmap='viridis')
cb = plt.colorbar()
cb.set_label(r'Intensity ($10^{14}$ W/cm^2)')
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'$y$ (um)')
if xlim != None:
plt.xlim(xlim)
plt.ylim(xlim)
if wrap_order == 0:
axis0 = 0
axis1 = 1
elif wrap_order == 1:
axis0 = 1
axis1 = 0
plt.subplot(132)
plt.imshow(np.unwrap(np.unwrap(beam.prep_data(phase), axis=axis0),
axis=axis1),
aspect='auto',
extent=ext,
cmap='viridis')
cb = plt.colorbar()
cb.set_label(r'Phase (rad)')
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'$y$ (um)')
if xlim != None:
plt.xlim(xlim)
plt.ylim(xlim)
plt.subplot(133)
plt.imshow(beam.prep_data(If),
aspect='auto',
extent=extf,
cmap='viridis')
cb = plt.colorbar()
cb.set_label(r'Intensity (arb unit)')
plt.xlabel(r'$f_x$ (um$^{-1}$)')
plt.ylabel(r'$f_y$ (um$^{-1}$)')
if flim != None:
plt.xlim(flim)
plt.ylim(flim)
plt.tight_layout()
plt.show()
# Lineouts
# We've already taken the transpose so y is the first index
indy = int(beam.Ny / 2)
indx = int(beam.Nx / 2)
x = beam.x
y = beam.y
plt.figure(figsize=(16, 4), dpi=150)
plt.subplot(131)
plt.plot(x, I[:, indy], label='y')
plt.plot(y, I[indx, :], 'm--', label='x')
plt.legend()
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'Intensity ($10^{14}$ W/cm^2)')
if xlim != None:
plt.xlim(xlim)
plt.subplot(132)
plt.plot(x, np.unwrap(phase[:, indy]), label='x')
plt.plot(y, np.unwrap(phase[indx, :]), 'm--', label='y')
plt.legend()
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'Phase (rad)')
if xlim != None:
plt.xlim(xlim)
plt.subplot(133)
plt.plot(fx, If[:, indy], label='x')
plt.plot(fy, If[indx, :], 'm--', label='y')
plt.legend()
plt.xlabel(r'$f_x$ (um$^{-1}$)')
plt.ylabel(r'Intensity (arb unit)')
if flim != None:
plt.xlim(flim)
plt.tight_layout()
plt.show()
if log == True:
# Lineouts
plt.figure(figsize=(16, 4), dpi=150)
plt.subplot(131)
plt.plot(x, I[:, indy], label='x')
plt.plot(y, I[indx, :], 'm--', label='y')
plt.legend()
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'Intensity ($10^{14}$ W/cm^2)')
plt.yscale('log')
if xlim != None:
plt.xlim(xlim)
plt.subplot(132)
plt.plot(x, np.unwrap(phase[:, indy]), label='x')
plt.plot(y, np.unwrap(phase[indx, :]), 'm--', label='y')
plt.legend()
plt.xlabel(r'$x$ (um)')
plt.ylabel(r'Phase (rad)')
plt.yscale('log')
if xlim != None:
plt.xlim(xlim)
plt.subplot(133)
plt.plot(fx, If[:, indy], label='x')
plt.plot(fy, If[indx, :], 'm--', label='y')
plt.legend()
plt.xlabel(r'$f_x$ (um$^{-1}$)')
plt.ylabel(r'Intensity (arb unit)')
plt.yscale('log')
if flim != None:
plt.xlim(flim)
plt.tight_layout()
plt.show()
output_num_units=10,
output_nonlinearity=lasagne.nonlinearities.softmax,
update=nesterov_momentum,
update_learning_rate=0.01,
update_momentum=0.9,
regression=False,
max_epochs=10,
verbose=1,
)
X, y = load() # load 2-d data
net2.fit(X, y)
# Training for 1000 epochs will take a while. We'll pickle the
# trained model so that we can load it back later:
import cPickle as pickle
with open('net2.pickle', 'wb') as f:
pickle.dump(net2, f, -1)
train_loss = np.array([i["train_loss"] for i in net2.train_history_])
valid_loss = np.array([i["valid_loss"] for i in net2.train_history_])
pyplot.plot(train_loss, linewidth=3, label="train")
pyplot.plot(valid_loss, linewidth=3, label="valid")
pyplot.grid()
pyplot.legend()
pyplot.xlabel("epoch")
pyplot.ylabel("loss")
pyplot.ylim(0, 1)
pyplot.yscale("log")
pyplot.show()
def gfl_cvx_2():
''' Graph Fused Lasso '''
numNode = 100
data = np.zeros((numNode, 2))
for k in range(11):
data[k] = data[k] + np.array([1, 1])
data[k + 11] = data[k + 11] + np.array([-1, 1])
data[k + 22] = data[k + 22] + np.array([2, 2])
data[k + 33] = data[k + 33] + np.array([-1, -1])
np.random.seed(1)
noise = 0.5 * np.random.normal(0, 1, (numNode, 2))
data = data + noise
graph = nx.grid_2d_graph(10, 10)
graph = nx.convert_node_labels_to_integers(graph)
print('graph:', list(graph.edges()))
temp_edges = []
for k in range(0, numNode):
for j in graph.neighbors(k):
if j == k + 1:
temp_edges.append([k, j])
graph0_edges = []
graph0_edges.append(temp_edges[0])
for edge in temp_edges:
if (edge[0] != graph0_edges[-1][1]) and (edge != graph0_edges[-1]):
graph0_edges.append(edge)
graph1_edges = [
edge for edge in list(graph.edges()) if edge not in graph0_edges
]
graph1_edges.sort()
graph0_nodes = np.unique(np.array(graph0_edges))
graph1_nodes = np.unique(np.array(graph1_edges))
graph0 = nx.Graph()
graph1 = nx.Graph()
graph0.add_nodes_from(graph0_nodes)
graph1.add_nodes_from(graph1_nodes)
graph0.add_edges_from(graph0_edges)
graph1.add_edges_from(graph1_edges)
print("graph0:", graph0_edges, "graph1:", graph1_edges)
# edgelist = [[k, k + 1] for k in range(numNode - 1)]
# graph = nx.Graph()
# graph.add_edges_from(edgelist)
# coloredge(graph)
# graph0, graph1 = decompose_graph(graph)
runtime = []
objerror = []
# rho_set = [2**k for k in range(7)]
rho_set = [1]
x_hat1, obj1, _, _ = solve_GroupFL(y=data,
G=graph,
G0=graph0,
G1=graph1,
maxsteps=20,
rho=1,
lam=1)
optobj = obj1[-1]
print(obj1[:20])
for rho in rho_set:
clock_time = time.time()
x_hat2, tevolution, obj, _ = solve_GroupFL_pool(y=data,
G=graph,
G0=graph0,
G1=graph1,
maxsteps=20,
rho=rho,
lam=1)
print('GraphLasso_pool:', time.time() - clock_time, ',rho:', rho)
objerror.append([i - optobj for i in obj])
runtime.append(tevolution)
print(obj[:20])
print((x_hat1 - x_hat2)[:10])
print([obj1[i] - obj[i] for i in range(len(obj))])
# plotting
plt.switch_backend('TkAgg')
plt.figure()
plt.plot(runtime[0], objerror[0], label=r'$\rho=2^0$')
# plt.plot(runtime[1], objerror[1], label=r'$\rho=2^1$')
# plt.plot(runtime[2], objerror[2], label=r'$\rho=2^2$')
# plt.plot(runtime[3], objerror[3], label=r'$\rho=2^3$')
# plt.plot(runtime[4], objerror[4], label=r'$\rho=2^4$')
# plt.plot(runtime[5], objerror[5], label=r'$\rho=2^5$')
# plt.plot(runtime[6], objerror[6], label=r'$\rho=2^6$')
plt.legend()
plt.xlim((0, 50))
plt.ylim((1e-5, 1e2))
plt.title('Graph Fused Lasso Algorithm (cvxpy)')
plt.xlabel('Running time (seconds)', fontsize=12)
plt.ylabel('Objective Value Error', fontsize=12)
plt.yscale('log')
def draw_static_compare():
'''
draw the static graph of comparison
draw two figures: one for time, one for memory
'''
# load data
c = 0.6
epsilon = 0.01
g_names = []
rlp_time = []
flp_time = []
pcg_time = []
rlp_mem = []
flp_mem = []
pcg_mem = []
lin_time = []
lin_mem = []
cloudwalker_time = []
cloudwalker_mem = []
for data in datasets:
g_names.append(data_names[data])
rlp_data = parse_exp_file(RLP_get_data_file_base(data, c, epsilon) + ".meta")
flp_data = parse_exp_file(FLP_get_data_file_base(data, c, epsilon) + ".meta")
lin_data = parse_lin_file(get_lineard_file(data))
cloudwalker_data = parse_lin_file(get_cloudwalker_file(data))
tkde_data = {}
print(rlp_data["time"], flp_data["time"])
print(rlp_data["mem"], flp_data["mem"])
# get tkde data
tkde_file = TKDE17_get_data_file_base(data, c, epsilon) + ".meta"
if (os.path.isfile(tkde_file)):
with open(tkde_file, "r") as f:
lines = f.readlines()
tkde_data["time"] = float(lines[0].strip())
tkde_data["mem"] = float(lines[1].strip())
else:
tkde_data["time"] = 0
tkde_data["mem"] = 0
print(rlp_data["time"], flp_data["time"], tkde_data["time"])
print(rlp_data["mem"], flp_data["mem"], tkde_data["mem"])
# file the data into the list
rlp_time.append(rlp_data["time"])
rlp_mem.append(rlp_data["mem"])
flp_time.append(flp_data["time"])
flp_mem.append(flp_data["mem"])
pcg_time.append(tkde_data["time"])
pcg_mem.append(tkde_data["mem"])
lin_time.append(lin_data["time"])
lin_mem.append(lin_data["mem"])
cloudwalker_time.append(cloudwalker_data["time"])
cloudwalker_mem.append(cloudwalker_data["mem"])
# draw the cpu figures
size_of_fig = (FIG_SIZE_MULTIPLE)
fig, ax = plt.subplots()
N = len(g_names)
width = 0.2 # the width of the bars
ind = 1.2 * np.arange(N) # the x locations for the groups
rlp_time_bar = ax.bar(ind, rlp_time, width, hatch="//", label="Opt-LP", fill=False)
flp_time_bar = ax.bar(ind + width, flp_time, width, hatch=".", label="FLP", fill=False)
lin_time_bar = ax.bar(ind + 3 * width, lin_time, width, hatch='x', label="LIN", fill=False)
cloudwalker_time_bar = ax.bar(ind + 2 * width, cloudwalker_time, width, hatch='-', label="MCAP", fill=False)
pcg_time_bar = ax.bar(ind + 4 * width, pcg_time, width, hatch='+', label="PCG", fill=False)
plt.yscale('log') # log scale of y
# plt.ylim(ymin=-2) # this line
ax.set_xticks(ind + 2 * width)
ax.set_xticklabels(g_names, fontsize=LABEL_SIZE)
ax.set_ylabel("CPU Time(s)", fontsize=LABEL_SIZE)
plt.xticks(fontsize=TICK_SIZE)
plt.yticks(fontsize=TICK_SIZE)
fig.set_size_inches(*size_of_fig) # set ratio
plt.legend(prop={'size': LEGEND_SIZE, "weight": "bold"}, loc="upper left", ncol=5) # number of algorithms
fig.savefig("./figures/" + 'static_cpu_compare.pdf', bbox_inches='tight', dpi=300)
# draw the memory figure
fig, ax = plt.subplots()
N = len(g_names)
ind = 1.2 * np.arange(N) # the x locations for the groups
rlp_mem_bar = ax.bar(ind, np.array(rlp_mem) / 1000, width, \
hatch="//", label="Opt-LP", fill=False)
flp_mem_bar = ax.bar(ind + width, np.array(flp_mem) / 1000, width, \
hatch=".", label="FLP", fill=False)
lin_mem_bar = ax.bar(ind + 3 * width, np.array(lin_mem) / 1000, width, hatch='x', label="LIN", fill=False)
cloudwalker_mem_bar = ax.bar(ind + 2 * width, np.array(cloudwalker_mem) / 1000, width, hatch='-', label="MCAP",
fill=False)
pcg_mem_bar = ax.bar(ind + 4 * width, np.array(pcg_mem) / 1000, width, \
hatch='+', label="PCG", fill=False)
plt.yscale('log') # log scale of y
# plt.ylim(ymin=-2) # this line
ax.set_xticks(ind + 2 * width)
ax.set_xticklabels(g_names, fontsize=LABEL_SIZE)
ax.set_ylabel("Memory Usage(MB)", fontsize=LABEL_SIZE)
plt.xticks(fontsize=TICK_SIZE)
plt.yticks(fontsize=TICK_SIZE)
fig.set_size_inches(*size_of_fig) # set ratio
plt.legend(prop={'size': LEGEND_SIZE, "weight": "bold"}, loc="upper left", ncol=5)
fig.savefig("./figures/" + 'static_mem_compare.pdf', bbox_inches='tight', dpi=300)
return
def plot_1d_hists(dim):
plot_label = dimension_labels[dim]
plt.figure(figsize=(12, 12))
plt.suptitle('step %d - Parameter - %s' % (e, plot_label))
plt.subplot(3, 3, 1)
#inital real
plt.title('Inital unseen test input', fontsize='x-small')
plt.hist(sample_to_test[:, 0, dim],
bins=50,
range=[
np.amin(sample_to_test[:, 0, dim]),
np.amax(sample_to_test[:, 0, dim])
])
plt.subplot(3, 3, 2)
#final real
plt.title('GEANT4 output', fontsize='x-small')
plt.hist(sample_to_test[:, 1, dim],
bins=50,
range=[
np.amin(sample_to_test[:, 1, dim]),
np.amax(sample_to_test[:, 1, dim])
],
color='g')
plt.subplot(3, 3, 4)
#inital fake
plt.title('Exact same test input', fontsize='x-small')
plt.hist(synthetic_test_output[:, 0, dim],
bins=50,
range=[
np.amin(sample_to_test[:, 0, dim]),
np.amax(sample_to_test[:, 0, dim])
])
plt.subplot(3, 3, 5)
#final fake
plt.title('GAN output - same range', fontsize='x-small')
plt.hist(synthetic_test_output[:, 1, dim],
bins=50,
range=[
np.amin(sample_to_test[:, 1, dim]),
np.amax(sample_to_test[:, 1, dim])
],
color='r')
plt.subplot(3, 3, 7)
#final fake - full range
plt.title('GAN output - full range', fontsize='x-small')
plt.hist(synthetic_test_output[:, 1, dim], bins=50, color='r')
plt.subplot(3, 3, 8)
#final fake
plt.title('Output overlays', fontsize='x-small')
plt.hist(
[sample_to_test[:, 1, dim], synthetic_test_output[:, 1, dim]],
histtype='step',
bins=50,
label=['GEANT4', 'GAN'],
range=[
np.amin(sample_to_test[:, 1, dim]),
np.amax(sample_to_test[:, 1, dim])
])
plt.legend(loc='upper right')
plt.tick_params(axis='y', which='both', labelsize=5)
plt.tick_params(axis='x', which='both', labelsize=5)
plt.subplot(3, 3, 9)
#final fake
plt.title('Output overlays', fontsize='x-small')
plt.hist(
[sample_to_test[:, 1, dim], synthetic_test_output[:, 1, dim]],
histtype='step',
bins=50,
label=['GEANT4', 'GAN'],
range=[
np.amin(sample_to_test[:, 1, dim]),
np.amax(sample_to_test[:, 1, dim])
])
plt.legend(loc='upper right')
plt.yscale('log')
plt.tick_params(axis='y', which='both', labelsize=5)
plt.tick_params(axis='x', which='both', labelsize=5)
plt.savefig('%s1D_hist_%d.png' % (output_location, dim),
bbox_inches='tight')
plt.savefig('%s%d/1D_hist_%d.png' % (output_location, dim, e),
bbox_inches='tight')
plt.close('all')
def do_fit(self, ):
# run emcee:
sampler = self.run_emcee()
# plot chains:
fig, axes = plt.subplots(self.ndim, figsize=(10, 7), sharex=True)
samples = sampler.get_chain()
labels = ["Fvb", "vb", "p", "log(f)"]
for i in range(self.ndim):
ax = axes[i]
ax.plot(samples[:, :, i], "k", alpha=0.3)
ax.set_xlim(0, len(samples))
ax.set_ylabel(labels[i])
ax.yaxis.set_label_coords(-0.1, 0.5)
axes[-1].set_xlabel("step number")
# plot 2D parameter posterior distributions:
burnin = self.burnin
flat_samples = sampler.get_chain(discard=burnin, thin=15, flat=True)
print(flat_samples.shape)
fig = corner.corner(flat_samples, labels=labels)
fig.savefig(f'{self.name}_2dposteriors.pdf')
print('----------------------------------------------------------')
print('MCMC results:')
# get autocorrelation time:
try:
tau = sampler.get_autocorr_time()
except:
print(
'**Warning** The chain is shorter than 50 times the integrated autocorrelation time for 4 parameter(s). Use this estimate with caution and run a longer chain!'
)
tau = np.inf
print(
f'The autocorrelation time is {tau}. You should run the chains for at least 10 x steps as this.'
)
# extract p plus uncertainties:
results = []
results_up = []
results_low = []
print('The MCMC fit parameters are:')
for i in range(self.ndim):
mcmc = np.percentile(flat_samples[:, i], [16, 50, 84])
q = np.diff(mcmc)
txt = "\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}"
txt = txt.format(mcmc[1], q[0], q[1], labels[i])
results.append(mcmc[1])
results_up.append(q[1])
results_low.append(q[0])
display(Math(txt))
print('----------------------------------------------------------')
# plot the observed and model spectra:
# plot flux density SED:
f = plt.figure(figsize=(8, 8))
vs = np.linspace(0, 30, 100)
emcee_flux = self.powerlaw(vs, results[0], results[1], results[2])
emcee_flux_up = self.powerlaw(vs, results_up[0], results_up[1],
results_up[2])
emcee_flux_low = self.powerlaw(vs, results_low[0], results_low[1],
results_low[2])
plt.scatter(self.frequency, self.flux_emission)
plt.errorbar(
self.frequency,
self.flux_emission,
yerr=[self.fd_err_up, self.fd_err_low],
fmt='.',
capsize=2,
)
plt.plot(vs, emcee_flux)
plt.plot(vs, emcee_flux + emcee_flux_up, color='grey', alpha=0.5)
plt.plot(vs, emcee_flux - emcee_flux_low, color='grey', alpha=0.5)
# plt.plot(frequency[5:], 4*frequency[5:]**-3)
# plt.axhline(y=np.max(flux_emission),label=r'F_p')
plt.xscale('log')
plt.yscale('log')
# plt.axis([1, 30, 0.5e-2,12e-2])
plt.xlabel('Frequency (GHz)')
plt.ylabel('Flux Density (mJy)')
plt.axvline(x=vs[np.where(emcee_flux == np.max(emcee_flux))],
ls='--',
color='grey')
plt.axhline(y=np.max(emcee_flux), ls='--', color='grey')
plt.savefig(f'{self.name}_model_spectrum.pdf')
# print peak flux, peak frequency, and p of spectrum:
print('----------------------------------------------------------')
print('The peak flux, peak frequency, and p of the spectrum are:')
up = emcee_flux_up[np.where(emcee_flux == np.max(emcee_flux))]
low = emcee_flux_low[np.where(emcee_flux == np.max(emcee_flux))]
Ferror = (up + low) / 2
print(f'Fp = {np.max(emcee_flux):.2f} +/- {Ferror[0]:.2f} mJy')
print(
f'vp = {vs[np.where(emcee_flux == np.max(emcee_flux))][0]:2f} GHz')
print(
f'p = {results[2]:.2f} +{results_up[2]:.2f} - {results_low[2]:.2f} '
)
print('----------------------------------------------------------')
Fvb = results[0]
Fvb_u = (results_up[0] + results_low[0]) / 2
vb = results[1]
vb_u = (results_up[1] + results_low[1]) / 2
p = results[2]
p_u = (results_up[2] + results_low[2]) / 2
Fp = np.max(emcee_flux)
Fp_u = Ferror[0]
vp = vs[np.where(emcee_flux == np.max(emcee_flux))][0]
return Fvb, vb, p, Fp, vp, Fvb_u, vb_u, p_u, Fp_u
def __get_aligned_nr_pn_amp_phase__(this,
lm,
kind,
plot=False,
verbose=False):
'''
Given optimal hybrid params for l=m=2, begin to estimate optimal phase alignment for PN
'''
# Import usefuls
from numpy import argmax, array, unwrap, angle, pi, mean, exp, arange
# Unpack l and m
l, m = lm
# Create shorthand for useful information
k0 = this.optimal_hybrid_params['k0']
dt = this.optimal_hybrid_params['dt']
mask = this.optimal_hybrid_params['mask']
T1 = this.optimal_hybrid_params['T1']
T2 = this.optimal_hybrid_params['T2']
phi22 = this.optimal_hybrid_params['phi0_22']
# Find the peak location for l=m=2 to be used for masking below
k_amp_max_22 = argmax(this.__unpacklm__((2, 2), kind)[1])
# Get the format aligned data for this multipole
t, nr_y, pn_y = this.__unpacklm__(lm, kind)
# Apply optimal time shift, NOTE the rescaling of phi22
pn_y, phi0 = this.__apply_hyb_params_to_pn__(pn_y,
nr_y,
k0 * dt,
MSK=mask,
TSMETH='index',
phi0=m * phi22 / 2)
# Begin phase alignment
__getphi__ = lambda X: unwrap(angle(X))
# Get phases aligned in mask region
def get_aligned_phi(a, b, __mask__):
'''Get phases aligned in mask region'''
A = __getphi__(a)
B = __getphi__(b)
L = pi / 2
n = round(mean(B[__mask__] - A[__mask__]) / L)
B -= n * L
return A, B
# Get NR phase
nr_phi = __getphi__(nr_y)
# Find a mask for the smoothest part
nr_smoothest_mask_amp = smoothest_part(abs(nr_y)[:k_amp_max_22])
nr_smoothest_mask_pha = smoothest_part(nr_phi[:k_amp_max_22])
# nr_smoothest_mask_pha = nr_smoothest_mask_amp
# Take the union of the amplitude and phase masks
nr_smoothest_mask = arange(
min(min(nr_smoothest_mask_amp), min(nr_smoothest_mask_pha)),
min(max(nr_smoothest_mask_amp), max(nr_smoothest_mask_pha)))
# Get the aligned phases (NOTE that nr_phi should not have changed)
# NOTE that below we wish to use T1 as the start of the alignment region if the entire NR waveform is erroneously deemed smooth (e.g. due to settings in smoothest_part)
nr_phi, pn_phi = get_aligned_phi(
nr_y, pn_y, nr_smoothest_mask[:10] if t[nr_smoothest_mask][0] > T1
else nr_smoothest_mask[t[nr_smoothest_mask] >= T1][:10])
# Get Amplitudes
nr_amp, pn_amp = abs(nr_y), abs(pn_y)
# --
# Impose scaling of PN amplitude to match NR to accomodate limitations of PN
# NOTE that this is an agressive approach
if this.aggressive:
# >>>
T1k = max(t[nr_smoothest_mask[0]], T1)
T2k = T1k + (T2 - T1) / this.optimal_hybrid_params['hybrid_cycles']
k = (t >= T1k) & (t <= T2k)
scale_factor = mean(nr_amp[k] / pn_amp[k])
if this.aggressive == 2:
warning(
'A scale factor of %f is applied to the PN amplitude.' %
scale_factor,
verbose=this.verbose)
pn_amp *= scale_factor
# >>>
phase_shift = mean(nr_phi[k] - pn_phi[k])
warning('The PN phase will be shifted by %f (rad).' % phase_shift)
pn_phi += phase_shift
# >>>
# --
# Plotting
if plot:
# Import usefuls
from matplotlib.pyplot import figure, figaspect, plot, xlim, ylim, xscale, subplot, show
from matplotlib.pyplot import yscale, axvline, axvspan, xlabel, ylabel, title, yscale, legend
# Create figure
fig = figure(figsize=3 * figaspect(4.0 / 7))
# Plot phases
subplot(2, 1, 2)
if this.aggressive:
# Hilight the aggressive alignment region
axvspan(T1k, T2k, color='lawngreen', alpha=0.2)
axvline(T1k, color='g', ls='--')
axvline(T2k, color='g', ls='--')
plot(t[nr_smoothest_mask],
nr_phi[nr_smoothest_mask],
color='k',
lw=3,
alpha=0.4,
ls='--')
plot(t, nr_phi)
plot(t, pn_phi, lw=1)
# Set plot limits
ylim(min(pn_phi), max(lim(nr_phi[nr_smoothest_mask], dilate=0.1)))
xlim(0, max(t[nr_smoothest_mask]))
# Highlight the hybridization region
axvspan(T1, T2, alpha=0.15, color='cyan')
axvline(T1, color='c', ls='--')
axvline(T2, color='c', ls='--')
# Set axis labels
xlabel(r'$t/M$')
ylabel(r'$\phi_{%i%i}=\arg(\psi_{%i%i})$' % (l, m, l, m))
# Plot amplitudes
subplot(2, 1, 1)
if this.aggressive:
# Hilight the aggressive alignment region
axvspan(T1k,
T2k,
color='lawngreen',
alpha=0.2,
label='Aggressive alignment region')
axvline(T1k, color='g', ls='--')
axvline(T2k, color='g', ls='--')
# Add amplitude lines
yscale('log')
plot(t[nr_smoothest_mask],
nr_amp[nr_smoothest_mask],
color='k',
lw=3,
alpha=0.4,
ls='--')
plot(t, nr_amp)
plot(t, pn_amp, lw=1)
# Set plot limits
ylim(min(pn_amp[pn_amp > 0]),
max(lim(nr_amp[nr_smoothest_mask], dilate=0.1)))
xlim(0, max(t[nr_smoothest_mask]))
# Highlight the hybridization region
axvspan(T1,
T2,
alpha=0.15,
color='cyan',
label='Final hybridization region')
axvline(T1, color='c', ls='--')
axvline(T2, color='c', ls='--')
# Set axis labels
ylabel(r'$|\psi_{%i%i}|$' % (l, m))
legend()
# xlabel(r'$t/M$')
# show()
# Package output
foo = {}
foo['nr_amp'] = nr_amp
foo['nr_phi'] = nr_phi
foo['pn_amp'] = pn_amp
foo['pn_phi'] = pn_phi
foo['nr_smoothest_mask'] = nr_smoothest_mask
foo['t'] = t
# Return aligned phases
return foo
def plot_p_against_pt(geant_array, gan_array):
geant_array = np.swapaxes(geant_array, 0, 1)[1]
gan_array = np.swapaxes(gan_array, 0, 1)[1]
geant_array = np.swapaxes(geant_array, 0, 1)
gan_array = np.swapaxes(gan_array, 0, 1)
trans_geant = np.sqrt(np.add(geant_array[2]**2, geant_array[3]**2))
trans_gan = np.sqrt(np.add(gan_array[2]**2, gan_array[3]**2))
total_mom_geant = np.add(trans_geant, geant_array[4])
total_mom_gan = np.add(trans_gan, gan_array[4])
plt.figure(figsize=(8, 8))
plt.suptitle('step %d' % (e))
plt.subplot(2, 2, 1)
plt.title('p vs pt GEANT4', fontsize='x-small')
plt.hist2d(total_mom_geant,
trans_geant,
bins=50,
norm=LogNorm(),
cmap=plt.cm.CMRmap)
plt.xlabel('P (GeV)')
plt.ylabel('P_t (GeV)')
plt.subplot(2, 2, 2)
plt.title('p vs pt GAN', fontsize='x-small')
plt.hist2d(total_mom_gan,
trans_gan,
bins=50,
norm=LogNorm(),
cmap=plt.cm.CMRmap)
plt.xlabel('P (GeV)')
plt.ylabel('P_t (GeV)')
plt.subplot(2, 2, 3)
plt.title('p overlay', fontsize='x-small')
plt.hist([total_mom_geant, total_mom_gan],
bins=50,
histtype='step',
label=['GEANT4', 'GAN'])
plt.xlabel('P (GeV)')
plt.yscale('log')
plt.legend(loc='upper right')
plt.subplot(2, 2, 4)
plt.title('p_t overlay', fontsize='x-small')
plt.hist([trans_geant, trans_gan],
bins=50,
histtype='step',
label=['GEANT4', 'GAN'])
plt.xlabel('P_t (GeV)')
plt.yscale('log')
plt.legend(loc='upper right')
plt.savefig('%smomentum/momentum_%d.png' % (output_location, e),
bbox_inches='tight')
plt.savefig('%scurrent_momentum.png' % output_location,
bbox_inches='tight')
plt.close('all')
def __calc_single_multipole_hybrid__(this, lm, kind, plot=False):
# Import usefuls
from numpy import zeros_like, argmax, arange, diff, roll, unwrap, mod, exp
# Create shorthand for useful information
l, m = lm
T1 = this.optimal_hybrid_params['T1']
T2 = this.optimal_hybrid_params['T2']
# Get the time and phase aligned waveforms
foo = this.__get_aligned_nr_pn_amp_phase__(lm, kind, plot=plot)
nr_amp = foo['nr_amp']
nr_phi = foo['nr_phi']
pn_amp = foo['pn_amp']
pn_phi = foo['pn_phi']
t = foo['t']
pn_t_amp_max = t[argmax(pn_amp)]
#
stride = 4 * (T2 - T1)
pnmask = (t >= max(T1 - stride, 0)) & (t <= T2)
nrmask = (t >= t[foo['nr_smoothest_mask']][0]) & (t <= min(
pn_t_amp_max, t[foo['nr_smoothest_mask']][0] + stride))
# Deermine if a bridge model is needed
# NOTE that a bridge model (between PN and NR regions over the noisey NR) is only needed if the smooth NR data does not contain the region bounded by [T1,T2].
BRIDGE = t[nrmask][0] > T1
#
if BRIDGE:
# Create training data for bridge models
__bridge_t = t.copy()
__bridge_t = __bridge_t[pnmask | nrmask]
#
__bridge_amp = zeros_like(t)
__bridge_amp[pnmask] = pn_amp[pnmask]
__bridge_amp[nrmask] = nr_amp[nrmask]
__bridge_amp = __bridge_amp[pnmask | nrmask]
#
__bridge_phi = zeros_like(t)
__bridge_phi[pnmask] = pn_phi[pnmask]
__bridge_phi[nrmask] = nr_phi[nrmask]
__bridge_phi = __bridge_phi[pnmask | nrmask]
# Model the AMPLITUDE with fixed symbols; TODO -- check for poles which seem to appear when both NR and PN are poor
rope = gmvrfit(__bridge_t, __bridge_amp)
# rope = mvrfit( __bridge_t, __bridge_amp, numerator_symbols=[('00')], denominator_symbols=['00','000'] )
# Model the PHASE with fixed symbols
plank = mvpolyfit(__bridge_t,
__bridge_phi,
basis_symbols=['K', '0', '000'])
# plank = gmvpfit( __bridge_t, __bridge_phi ) # NOTE -- this works but is slower
# Create blending window towards connecting models with NR
bridge_amp = rope.eval(t)
bridge_phi = plank.eval(t)
bridge_state = lim(arange(len(t))[nrmask])
window = maketaper(t, bridge_state)
# Extend NR towards PN using models
u = window
extended_nr_amp = (1 - u) * bridge_amp + u * nr_amp
extended_nr_phi = (1 - u) * bridge_phi + u * nr_phi
else:
#
warning(
'The NR data appares to be sufficiently smooth. No bridge model will be used.',
verbose=this.verbose)
# No extension to the left (i.e. bridge) is required
extended_nr_amp = nr_amp
extended_nr_phi = nr_phi
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# Create final hybrid multipole by repeating the process above for the extended nr and pn
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# Select hybridization region & make related taper
mask = (t >= T1) & (t <= T2)
hybrid_state = lim(arange(len(t))[mask])
u = maketaper(t, hybrid_state)
# Currently, the taper removes PN data that may be to the right of NR data due to wrapping around when time shifting, so we correct for this by turning the taper off
k_pn_start = int(mod(this.optimal_hybrid_params['k0'], len(t)))
u[k_pn_start:] = 0
hybrid_amp = (1 - u) * pn_amp + u * extended_nr_amp
hybrid_phi = (1 - u) * pn_phi + u * extended_nr_phi
# hybrid_amp = roll(hybrid_amp,k_pn_start)
# hybrid_phi = unwrap(roll(hybrid_phi,k_pn_start))
# Plotting
fig, (ax1, ax2) = None, (None, None)
if plot:
# Import usefuls
from matplotlib.pyplot import figure, figaspect, plot, xlim, ylim, xscale, subplot, show
from matplotlib.pyplot import yscale, axvline, axvspan, xlabel, ylabel, title, yscale, legend
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# Informative plots of intermediate information
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# Create figure
fig = figure(figsize=3 * figaspect(4.0 / 7))
#
ax1 = subplot(2, 1, 1)
# Show training regions
if BRIDGE:
axvspan(min(t[pnmask]),
max(t[pnmask]),
color='cyan',
alpha=0.15,
label='Bridge Training Region')
axvspan(min(t[nrmask]),
max(t[nrmask]),
color='cyan',
alpha=0.15)
axvline(min(t[pnmask]), color='c', ls='--')
axvline(max(t[pnmask]), color='c', ls='--')
axvline(min(t[nrmask]), color='c', ls='--')
axvline(max(t[nrmask]), color='c', ls='--')
# Plot amplitudes
plot(t, nr_amp, label='NR', alpha=0.2, lw=4, ls='--')
if BRIDGE: plot(t, rope.eval(t), lw=2, label='Bridge Model')
plot(t, hybrid_amp, '-', label='Hybrid')
plot(t,
pn_amp,
label='PN',
linewidth=2,
ls='--',
color='k',
alpha=0.5)
# Set plot limits
yscale('log')
ylim(min(pn_amp[pn_amp > 0]),
max(lim(nr_amp[foo['nr_smoothest_mask']], dilate=0.1)))
xlim(0, max(t[foo['nr_smoothest_mask']]))
# Set axis labels and legend
ylabel(r'$|\psi_{%i%i}|$' % (l, m))
legend(frameon=True, framealpha=1, edgecolor='k', fancybox=False)
#
ax2 = subplot(2, 1, 2)
# Show training regions
if BRIDGE:
axvspan(min(t[pnmask]),
max(t[pnmask]),
color='cyan',
alpha=0.15,
label='Bridge Training Region')
axvspan(min(t[nrmask]),
max(t[nrmask]),
color='cyan',
alpha=0.15)
axvline(min(t[pnmask]), color='c', ls='--')
axvline(max(t[pnmask]), color='c', ls='--')
axvline(min(t[nrmask]), color='c', ls='--')
axvline(max(t[nrmask]), color='c', ls='--')
# Plot phases
plot(t, nr_phi, label='NR', alpha=0.2, lw=4, ls='--')
if BRIDGE: plot(t, plank.eval(t), lw=2, label='Bridge Model')
plot(t, hybrid_phi, '-')
plot(t,
pn_phi,
label='PN',
linewidth=2,
ls='--',
color='k',
alpha=0.5)
# Set plot limits
ylim(min(pn_phi),
max(lim(nr_phi[foo['nr_smoothest_mask']], dilate=0.1)))
xlim(0, max(t[foo['nr_smoothest_mask']]))
# plot( t, window*diff(ylim())+min(ylim()), color='k',alpha=0.3 )
# plot( t, (1-window)*diff(ylim())+min(ylim()), color='k',alpha=0.3 )
# Set axis labels
xlabel(r'$t/M$')
ylabel(r'$\phi_{%i%i}=\arg(\psi_{%i%i})$' % (l, m, l, m))
# # ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# # Informative plots of final hybrid and original pn+nr
# # ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- #
# # Create figure
# fig = figure( figsize=1.5*figaspect(4.0/7) )
# #
# ax1 = subplot(2,1,1)
# # plot( t, nr_amp, label='NR', alpha = 0.5 )
# plot( t, roll(hybrid_amp,int(this.optimal_hybrid_params['k0'])), label='Hybrid' )
# plot( t, roll(pn_amp,int(this.optimal_hybrid_params['k0'])), label='PN' )
# # Set plot limits
# yscale('log')
# # ylim( min(pn_amp[pn_amp>0]),max(lim( nr_amp[foo['nr_smoothest_mask']], dilate=0.1)) )
# # xlim( 0,max(t[foo['nr_smoothest_mask']]) )
# # Set axis labels and legend
# ylabel(r'$|\psi_{%i%i}|$'%(l,m))
# legend()
# #
# ax2 = subplot(2,1,2)
# # plot( t, nr_phi, label='NR', alpha = 0.5 )
# plot( t, unwrap(roll(hybrid_phi,int(this.optimal_hybrid_params['k0']))), label='Hybrid' )
# plot( t, unwrap(roll(pn_phi,int(this.optimal_hybrid_params['k0']))), label='PN' )
# # Set plot limits
# # ylim( min(pn_phi),max(lim( nr_phi[foo['nr_smoothest_mask']], dilate=0.1)) )
# # xlim( 0,max(t[foo['nr_smoothest_mask']]) )
# # Set axis labels
# xlabel(r'$t/M$'); ylabel(r'$\phi_{%i%i}=\arg(\psi_{%i%i})$'%(l,m,l,m))
# Output data for further processing
return t, hybrid_amp, hybrid_phi
]
barWidth = 0.2
opacity = 0.4
index = np.arange(15) + 1
plt.bar(index - 0.1,
pythonResults,
barWidth,
alpha=opacity,
color='r',
label='python')
plt.bar(index + 0.1,
numpyResults,
barWidth,
alpha=opacity,
color='g',
label='numpy')
plt.yscale('symlog')
# ax.yaxis.set_ticks(np.arange(min(npy), max(npy)*0.02, 0.02))
plt.ylabel('Running time in seconds')
plt.title("Running time comparison between python and numpy")
plt.legend()
plt.show()
def plot_MVAoutput(y_truth, y_score, out_path, label='', nbins=100):
"""
Plots the MVA output as histogram and returns the underlying
distributions of the positive and the negative class.
"""
print(':: Creating MVA output plot...')
y_score_truePos = y_score[np.array(y_truth==1)]
y_score_trueNeg = y_score[np.array(y_truth==0)]
y_score_trueBG = y_score[np.array(y_truth==99)]
plt.figure()
n_col = 2
n_total, bins_total, patches_total = \
plt.hist(y_score,
bins=nbins,
range=(0., 1.),
alpha=.25,
color='black',
label='S+B')
if y_score_truePos.shape[0] > 0:
n_truePos, bins_truePos, patches_truePos = \
plt.hist(y_score_truePos,
bins=nbins,
range=(0., 1.),
alpha=0.5,
color='green',
label='Sig')
n_col = n_col + 1
else:
n_truePos = None
n_trueNeg, bins_trueNeg, patches_trueNeg = \
plt.hist(y_score_trueNeg,
bins=nbins,
range=(0., 1.),
alpha=0.5,
color='#dd0000',
label='Bg')
if y_score_trueBG.shape[0] > 0:
n_trueBG, bins_trueBG, patches_trueBG = \
plt.hist(y_score_trueBG,
bins=nbins,
range=(0., 1.),
alpha=0.5,
color='yellow',
label='Generated Bg')
n_trueNeg = n_trueNeg+n_trueBG
n_col = n_col + 1
if n_col == 3:
x_txt = 0.546
leg_loc = 1
else:
x_txt = 0.03
leg_loc = 2
#plt.title('MVA output distribution (positive class)')
plt.xlim(-0.05, 1.05)
plt.xlabel('MVA output', fontsize=17)
plt.ylabel('Entries', fontsize=17)
plt.legend(loc=leg_loc, ncol=n_col,fontsize=12)
plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.annotate('ALICE simulation, this work\nPythia-8 MBR {}\n{} TeV'.format(
r'$\varepsilon=0.104$', r'$\sqrt{s} = 13$'),
xy=(x_txt,0.74),
xycoords='axes fraction',
fontsize=12,
bbox=dict(boxstyle="round", fc="w", ec="0.5", alpha=0.4))
plt.tight_layout()
plt.savefig(out_path + 'MVAoutput_distr_' + label + '.png')
plt.savefig(out_path + 'MVAoutput_distr_' + label + '.pdf')
plt.yscale('log')
plt.savefig(out_path + 'MVAoutput_distr_' + label + '_log.png')
plt.savefig(out_path + 'MVAoutput_distr_' + label + '_log.pdf')
return n_truePos, n_trueNeg
def __calc_l2m2_hybrid_params__(this, plot=False):
# Import usefuls
from scipy.optimize import minimize
from numpy import pi, linspace, mean, std, unwrap, angle, exp
# Setup plotting
__plot__ = plot
if __plot__:
# Import usefuls
from matplotlib.pyplot import plot,ylim,xlim,xlabel,ylabel,\
figure,figaspect,yscale,xscale,axvline,axhline,show,title
alert('Plots will be generated.', verbose=this.verbose)
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Extract domain and range values of interest
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
kind = this.__kind__
pno = this.__pn__
nro = this.gwylmo
pn_t, pn_y, nr_t, nr_y = pno.pn_gwylmo.t, pno.pn_gwylmo[
2, 2][kind].y, nro.t, nro[2, 2][kind].y
alert(
'The l=m=2 %s will be used for determining (initial) optimal hybrid params.'
% cyan(kind),
verbose=this.verbose)
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Format data in a common way
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
alert('Aligning formats of NR and PN data', verbose=this.verbose)
t, pn_y, nr_y = format_align(pn_t,
pn_y,
nr_t,
nr_y,
center_domains=True)
alert('Storing hybrid time series.', verbose=this.verbose)
this.t = t
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Align data for initial guesses
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
alert('Using cross-correlation to estimate optimal params.',
verbose=this.verbose)
t, pn_, t, nr_, foo = corr_align(t, abs(pn_y), t, abs(nr_y))
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Calculate time resolution
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
dt = t[1] - t[0]
this.dt = dt
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Define limits of fitting region
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
N = 3 # Width of hybrid region in number of cycles (approx)
alert('We will use %i cycles for the hybridization region\'s width.' %
N,
verbose=this.verbose)
T1 = (nro.t - nro.t[0])[nro.startindex]
T2 = (nro.t - nro.t[0])[nro.startindex + int(N * (2 * pi) /
(nro.wstart_pn / 2))]
mask = (t >= T1) & (t <= T2)
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Define methods for additional alignment
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Method: Time shift the PN data
pn_time_shift = lambda TSHIFT, PN=pn_y, METH=None: tshift(
t, PN, TSHIFT, method=METH) # roll( pn_y, int(t0/dt) )
# Method: Align in phase using average phase difference
def pn_phi_align(PN, NR, return_phi=False, MSK=mask, phi0=None):
# Apply an optimal phase shift
getphi = lambda X: unwrap(angle(X))
if phi0 == None: phi0 = mean((getphi(PN) - getphi(NR))[MSK])
if return_phi:
return PN * exp(-1j * phi0), phi0
else:
return PN * exp(-1j * phi0)
# Method: Calculate estimator
estimartor_fun = lambda PN, NR=nr_y, MSK=mask: abs(
std(PN[MSK] - NR[MSK]) / std(NR[MSK]))
# Store usefuls
alert('Storing shifting functions for future reference.',
verbose=this.verbose)
this.__pn_time_shift__ = pn_time_shift
this.__estimator_fun__ = estimartor_fun
this.__pn_phi_align__ = pn_phi_align
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Define work function for optimizer
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
def work(t0):
# Shift the PN waveform by t0
pn_y_ = pn_time_shift(t0)
# Apply an optimal phase shift
pn_y_ = pn_phi_align(pn_y_, nr_y)
# Compute a residual error statistic
frmse = estimartor_fun(pn_y_)
# Return estimator
return frmse
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Numerically Determine Optimal Tims Shift
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
alert('Finding optimal time-shift using scipy.optimize.minimize',
verbose=this.verbose)
t0_guess = len(t) - foo['domain_shift']
bear = minimize(work, t0_guess)
est0 = bear.fun
t0 = bear.x[0]
k0 = round(t0 / dt)
if __plot__:
t0_space = linspace(t0 - 200 * dt, t0 + 200 * dt, 21)
figure()
plot(t0_space, map(work, t0_space))
xlim(lim(t0_space))
axvline(t0, color='r', ls='--')
axvline(t0_guess, color='k', ls='-', alpha=0.3)
title('(%f,%f)' % (t0, t0_guess))
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Apply optimal parameters
# ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~ #
# Define general functoin for applying hybrid params
def apply_hyb_params_to_pn(PN,
NR,
T0,
MSK=mask,
TSMETH=None,
phi0=None):
# Apply optimal time shift
PN_ = this.__pn_time_shift__(T0, PN=PN, METH=TSMETH)
# Compute and apply an optimal phase shift
PN_, phi0 = pn_phi_align(PN_,
NR,
MSK=mask,
phi0=phi0,
return_phi=True)
# Return deliverables
return PN_, phi0
# Store method
this.__apply_hyb_params_to_pn__ = apply_hyb_params_to_pn
# Apply optimal time shift
pn_y_, phi0_22 = this.__apply_hyb_params_to_pn__(pn_y,
nr_y,
t0,
MSK=mask)
if __plot__:
figure(figsize=1 * figaspect(1.0 / 7))
plot(t, pn_y_.real)
plot(t, nr_y.real)
plot(t, abs(nr_y))
plot(t, abs(pn_y_))
xlim([100, 2500])
axvline(T1, color='b')
axvline(T2, color='b')
yscale('log', nonposy='clip')
ylim([1e-6, 5e-3])
# Store optimals
alert('Storing optimal params to this.optimal_hybrid_params',
verbose=this.verbose)
this.optimal_hybrid_params = {
't0': t0,
'dt': dt,
'k0': k0,
'phi0_22': phi0_22,
'mask': mask,
'T1': T1,
'T2': T2,
'hybrid_cycles': N
}
if this.verbose: print this.optimal_hybrid_params
def plot_smoothed(data,
N,
observable,
method,
t_odes,
fit_range=None,
plot=True,
mags=[]):
mag_vals = {
0.0: 0.33332738590880195,
0.025: 0.33307956586891685,
0.05: 0.3326564798699867,
0.07500000000000001: 0.3320883979754039,
0.1: 0.3313545522832395,
0.125: 0.3302569822117752,
0.15000000000000002: 0.3290837899991117,
0.17500000000000002: 0.327625550776773,
0.2: 0.32559085983104563,
0.225: 0.32329927512893614
}
megam = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225]
multiplier = 1
plt.xscale("log")
plt.yscale("log")
plt.xlabel(r"$\delta$ = 1 + $\epsilon$")
plt.ylabel("Thermalization time ")
cmap = plt.get_cmap('tab10')
therm_sequence = data[observable][N]
prefix = r"$T_{th}$ from $\overline{\mathcal{O}(t)}, $" + " {}, method: {}".format(
observable, method)
"""Getting time at which average goes over threshold"""
mean_therm_times = []
hams = []
deltas = list(t_odes[N].keys())
deltas.sort()
for delta in deltas:
if delta > 900:
continue
t_therm = 0
for megamacek in megam:
if type(therm_sequence[1000 * delta + megamacek]) == np.float64:
continue
if observable == "bondz_mean":
limit = (1 - 0.001 * delta) / 3
else:
limit = mag_vals[return_closest(megamacek / 1000,
mag_vals.keys())]
if method[:3] == "rel":
threshold = limit * float(method[3:])
else:
threshold = limit - float(method[3:])
if threshold > 0:
if therm_sequence[1000 * delta + megamacek][-1] > threshold:
index = np.where(
therm_sequence[1000 * delta +
megamacek] > threshold)[0][0]
mag_factor = len([
x for x in mags[N][delta]
if megamacek == return_closest(1000 * x, megam)
]) / len(mags[N][delta])
t_therm += t_odes[N][delta] * (index) / len(
therm_sequence[delta]) * mag_factor
else:
t_therm += np.inf
mean_therm_times.append(t_therm)
if delta > 1000:
hams.append(2 - 0.001 * delta)
else:
hams.append(0.001 * delta)
temp_sum = 0
hams_log = np.log(np.array(hams))
mean_therm_times_log = np.log(mean_therm_times)
if plot:
if fit_range:
start, end = fit_range
coefficients, residuals, a, b, c = np.polyfit(
hams_log[start:end],
mean_therm_times_log[start:end],
1,
full=True)
print(N, "Thermalization time residuals are ", residuals[0])
polynomial = np.poly1d(coefficients)
ys = polynomial(hams_log)
plt.plot(hams[start:end],
multiplier * np.exp(ys[start:end]),
color=cmap.colors[1])
#, label = "N=" + str(N)+ ",exponent = " + str(coefficients[0])[:7])
plt.scatter(hams,
multiplier * np.array(mean_therm_times),
label=prefix + ", exp:" + str(coefficients[0])[:5] +
", res: " + str(residuals[0])[:6])
else:
plt.scatter(hams,
mean_therm_times,
label=prefix + " N = " + str(N))
return dict(zip(hams, mean_therm_times))
def plot_feature(x_sig, x_bg, out_path, **kwargs):
"""
Plots the features comparing signal and background as histogram
"""
x_total = np.concatenate([x_sig, x_bg])
n_unique = np.unique(x_total).shape[0]
if n_unique == 0:
n_unique = 1
hist_range = (x_total.min()-1., x_total.max()+1)
else:
hist_range = np.percentile(np.hstack([x_total]), [0.01, 99.99])
if n_unique < 40:
nbins = n_unique*2
else:
nbins = 100
plt.figure()
title = kwargs.pop('title', None)
xlabel = kwargs.pop('xlabel', 'x')
xlabel = xlabel.replace('_', ' ')
sig_label = kwargs.pop('sig_label', 'Signal')
bg_label = kwargs.pop('bg_label', 'BG')
combined_label = kwargs.pop('combined_label', 'S+B')
normed = kwargs.pop('normed', None)
if kwargs:
raise TypeError('Invalid kwargs passed: {}'.format(kwargs))
# #### Kolmogorov-Smirnov test seems not to work at the moment ####
# h_sig_norm = np.histogram(x_sig, nbins, density=True)[0]
# h_bg_norm = np.histogram(x_bg, nbins, density=True)[0]
# ks_p_val = stats.ks_2samp(h_sig_norm, h_bg_norm)[1]
# plt.plot([], [], ' ', label='KS p-value: {:.3f}'.format(ks_p_val))
n_total, bins_total, patches_total = \
plt.hist(x_total,
bins=nbins,
range=hist_range,
normed=normed,
alpha=.25,
color='black',
label=combined_label)
n_trueNeg, bins_trueNeg, patches_trueNeg = \
plt.hist(x_bg,
bins=nbins,
range=hist_range,
normed=normed,
alpha=0.5,
color='#dd0000',
label=bg_label)
n_truePos, bins_truePos, patches_truePos = \
plt.hist(x_sig,
bins=nbins,
range=hist_range,
normed=normed,
alpha=0.5,
color='green',
label=sig_label)
# currently not working -> some binning problem
# zeros_in_neg = np.where(n_trueNeg==0)[0].tolist()
# zeros_in_pos = np.where(n_truePos==0)[0].tolist()
# zeros_in_both = sorted(list(set(zeros_in_pos).intersection(zeros_in_neg)))
# all_idx = list(range(n_trueNeg.shape[0]))
# non_zero_lst = [x for x in all_idx if x not in zeros_in_both]
# ks_p_val = stats.ks_2samp(n_trueNeg[non_zero_lst], n_truePos[non_zero_lst])[1]
# plt.plot([], [], ' ', label='KS p-value: {:.3f}'.format(ks_p_val))
# plt.title(title, fontsize=18)
# plt.xlim(-0.05, 1.05)
plt.xlabel(xlabel, fontsize=17)
plt.ylabel('Entries', fontsize=17)
plt.legend(loc='best',fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
# plt.annotate('ALICE simulation, this work\nPythia-8 MBR {}\n{} TeV'.format(
# r'$\varepsilon=0.104$', r'$\sqrt{s} = 13$'),
# xy=(0.5,0.81),
# xycoords='axes fraction',
# fontsize=13,
# bbox=dict(boxstyle="round", fc="w", ec="0.5", alpha=0.5))
plt.tight_layout()
plt.savefig(out_path + 'feature_' + title + '_' + xlabel + '.png')
plt.savefig(out_path + 'feature_' + title + '_' + xlabel + '.pdf')
plt.yscale('log')
plt.savefig(out_path + 'feature_' + title + '_' + xlabel + '_log.png')
plt.savefig(out_path + 'feature_' + title + '_' + xlabel + '_log.pdf')
return n_truePos, n_trueNeg
def plot_simlum(self,isofile='lumfuncs/initial.dat',linestyle='solid',overlay=False,pop='multiple'):
def apparent(M,dguess):
m = M + 5*np.log10(dguess/10)
return m
def merr(dist,derr):
m=5*np.log10(dist/10)-5*np.log10((dist-derr)/10)
return m
def mag(a,b):
c=np.sqrt(a**2+b**2)
return c
self.merr=merr
self.mag=mag
dists=[(680000,30000),(630000,30000),(820000,30000),(785000,25000),(0,0)]
for i in range(len(self.galaxies)):
if self.galaxy==self.galaxies[i]:
distance=dists[i][0]
disterr=dists[i][1]
break
names=['age','z','magbin','mbolmag','zmag','ymag','jmag','hmag','kmag']
iso=ascii.read(isofile,names=names)
iso=iso.to_pandas()
iso.magbin=apparent(iso.magbin,distance)
#label=8 is the AGB phase in padova isochrones
#same method for seperating out the different isochrones in the set
#as in self.isoplot
xdata=iso.magbin
ydata=iso.kmag
indices=[]
for i in range(1,len(iso.age)):
if iso.age[i]!=iso.age[i-1] or iso.z[i]!=iso.z[i-1]:
indices.append(i)
if pop!='single':
plt.plot(xdata[:indices[0]],ydata[:indices[0]],linestyle=linestyle,label='log(t) = ' + str(iso.age[0]) + ', Z = ' +str(iso.z[0]))
print(xdata[:indices[0]])
print(ydata[:indices[0]])
for i in range(len(indices)):
if i==(len(indices)-1):
plt.plot(xdata[indices[i]:],ydata[indices[i]:],linestyle=linestyle,label='log(t) = ' + str(iso.age[indices[i]]) + ', Z = ' +str(iso.z[indices[i]]))
break
else:
plt.plot(xdata[indices[i]:indices[i+1]],ydata[indices[i]:indices[i+1]],linestyle=linestyle,label='log(t) = ' + str(iso.age[indices[i]]) + ', Z = ' +str(iso.z[indices[i]]))
else:
plt.plot(xdata,ydata,linestyle=linestyle,marker='o',markersize='1',label='log(t) = ' + str(iso.age[0]) + ', Z = ' + str(iso.z[0]))
if overlay==False:
plt.ylabel('N_stars')
plt.xlabel('$K_0$')
plt.yscale('log')
plt.gca().invert_xaxis()
plt.legend()
for g in range(len(c2f)):
cl2.append(c2f[g] - (1.0/(2*g+1)*((abs(alm2[g]))**2)))
#print(alm2)
for z in range(len(cl2)):
c2[z] = c2[z] + cl2[z]
for c in range(len(cl)):
c2[c] = c2[c] / (1.0*events2)
for i in range(len(cl)):
cl[i] = cl[i] - c2[i]
'''
'''
plt.yscale('log')
plt.plot(c)
plt.savefig("powerspect_AMPT.png")
'''
'''
b11 = math.sqrt((2*1+1)/(4*math.pi)*1/math.factorial(2))* -1.56727
b00 = math.sqrt((2*0+1)/(4*math.pi)*1/math.factorial(0))* 1.96962
b22 = math.sqrt((2*2+1)/(4*math.pi)*1/math.factorial(4))* 3.99862
b33 = math.sqrt((2*3+1)/(4*math.pi)*1/math.factorial(6))* -17.6705
