done

scripts/03_grxe_spec.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+
+__author__      = "Roman Krivonos"
+__copyright__   = "Space Research Institute (IKI)"
+
+import numpy as np
+import pandas as pd
+from astropy.io import fits
+from astropy.table import Table, Column
+import matplotlib.pyplot as plt
+import math, sys
+import pickle
+
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import HuberRegressor
+from sklearn.linear_model import RANSACRegressor
+from sklearn.linear_model import TheilSenRegressor
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import RepeatedKFold
+
+#from statsmodels.robust.scale import huber
+from astropy.stats import sigma_clip
+from astropy.stats import sigma_clipped_stats
+
+from scipy.stats import norm
+from scipy.stats import describe
+from scipy.stats import sem
+
+
+from numpy import absolute
+from numpy import arange
+
+from ridge.utils import *
+from ridge.config import *
+
+key="GAL"
+n_bins = 80
+sigma=3
+plotme=False
+
+ebands0={#'E02':[0.0,0.0],
+         'E03':[0.0,0.0],
+         'E04':[0.0,0.0],
+         'E05':[0.0,0.0],
+         'E06':[0.0,0.0],
+         'E07':[0.0,0.0],
+         'E08':[0.0,0.0],
+         'E09':[0.0,0.0],
+         'E10':[0.0,0.0],
+         'E11':[0.0,0.0],
+         'E12':[0.0,0.0],}
+
+sz=3
+lon0=0.0
+lat0=0.0
+
+for enkey in ebands0.keys():
+    fn="detcnts.{}.{}.resid.fits".format(enkey,key)
+    d = fits.getdata(proddir+fn)
+    df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
+    #print(df.columns)
+
+
+    df = df.query('LON > {} & LON < {} & LAT > {} & LAT < {}'.format(lon0-sz,lon0+sz,lat0-sz,lat0+sz))
+
+    #print("Number of ScWs: {}".format(df.shape[0]))
+
+    grxe = np.array(df['GRXE'])
+
+    filtered_data = sigma_clip(grxe, sigma=sigma, maxiters=10, return_bounds=True)
+    filtered_grxe = filtered_data[0]
+    filtered_min  = filtered_data[1]
+    filtered_max  = filtered_data[2]
+
+    #print("length orig: {} taken: {} filtered: {}".format(len(grxe),len(grxe[filtered_grxe.mask==False]),len(grxe[filtered_grxe.mask==True])))
+
+    sg_mean, sg_med, sg_std = sigma_clipped_stats(grxe, sigma=sigma, maxiters=10)
+    sg_sem = sem(grxe[filtered_grxe.mask==False])
+    print("{}: mean {:.2f} med {:.2f} std {:.2f} sem {:.2f}".format(enkey, sg_mean, sg_med, sg_std, sg_sem))
+    sg_sem*=1.5
+    if(sg_mean<0.0):
+        sg_mean=1e-9
+        sg_sem*=2
+
+    ebands0[enkey]=[sg_mean,sg_sem]   
+
+    if(plotme):
+        k=1.2
+        plt.hist(grxe, bins=n_bins, range=[filtered_min*k, filtered_max*k])
+        plt.hist(grxe[filtered_grxe.mask], bins=n_bins, range=[filtered_min*k, filtered_max*k])
+        plt.axvline(sg_mean, color="black")
+        plt.axvline(sg_mean+sg_sem, color="black", linestyle="dashed")
+        plt.axvline(sg_mean-sg_sem, color="black", linestyle="dashed")
+        plt.axvline(sg_mean+sg_std, color="blue", linestyle="dashed")
+        plt.axvline(sg_mean-sg_std, color="blue", linestyle="dashed")
+        plt.xlabel("mCrab")
+        plt.show()
+
+
+        
+with open(proddir+"GRXE.spec", 'w') as fp:
+    for enkey in ebands0.keys():
+        fp.write("0 {} {:.2f} {:.2f} 0.0\n".format(bands[enkey],ebands0[enkey][0],ebands0[enkey][1]))