def set_xspec(obsid, model = None,
rmffile='/Users/corcoran/Dropbox/nicer_cal/nicer_v0.06.rmf',
arffile='/Users/corcoran/Dropbox/nicer_cal/ni_xrcall_onaxis_v0.06.arf',
workdir='/Users/corcoran/research/WR140/NICER/work/',
ignore='0.0-0.45, 5.-**', showplot=False, showmodel=False
):
"""
sets the pha and model instances for a given obsid
:param obsid: NICER observation id (integer)
:param rmffile: NICER response file
:param arffile: NICER effective area file
:param workdir: NICER dataset working directory
:param ignore: energy range in keV to ignore when fitting
:param showplot: if True plot the model and spectrum vs. energy
:param showmodel: if True print the model parameters
:return:
"""
import xspec
import matplotlib as plt
obs = str(obsid)
xspec.AllData.clear()
xspec.AllModels.clear()
xspecdir = os.path.join(workdir, obs)
phaname = os.path.join(xspecdir, 'ni{obs}_0mpu7_cl.pha'.format(obs=obsid))
print "phaname = {phaname}".format(phaname=phaname)
try:
pha = xspec.Spectrum(phaname)
except:
print "Can't find {phaname}; returning".format(phaname=phaname)
return 0, 0
pha.response = rmffile
pha.response.arf = arffile
pha.ignore(ignore)
#
# Define a model
#
if not model:
model = set_model()
if showmodel:
with sys_pipes():
model.show()
if showplot:
xspec.Plot.setRebin(minSig=3, maxBins=30)
xspec.Plot.device = "/null"
xspec.Plot.xAxis = "keV"
xspec.Plot.yLog = "True"
xspec.Plot("data")
plt.figure(figsize=[10, 6])
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Energy (keV)', fontsize=16)
plt.step(xspec.Plot.x(), xspec.Plot.y())
plt.step(xspec.Plot.x(), xspec.Plot.model())
return pha, model
def plot_gti_lc(obsid, rootdir, flagtype="slow", binwidth=2, ymax=30, save=False,
chanmin=None, chanmax=None,
figdir='/Users/corcoran/research/WR140/NICER/plots',
figsize=[15, 4]):
"""
plots the lightcurve for the specified obsid for all the gtis
"""
nobs = nicerObs(obsid, rootdir)
gti = nobs.get_gti()
fig = plt.figure(figsize=figsize)
nrows = int(len(gti) / 2)
for i in range(2 * nrows):
tstart = gti['START'][i]
tstop = gti['STOP'][i]
# print tstart,tstop
subplot(nrows, 2, i + 1)
fig.tight_layout(pad=1.2)
ylim(0, ymax)
plt.xlabel('Time - {0} (seconds)'.format(tstart), fontsize=16)
plt.ylabel('XTI Counts s$^{-1}$', fontsize=16)
if chanmin:
titl = "{0} {1}-{2}".format(obsid, chanmin, chanmax)
else:
titl = obsid
plt.title(titl)
ctsbin, bincen, binwidths = nobs.get_lc(gtinum=i, binwidth=binwidth, flagtype=flagtype, chanmin=chanmin,
chanmax=chanmax)
plt.step(bincen - gti['START'][i], ctsbin / binwidth)
if save:
figname = os.path.join(figdir, "{0}_plot_gti.pdf".format(obsid))
plt.savefig(figname)
def save_controls(control_name, fname, action):
steps = [
i * environment_configs.get_interval_width()
for i in range(action.shape[0])
]
plt.clf()
plt.step(steps, action)
plt.ylabel(control_name)
plt.xlabel('t')
plt.savefig(fname + '_' + control_name)
def plot_ecdf(data, name):
# sort the values
sorted_data = np.sort(data)
# eval y
n = sorted_data.size
F_x = [sorted_data[sorted_data <= x].size / n for x in sorted_data]
# plot the plot
plt.step(sorted_data, F_x, linewidth=4, label=name)
return
def fit_spectrum(obsid, pha, model, writexcm=True,
workdir='/Users/corcoran/research/WR140/NICER/work/',
statMethod='cstat'
):
import xspec
from wurlitzer import sys_pipes
import pylab as plt
from heasarc.utils import xspec_utils as xu
if type(pha) != int:
#
# do fit
#
xspec.Fit.statMethod = statMethod
xspec.Fit.perform()
print("Best Fit Model is\n")
with sys_pipes():
model.show()
pha.notice("0.4-10.0")
xspec.Plot.setRebin(minSig=3, maxBins=30)
xspec.Plot.device = "/null"
xspec.Plot.xAxis = "keV"
xspec.Plot.yLog = "True"
xspec.Plot("data")
plt.figure(figsize=[10, 6])
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Energy (keV)', fontsize=16)
plt.title("{obs}".format(obs=obsid), fontsize=16)
plt.errorbar(xspec.Plot.x(), xspec.Plot.y(),
xerr=xspec.Plot.xErr(), yerr=xspec.Plot.yErr(), fmt='.')
plt.step(xspec.Plot.x(), xspec.Plot.model(), where="mid")
band = "0.5-10 keV"
xspec.AllModels.calcFlux(band.replace(' keV', '').replace('-',' '))
print "Flux is {0:.3e} in the {1} band".format(pha.flux[0], band)
if writexcm:
xcmfile = os.path.join(workdir, str(obsid), 'ni{obsid}_0mpu7_cl'.format(obsid=obsid))
xu.write_xcm(xcmfile, pha, model=model)
else:
print "Can't fit OBSID {obs}".format(obs=obsid)
return pha, model
def scree_plot(self):
total = sum(self.eig_vals)
explained_var = [(i / total) * 100
for i in sorted(self.eig_vals, reverse=True)]
cum_var = np.cumsum(explained_var)
with plt.style.context('seaborn-darkgrid'):
plt.bar(range(len(explained_var)),
explained_var,
align='center',
label='individual explained variance')
plt.step(range(len(explained_var)),
cum_var,
where='mid',
label='cumulative explained variance',
color="red")
plt.ylabel("Cumulative variance")
plt.xlabel("Principal components")
plt.tight_layout()
plt.legend(loc='best')
plt.show()
classifyReports[j] = report
preRecFSupports[j] = prfs
microAverageList = [v["micro"] for v in APList.values()]
microAverage = np.mean(microAverageList)
print('Average precision score, micro-averaged over all classes: {0:0.2f}'.
format(microAverage))
# %%
print(classifyReports[2])
# %%
print(preRecFSupports[4])
# %%
plt.figure()
plt.step(recall['micro'], precision['micro'], where='post')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title(
'Average precision score, micro-averaged over all classes: AP={0:0.2f}'.
format(average_precision["micro"]))
# %%
svc.decision_function
# %%
from itertools import cycle
# setup plot details
n_classes = 3
# ---------------- plot PRC ----------------
average_precision = average_precision_score(test_label_trans, lp_probs_trans)
precision, recall, thresholds = precision_recall_curve(test_label_trans, lp_probs_trans)
pos_rate = sum(test_label_trans)/len(test_label_trans)
f=plt.figure()
matplotlib.rcParams.update({'font.size': 20})
# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument
step_kwargs = ({'step': 'post'}
if 'step' in signature(plt.fill_between).parameters
else {})
plt.step(recall, precision, color='b', alpha=0.2,
where='post', label='AP = %0.2f' % average_precision)
plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)
plt.plot([0, 1], [pos_rate, pos_rate], 'r--')
plt.legend(loc='upper right')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
# plt.title('PRC for skip-chain CRF with threads \n AP={0:0.2f}'.format(average_precision))
f.savefig("data_cmv/figures/exp-skip-thread-prc-large-6.pdf", bbox_inches='tight')
print(precision)
print(recall)
