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Roman Krivonos
2024-09-27 19:46:24 +03:00
parent 462133ce2e
commit 0839938594
149 changed files with 157799 additions and 157347 deletions
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24
scripts/02_grxe_resid_plot.py
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@@ -62,7 +62,7 @@ t = Table.from_pandas(df)
t.write("{}/{}.{}.resid_filtered_rev.fits".format(proddir,inkey,enkey),overwrite=True)
fresid1="{}/{}.{}.resid_filtered_spec.fits".format(proddir,inkey,enkey)
sg_mean,sg_sem = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, gaussfit=True, fout=fresid1)
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"
@@ -79,7 +79,7 @@ t = Table.from_pandas(df)
t.write("{}/{}.{}.resid_filtered_rev.fits".format(proddir,inkey,enkey),overwrite=True)
fresid2="{}/{}.{}.resid_filtered_spec.fits".format(proddir,inkey,enkey)
sg_mean,sg_sem = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, gaussfit=True, fout=fresid2)
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"
@@ -96,7 +96,7 @@ t = Table.from_pandas(df)
t.write("{}/{}.{}.resid_filtered_rev.fits".format(proddir,inkey,enkey),overwrite=True)
fresid3="{}/{}.{}.resid_filtered_spec.fits".format(proddir,inkey,enkey)
sg_mean,sg_sem = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, gaussfit=True, fout=fresid3)
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=fresid3)
###
### Plot light curve
@@ -218,6 +218,22 @@ n, bins, patches = ax3.hist(data, nbins, density=True, facecolor='green', alpha=
# add a 'best fit' line
y = norm.pdf(bins, mu, sg)
l = ax3.plot(bins, y, 'r--', linewidth=2)
area = np.sum(n * np.diff(bins))
xdata = bins[:-1]+np.diff(bins)/2
ydata = n
print("Initial Gaiss fit: mu={:.2f} sigma={:.2f}".format(mu,sg))
y_peak = norm.pdf(mu, mu, sg)
params=[
y_peak*area, # height
mu, # mu
sg, # sigma
0.0, # const 1
0.0, # const 2
]
pfit, perr = fit_leastsq(params, xdata, ydata, const_gaussian_ff)
#plt.plot(bins, const_gaussian_ff(bins, pfit), c='black' )
#plot
ax3.axvline(mu, color="black", linewidth=2)
ax3.axvline(mu+sg, color="blue", linestyle="dashed", linewidth=2)
@@ -232,7 +248,7 @@ plt.xlabel('Flux, mCrab',fontsize=14, fontweight='normal')
#ax2.set_ylabel('No, x10$^{-3}$ cts s$^{-1}$ pix$^{-1}$',fontsize=14, fontweight='normal')
#plt.xscale('linear')
#plt.yscale('linear')
plt.yscale('linear')
plt.savefig(proddir+'bkgmodel_histogram.png', bbox_inches='tight')
plt.close(fig)