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def fn_max ( self , a , axis = None ) : return numpy . nanmax ( self . _to_ndarray ( a ) , axis = axis )
Return the maximum of an array ignoring any NaNs .
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def fn_median ( self , a , axis = None ) : return numpy . nanmedian ( self . _to_ndarray ( a ) , axis = axis )
Compute the median of an array ignoring NaNs .
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def fn_mean ( self , a , axis = None ) : return numpy . nanmean ( self . _to_ndarray ( a ) , axis = axis )
Compute the arithmetic mean of an array ignoring NaNs .
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def fn_std ( self , a , axis = None ) : return numpy . nanstd ( self . _to_ndarray ( a ) , axis = axis )
Compute the standard deviation of an array ignoring NaNs .
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def fn_var ( self , a , axis = None ) : return numpy . nanvar ( self . _to_ndarray ( a ) , axis = axis )
Compute the variance of an array ignoring NaNs .
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def fn_ceil ( self , value ) : if is_ndarray ( value ) or isinstance ( value , ( list , tuple ) ) : return numpy . ceil ( self . _to_ndarray ( value ) ) else : return math . ceil ( value )
Return the ceiling of a number .
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def fn_int ( self , value ) : if is_ndarray ( value ) or isinstance ( value , ( list , tuple ) ) : return self . _to_ndarray ( value ) . astype ( 'int' ) else : return int ( value )
Return the value cast to an int .
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def fn_float ( self , value ) : if is_ndarray ( value ) or isinstance ( value , ( list , tuple ) ) : return self . _to_ndarray ( value ) . astype ( 'float' ) else : return float ( value )
Return the value cast to a float .
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def make_datetime ( dt , date_parser = parse_date ) : if ( isinstance ( dt , ( datetime . datetime , datetime . date , datetime . time , pd . Timestamp , np . datetime64 ) ) or dt in ( float ( 'nan' ) , float ( 'inf' ) , float ( '-inf' ) , None , '' ) ) : return dt if isinstance ( dt , ( float , int ) ) : return dateti...
Coerce a datetime or string into datetime . datetime object
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def quantize_datetime ( dt , resolution = None ) : # FIXME: this automatically truncates off microseconds just because timtuple() only goes out to sec resolution = int ( resolution or 6 ) if hasattr ( dt , 'timetuple' ) : dt = dt . timetuple ( ) # strips timezone info if isinstance ( dt , time . struct_time ) : # strip...
Quantize a datetime to integer years months days hours minutes seconds or microseconds
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def timetag_str ( dt = None , sep = '-' , filler = '0' , resolution = 6 ) : resolution = int ( resolution or 6 ) if sep in ( None , False ) : sep = '' sep = str ( sep ) dt = datetime . datetime . now ( ) if dt is None else dt # FIXME: don't use timetuple which truncates microseconds return sep . join ( ( '{0:' + filler...
Generate a date - time tag suitable for appending to a file name .
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def make_tz_aware ( dt , tz = 'UTC' , is_dst = None ) : # make sure dt is a datetime, time, or list of datetime/times dt = make_datetime ( dt ) if not isinstance ( dt , ( list , datetime . datetime , datetime . date , datetime . time , pd . Timestamp ) ) : return dt # TODO: deal with sequence of timezones try : tz = dt...
Add timezone information to a datetime object only if it is naive .
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def translate_addresstype ( f ) : @ wraps ( f ) def wr ( r , pc ) : at = r [ "addressType" ] try : r . update ( { "addressType" : POSTCODE_API_TYPEDEFS_ADDRESS_TYPES [ at ] } ) except : logger . warning ( "Warning: {}: " "unknown 'addressType': {}" . format ( pc , at ) ) return f ( r , pc ) return wr
decorator to translate the addressType field .
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def translate_purposes ( f ) : @ wraps ( f ) def wr ( r , pc ) : tmp = [ ] for P in r [ "purposes" ] : try : tmp . append ( POSTCODE_API_TYPEDEFS_PURPOSES [ P ] ) except : logger . warning ( "Warning: {}: " "cannot translate 'purpose': {}" . format ( pc , P ) ) tmp . append ( P ) r . update ( { "purposes" : tmp } ) ret...
decorator to translate the purposes field .
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def quantile ( data , num_breaks ) : def scipy_mquantiles ( a , prob = list ( [ .25 , .5 , .75 ] ) , alphap = .4 , betap = .4 , axis = None , limit = ( ) ) : """ function copied from scipy 0.13.3::scipy.stats.mstats.mquantiles """ def _quantiles1D ( data , m , p ) : x = numpy . sort ( data . compressed ( ) ) n = len ( ...
Calculate quantile breaks .
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def equal ( data , num_breaks ) : step = ( numpy . amax ( data ) - numpy . amin ( data ) ) / num_breaks return numpy . linspace ( numpy . amin ( data ) + step , numpy . amax ( data ) , num_breaks )
Calculate equal interval breaks .
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def add_column ( filename , column , formula , force = False ) : columns = parse_formula ( formula ) logger . info ( "Running file: %s" % filename ) logger . debug ( " Reading columns: %s" % columns ) data = fitsio . read ( filename , columns = columns ) logger . debug ( ' Evaluating formula: %s' % formula ) col = eval...
Add a column to a FITS file .
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def load_files ( filenames , multiproc = False , * * kwargs ) : filenames = np . atleast_1d ( filenames ) logger . debug ( "Loading %s files..." % len ( filenames ) ) kwargs = [ dict ( filename = f , * * kwargs ) for f in filenames ] if multiproc : from multiprocessing import Pool processes = multiproc if multiproc > 0...
Load a set of FITS files with kwargs .
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def applyFracdet ( self , lon , lat ) : self . loadFracdet ( ) fracdet_core = meanFracdet ( self . m_fracdet , lon , lat , np . tile ( 0.1 , len ( lon ) ) ) fracdet_wide = meanFracdet ( self . m_fracdet , lon , lat , np . tile ( 0.5 , len ( lon ) ) ) return ( fracdet_core >= self . config [ self . algorithm ] [ 'fracde...
We want to enforce minimum fracdet for a satellite to be considered detectable
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def applyHotspot ( self , lon , lat ) : self . loadRealResults ( ) cut_detect_real = ( self . data_real [ 'SIG' ] >= self . config [ self . algorithm ] [ 'sig_threshold' ] ) lon_real = self . data_real [ 'RA' ] [ cut_detect_real ] lat_real = self . data_real [ 'DEC' ] [ cut_detect_real ] cut_hotspot = np . tile ( True ...
Exclude objects that are too close to hotspot
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def predict ( self , lon , lat , * * kwargs ) : assert self . classifier is not None , 'ERROR' pred = np . zeros ( len ( lon ) ) cut_geometry , flags_geometry = self . applyGeometry ( lon , lat ) x_test = [ ] for key , operation in self . config [ 'operation' ] [ 'params_intrinsic' ] : assert operation . lower ( ) in [...
distance abs_mag r_physical
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def catalogFactory ( name , * * kwargs ) : fn = lambda member : inspect . isclass ( member ) and member . __module__ == __name__ catalogs = odict ( inspect . getmembers ( sys . modules [ __name__ ] , fn ) ) if name not in list ( catalogs . keys ( ) ) : msg = "%s not found in catalogs:\n %s" % ( name , list ( kernels . ...
Factory for various catalogs .
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def write_results ( filename , config , srcfile , samples ) : results = createResults ( config , srcfile , samples = samples ) results . write ( filename )
Package everything nicely
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def estimate ( self , param , burn = None , clip = 10.0 , alpha = 0.32 ) : # FIXME: Need to add age and metallicity to composite isochrone params (currently properties) if param not in list ( self . samples . names ) + list ( self . source . params ) + [ 'age' , 'metallicity' ] : msg = 'Unrecognized parameter: %s' % pa...
Estimate parameter value and uncertainties
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def estimate_params ( self , burn = None , clip = 10.0 , alpha = 0.32 ) : mle = self . get_mle ( ) out = odict ( ) for param in mle . keys ( ) : out [ param ] = self . estimate ( param , burn = burn , clip = clip , alpha = alpha ) return out
Estimate all source parameters
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def estimate_position_angle ( self , param = 'position_angle' , burn = None , clip = 10.0 , alpha = 0.32 ) : # Transform so peak in the middle of the distribution pa = self . samples . get ( param , burn = burn , clip = clip ) peak = ugali . utils . stats . kde_peak ( pa ) shift = 180. * ( ( pa + 90 - peak ) > 180 ) pa...
Estimate the position angle from the posterior dealing with periodicity .
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def date_range_for_webtrends ( cls , start_at = None , end_at = None ) : if start_at and end_at : start_date = cls . parse_standard_date_string_to_date ( start_at ) end_date = cls . parse_standard_date_string_to_date ( end_at ) return [ ( cls . parse_date_for_query ( start_date ) , cls . parse_date_for_query ( end_date...
Get the start and end formatted for query or the last hour if none specified . Unlike reports this does not aggregate periods and so it is possible to just query a range and parse out the individual hours .
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def get_ugali_dir ( ) : dirname = os . getenv ( 'UGALIDIR' ) # Get the HOME directory if not dirname : dirname = os . path . join ( os . getenv ( 'HOME' ) , '.ugali' ) if not os . path . exists ( dirname ) : from ugali . utils . logger import logger msg = "Creating UGALIDIR:\n%s" % dirname logger . warning ( msg ) retu...
Get the path to the ugali data directory from the environment
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def get_iso_dir ( ) : dirname = os . path . join ( get_ugali_dir ( ) , 'isochrones' ) if not os . path . exists ( dirname ) : from ugali . utils . logger import logger msg = "Isochrone directory not found:\n%s" % dirname logger . warning ( msg ) return dirname
Get the ugali isochrone directory .
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def registerParser ( self , parser ) : if not isinstance ( parser , Subparser ) : raise TypeError ( "%s is not an instance of a subparser." % parser ) self . parsers . append ( parser )
Registers a parser to parse configuration inputs .
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def addConfig ( self , name , default = None , cast = None , required = False , description = None ) : # Validate the name if not self . configNameRE . match ( name ) : raise InvalidConfigurationException ( "Invalid configuration name: %s" % name ) self . configs [ self . _sanitizeName ( name ) ] = { 'default' : defaul...
Adds the given configuration option to the ConfigManager .
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def parse ( self ) : self . _config = _Config ( ) self . _setDefaults ( ) for parser in self . parsers : for key , value in parser . parse ( self , self . _config ) . items ( ) : key = self . _sanitizeName ( key ) if key not in self . configs : raise UnknownConfigurationException ( key ) if value is not None : self . _...
Executes the registered parsers to parse input configurations .
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def _setDefaults ( self ) : for configName , configDict in self . configs . items ( ) : self . _setConfig ( configName , configDict [ 'default' ] )
Sets all the expected configuration options on the config object as either the requested default value or None .
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def _cast ( self ) : for configName , configDict in self . configs . items ( ) : if configDict [ 'cast' ] is not None : configValue = getattr ( self . _config , configName ) if configValue is not None : try : self . _setConfig ( configName , configDict [ 'cast' ] ( configValue ) ) except : raise InvalidConfigurationExc...
Iterates through our parsed configuration options and cast any options with marked cast types .
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def list_models ( self , macaroons ) : return make_request ( "{}model" . format ( self . url ) , timeout = self . timeout , client = self . _client , cookies = self . cookies )
Get the logged in user s models from the JIMM controller .
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def write ( self , novel_title = 'novel' , filetype = 'txt' ) : self . _compose_chapters ( ) self . _write_to_file ( novel_title , filetype )
Composes chapters and writes the novel to a text file
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def _compose_chapters ( self ) : for count in range ( self . chapter_count ) : chapter_num = count + 1 c = Chapter ( self . markov , chapter_num ) self . chapters . append ( c )
Creates a chapters and appends them to list
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def valid_address ( address ) : if not address : return False components = str ( address ) . split ( ':' ) if len ( components ) > 2 or not valid_hostname ( components [ 0 ] ) : return False if len ( components ) == 2 and not valid_port ( components [ 1 ] ) : return False return True
Determines whether the specified address string is valid .
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def valid_hostname ( host ) : if len ( host ) > 255 : return False if host [ - 1 : ] == '.' : host = host [ : - 1 ] return all ( _hostname_re . match ( c ) for c in host . split ( '.' ) )
Returns whether the specified string is a valid hostname .
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def sample ( self , n , mass_min = 0.1 , mass_max = 10. , steps = 10000 , seed = None ) : if seed is not None : np . random . seed ( seed ) d_mass = ( mass_max - mass_min ) / float ( steps ) mass = np . linspace ( mass_min , mass_max , steps ) cdf = np . insert ( np . cumsum ( d_mass * self . pdf ( mass [ 1 : ] , log_m...
Sample initial mass values between mass_min and mass_max following the IMF distribution .
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def pdf ( cls , mass , log_mode = True ) : log_mass = np . log10 ( mass ) # From Eq 2 mb = mbreak = [ 0.08 , 0.5 ] # Msun a = alpha = [ 0.3 , 1.3 , 2.3 ] # alpha # Normalization set from 0.1 -- 100 Msun norm = 0.27947743949440446 b = 1. / norm c = b * mbreak [ 0 ] ** ( alpha [ 1 ] - alpha [ 0 ] ) d = c * mbreak [ 1 ] *...
PDF for the Kroupa IMF .
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def pdf ( cls , mass , log_mode = True ) : alpha = 2.35 a = 0.060285569480482866 dn_dm = a * mass ** ( - alpha ) if log_mode : # Number per logarithmic mass range, i.e., dN/dlog(M) return dn_dm * ( mass * np . log ( 10 ) ) else : # Number per linear mass range, i.e., dN/dM return dn_dm
PDF for the Salpeter IMF .
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def _getConfigFile ( self , config ) : joinPath = lambda p : ( os . path . join ( p ) if isinstance ( p , ( tuple , list ) ) else p ) if self . filepathConfig is not None and self . filenameConfig is not None : if hasattr ( config , self . filepathConfig ) and hasattr ( config , self . filenameConfig ) : path = joinPat...
Retrieves a file descriptor to a configuration file to process .
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def _count_citations ( aux_file ) : counter = defaultdict ( int ) with open ( aux_file ) as fobj : content = fobj . read ( ) for match in CITE_PATTERN . finditer ( content ) : name = match . groups ( ) [ 0 ] counter [ name ] += 1 return counter
Counts the citations in an aux - file .
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def _setup_logger ( self ) : log = logging . getLogger ( 'latexmk.py' ) handler = logging . StreamHandler ( ) log . addHandler ( handler ) if self . opt . verbose : log . setLevel ( logging . INFO ) return log
Set up a logger .
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def _parse_texlipse_config ( self ) : # If Eclipse's workspace refresh, the # ".texlipse"-File will be newly created, # so try again after short sleep if # the file is still missing. if not os . path . isfile ( '.texlipse' ) : time . sleep ( 0.1 ) if not os . path . isfile ( '.texlipse' ) : self . log . error ( '! Fata...
Read the project name from the texlipse config file . texlipse .
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def _read_latex_files ( self ) : if os . path . isfile ( '%s.aux' % self . project_name ) : cite_counter = self . generate_citation_counter ( ) self . read_glossaries ( ) else : cite_counter = { '%s.aux' % self . project_name : defaultdict ( int ) } fname = '%s.toc' % self . project_name if os . path . isfile ( fname )...
Check if some latex output files exist before first latex run process them and return the generated data .
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def read_glossaries ( self ) : filename = '%s.aux' % self . project_name with open ( filename ) as fobj : main_aux = fobj . read ( ) pattern = r'\\@newglossary\{(.*)\}\{.*\}\{(.*)\}\{(.*)\}' for match in re . finditer ( pattern , main_aux ) : name , ext_i , ext_o = match . groups ( ) self . glossaries [ name ] = ( ext_...
Read all existing glossaries in the main aux - file .
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def check_errors ( self ) : errors = ERROR_PATTTERN . findall ( self . out ) # "errors" is a list of tuples if errors : self . log . error ( '! Errors occurred:' ) self . log . error ( '\n' . join ( [ error . replace ( '\r' , '' ) . strip ( ) for error in chain ( * errors ) if error . strip ( ) ] ) ) self . log . error...
Check if errors occured during a latex run by scanning the output .
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def generate_citation_counter ( self ) : cite_counter = dict ( ) filename = '%s.aux' % self . project_name with open ( filename ) as fobj : main_aux = fobj . read ( ) cite_counter [ filename ] = _count_citations ( filename ) for match in re . finditer ( r'\\@input\{(.*.aux)\}' , main_aux ) : filename = match . groups (...
Generate dictionary with the number of citations in all included files . If this changes after the first latex run you have to run bibtex .
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def latex_run ( self ) : self . log . info ( 'Running %s...' % self . latex_cmd ) cmd = [ self . latex_cmd ] cmd . extend ( LATEX_FLAGS ) cmd . append ( '%s.tex' % self . project_name ) try : with open ( os . devnull , 'w' ) as null : Popen ( cmd , stdout = null , stderr = null ) . wait ( ) except OSError : self . log ...
Start latex run .
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def bibtex_run ( self ) : self . log . info ( 'Running bibtex...' ) try : with open ( os . devnull , 'w' ) as null : Popen ( [ 'bibtex' , self . project_name ] , stdout = null ) . wait ( ) except OSError : self . log . error ( NO_LATEX_ERROR % 'bibtex' ) sys . exit ( 1 ) shutil . copy ( '%s.bib' % self . bib_file , '%s...
Start bibtex run .
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def makeindex_runs ( self , gloss_files ) : gloss_changed = False for gloss in self . glossaries : make_gloss = False ext_i , ext_o = self . glossaries [ gloss ] fname_in = '%s.%s' % ( self . project_name , ext_i ) fname_out = '%s.%s' % ( self . project_name , ext_o ) if re . search ( 'No file %s.' % fname_in , self . ...
Check for each glossary if it has to be regenerated with makeindex .
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def open_preview ( self ) : self . log . info ( 'Opening preview...' ) if self . opt . pdf : ext = 'pdf' else : ext = 'dvi' filename = '%s.%s' % ( self . project_name , ext ) if sys . platform == 'win32' : try : os . startfile ( filename ) except OSError : self . log . error ( 'Preview-Error: Extension .%s is not linke...
Try to open a preview of the generated document . Currently only supported on Windows .
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def need_latex_rerun ( self ) : for pattern in LATEX_RERUN_PATTERNS : if pattern . search ( self . out ) : return True return False
Test for all rerun patterns if they match the output .
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def run ( self ) : # store files self . old_dir = [ ] if self . opt . clean : self . old_dir = os . listdir ( '.' ) cite_counter , toc_file , gloss_files = self . _read_latex_files ( ) self . latex_run ( ) self . read_glossaries ( ) gloss_changed = self . makeindex_runs ( gloss_files ) if gloss_changed or self . _is_to...
Run the LaTeX compilation .
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def command ( self , outfile , configfile , pix ) : params = dict ( script = self . config [ 'scan' ] [ 'script' ] , config = configfile , outfile = outfile , nside = self . nside_likelihood , pix = pix , verbose = '-v' if self . verbose else '' ) cmd = '%(script)s %(config)s %(outfile)s --hpx %(nside)i %(pix)i %(verbo...
Generate the command for running the likelihood scan .
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def submit_all ( self , coords = None , queue = None , debug = False ) : if coords is None : pixels = np . arange ( hp . nside2npix ( self . nside_likelihood ) ) else : coords = np . asarray ( coords ) if coords . ndim == 1 : coords = np . array ( [ coords ] ) if coords . shape [ 1 ] == 2 : lon , lat = coords . T radiu...
Submit likelihood analyses on a set of coordinates . If coords is None submit all coordinates in the footprint .
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def check ( cls ) : if cls == AppSettings : return None exceptions = [ ] for setting in cls . settings . values ( ) : try : setting . check ( ) # pylama:ignore=W0703 except Exception as e : exceptions . append ( str ( e ) ) if exceptions : raise ImproperlyConfigured ( "\n" . join ( exceptions ) )
Class method to check every settings .
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def parse_args ( name = "" , args = None ) : def _load_json_file ( path ) : with open ( path ) as f : json_data = json . load ( f ) json_data [ 'path_to_json_file' ] = path return json_data parser = argparse . ArgumentParser ( description = "%s collector for sending" " data to the performance" " platform" % name ) pars...
Parse command line argument for a collector
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def hash ( self ) : renderer_str = "{}|{}|{}|{}" . format ( self . renderer . __class__ . __name__ , self . renderer . colormap , self . renderer . fill_value , self . renderer . background_color ) if isinstance ( self . renderer , StretchedRenderer ) : renderer_str = "{}|{}|{}" . format ( renderer_str , self . rendere...
Returns a hash of this render configuration from the variable renderer and time_index parameters . Used for caching the full - extent native projection render so that subsequent requests can be served by a warp operation only .
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def factory ( type , module = None , * * kwargs ) : cls = type if module is None : module = __name__ fn = lambda member : inspect . isclass ( member ) and member . __module__ == module classes = odict ( inspect . getmembers ( sys . modules [ module ] , fn ) ) members = odict ( [ ( k . lower ( ) , v ) for k , v in class...
Factory for creating objects . Arguments are passed directly to the constructor of the chosen class .
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def get_definition_from_renderer ( renderer ) : config = { 'colors' : [ [ x [ 0 ] , x [ 1 ] . to_hex ( ) ] for x in renderer . colormap ] , 'options' : { } } if renderer . fill_value : config [ 'options' ] [ 'fill_value' ] = renderer . fill_value if isinstance ( renderer , StretchedRenderer ) : config [ 'type' ] = 'str...
Returns a dictionary definition of the given renderer
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def set_model ( self , name , model ) : # Careful to not use `hasattr` # https://hynek.me/articles/hasattr/ try : self . __getattribute__ ( 'models' ) except AttributeError : object . __setattr__ ( self , 'models' , odict ( ) ) self . models [ name ] = model
Set a model .
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def set_params ( self , * * kwargs ) : for key , value in list ( kwargs . items ( ) ) : setattr ( self , key , value )
Set the parameter values
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def get_params ( self ) : return odict ( [ ( key , param . value ) for key , param in self . params . items ( ) ] )
Get an odict of the parameter names and values
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def get_free_params ( self ) : return odict ( [ ( key , param . value ) for key , param in self . params . items ( ) if param . free ] )
Get an odict of free parameter names and values
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def iter_finds ( regex_obj , s ) : if isinstance ( regex_obj , str ) : for m in re . finditer ( regex_obj , s ) : yield m . group ( ) else : for m in regex_obj . finditer ( s ) : yield m . group ( )
Generate all matches found within a string for a regex and yield each match as a string
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def composite_decorator ( func ) : @ wraps ( func ) def wrapper ( self , * args , * * kwargs ) : total = [ ] for weight , iso in zip ( self . weights , self . isochrones ) : subfunc = getattr ( iso , func . __name__ ) total . append ( weight * subfunc ( * args , * * kwargs ) ) return np . sum ( total , axis = 0 ) retur...
Decorator for wrapping functions that calculate a weighted sum
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def mergeCatalogs ( catalog_list ) : # Check the columns for c in catalog_list : if c . data . dtype . names != catalog_list [ 0 ] . data . dtype . names : msg = "Catalog data columns not the same." raise Exception ( msg ) data = np . concatenate ( [ c . data for c in catalog_list ] ) config = catalog_list [ 0 ] . conf...
Merge a list of Catalogs .
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def applyCut ( self , cut ) : return Catalog ( self . config , data = self . data [ cut ] )
Return a new catalog which is a subset of objects selected using the input cut array .
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def bootstrap ( self , mc_bit = 0x10 , seed = None ) : if seed is not None : np . random . seed ( seed ) data = copy . deepcopy ( self . data ) idx = np . random . randint ( 0 , len ( data ) , len ( data ) ) data [ self . config [ 'catalog' ] [ 'mag_1_field' ] ] [ : ] = self . mag_1 [ idx ] data [ self . config [ 'cata...
Return a random catalog by boostrapping the colors of the objects in the current catalog .
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def project ( self , projector = None ) : msg = "'%s.project': ADW 2018-05-05" % self . __class__ . __name__ DeprecationWarning ( msg ) if projector is None : try : self . projector = ugali . utils . projector . Projector ( self . config [ 'coords' ] [ 'reference' ] [ 0 ] , self . config [ 'coords' ] [ 'reference' ] [ ...
Project coordinates on sphere to image plane using Projector class .
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def spatialBin ( self , roi ) : if hasattr ( self , 'pixel_roi_index' ) and hasattr ( self , 'pixel' ) : logger . warning ( 'Catalog alread spatially binned' ) return # ADW: Not safe to set index = -1 (since it will access last entry); # np.inf would be better... self . pixel = ang2pix ( self . config [ 'coords' ] [ 'n...
Calculate indices of ROI pixels corresponding to object locations .
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def write ( self , outfile , clobber = True , * * kwargs ) : fitsio . write ( outfile , self . data , clobber = True , * * kwargs )
Write the current object catalog to FITS file .
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def _parse ( self , roi = None , filenames = None ) : if ( roi is not None ) and ( filenames is not None ) : msg = "Cannot take both roi and filenames" raise Exception ( msg ) if roi is not None : pixels = roi . getCatalogPixels ( ) filenames = self . config . getFilenames ( ) [ 'catalog' ] [ pixels ] elif filenames is...
Parse catalog FITS files into recarray .
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def _defineVariables ( self ) : logger . info ( 'Catalog contains %i objects' % ( len ( self . data ) ) ) mc_source_id_field = self . config [ 'catalog' ] [ 'mc_source_id_field' ] if mc_source_id_field is not None : if mc_source_id_field not in self . data . dtype . names : array = np . zeros ( len ( self . data ) , dt...
Helper funtion to define pertinent variables from catalog data .
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def add_node ( self , node_id , task , inputs ) : if node_id in self . nodes_by_id : raise ValueError ( 'The node {0} already exists in this workflow.' . format ( node_id ) ) node = WorkflowNode ( node_id , task , inputs ) self . nodes_by_id [ node_id ] = node for source , value in six . itervalues ( inputs ) : if sour...
Adds a node to the workflow .
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def map_output ( self , node_id , node_output_name , parameter_name ) : self . output_mapping [ parameter_name ] = ( node_id , node_output_name ) dependents = self . dependents_by_node_id . get ( node_id , set ( ) ) dependents . add ( 'output_{}' . format ( parameter_name ) ) self . dependents_by_node_id [ node_id ] = ...
Maps the output from a node to a workflow output .
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def to_json ( self , indent = None ) : inputs = ParameterCollection ( self . inputs ) d = { 'meta' : { 'name' : self . name , 'description' : self . description } , 'inputs' : [ ] , 'workflow' : [ ] , 'outputs' : [ { 'name' : k , 'node' : v } for k , v in six . iteritems ( self . output_mapping ) ] } for parameter in s...
Serialize this workflow to JSON
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def from_json ( cls , text ) : d = json . loads ( text ) meta = d . get ( 'meta' , { } ) workflow = cls ( name = meta . get ( 'name' ) , description = meta . get ( 'description' ) ) for workflow_input in d . get ( 'inputs' , [ ] ) : parameter_cls = Parameter . by_id ( workflow_input [ 'type' ] ) args = [ workflow_input...
Return a new workflow deserialized from a JSON string
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def get_handler ( self , * args , * * options ) : handler = super ( ) . get_handler ( * args , * * options ) use_static_handler = options [ 'use_static_handler' ] insecure_serving = options [ 'insecure_serving' ] if use_static_handler and ( settings . DEBUG or insecure_serving ) : return CRAStaticFilesHandler ( handler...
Return the static files serving handler wrapping the default handler if static files should be served . Otherwise return the default handler .
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def surveyPixel ( lon , lat , nside_pix , nside_subpix = None ) : pix = np . unique ( ang2pix ( nside_pix , lon , lat ) ) if nside_subpix is None : return pix else : subpix_array = [ ] for ii in range ( 0 , len ( pix ) ) : subpix = subpixel ( pix [ ii ] , nside_pix , nside_subpix ) subpix_array . append ( subpix ) retu...
Return the set of HEALPix pixels that cover the given coordinates at resolution nside . Optionally return the set of subpixels within those pixels at resolution nside_subpix
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def allSkyCoordinates ( nside ) : lon , lat = pix2ang ( nside , np . arange ( 0 , hp . nside2npix ( nside ) ) ) return lon , lat
Generate a set of coordinates at the centers of pixels of resolutions nside across the full sky .
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def randomPositionsMask ( mask , nside_pix , n ) : npix = len ( mask ) nside = hp . npix2nside ( npix ) # Estimate the number of points that need to be thrown based off # coverage fraction of the HEALPix mask coverage_fraction = float ( np . sum ( mask ) ) / len ( mask ) n_throw = int ( n / coverage_fraction ) lon , la...
Generate n random positions within a HEALPix mask of booleans .
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def embed_ising ( source_linear , source_quadratic , embedding , target_adjacency , chain_strength = 1.0 ) : # store variables in the target graph that the embedding hasn't used unused = { v for v in target_adjacency } - set ( ) . union ( * embedding . values ( ) ) # ok, let's begin with the linear biases. # we spread ...
Embeds a logical Ising model onto another graph via an embedding .
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def chain_break_frequency ( samples , embedding ) : counts = { v : 0 for v in embedding } total = 0 for sample in samples : for v , chain in iteritems ( embedding ) : vals = [ sample [ u ] for u in chain ] if not _all_equal ( vals ) : counts [ v ] += 1 total += 1 return { v : counts [ v ] / total for v in embedding }
Determines the frequency of chain breaks in the given samples .
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def unembed_samples ( samples , embedding , chain_break_method = None ) : if chain_break_method is None : chain_break_method = majority_vote return list ( itertools . chain ( * ( chain_break_method ( sample , embedding ) for sample in samples ) ) )
Return samples over the variables in the source graph .
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def discard ( sample , embedding ) : unembeded = { } for v , chain in iteritems ( embedding ) : vals = [ sample [ u ] for u in chain ] if _all_equal ( vals ) : unembeded [ v ] = vals . pop ( ) else : return yield unembeded
Discards the sample if broken .
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def majority_vote ( sample , embedding ) : unembeded = { } for v , chain in iteritems ( embedding ) : vals = [ sample [ u ] for u in chain ] if _all_equal ( vals ) : unembeded [ v ] = vals . pop ( ) else : unembeded [ v ] = _most_common ( vals ) yield unembeded
Determines the sample values by majority vote .
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def weighted_random ( sample , embedding ) : unembeded = { } for v , chain in iteritems ( embedding ) : vals = [ sample [ u ] for u in chain ] # pick a random element uniformly from all vals, this weights them by # the proportion of each unembeded [ v ] = random . choice ( vals ) yield unembeded
Determines the sample values by weighed random choice .
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def _all_equal ( iterable ) : iterator = iter ( iterable ) first = next ( iterator ) return all ( first == rest for rest in iterator )
True if all values in iterable are equal else False .
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def _most_common ( iterable ) : data = Counter ( iterable ) return max ( data , key = data . __getitem__ )
Returns the most common element in iterable .
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def lnlike ( self , theta ) : params , loglike = self . params , self . loglike kwargs = dict ( list ( zip ( params , theta ) ) ) try : lnlike = loglike . value ( * * kwargs ) except ValueError as AssertionError : lnlike = - np . inf return lnlike
Logarithm of the likelihood
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def lnprior ( self , theta ) : params , priors = self . params , self . priors kwargs = dict ( list ( zip ( params , theta ) ) ) err = np . seterr ( invalid = 'raise' ) try : lnprior = np . sum ( np . log ( [ priors [ k ] ( v ) for k , v in list ( kwargs . items ( ) ) ] ) ) except ( FloatingPointError , ValueError ) : ...
Logarithm of the prior
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def lnprob ( self , theta ) : global niter params , priors , loglike = self . params , self . priors , self . loglike # Avoid extra likelihood calls with bad priors _lnprior = self . lnprior ( theta ) if np . isfinite ( _lnprior ) : _lnlike = self . lnlike ( theta ) else : _lnprior = - np . inf _lnlike = - np . inf _ln...
Logarithm of the probability
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def write_membership ( filename , config , srcfile , section = None ) : source = Source ( ) source . load ( srcfile , section = section ) loglike = createLoglike ( config , source ) loglike . write_membership ( filename )
Top level interface to write the membership from a config and source model .
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def createCatalog ( config , roi = None , lon = None , lat = None ) : import ugali . observation . catalog if roi is None : roi = createROI ( config , lon , lat ) catalog = ugali . observation . catalog . Catalog ( config , roi = roi ) return catalog
Create a catalog object
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def simulateCatalog ( config , roi = None , lon = None , lat = None ) : import ugali . simulation . simulator if roi is None : roi = createROI ( config , lon , lat ) sim = ugali . simulation . simulator . Simulator ( config , roi ) return sim . catalog ( )
Simulate a catalog object .
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def calc_observable_fraction ( self , distance_modulus ) : # This is the observable fraction after magnitude cuts in each # pixel of the ROI. observable_fraction = self . isochrone . observableFraction ( self . mask , distance_modulus ) if not observable_fraction . sum ( ) > 0 : msg = "No observable fraction" msg += ( ...
Calculated observable fraction within each pixel of the target region .
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