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 animate_plotting(subdir_path,):
average_filename = 'averaged_out.txt'
if os.path.exists( os.path.join(subdir_path,average_filename) ):
print(subdir_path+average_filename+' already exists please use hotPlot.py')
#import existing data for average at the end
# data_out = numpy.genfromtxt(os.path.join(subdir_path,average_filename))
# averaged_data = numpy.array(data_out[:,1])
# angles = data_out[:,0]
#os.remove( os.path.join(subdir_path,average_filename))
else:
files = os.listdir(subdir_path)
#files = [d for d in os.listdir(subdir_path) if os.path.isdir(os.path.join(subdir_path, d))]
onlyfiles_path = [os.path.join(subdir_path,f) for f in files if os.path.isfile(os.path.join(subdir_path,f))]
onlyfiles_path = natsort.natsorted(onlyfiles_path)
averaged_data = []
angles = []
for f in onlyfiles_path:
data = numpy.genfromtxt(f,delimiter = ',')
#data = pandas.read_csv(f)
averaged_data.append(numpy.mean(data))
angle = os.path.basename(f).split('_')[0]
angles.append(float(angle))
fig = plt.plot(angles, averaged_data,'o')
plt.yscale('log')
plt.xscale('log')
plt.legend(loc='upper right')
plt.title(base_path)
plt.grid(True)
plt.xlabel(r'$\theta$ $[deg.]}$')
#plt.xlabel(r'$\mathrm{xlabel\;with\;\LaTeX\;font}$')
plt.ylabel(r'I($\theta$) $[a.u.]$')
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 curve in plot.curves:
if curve[1] == None: p.plot(curve[0],curve[2], label=curve[3])
else: p.plot(curve[0], curve[1], curve[2], label=curve[3])
p.rc('legend', fontsize='small')
p.legend(shadow=0, loc='best')
p.axes().set_aspect(plot.aspect)
if not plot.dir: plot.dir = './plots/'
if not plot.name: plot.name = self.__global_name+'_%0*i'%(2,n)
if not os.path.isdir(plot.dir): os.mkdir(plot.dir)
if plot.pgf: p.savefig(plot.dir+plot.name+'.pgf')
else: p.savefig(plot.dir+plot.name+'.pdf', bbox_inches='tight')
p.close()
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 animate_plotting(subdir_path, ):
average_filename = 'averaged_out.txt'
if os.path.exists(os.path.join(subdir_path, average_filename)):
print(subdir_path + average_filename +
' already exists please use hotPlot.py')
#import existing data for average at the end
# data_out = numpy.genfromtxt(os.path.join(subdir_path,average_filename))
# averaged_data = numpy.array(data_out[:,1])
# angles = data_out[:,0]
#os.remove( os.path.join(subdir_path,average_filename))
else:
files = os.listdir(subdir_path)
#files = [d for d in os.listdir(subdir_path) if os.path.isdir(os.path.join(subdir_path, d))]
onlyfiles_path = [
os.path.join(subdir_path, f) for f in files
if os.path.isfile(os.path.join(subdir_path, f))
]
onlyfiles_path = natsort.natsorted(onlyfiles_path)
averaged_data = []
angles = []
for f in onlyfiles_path:
data = numpy.genfromtxt(f, delimiter=',')
#data = pandas.read_csv(f)
averaged_data.append(numpy.mean(data))
angle = os.path.basename(f).split('_')[0]
angles.append(float(angle))
fig = plt.plot(angles, averaged_data, 'o')
plt.yscale('log')
plt.xscale('log')
plt.legend(loc='upper right')
plt.title(base_path)
plt.grid(True)
plt.xlabel(r'$\theta$ $[deg.]}$')
#plt.xlabel(r'$\mathrm{xlabel\;with\;\LaTeX\;font}$')
plt.ylabel(r'I($\theta$) $[a.u.]$')
# Import numpy as np
import numpy as np
import matplotlib as plt
pop = [1, 2, 5, 20, 50, 60, 5, 84, 32]
# Store pop as a numpy array: np_pop
np_pop = np.array(pop)
# Double np_pop
np_pop.value = np_pop * 2
# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s=np_pop)
# Previous customizations
plt.xscale('log')
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000, 10000, 100000], ['1k', '10k', '100k'])
# Display the plot
plt.show()
print(life_exp[-1])
# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap, life_exp)
# Display the plot
plt.show()
plt.clf()
# Scatter plot (1)
# Change the line plot below to a scatter plot
# plt.plot(gdp_cap, life_exp)
plt.scatter(gdp_cap, life_exp)
# Put the x-axis on a logarithmic scale
plt.xscale("log")
# Show plot
plt.show()
# Scatter plot (2)
# Build Scatter plot
plt.scatter(pop, life_exp)
# Show plot
plt.show()
plt.clf()
######################################################################
# Histogram -------------------------------------------
# Build a histogram (1)
#compute corr coef line.
lin_fit = numpy.polyfit(numpy.array(fc),numpy.array(fc2),1) # this returns the coef of the polynomial fit
corr_coef = numpy.corrcoef(numpy.array(fc),numpy.array(fc2))[0][1] # this is R
line_x = numpy.linspace(numpy.array(fc).min(),numpy.array(fc).max()) # this is to have some points to actually draw the line.
plots=[]
for i,classe in enumerate(all_classes):
a=plt.plot(numpy.array(fc)[all_classes[classe]],numpy.array(fc2)[all_classes[classe]],'o',alpha=.5, color=colors[i], marker=markers[i], label=classe)
plots.append(a)
plots.append( plt.plot(line_x, line_x*lin_fit[0] + lin_fit[1] , '--b', label='$R^2$ = %.2f'%(corr_coef*corr_coef) )) #we append the plot of the line here
kwarg={'size':6 }
plt.legend(loc='upper right', prop=kwarg)
if log2gene1:
plt.xscale('log', basex=2)
plt.xlabel('log2 expression of %s'%in_gene)
plt.xlim(xmax=plt.xlim()[1]+2^10) #make room for the legend
else:
plt.xlabel('%s'%in_gene)
plt.xlim(xmax=plt.xlim()[1]+1000) #make room for the legend
if log2gene2:
plt.yscale('log', basey=2)
plt.ylabel('log2 expression of %s'%in_gene2)
plt.xlim(xmax=plt.xlim()[1]+2^10) #make room for the legend
else:
plt.xlabel('%s'%in_gene)
plt.xlim(xmax=plt.xlim()[1]+1000) #make room for the legend
