done

scripts/03_grxe_flux.py

@@ -0,0 +1,95 @@
+#!/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"
+enkey = sys.argv[1]
+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('abs(LAT) < {} & abs(LON) < {}'.format(3,3))
+
+sz=3
+lon0=0.0
+lat0=0.0
+df = df.query('LON > {} & LON < {} & LAT > {} & LAT < {}'.format(lon0-sz,lon0+sz,lat0-sz,lat0+sz))
+
+print("Number of ScWs: {}".format(df.shape[0]))
+
+n_bins = 80
+sigma=3
+
+grxe = np.array(df['GRXE'])
+
+mean = np.mean(grxe)
+std = np.std(grxe)
+print("\n*** Unfiltered:")
+print("mean {:.2f} std {:.2f}".format(mean,std))
+print("min    {:.2f}".format(grxe.min()))
+print("max    {:.2f}".format(grxe.max()))
+print("mean   {:.2f}".format(grxe.mean()))
+print("median {:.2f}".format(np.median(grxe)))
+print("var    {:.2f}".format(grxe.var()))
+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))
+
+print("\n***")
+
+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("Sigma clipping: mean {:.2f} med {:.2f} std {:.2f} sem {:.2f}".format(sg_mean, sg_med, sg_std, sg_sem))
+
+
+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()