Added examples

examples/1_save_results.py

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+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

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+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

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+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

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+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))