MANIFEST.in

@@ -1,3 +1,2 @@
-include nuwavdet/pixpos/*
-include nuwavdet/badpix_headers/*
-
+include nuwavsource/pixpos/*
+include nuwavsource/badpix_headers/*

README.md

@@ -1,67 +1,10 @@
-# nuwavdet
+# nuwavsource
 
-This pacakge is used to generate region masks separating any focused X-ray flux from background signal in NuSTAR observations.
+This package is supposed to be used to detect the sources in NuStar observations and generate a mask excluding the signal from the sources of any kind. 
 
-## Installation
-This package is to be used with Python 3.x.x
-```bash
-pip install git+http://heagit.cosmos.ru/nustar/nuwavdet.git
-```
+Additionaly, it generates a table containing:
 
-To update the package to the current version one should delete the previous version
-```bash
-pip uninstall nuwavdet
-```
-
-And simply repeat the intallation procedure again from the repository.
-
-## Installation verification
-If the installation was successful the package can be used with the following import:
-
-```python
-from nuwavdet import nuwavdet as nw
-```
-
-To verify the installation we suggest running a simple script:
-
-```python
-from nuwavdet import nuwavdet as nw
-
-print(nw.binary_array(2))
-```
-
-The output of the script should be
-
-```bash
-[[False False]
- [False  True]
- [ True False]
- [ True  True]]
-```
-
-## Main use
-
-The main functionality of the package is presented with a single function
-```python
-nw.process(obs_path, thresh)
-```
-
-Inputs are string with path to the _cl.evt file to use and a tuple of thresholds, e.g.
-```python
-nw.process('D:\\Data\\obs_cl.evt', (3, 2))
-```
-
-The detailed script description of the data extraction with the script is presented in the examples folder of the repository.
-
-The function nw.process returns severl python objects:
-1. python-dictionary with some metadata and properties of the observation after mask generation procedure.
-2. region array with mask in DET1 coordinate frame. Note that this mask is for numpy mask application so  True (1) corresponds to masked pixel and False (0) otherwise.
-3. custom bad pixel table with flagged pixels in RAW coordinates. It can be exported as fits file for further application to the nupipeline as fpma_userbpfile or fpmb_userbpfile. 
-4. array with the sum of wavelet planes for potential alternative applications.
-
-Metadata about the observation returned by the nw.process is:
-
-Observation metadata: 
+Useful data about the observation:
 
 1. OBS_ID
 2. Detector
@@ -71,14 +14,36 @@ Observation metadata:
 
 Useful algorythm-related data:
 
-6. Average count rate of unmasked area
-7. Fraction of unmasked area
-8. Modified Cash-statistic per bin before and after masking the detected sources
+6. Average count rate on unmasked area
+7. Portion of unmasked area
+8. Specific statistical metric[1] before and after masking the detected sources
+9. Root-mean-square of counts in unmasked area
 
-## Other uses
+## Installation
+This package is to be used with Python 3.x.x
 
-Other possbile usecases are shown in the examples folder.
+To install tha package write
 
-## Contact information
+```bash
+pip install nuwavsource
+```
 
-If you have any questions or issues with the code, feel free to contact Andrey Mukhin: amukhin@cosmos.ru
+## Usage
+
+To use the package in your project, import it in by writing
+
+```python
+from nuwavsource import nuwavsource
+```
+
+You can process the cl.evt file by creating an Observation class object:
+
+```python
+obs = nuwavsource.Observation(path_to_evt_file)
+```
+
+Additionally, the energy band in KeV to get events from can be passed as an argument. The default value is [3,20].
+
+```python
+obs = nuwavsource.Observation(path_to_evt_file,E_borders=[E_min,E_max])
+```

examples/1_save_results.py

@@ -1,54 +0,0 @@
-from nuwavdet import nuwavdet as nw
-
-
-OBS_PATH = r'.//path_to_obs//nu<obsid><DET>01_cl.evt'
-THRESH = (3, 2)
-
-SAVE_BADPIX_PATH = r'.//out//badpix.fits'
-SAVE_REGION_PATH = r'.//out//region.fits'
-SAVE_WAVSUM_PATH = r'.//out//wavsum.fits'
-
-METADATA_PATH = r'.//out//metadata.csv'
-METADATA_FITS_PATH = r'.//out//metadata.fits'
-
-
-if __name__ == '__main__':
-    # PROCESS THE OBSERVATION WITH GIVEN THRESHOLD
-    result, region, region_raw, wav_sum = nw.process(OBS_PATH, thresh=THRESH)
-
-    # SAVE THE REGION BAD PIXEL FILES TO THE FITS FILE WITH NUPIPELINE
-    # COMPATIBLE FORMAT AND HEADERS.
-    region_raw.writeto(SAVE_BADPIX_PATH)
-
-    # SAVE REGION MASK AS A FITS IMAGE
-    nw.save_region(region, SAVE_REGION_PATH, overwrite=False)
-    # Note that the Python script uses numpy masked array with
-    # True (1) as as masked and False (0) as unmasked pixel.
-    # nw.save_region transfers the numpy masked array to
-    # conventional format with 1 for unmasked and 0 for masked pixel.
-    # However, if mask is used in the Python you need to transfer it back with
-    # numpy.logical_not(mask).
-
-    # SAVE WAVSUM ARRAY AS A FITS IMAGE
-    nw.fits.writeto(SAVE_WAVSUM_PATH, wav_sum, overwrite=False)
-
-    # SAVE METADATA
-    # WE SUGGEST SAVING ALL THE METADATA FOR SEVERAL OBSERVATIONS
-    # IN ONE FILE.
-
-    # CREATE CSV FILE TO SAVE DATA
-    # IF FILE ALREADY EXISTS YOU SHOULD REMOVE THIS BLOCK FROM YOUR CODE
-    table = {
-        'obs_id': [], 'detector': [], 'ra': [], 'dec': [],
-        'lon': [], 'lat': [], 't_start': [], 'exposure': [],
-        'count_rate': [], 'remaining_area': [], 'cash_stat': [],
-        'cash_stat_full': []
-    }
-    out_table = nw.DataFrame(table)
-    out_table.to_csv(METADATA_PATH)
-
-    # SAVE DATA TO CREATED CSV
-    nw.DataFrame(result, index=[0]).to_csv(METADATA_PATH, mode='a', header=False)
-
-    # TRANSFORM THE CSV TO FITS-TABLE
-    nw.csv_to_table(METADATA_PATH, METADATA_FITS_PATH)

examples/2_directory_processing.py

@@ -1,33 +0,0 @@
-from nuwavdet import nuwavdet as nw
-
-INPUT_FOLDER = r'path_to_directory'
-OUTPUT_FOLDER = r'.//Output'
-
-
-if __name__ == '__main__':
-    # BEGIN PROCESSING ALL THE OBSERVATIONS INSIDE THE FOLDER
-    nw.process_folder(input_folder=INPUT_FOLDER,
-                      start_new_file='y',
-                      fits_folder=OUTPUT_FOLDER,
-                      thresh=(3, 2),
-                      cpu_num=10
-                      )
-
-    # IF THE PROCESSING WAS INTERRUPTED YOU CAN CONTINUE IT WITH THE SAME CODE
-    # BY CHANGING THE start_new_file TO 'n'.
-
-    # THE STRUCTURE OF THE FOLDER IS
-    # OUTPUT_FOLDER
-    # __overview.csv                csv-table with observations metadata
-    # __overvies.fits               fits-table with the same metadata
-    # __overview_skipped.csv        csv-table with the skipped observations
-    # __Region                      folder for region mask images
-    # ____<obsid><DET>_region.fits
-    # __Region_raw                  folder for region masks in RAW coordinates
-    # ____<obsid><DET>_reg_raw.fits
-    # __Wav_sum                     folder for sum of wavelet layers
-    # ____<obsid><DET>_wav_sum.fits
-
-    # Note nw.process_folder uses multiprocessing with cpu_num cores.
-    # The number of cores can be manually chosen or automatically
-    # detected if cpu_num = 0. 

examples/3_wavelet.py

@@ -1,23 +0,0 @@
-from nuwavdet import nuwavdet as nw
-
-OBS_PATH = r'.//path_to_obs//nu<obsid><DET>01_cl.evt'
-THRESH = (3, 2)
-
-
-if __name__ == '__main__':
-    # CREATE THE OBSERVATION CLASS OBJECT
-    obs = nw.Observation(OBS_PATH)
-
-    # CALCULATE THE WAVLET LAYERS WITH GIVEN THRESHOLD
-    wav_layers = obs.wavdecomp(mode='atrous', occ_coeff=True, thresh=THRESH)
-
-    # ALL THE LAYERS CAN BE ACCESSED AS AN ELEMENT OF wav_layers VARIABLE
-    # wav_layers[0] for the 1st wavelet layer
-    # wav_layers[4] for 5th wavelet layer
-    # wav_layers[-1] for the last wavelet layer
-    # wav_layers[2:5] for the list of the layers from 3 to 5
-    # wav_layers[[1, 3, 5]] for the list of layers 2, 4 and 6
-
-    # To calculate the sum of wavelet layers one should use sum() method
-    # wav_layers[2:7].sum(0) returns a sum of layers from 3 to 7
-    # wav_layers[[1, 3, 5]].sum(0) returns a sum of layers 2, 4 and 6.

examples/4_cstat.py

@@ -1,23 +0,0 @@
-from nuwavdet import nuwavdet as nw
-import numpy as np
-
-
-OBS_PATH = r'.//path_to_obs//nu<obsid><DET>01_cl.evt'
-MASK_PATH = r'.//path_to_mask//<obsid><DET>.fits'
-
-
-if __name__ == '__main__':
-    # CREATE THE OBSERVATION CLASS OBJECT
-    obs = nw.Observation(OBS_PATH)
-
-    # READ THE REGION MASK FILE
-    region = nw.fits.getdata(MASK_PATH)
-
-    # TRANSFORM REGION MASK DATA TO NUMPY MASK DATA (SEE 1_save_results.py).
-    region = np.logical_not(region.astype(bool))
-
-    # CREATE MASKED ARRAY CLASS OBJECT
-    masked_data = np.ma.masked_array(obs, mask=region)
-
-    # CALCULATE THE CSTAT ON THE MASKED DATA
-    print(nw.сstat(masked_data.mean(), masked_data))

nuwavdet/__init__.py

@@ -1 +0,0 @@
-name = 'nuwavdet'

nuwavsource/__init__.py

@@ -0,0 +1 @@
+name = 'nuwavsource'

setup.py

@@ -4,14 +4,14 @@ with open("README.md", "r") as fh:
     long_description = fh.read()
 
 setuptools.setup(
-    name="nuwavdet",
-    version="0.1.1",
+    name="nuwavsource",
+    version="0.0.8",
     author="Andrey Mukhin",
-    author_email="amukhin@cosmos.ru",
-    description="A package for source exclusion in NuSTAR observation data using wavelet decomposition",
+    author_email="amukhin@phystech.edu",
+    description="A package for source exclusion in NuStar observation data using wavelet decomposition",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://github.com/andrey-rrousan/nuwavdet",
+    url="https://github.com/Andreyousan/nuwavsource",
     packages=setuptools.find_packages(),
     include_package_data=True,
     classifiers=(