more filtering

scripts/03_plot_skyreg.py

@@ -71,6 +71,7 @@ plt.show()
 
 rev = np.array(df['REV'])
 grxe = np.array(df['GRXE'])
+grxe_err = np.array(df['GRXE_ERR'])
 
 
 
@@ -86,6 +87,35 @@ sstr = '%-14s mean = %6.4f, variance = %6.4f, skew = %6.4f, kurtosis = %6.4f'
 n, (smin, smax), sm, sv, ss, sk = describe(grxe)
 print(sstr % ('sample:', sm, sv, ss, sk))
     
+# Calculate the percentiles across the x dimension
+errmax=np.max(grxe_err)
+perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
+print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(skey,enkey,sem_cut,perc))
+idx=np.where(grxe_err < perc)
+
+plt.hist(grxe_err, bins=n_bins, range=[0,errmax], color="red")
+rev=rev[idx]
+grxe=grxe[idx]
+grxe_err=grxe_err[idx]
+plt.hist(grxe_err, bins=n_bins, range=[0,errmax], color="grey")
+
+plt.xlabel("GRXE ERROR, mCrab")
+plt.title("Distribution of errors {}".format(skey))
+plt.show()
+
+grxe_sign=np.divide(grxe,grxe_err)
+plt.hist(grxe_sign, bins=n_bins)
+plt.xlabel("GRXE S/N")
+plt.title("Distribution of significance {}".format(skey))
+plt.show()
+
+A=np.sum(grxe/grxe_err**2)
+B=np.sum(1.0/grxe_err**2)
+wgt_mean=A/B
+wgt_mean_err=np.sqrt(1.0/B)
+
+print("Weighted mean: {:.2f}+/-{:.2f} normal mean: {:.2f}".format(wgt_mean,wgt_mean_err,np.mean(grxe)))
+
 #filtered_grxe = sigma_clip(grxe, sigma=sigma, maxiters=10)
 filtered_data = sigma_clip(grxe, sigma=sigma, maxiters=10, return_bounds=True)
 filtered_grxe = filtered_data[0]