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clean

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Roman Krivonos
2024-11-01 11:40:05 +03:00
parent 770ee50a62
commit bb3b99abd3
1115 changed files with 62 additions and 1376385 deletions
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23
scripts/02_grxe_resid_plot.py
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@@ -48,7 +48,7 @@ with open(ignored_rev_file, 'rb') as fp:
ign=ignored_rev.tolist()
enkey="E01"
enkey="A01"
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
dat = Table.read(proddir+fn, unit_parse_strict='silent')
df = dat.to_pandas()
@@ -65,7 +65,7 @@ fresid1="{}/{}.{}.resid_filtered_spec.fits".format(proddir,inkey,enkey)
sg_mean,sg_sem,skew_val,skew_err = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, gaussfit=True, fout=fresid1)
enkey="E14"
enkey="A02"
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
dat = Table.read(proddir+fn, unit_parse_strict='silent')
df = dat.to_pandas()
@@ -82,7 +82,7 @@ fresid2="{}/{}.{}.resid_filtered_spec.fits".format(proddir,inkey,enkey)
sg_mean,sg_sem, skew_val, skew_err = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, gaussfit=True, fout=fresid2)
enkey="E13"
enkey="A03"
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
dat = Table.read(proddir+fn, unit_parse_strict='silent')
df = dat.to_pandas()
@@ -250,8 +250,10 @@ plt.xlabel('Flux, mCrab',fontsize=14, fontweight='normal')
#plt.xscale('linear')
plt.yscale('linear')
plt.savefig(proddir+'bkgmodel_histogram.png', bbox_inches='tight')
filename=figdir+'bkgmodel_histogram.png'
plt.savefig(filename, bbox_inches='tight')
plt.close(fig)
print("\nResult is saved as {}".format(filename))
###
@@ -277,7 +279,7 @@ ax3.ticklabel_format(style='sci', axis='y', scilimits=(-4,4))
data=(df1['CLEAN']-df1['MODEL'])/df1['CLEAN']*100
(mu, sg) = norm.fit(data)
print(mu, sg)
#print(mu, sg)
txt="25-60 keV, $\\sigma=${:.1f}%".format(sg)
ax1.set_title(txt)
n, bins, patches = ax1.hist(data, nbins, density=True, facecolor='green', alpha=0.75)
@@ -292,7 +294,7 @@ ax1.grid(visible=True)
data=(df2['CLEAN']-df2['MODEL'])/df2['CLEAN']*100
(mu, sg) = norm.fit(data)
print(mu, sg)
#print(mu, sg)
txt="60-80 keV, $\\sigma=${:.1f}%".format(sg)
ax2.set_title(txt)
n, bins, patches = ax2.hist(data, nbins, density=True, facecolor='green', alpha=0.75)
@@ -308,7 +310,7 @@ ax2.grid(visible=True)
data=(df3['CLEAN']-df3['MODEL'])/df3['CLEAN']*100
(mu, sg) = norm.fit(data)
print(mu, sg)
#print(mu, sg)
txt="80-200 keV, $\\sigma=${:.1f}%".format(sg)
ax3.set_title(txt)
@@ -333,5 +335,10 @@ plt.xlabel('Residuals, %',fontsize=14, fontweight='normal')
#plt.xscale('linear')
#plt.yscale('linear')
plt.savefig(proddir+'bkgmodel_systematic.png', bbox_inches='tight')
if not os.path.exists(figdir):
os.makedirs(figdir)
filename=figdir+'bkgmodel_systematic.png'
plt.savefig(filename, bbox_inches='tight')
plt.close(fig)
print("Result is saved as {}".format(filename))