Functions¶
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skysearcher.skysearch_lib.time() → floating point number¶ -
Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them.
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skysearcher.skysearch_lib.clear_tables(_target_dir)[source]¶ -
Clear all MPI temp tables from MPI_TABLE_DIR before creating new ones.
Parameters: _target_dir (str, optional) – PATH to target directory.
Raises: IOError– In case they cant be deleted.TypeError– MPI_TABLE_DIR may not be defined.
Example
>>> from skysearcher.skysearch_lib import * >>> import os >>> os.listdir(MPI_TABLE_DIR) ['17.hdf5', '2.hdf5', '11.hdf5', ..., '0.hdf5', '15.hdf5', '12.hdf5'] >>> clear_tables() >>> os.listdir(MPI_TABLE_DIR) []
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skysearcher.skysearch_lib.pause(rnk=0)[source]¶ -
Slow down MPI process according to their rank.
Parameters: rnk (int, optional) – Integer representing MPI process rank Example
>>> skysearcher.skysearch_lib import pause >>> pause(rnk=1)
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skysearcher.skysearch_lib.clear_tables(_target_dir)[source] -
Clear all MPI temp tables from MPI_TABLE_DIR before creating new ones.
Parameters: _target_dir (str, optional) – PATH to target directory.
Raises: IOError– In case they cant be deleted.TypeError– MPI_TABLE_DIR may not be defined.
Example
>>> from skysearcher.skysearch_lib import * >>> import os >>> os.listdir(MPI_TABLE_DIR) ['17.hdf5', '2.hdf5', '11.hdf5', ..., '0.hdf5', '15.hdf5', '12.hdf5'] >>> clear_tables() >>> os.listdir(MPI_TABLE_DIR) []
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skysearcher.skysearch_lib.grid_list()[source]¶ -
Load file handles for numpy data arrays.
Provide a list of all available numpy data arrays (grids).
Returns: List of grids to be used in search. (halo, PATH) Return type: list Example
>>> from skysearcher.skysearch_lib import * >>> grids = grid_list() >>> grids [('halo07', '$PATH/skysearcher/data/grids/halo07_4.0Mpc_h158_grid.npy'), ('halo09', '$PATH/skysearcher/data/grids/halo09_4.0Mpc_h158_grid.npy'), ('halo20', '$PATH/skysearcher/data/grids/halo20_4.0Mpc_h158_grid.npy'), ... ('halo08', '$PATH/skysearcher/data/grids/halo08_4.0Mpc_h158_grid.npy'), ('halo15', '$PATH/skysearcher/data/grids/halo15_4.0Mpc_h158_grid.npy'), ('halo02', '$PATH/skysearcher/data/grids/halo02_4.0Mpc_h158_grid.npy')]
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skysearcher.skysearch_lib.kpc_to_arcmin[source]¶ -
Make the mod for the distance.
Parameters: d_mpc (float, optional) – Distance in Mpc. Returns: Ratio of Kpc to Arc-min. Return type: float Example
>>> from skysearcher.skysearch_lib import kpc_to_arcmin >>> mod = kpc_to_arcmin(d_mpc=4.0) >>> mod 0.7386310975322501
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skysearcher.skysearch_lib.record_table(_names=[('halo', 'i'), ('radius', 'i'), ('r0', 'f'), ('r1', 'f'), ('annuli_step', 'f'), ('Log10(mu)', 'f'), ('xbox_max', 'f'), ('Log10(n_stars_max)', 'f'), ('domsat_purity', 'f'), ('domsat_id', 'i'), ('domsat_sig', 'f'), ('nsats', 'f'), ('domsat_mass', 'f'), ('domsat_atime', 'f'), ('domsat_j', 'f'), ('deg0', 'f'), ('extent', 'f')], _meta=True)[source]¶ -
Load the record table (r_table).
Make a new astropy table to use for MPI output.
Parameters: Returns: Record table to use for storing MPI data.
Return type: astropy.tables.Table
Example
>>> from skysearcher.skysearch_lib import * >>> record_table(_names=TABLE_COLUMNS) <Table length=0> halo radius r0 r1 annuli_step ... domsat_atime domsat_j deg0 extent int32 int32 float32 float32 float32 ... float32 float32 float32 float32 ----- ------ ------- ------- ----------- ... ------------ -------- ------- -------
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skysearcher.skysearch_lib.radii()[source]¶ -
Load list of radii (_radii). i.e radii[x] = (r, r_start, r_stop)
The list of radii to search. Set from rc.cfg file.
Returns: List of radii in the form (center, inner, outer) Return type: list Example
>>> from skysearcher.skysearch_lib import radii >>> _radii = radii() >>> _radii [(5, 4.75, 5.25), (6, 5.7, 6.3), (7, 6.65, 7.35), ... (11, 10.45, 11.55), (12, 11.4, 12.6), (13, 12.35, 13.65)]
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skysearcher.skysearch_lib.save_record_table(_table, _rnk=0)[source]¶ -
Save record hdf5 table.
Parameters: Raises: Example
>>> from skysearcher.skysearch_lib import * >>> r_table = record_table(_names=TABLE_COLUMNS) >>> save_record_table(_table=r_table)
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skysearcher.skysearch_lib.fix_rslice(_grid, _dtype=<class 'numpy.float32'>)[source]¶ -
Summary
Parameters: - _grid (TYPE) – Description
- _dtype (TYPE, optional) – Description
Returns: Description
Return type: TYPE
Raises: IndexError– Description
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skysearcher.skysearch_lib.load_grid(_grid_fh)[source]¶ -
Get the data grid for this halo (grid).
Parameters: _grid_fh (str) – File handle (absolute PATH) for a grid file. Returns: List of Tuples [(halo_name, halo_data_path), …] Return type: list Example
>>> from skysearcher.skysearch_lib import * >>> grids = grid_list() >>> grid_info = grids[0] >>> grid_info ('halo07', '$PATH/skysearcher/data/grids/halo07_4.0Mpc_h158_grid.npy') >>> halo, grid_fh = grid_info >>> halo 'halo07' >>> grid_fh '$PATH/skysearcher/data/grids/halo07_4.0Mpc_h158_grid.npy' >>> grid = load_grid(grid_fh) >>> grid array([[[ 0., 0., 0., ..., 0., 423.55697632, -2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, -2.35786676], [ 0., 0., 0., ..., 0., 422.14511108, -2.35954452], ..., [ 0., 0., 0., ..., 0., 422.14511108, 2.35954452], [ 0., 0., 0., ..., 0., 422.85043335, 2.35786676], [ 0., 0., 0., ..., 0., 423.55697632, 2.3561945 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -2.35452223], [ 0., 0., 0., ..., 0., 422.14276123, -2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, -2.35787249], ..., [ 0., 0., 0., ..., 0., 421.43624878, 2.35787249], [ 0., 0., 0., ..., 0., 422.14276123, 2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, 2.35452223] ], [[ 0., 0., 0., ..., 0., 422.14511108, -2.35284448], [ 0., 0., 0., ..., 0., 421.43624878, -2.35451674], [ 0., 0., 0., ..., 0., 420.72854614, -2.3561945 ], ..., [ 0., 0., 0., ..., 0., 420.72854614, 2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, 2.35451674], [ 0., 0., 0., ..., 0., 422.14511108, 2.35284448]], ..., [[ 0., 0., 0., ..., 0., 422.14511108, -0.7887482 ], [ 0., 0., 0., ..., 0., 421.43624878, -0.787076 ], [ 0., 0., 0., ..., 0., 420.72854614, -0.78539819], ..., [ 0., 0., 0., ..., 0., 420.72854614, 0.78539819], [ 0., 0., 0., ..., 0., 421.43624878, 0.787076 ], [ 0., 0., 0., ..., 0., 422.14511108, 0.7887482 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -0.78707045], [ 0., 0., 0., ..., 0., 422.14276123, -0.78539819], [ 0., 0., 0., ..., 0., 421.43624878, -0.78372031], ..., [ 0., 0., 0., ..., 0., 421.43624878, 0.78372031], [ 0., 0., 0., ..., 0., 422.14276123, 0.78539819], [ 0., 0., 0., ..., 0., 422.85043335, 0.78707045] ], [[ 0., 0., 0., ..., 0., 423.55697632, -0.78539819], [ 0., 0., 0., ..., 0., 422.85043335, -0.78372592], [ 0., 0., 0., ..., 0., 422.14511108, -0.78204811], ..., [ 0., 0., 0., ..., 0., 422.14511108, 0.78204811], [ 0., 0., 0., ..., 0., 422.85043335, 0.78372592], [ 0., 0., 0., ..., 0., 423.55697632, 0.78539819] ]], dtype=float32) >>> grid.shape (600, 600, 7)
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skysearcher.skysearch_lib.satid_setup(_halo, attempts=0)[source]¶ -
Get a list of satid’s and a table for counting satids per region (satid_list) (satid_table).
Parameters: Returns: (list, astropy.table.Table)
Return type: Raises: Example
>>> from skysearcher.skysearch_lib import * >>> halo = "halo07" >>> satid_list, satid_table = satid_setup(halo) >>> satid_list [215, 217, 218, ..., 316, 317, 318] >>> satid_table <Table length=8243050> satids Phi Rads int16 rad kpc uint16 float16 uint16 ------ --------- ------ 225 -3.0566 229 225 -3.0605 229 254 -3.0664 229 ... ... ... 225 0.076111 229 225 0.080444 229 225 0.084778 229 >>> satid_table.keys() ['satids', 'Phi', 'Rads'] >>> satid_table["satids"].min() >= min(satid_list) True >>> satid_table["satids"].max() <= max(satid_list) True
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skysearcher.skysearch_lib.mu_idx(_grid, _r0, _r1)[source]¶ -
Get the mean number of stars for this annulus (mu) and an array of indices’s representing the corresponding array segments (r_idx).
Parameters: Returns: (float, numpy.ndarray)
Return type: Example
>>> from skysearcher.skysearch_lib import * >>> grids = grid_list() >>> grid_info = grids[0] >>> halo, grid_fh = grid_info >>> grid = load_grid(grid_fh) >>> _radii = radii() >>> r, r_start, r_stop = _radii[0] >>> mu, r_idx = mu_idx(grid, r_start, r_stop) >>> mu 6650.8335 >>> r_idx array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], dtype=bool)
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skysearcher.skysearch_lib.mu_idx2(_grid, _r_indx, d0, d1)[source]¶ -
Get the mean number of stars for this sub-annulus-section (mu) and the array indices’s for the corresponding array elements (r_idx2).
Parameters: Returns: float, numpy.ndarray
Return type: Example
>>> from skysearcher.skysearch_lib import * >>> grids = grid_list() >>> grid_info = grids[0] >>> halo, grid_fh = grid_info >>> grid = load_grid(grid_fh) >>> grid array([[[ 0., 0., 0., ..., 0., 423.55697632, -2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, -2.35786676], [ 0., 0., 0., ..., 0., 422.14511108, -2.35954452], ..., [ 0., 0., 0., ..., 0., 422.14511108, 2.35954452 ], [ 0., 0., 0., ..., 0., 422.85043335, 2.35786676 ], [ 0., 0., 0., ..., 0., 423.55697632, 2.3561945 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -2.35452223], [ 0., 0., 0., ..., 0., 422.14276123, -2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, -2.35787249], ..., [ 0., 0., 0., ..., 0., 421.43624878, 2.35787249 ], [ 0., 0., 0., ..., 0., 422.14276123, 2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, 2.35452223 ] ], [[ 0., 0., 0., ..., 0., 422.14511108, -2.35284448], [ 0., 0., 0., ..., 0., 421.43624878, -2.35451674], [ 0., 0., 0., ..., 0., 420.72854614, -2.3561945 ], ..., [ 0., 0., 0., ..., 0., 420.72854614, 2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, 2.35451674 ], [ 0., 0., 0., ..., 0., 422.14511108, 2.35284448 ]], ..., [[ 0., 0., 0., ..., 0., 422.14511108, -0.7887482 ], [ 0., 0., 0., ..., 0., 421.43624878, -0.787076 ], [ 0., 0., 0., ..., 0., 420.72854614, -0.78539819], ..., [ 0., 0., 0., ..., 0., 420.72854614, 0.78539819 ], [ 0., 0., 0., ..., 0., 421.43624878, 0.787076 ], [ 0., 0., 0., ..., 0., 422.14511108, 0.7887482 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -0.78707045], [ 0., 0., 0., ..., 0., 422.14276123, -0.78539819], [ 0., 0., 0., ..., 0., 421.43624878, -0.78372031], ..., [ 0., 0., 0., ..., 0., 421.43624878, 0.78372031 ], [ 0., 0., 0., ..., 0., 422.14276123, 0.78539819 ], [ 0., 0., 0., ..., 0., 422.85043335, 0.78707045 ] ], [[ 0., 0., 0., ..., 0., 423.55697632, -0.78539819], [ 0., 0., 0., ..., 0., 422.85043335, -0.78372592], [ 0., 0., 0., ..., 0., 422.14511108, -0.78204811], ..., [ 0., 0., 0., ..., 0., 422.14511108, 0.78204811 ], [ 0., 0., 0., ..., 0., 422.85043335, 0.78372592 ], [ 0., 0., 0., ..., 0., 423.55697632, 0.78539819 ] ]], dtype=float32) >>> grid.shape (600, 600, 7) >>> _radii = radii() >>> r, r_start, r_stop = _radii[0] >>> r 5 >>> r_start 4.75 >>> r_stop 5.25 >>> mu, r_idx = mu_idx(grid, r_start, r_stop) >>> mu 6650.8335 >>> r_idx array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], dtype=bool) >>> annuli, annuli_step = get_annuli(r) >>> annuli [(-3.1730085801256909, -3.1642795331377247), (-3.1642795331377247, -3.1555504861497585), (-3.1555504861497585, -3.1468214391617924), ..., (3.1468214391617924, 3.1555504861497585), (3.1555504861497585, 3.1642795331377247), (3.1642795331377247, 3.1730085801256909)] >>> annuli_step 0.0087290469879661367 >>> _deg0, _deg1 = annuli[0] >>> _deg0 -3.1730085801256909 >>> _deg1 -3.1642795331377247 >>> mu, r_idx2 = mu_idx2(grid, r_idx, _deg0, _deg1) >>> mu 1.0 >>> r_idx2 array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], dtype=bool)
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skysearcher.skysearch_lib.get_annuli(_r)[source]¶ -
Load array of annuli segments and step value (annuli) & ( annuli_step).
Parameters: _r (int) – Radius Kpc. Returns: - (list of tuples, float)
- list of tuples: annuli_step = float annuli[x] = (deg_0, deg_1) annuli, annuli_step
Return type: tuple Example
>>> from skysearcher.skysearch_lib import * >>> r = 10 >>> annuli, annuli_step = get_annuli(r) >>> annuli [(-3.1730085801256909, -3.1642795331377247), (-3.1642795331377247, -3.1555504861497585), (-3.1555504861497585, -3.1468214391617924), ... (3.1468214391617924, 3.1555504861497585), (3.1555504861497585, 3.1642795331377247), (3.1642795331377247, 3.1730085801256909)] >>> annuli_step 0.0086340369527229677
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skysearcher.skysearch_lib.get_idx[source]¶ -
The grid index for grid spaces within this segment (idx).
Parameters: Returns: Return type: numpy.ndarray
Example
>>> from skysearcher.skysearch_lib import * >>> grids = grid_list() >>> grid_info = grids[0] >>> halo, grid_fh = grid_info >>> grid = load_grid(grid_fh) >>> grid array([[[ 0., 0., 0., ..., 0., 423.55697632, -2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, -2.35786676], [ 0., 0., 0., ..., 0., 422.14511108, -2.35954452], ..., [ 0., 0., 0., ..., 0., 422.14511108, 2.35954452 ], [ 0., 0., 0., ..., 0., 422.85043335, 2.35786676 ], [ 0., 0., 0., ..., 0., 423.55697632, 2.3561945 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -2.35452223], [ 0., 0., 0., ..., 0., 422.14276123, -2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, -2.35787249], ..., [ 0., 0., 0., ..., 0., 421.43624878, 2.35787249 ], [ 0., 0., 0., ..., 0., 422.14276123, 2.3561945 ], [ 0., 0., 0., ..., 0., 422.85043335, 2.35452223 ] ], [[ 0., 0., 0., ..., 0., 422.14511108, -2.35284448], [ 0., 0., 0., ..., 0., 421.43624878, -2.35451674], [ 0., 0., 0., ..., 0., 420.72854614, -2.3561945 ], ..., [ 0., 0., 0., ..., 0., 420.72854614, 2.3561945 ], [ 0., 0., 0., ..., 0., 421.43624878, 2.35451674 ], [ 0., 0., 0., ..., 0., 422.14511108, 2.35284448 ]], ..., [[ 0., 0., 0., ..., 0., 422.14511108, -0.7887482 ], [ 0., 0., 0., ..., 0., 421.43624878, -0.787076 ], [ 0., 0., 0., ..., 0., 420.72854614, -0.78539819], ..., [ 0., 0., 0., ..., 0., 420.72854614, 0.78539819 ], [ 0., 0., 0., ..., 0., 421.43624878, 0.787076 ], [ 0., 0., 0., ..., 0., 422.14511108, 0.7887482 ] ], [[ 0., 0., 0., ..., 0., 422.85043335, -0.78707045], [ 0., 0., 0., ..., 0., 422.14276123, -0.78539819], [ 0., 0., 0., ..., 0., 421.43624878, -0.78372031], ..., [ 0., 0., 0., ..., 0., 421.43624878, 0.78372031 ], [ 0., 0., 0., ..., 0., 422.14276123, 0.78539819 ], [ 0., 0., 0., ..., 0., 422.85043335, 0.78707045 ] ], [[ 0., 0., 0., ..., 0., 423.55697632, -0.78539819], [ 0., 0., 0., ..., 0., 422.85043335, -0.78372592], [ 0., 0., 0., ..., 0., 422.14511108, -0.78204811], ..., [ 0., 0., 0., ..., 0., 422.14511108, 0.78204811 ], [ 0., 0., 0., ..., 0., 422.85043335, 0.78372592 ], [ 0., 0., 0., ..., 0., 423.55697632, 0.78539819 ] ]], dtype=float32) >>> grid.shape (600, 600, 7) >>> _radii = radii() >>> r, r_start, r_stop = _radii[0] >>> r 5 >>> r_start 4.75 >>> r_stop 5.25 >>> mu, r_idx = mu_idx(grid, r_start, r_stop) >>> mu 6650.8335 >>> r_idx array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], dtype=bool) >>> annuli, annuli_step = get_annuli(r) >>> annuli [(-3.1730085801256909, -3.1642795331377247), (-3.1642795331377247, -3.1555504861497585), (-3.1555504861497585, -3.1468214391617924), ..., (3.1468214391617924, 3.1555504861497585), (3.1555504861497585, 3.1642795331377247), (3.1642795331377247, 3.1730085801256909)] >>> annuli_step 0.0087290469879661367 >>> _deg0, _deg1 = annuli[0] >>> _deg0 -3.1730085801256909 >>> _deg1 -3.1642795331377247 >>> mu, r_idx2 = mu_idx2(grid, r_idx, _deg0, _deg1) >>> mu 1.0 >>> r_idx2 array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], dtype=bool) >>> idx = get_idx(grid, _deg0, _deg1, r_idx2) (array([], dtype=int64), array([], dtype=int64)) >>> type(idx) tuple >>> type(idx[0]) numpy.ndarray
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skysearcher.skysearch_lib.get_xbox[source]¶ -
The array of grid spaces from this segment’s contrast density value (xbox).
Parameters: Returns: Return type: numpy.ndarray
Example
>>> n_boxes_in_seg = len(idx[0]) >>> xbox = get_xbox(grid, idx, mu) >>> n_stars_here = grid[:, :, 1][idx].sum()
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skysearcher.skysearch_lib.new_sat_stars(_id_lst)[source]¶ -
Start a dictionary for counting stars per satid (sat_stars).
Parameters: _id_lst (list) – List of satid’s‘ for this halo Returns: Dictionary with satid’s for keys and corresponding number of stars for each satid counted. Return type: dict Example
>>> sat_stars = new_sat_stars(satid_list) >>> sat_stars {0: 0, 1: 0, 2: 0, 3: 0, ... 110: 0, 111: 0, 112: 0}
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skysearcher.skysearch_lib.count_strs[source]¶ -
Count stars per satid.
Parameters: - _dict (dict) – Dictionary for stars per satid (sat_stars).
- _region (list) – Put all region info into a list (r_info).
- _table (astropy.table.Table) – Local_satid_table: This is so we don’t need to index the whole table every loop of the following for loop (local_satid_table).
Returns: An updated sat_stars dictionary with satid’s for keys and corresponding number of stars for each satid counted.
Return type: Example
>>> sat_stars = new_sat_stars(satid_list) >>> local_satid_table = satid_table[np.logical_and( ...: satid_table["Rads"] >= r_start, ...: satid_table["Rads"] < r_stop)] >>> r_info = [r_start, r_stop, _deg0, _deg1] >>> sat_stars = count_strs(sat_stars, r_info, local_satid_table)
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skysearcher.skysearch_lib.dom_satid(input_dict, _dict, rtn=False)[source]¶ -
The dominate satellite number (domsat_id). Domsats’s % of all stars (domsat_purity).
Parameters: _dict (dict) – A dictionary for counting stars per satid (sat_stars). Returns: (_domsat_id, _domsat_per, _standout, n_sats) _domsat_id : Dominate satellite id. _domsat_per : Percentage that the _domsat_id represents from all satid’s. _standout : _domsat_per / 0.01 n_sats : Number of staid’s present. Return type: tuple Example
>>> domsat_id, domsat_purity, standout, nsats = dom_satid(sat_stars)
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skysearcher.skysearch_lib.report(_type, _info)[source]¶ -
_dict = sorted(_dict, key=_dict.__getitem__, reverse=True) if _type == “starting halo”: STDOUT.write(“rank ” + str(MPI_RANK) + ” [ LOADED ] ” + halo + “n”) STDOUT.flush() if _type == “end annulus”: line = (“rank ” + str(MPI_RANK) + ” : halo” + halo[-2:] + ” - ” + str(round(r, 1)) + ” Kpc : –> ” + str(round((time() - a_tic), 1)) + ” secs”) STDOUT.write(line + “n”) STDOUT.flush if _type == “end halo”: if _type == “exit”: msg = (“rank ” + str(MPI_RANK) + ” [ FINISHED ] [ ” + str(round((time() - tic) / 60.0, 1)) + ” minutes ]n”) STDOUT.write(msg) STDOUT.flush() –> TIME
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skysearcher.mpi_search.main()[source]¶ -
This is the main parallel skysearcher program.
Example
>>> mpiexec -n <nproc> -machinefile <mf> python mpi_search.py
-nproc {int} – number of processes -machinefile {file} – list of hosts each on a new line
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skysearcher.new_cfg.new_rc(rc_fh=None, output=True, tbl_ext='hdf5')[source]¶ -
Create a new rc configuration file to the provided file handle.
Parameters: Keyword Arguments: - {str} -- full path and name of rc file (default (rc_fh) –
- {str} -- extention for tables. (default (tbl_ext) –
Returns: Return type: rc file handle str.
Example
>>> import ConfigParser >>> config = ConfigParser.RawConfigParser() >>> config.read("rc.cfg")
user@machine$ python new_cfg.py [ PATH ] data_dir = ~/$USER/skysearcher/data plot_dir = ~/$USER/skysearcher/data/plots table_dir = ~/$USER/skysearcher/data/tables mpi_table_dir = ~/$USER/skysearcher/data/tables/groupfinder/mpi grid_dir = ~/$USER/skysearcher/data/grids grid_file_designator = grid grid_ext = npy table_file_designator = table table_format = hdf5 table_hdf5_path = data table_ext = .hdf5 [ Search Extent ] r_start = 5 r_stop = 285 r_step = 1 annulus_scale = 0.05 annulus_phi_step = 720 [ Accept Reject ] xbox_cut = 0.1 min_log_nstars = 2.00 min_n_segments = 4 n_skips = 2 [ Run Time ] save_interval = 2 [ Data ] d_mpc = 4.0
See also
ConfigParser: http://docs.python.org/2/library/configparser.html