checking diffs

beast/tools/split_catalog_using_map.py

@@ -1,27 +1,18 @@
 #!/usr/bin/env python3
 """
-Split a catalog and a set of AST results, by source or background
-density bin. Uses one of the maps created by
-'create_background_density_map'. From the split AST catalog, individual
-noise models for different regions can be made, which can then be used
-to fit the stars of the observed catalog which also fall in those
-regions.
-
+Split a simulated catalog and a set of AST results by number of sources
+(to improve fitting efficiency for large grids).
 """
 import argparse
 import numpy as np
 from astropy.table import Table
-from beast.tools.density_map import BinnedDensityMap
-
-from beast.tools import beast_settings
 
 
 def split_main(
  beast_settings_info,
  catfile,
  astfile,
- mapfile,
- n_per_file=6250,
+ n_per_file=1000,
  min_n_subfile=None,
  sort_col="F475W_RATE",
 ):
@@ -41,9 +32,6 @@ def split_main(
  astfile : string
  name of the ast catalog file
 
- mapfile : string
- background or source density map file
-
  n_per_file : int or None (default=6250)
  If set, divide the split catalog into sub-catalogs with length
  n_per_file. Good for photometry, not useful for ASTs.
@@ -61,57 +49,18 @@ def split_main(
 
  """
 
- # process beast settings info
- if isinstance(beast_settings_info, str):
- settings = beast_settings.beast_settings(beast_settings_info)
- elif isinstance(beast_settings_info, beast_settings.beast_settings):
- settings = beast_settings_info
- else:
- raise TypeError(
- "beast_settings_info must be string or beast.tools.beast_settings.beast_settings instance"
- )
-
- # Create a binned density map, so both the observed and the ast
- # catalog can be split using a consistent grouping (= binning) of
- # the tiles
-
- if all(
- hasattr(settings, attr)
- for attr in ["sd_binmode", "sd_Nbins", "sd_binwidth", "sd_custom"]
- ):
- bdm = BinnedDensityMap.create(
- mapfile,
- bin_mode=settings.sd_binmode,
- N_bins=settings.sd_Nbins,
- bin_width=settings.sd_binwidth,
- custom_bins=settings.sd_custom,
- )
- else:
- raise RuntimeError(
- "You need to specify all source density binning parameters (sd_binmode, sd_Nbins, sd_binwidth, sd_custom) in the beast_settings file. Unused parameters should be set to 'None'."
- )
-
  print("Splitting catalog")
- split_catalog_using_map(
+ split_simulated_catalog(
  catfile,
- bdm,
  n_per_file=n_per_file,
  min_n_subfile=min_n_subfile,
  sort_col=sort_col,
  )
- print("")
- print("Splitting ASTs")
- split_catalog_using_map(
- astfile, bdm, ra_colname="RA_J2000", dec_colname="DEC_J2000", n_per_file=None
- )
 
 
-def split_catalog_using_map(
+def split_simulated_catalog(
  catfile,
- binned_density_map,
- ra_colname="RA",
- dec_colname="DEC",
- n_per_file=6250,
+ n_per_file=1000,
  min_n_subfile=None,
  sort_col="F475W_RATE",
 ):
@@ -123,12 +72,6 @@ def split_catalog_using_map(
  catfile : string
  name of the photometry catalog file
 
- binned_density_map : BinnedDensityMap object
- the binned density map for the field
-
- ra_colname, dec_colname : string
- labels for the RA and DEC columns
-
  n_per_file : int or None (default=6250)
  If set, divide the split catalog into sub-catalogs with length
  n_per_file. Good for photometry, not useful for ASTs.
@@ -147,96 +90,58 @@ def split_catalog_using_map(
  """
  cat = Table.read(catfile)
 
- ras = cat[ra_colname]
- decs = cat[dec_colname]
-
- bin_foreach_source = np.zeros(len(cat), dtype=int)
- for i in range(len(cat)):
- bin_foreach_source[i] = binned_density_map.bin_for_position(ras[i], decs[i])
-
- binnrs = np.unique(bin_foreach_source)
-
- for b in binnrs:
- # write out file for this bin
- sources_for_bin = np.where(bin_foreach_source == b)
- print("bin {0}: {1} sources".format(b, len(sources_for_bin[0])))
- subcat = cat[sources_for_bin]
- subcat.write(catfile.replace(".fits", "_bin{}.fits".format(b)), overwrite=True)
-
- # write out sub-files, if chosen
- if (n_per_file is not None) or (min_n_subfile is not None):
-
- # calculate number of subfiles and number of stars per file
- # - only n_per_file set
- if (n_per_file is not None) and (min_n_subfile is None):
- tot_subfiles = int(np.ceil(len(sources_for_bin[0]) / n_per_file))
+ # write out sub-files, if chosen
+ if (n_per_file is not None) or (min_n_subfile is not None):
+
+ # calculate number of subfiles and number of stars per file
+ # - only n_per_file set
+ if (n_per_file is not None) and (min_n_subfile is None):
+ tot_subfiles = int(np.ceil(len(cat) / n_per_file))
+ curr_n_per_file = n_per_file
+ # - only min_n_subfile set
+ if (n_per_file is None) and (min_n_subfile is not None):
+ tot_subfiles = min_n_subfile
+ curr_n_per_file = int(np.ceil(len(cat) / tot_subfiles))
+ # - both are set: make sure the largest number of subfiles is used
+ if (n_per_file is not None) and (min_n_subfile is not None):
+ temp_tot_subfiles = int(np.ceil(len(cat) / n_per_file))
+ # n_per_file makes at least min_n_subfile -> use value from n_per_file
+ if min_n_subfile <= temp_tot_subfiles:
+ tot_subfiles = temp_tot_subfiles
  curr_n_per_file = n_per_file
- # - only min_n_subfile set
- if (n_per_file is None) and (min_n_subfile is not None):
+ # n_per_file doesn't make enough subfiles -> use min_n_subfile
+ else:
  tot_subfiles = min_n_subfile
- curr_n_per_file = int(np.ceil(len(sources_for_bin[0]) / tot_subfiles))
- # - both are set: make sure the largest number of subfiles is used
- if (n_per_file is not None) and (min_n_subfile is not None):
- temp_tot_subfiles = int(np.ceil(len(sources_for_bin[0]) / n_per_file))
- # n_per_file makes at least min_n_subfile -> use value from n_per_file
- if min_n_subfile <= temp_tot_subfiles:
- tot_subfiles = temp_tot_subfiles
- curr_n_per_file = n_per_file
- # n_per_file doesn't make enough subfiles -> use min_n_subfile
- else:
- tot_subfiles = min_n_subfile
- curr_n_per_file = int(
- np.ceil(len(sources_for_bin[0]) / tot_subfiles)
- )
+ curr_n_per_file = int(
+ np.ceil(len(cat) / tot_subfiles)
+ )
 
-  print(
-  "dividing into "
-  + str(tot_subfiles)
-  + " subfiles for later fitting speed"
-  )
+ print(
+ "dividing into "
+ + str(tot_subfiles)
+ + " subfiles for later fitting speed"
+ )
 
-  # Sort the stars
-  sort_indxs = np.argsort(subcat[sort_col])
+ # Sort the stars
+ sort_indxs = np.argsort(cat[sort_col])
 
-  for i in range(tot_subfiles):
-  min_k = i * curr_n_per_file
-  if i < tot_subfiles:
-  max_k = (i + 1) * curr_n_per_file
-  else:
-  max_k = len(subcat)
+ for i in range(tot_subfiles):
+ min_k = i * curr_n_per_file
+ if i < tot_subfiles:
+ max_k = (i + 1) * curr_n_per_file
+ else:
+ max_k = len(cat)
 
-  subcat[sort_indxs[min_k:max_k]].write(
-  catfile.replace(".fits", "_bin{0}_sub{1}.fits".format(b, i)),
-  overwrite=True,
-  )
+ cat[sort_indxs[min_k:max_k]].write(
+ catfile.replace(".fits", "_bin0_sub{0}.fits".format(i)),
+ overwrite=True,
+ )
 
 
 if __name__ == "__main__": # pragma: no cover
  parser = argparse.ArgumentParser()
- parser.add_argument(
- "beast_settings_info",
- type=str,
- help="file name with beast settings",
- )
  parser.add_argument("catfile", type=str, help="catalog FITS file")
  parser.add_argument("astfile", type=str, help="ast results fits file")
- parser.add_argument(
- "mapfile", type=str, help="background or source density map file"
- )
-
- nbin_or_binwidth = parser.add_mutually_exclusive_group()
- nbin_or_binwidth.add_argument(
- "--n",
- type=int,
- help="Number of regions to split the catalog in, the number of density bins",
- )
- nbin_or_binwidth.add_argument(
- "--bin_width",
- type=float,
- default=None,
- help="Width of the density bins for splitting catalog",
- )
-
  parser.add_argument(
  "--n_per_file", type=int, default=6250, help="Number of sources per subfile"
  )
@@ -259,10 +164,8 @@ def split_catalog_using_map(
  args = parser.parse_args()
 
  split_main(
- args.beast_settings_info,
  args.catfile,
  args.astfile,
- args.mapfile,
  args.n_per_file,
  args.min_n_subfile,
  args.sort_col,