Merge pull request 'Fixed bad-pix files generation' (#1) from amukhin/nuwavdet:master into master

Reviewed-on: #1

README.md

@@ -5,12 +5,12 @@ This pacakge is used to generate region masks separating any focused X-ray flux
 ## Installation
 This package is to be used with Python 3.x.x
 ```python
-pip install git+https://github.com/andrey-rrousan/nuwavdet
+pip install git+http://heagit.cosmos.ru/nustar/nuwavdet.git
 ```
 
 ## Main use
 
-To use the package in your project, import it in by writing
+To use the package in your project, import it in by writing:
 
 ```python
 from nuwavdet import nuwavdet as nw

nuwavdet/nuwavdet.py

@@ -235,7 +235,7 @@ class Observation:
         resized_coeff = (coeff).reshape(2, 2).repeat(180, 0).repeat(180, 1)
         return resized_coeff
 
-    def get_data(self, file, E_borders=[3, 20]):
+    def get_data(self, file, E_borders=[3, 20], generate_mask=True):
         """
         Returns masked array with DET1 image data for given energy band.
         Mask is created from observations badpix tables and to mask the border and gaps. 
@@ -248,10 +248,12 @@ class Observation:
         data_mask = data[np.logical_not(idx_mask)]
         build_hist = lambda array: np.histogram2d(array['DET1Y'], array['DET1X'], 360, [[0, 360], [0, 360]])[0]
         output = build_hist(data_output)
+        if generate_mask:
             mask = build_hist(data_mask)
             mask = np.logical_or(mask, add_borders(output))
             mask = np.logical_or(mask, self.get_bad_pix(file))
             return output, mask
+        return output
 
     def get_bad_pix(self, file, threshold=0.9):
         """
@@ -330,7 +332,7 @@ class Observation:
         """
         Returns a hdu_list with positions of masked pixels in RAW coordinates.
         """
-        x_region, y_region = np.where(region)
+        y_region, x_region = np.where(region)
         hdus = []
         for i in range(4):
             current_dir = os.path.dirname(__file__)
@@ -411,12 +413,12 @@ def process(obs_path, thresh):
         rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
         masked_obs = np.ma.masked_array(obs.data, mask=region)
         good_lvl = np.zeros(bin_num, dtype=bool)
-        good_idx = 0
         if obs.exposure > 1000:
             wav_obs = obs.wavdecomp('atrous', thresh, occ_coeff=True)
             wav_sum = wav_obs[2:-1].sum(0)
             occ_coeff = obs.get_coeff()
             binary_arr = binary_array(bin_num)
+            good_idx = len(binary_arr) - 1
 
             for idx, lvl in enumerate(binary_arr):
                 try:
@@ -434,12 +436,9 @@ def process(obs_path, thresh):
                 rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
 
             for idx in range(len(poiss_comp)):
-                if ((poiss_comp[idx] < poiss_comp[good_idx]) and
+                if ((poiss_comp[idx] < poiss_comp[-1] + 0.05) and
-                    (poiss_comp[idx] < poiss_comp[-1] + 0.05) and
+                    (rem_area[idx] > rem_area[good_idx])):
-                    (rem_area[idx] > rem_area[-1])):
                     good_idx = idx
-            if good_idx == 0:
-                good_idx = len(binary_arr) - 1
             good_lvl = binary_arr[good_idx]
 
             try: