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def calc_signal_spatial ( self ) : # Calculate the surface intensity self . surface_intensity_sparse = self . calc_surface_intensity ( ) # Calculate the probability per object-by-object level self . surface_intensity_object = self . kernel . pdf ( self . catalog . lon , self . catalog . lat ) # Spatial component of sig...
Calculate the spatial signal probability for each catalog object .
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def fit_richness ( self , atol = 1.e-3 , maxiter = 50 ) : # Check whether the signal probability for all objects are zero # This can occur for finite kernels on the edge of the survey footprint if np . isnan ( self . u ) . any ( ) : logger . warning ( "NaN signal probability found" ) return 0. , 0. , None if not np . a...
Maximize the log - likelihood as a function of richness .
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def resolve_reference ( self , ref ) : url , resolved = self . resolver . resolve ( ref ) return resolved
Resolve a JSON Pointer object reference to the object itself .
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def get_path ( self , path ) : mapping = self . get_path_mapping ( path ) return self . path_class ( api = self , path = path , mapping = mapping )
Construct a Path object from a path string .
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def from_file ( cls , filename ) : with open ( filename ) as infp : if filename . endswith ( '.yaml' ) or filename . endswith ( '.yml' ) : import yaml data = yaml . safe_load ( infp ) else : import json data = json . load ( infp ) return cls . from_data ( data )
Construct an APIDefinition by parsing the given filename .
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def _server_error_message ( url , message ) : msg = _error_message . format ( url = url , message = message ) log . error ( msg ) return msg
Log and return a server error message .
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def make_request ( url , method = 'GET' , query = None , body = None , auth = None , timeout = 10 , client = None , macaroons = None ) : headers = { } kwargs = { 'timeout' : timeout , 'headers' : headers } # Handle the request body. if body is not None : if isinstance ( body , collections . Mapping ) : body = json . du...
Make a request with the provided data .
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def get_plans ( self , reference ) : response = make_request ( '{}charm?charm-url={}' . format ( self . url , 'cs:' + reference . path ( ) ) , timeout = self . timeout , client = self . _client ) try : return tuple ( map ( lambda plan : Plan ( url = plan [ 'url' ] , plan = plan [ 'plan' ] , created_on = datetime . date...
Get the plans for a given charm .
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def list_wallets ( self ) : response = make_request ( '{}wallet' . format ( self . url ) , timeout = self . timeout , client = self . _client ) try : total = response [ 'total' ] return { 'credit' : response [ 'credit' ] , 'total' : WalletTotal ( limit = total [ 'limit' ] , budgeted = total [ 'budgeted' ] , available =...
Get the list of wallets .
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def get_wallet ( self , wallet_name ) : response = make_request ( '{}wallet/{}' . format ( self . url , wallet_name ) , timeout = self . timeout , client = self . _client ) try : total = response [ 'total' ] return { 'credit' : response [ 'credit' ] , 'limit' : response [ 'limit' ] , 'total' : WalletTotal ( limit = tot...
Get a single wallet .
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def update_wallet ( self , wallet_name , limit ) : request = { 'update' : { 'limit' : str ( limit ) , } } return make_request ( '{}wallet/{}' . format ( self . url , wallet_name ) , method = 'PATCH' , body = request , timeout = self . timeout , client = self . _client )
Update a wallet with a new limit .
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def delete_wallet ( self , wallet_name ) : return make_request ( '{}wallet/{}' . format ( self . url , wallet_name ) , method = 'DELETE' , timeout = self . timeout , client = self . _client )
Delete a wallet .
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def create_budget ( self , wallet_name , model_uuid , limit ) : request = { 'model' : model_uuid , 'limit' : limit , } return make_request ( '{}wallet/{}/budget' . format ( self . url , wallet_name ) , method = 'POST' , body = request , timeout = self . timeout , client = self . _client )
Create a new budget for a model and wallet .
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def delete_budget ( self , model_uuid ) : return make_request ( '{}model/{}/budget' . format ( self . url , model_uuid ) , method = 'DELETE' , timeout = self . timeout , client = self . _client )
Delete a budget .
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def confusion ( df , labels = [ 'neg' , 'pos' ] ) : c = pd . DataFrame ( np . zeros ( ( 2 , 2 ) ) , dtype = int ) a , b = df . columns [ : 2 ] # labels[df.columns[:2]] c . columns = sorted ( set ( df [ a ] ) ) [ : 2 ] c . columns . name = a c . index = list ( c . columns ) c . index . name = b c1 , c2 = c . columns c [...
Binary classification confusion
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def thresh_from_spec ( spec , labels , scores , * * kwargs ) : cost_fun . verbose = kwargs . pop ( 'verbose' , cost_fun . verbose ) cost_fun . target = spec return minimize ( cost_fun , x0 = [ .5 ] , args = ( labels , scores ) , method = 'SLSQP' , constraints = ( { 'type' : 'ineq' , 'fun' : lambda x : np . array ( [ x ...
r Find the threshold level that accomplishes the desired specificity
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def add_dicts ( d1 , d2 ) : if d1 is None : return d2 if d2 is None : return d1 keys = set ( d1 ) keys . update ( set ( d2 ) ) ret = { } for key in keys : v1 = d1 . get ( key ) v2 = d2 . get ( key ) if v1 is None : ret [ key ] = v2 elif v2 is None : ret [ key ] = v1 else : ret [ key ] = v1 + v2 return ret
Merge two dicts of addable values
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def _update_capacity ( self , data ) : if 'ConsumedCapacity' in data : # This is all for backwards compatibility consumed = data [ 'ConsumedCapacity' ] if not isinstance ( consumed , list ) : consumed = [ consumed ] for cap in consumed : self . capacity += cap . get ( 'CapacityUnits' , 0 ) self . table_capacity += cap ...
Update the consumed capacity metrics
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def fetch ( self ) : self . limit . set_request_args ( self . kwargs ) data = self . connection . call ( * self . args , * * self . kwargs ) self . limit . post_fetch ( data ) self . last_evaluated_key = data . get ( 'LastEvaluatedKey' ) if self . last_evaluated_key is None : self . kwargs . pop ( 'ExclusiveStartKey' ,...
Fetch more results from Dynamo
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def build_kwargs ( self ) : keys , self . keys = self . keys [ : MAX_GET_BATCH ] , self . keys [ MAX_GET_BATCH : ] query = { 'ConsistentRead' : self . consistent } if self . attributes is not None : query [ 'ProjectionExpression' ] = self . attributes if self . alias : query [ 'ExpressionAttributeNames' ] = self . alia...
Construct the kwargs to pass to batch_get_item
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def fetch ( self ) : kwargs = self . build_kwargs ( ) data = self . connection . call ( 'batch_get_item' , * * kwargs ) if 'UnprocessedKeys' in data : for items in six . itervalues ( data [ 'UnprocessedKeys' ] ) : self . keys . extend ( items [ 'Keys' ] ) # Getting UnprocessedKeys indicates that we are exceeding our # ...
Fetch a set of items from their keys
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def copy ( self ) : return Limit ( self . scan_limit , self . item_limit , self . min_scan_limit , self . strict , self . filter )
Return a copy of the limit
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def set_request_args ( self , args ) : if self . scan_limit is not None : args [ 'Limit' ] = self . scan_limit elif self . item_limit is not None : args [ 'Limit' ] = max ( self . item_limit , self . min_scan_limit ) else : args . pop ( 'Limit' , None )
Set the Limit parameter into the request args
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def complete ( self ) : if self . scan_limit is not None and self . scan_limit == 0 : return True if self . item_limit is not None and self . item_limit == 0 : return True return False
Return True if the limit has been reached
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def accept ( self , item ) : accept = self . filter ( item ) if accept and self . item_limit is not None : if self . item_limit > 0 : self . item_limit -= 1 elif self . strict : return False return accept
Apply the filter and item_limit and return True to accept
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def returned ( n ) : ## `takei` yield lazily so we can short-circuit and avoid computing the rest of the walk for pos in randwalk ( ) >> drop ( 1 ) >> takei ( xrange ( n - 1 ) ) : if pos == Origin : return True return False
Generate a random walk and return True if the walker has returned to the origin after taking n steps .
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def first_return ( ) : walk = randwalk ( ) >> drop ( 1 ) >> takewhile ( lambda v : v != Origin ) >> list return len ( walk )
Generate a random walk and return its length upto the moment that the walker first returns to the origin .
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def seq ( start = 0 , step = 1 ) : def seq ( a , d ) : while 1 : yield a a += d return seq ( start , step )
An arithmetic sequence generator . Works with any type with + defined .
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def pipe ( inpipe , outpipe ) : if hasattr ( outpipe , '__pipe__' ) : return outpipe . __pipe__ ( inpipe ) elif hasattr ( outpipe , '__call__' ) : return outpipe ( inpipe ) else : raise BrokenPipe ( 'No connection mechanism defined' )
Connect inpipe and outpipe . If outpipe is not a Stream instance it should be an function callable on an iterable .
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def submit ( self , * items ) : with self . lock : if self . closed : raise BrokenPipe ( 'Job submission has been closed.' ) id = self . jobcount self . _status += [ 'SUBMITTED' ] * len ( items ) self . jobcount += len ( items ) for item in items : self . waitqueue . put ( ( id , item ) ) id += 1 if len ( items ) == 1 ...
Return job ids assigned to the submitted items .
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def cancel ( self , * ids ) : ncancelled = 0 with self . lock : for id in ids : try : if self . _status [ id ] == 'SUBMITTED' : self . _status [ id ] = 'CANCELLED' ncancelled += 1 except IndexError : pass return ncancelled
Try to cancel jobs with associated ids . Return the actual number of jobs cancelled .
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def shutdown ( self ) : with self . lock : self . pool . inqueue . put ( StopIteration ) # Stop the pool workers self . waitqueue . put ( StopIteration ) # Stop the input_feeder _iterqueue ( self . waitqueue ) >> item [ - 1 ] # Exhaust the waitqueue self . closed = True self . join ( )
Shut down the Executor . Suspend all waiting jobs . Running workers will terminate after finishing their current job items . The call will block until all workers are terminated .
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def main ( ) : arguments = docopt ( __doc__ , version = __version__ ) if arguments [ 'configure' ] and flag : configure ( ) if arguments [ 'cuisine' ] : if arguments [ 'list' ] : cuisine ( 'list' ) else : cuisine ( arguments [ '<cuisine-id>' ] ) elif arguments [ 'surprise' ] : surprise ( ) elif arguments [ 'reviews' ] ...
monica helps you order food from the timeline
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def _get_requirements ( fname ) : packages = _read ( fname ) . split ( '\n' ) packages = ( p . strip ( ) for p in packages ) packages = ( p for p in packages if p and not p . startswith ( '#' ) ) return list ( packages )
Create a list of requirements from the output of the pip freeze command saved in a text file .
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def TermsProcessor ( instance , placeholder , rendered_content , original_context ) : if 'terms' in original_context : return rendered_content return mark_safe ( replace_terms ( rendered_content ) )
Adds links all placeholders plugins except django - terms plugins
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def time_stops ( self ) : if not self . supports_time : return [ ] if self . service . calendar == 'standard' : units = self . service . time_interval_units interval = self . service . time_interval steps = [ self . time_start ] if units in ( 'years' , 'decades' , 'centuries' ) : if units == 'years' : years = interval ...
Valid time steps for this service as a list of datetime objects .
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def _parse_coords ( self , opts ) : # The coordinates are mutually exclusive, so # shouldn't have to worry about over-writing them. if 'coords' in vars ( opts ) : return radius = vars ( opts ) . get ( 'radius' , 0 ) gal = None if vars ( opts ) . get ( 'gal' ) is not None : gal = opts . gal elif vars ( opts ) . get ( 'c...
Parse target coordinates in various ways ...
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def default_value ( self ) : if callable ( self . default ) and self . call_default : return self . default ( ) return self . default
Property to return the default value .
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def raw_value ( self ) : if self . parent_setting is not None : return self . parent_setting . raw_value [ self . full_name ] else : return getattr ( settings , self . full_name )
Property to return the variable defined in django . conf . settings .
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def get_value ( self ) : try : value = self . raw_value except ( AttributeError , KeyError ) as err : self . _reraise_if_required ( err ) default_value = self . default_value if self . transform_default : return self . transform ( default_value ) return default_value else : return self . transform ( value )
Return the transformed raw or default value .
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def run_validators ( self , value ) : errors = [ ] for validator in self . validators : try : validator ( value ) except ValidationError as error : errors . extend ( error . messages ) if errors : raise ValidationError ( errors )
Run the validators on the setting value .
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def transform ( self , path ) : if path is None or not path : return None obj_parent_modules = path . split ( "." ) objects = [ obj_parent_modules . pop ( - 1 ) ] while True : try : parent_module_path = "." . join ( obj_parent_modules ) parent_module = importlib . import_module ( parent_module_path ) break except Impor...
Transform a path into an actual Python object .
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def get_value ( self ) : try : self . raw_value except ( AttributeError , KeyError ) as err : self . _reraise_if_required ( err ) default_value = self . default_value if self . transform_default : return self . transform ( default_value ) return default_value else : # If setting is defined, load values of all subsettin...
Return dictionary with values of subsettings .
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def sum_mags ( mags , weights = None ) : flux = 10 ** ( - np . asarray ( mags ) / 2.5 ) if weights is None : return - 2.5 * np . log10 ( np . sum ( flux ) ) else : return - 2.5 * np . log10 ( np . sum ( weights * flux ) )
Sum an array of magnitudes in flux space .
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def absolute_magnitude ( distance_modulus , g , r , prob = None ) : V = g - 0.487 * ( g - r ) - 0.0249 flux = np . sum ( 10 ** ( - ( V - distance_modulus ) / 2.5 ) ) Mv = - 2.5 * np . log10 ( flux ) return Mv
Calculate the absolute magnitude from a set of bands
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def observableFractionCDF ( self , mask , distance_modulus , mass_min = 0.1 ) : method = 'step' mass_init , mass_pdf , mass_act , mag_1 , mag_2 = self . sample ( mass_min = mass_min , full_data_range = False ) mag_1 = mag_1 + distance_modulus mag_2 = mag_2 + distance_modulus mask_1 , mask_2 = mask . mask_roi_unique . T...
Compute observable fraction of stars with masses greater than mass_min in each pixel in the interior region of the mask . Incorporates simplistic photometric errors .
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def histogram2d ( self , distance_modulus = None , delta_mag = 0.03 , steps = 10000 ) : if distance_modulus is not None : self . distance_modulus = distance_modulus # Isochrone will be binned, so might as well sample lots of points mass_init , mass_pdf , mass_act , mag_1 , mag_2 = self . sample ( mass_steps = steps ) #...
Return a 2D histogram the isochrone in mag - mag space .
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def pdf_mmd ( self , lon , lat , mag_1 , mag_2 , distance_modulus , mask , delta_mag = 0.03 , steps = 1000 ) : logger . info ( 'Running MMD pdf' ) roi = mask . roi mmd = self . signalMMD ( mask , distance_modulus , delta_mag = delta_mag , mass_steps = steps ) # This is fragile, store this information somewhere else... ...
Ok now here comes the beauty of having the signal MMD .
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def raw_separation ( self , mag_1 , mag_2 , steps = 10000 ) : # http://stackoverflow.com/q/12653120/ mag_1 = np . array ( mag_1 , copy = False , ndmin = 1 ) mag_2 = np . array ( mag_2 , copy = False , ndmin = 1 ) init , pdf , act , iso_mag_1 , iso_mag_2 = self . sample ( mass_steps = steps ) iso_mag_1 += self . distanc...
Calculate the separation in magnitude - magnitude space between points and isochrone . Uses a dense sampling of the isochrone and calculates the metric distance from any isochrone sample point .
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def separation ( self , mag_1 , mag_2 ) : iso_mag_1 = self . mag_1 + self . distance_modulus iso_mag_2 = self . mag_2 + self . distance_modulus def interp_iso ( iso_mag_1 , iso_mag_2 , mag_1 , mag_2 ) : interp_1 = scipy . interpolate . interp1d ( iso_mag_1 , iso_mag_2 , bounds_error = False ) interp_2 = scipy . interpo...
Calculate the separation between a specific point and the isochrone in magnitude - magnitude space . Uses an interpolation
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def get_handler ( self , operation_id ) : handler = ( self . handlers . get ( operation_id ) or self . handlers . get ( snake_case ( operation_id ) ) ) if handler : return handler raise MissingHandler ( 'Missing handler for operation %s (tried %s too)' % ( operation_id , snake_case ( operation_id ) ) )
Get the handler function for a given operation .
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def add_handlers ( self , namespace ) : if isinstance ( namespace , str ) : namespace = import_module ( namespace ) if isinstance ( namespace , dict ) : namespace = namespace . items ( ) else : namespace = vars ( namespace ) . items ( ) for name , value in namespace : if name . startswith ( '_' ) : continue if isfuncti...
Add handler functions from the given namespace for instance a module .
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def get_context ( self , arr , expr , context ) : expression_names = [ x for x in self . get_expression_names ( expr ) if x not in set ( context . keys ( ) ) . union ( [ 'i' ] ) ] if len ( expression_names ) != 1 : raise ValueError ( 'The expression must have exactly one variable.' ) return { expression_names [ 0 ] : a...
Returns a context dictionary for use in evaluating the expression .
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def execute ( self , array_in , expression , * * kwargs ) : context = self . get_context ( array_in , expression , kwargs ) context . update ( kwargs ) return ma . masked_where ( self . evaluate_expression ( expression , context ) , array_in )
Creates and returns a masked view of the input array .
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def timeout_error ( url , timeout ) : msg = 'Request timed out: {} timeout: {}s' . format ( url , timeout ) log . warning ( msg ) return ServerError ( msg )
Raise a server error indicating a request timeout to the given URL .
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def histogram ( title , title_x , title_y , x , bins_x ) : plt . figure ( ) plt . hist ( x , bins_x ) plt . xlabel ( title_x ) plt . ylabel ( title_y ) plt . title ( title )
Plot a basic histogram .
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def twoDimensionalHistogram ( title , title_x , title_y , z , bins_x , bins_y , lim_x = None , lim_y = None , vmin = None , vmax = None ) : plt . figure ( ) mesh_x , mesh_y = np . meshgrid ( bins_x , bins_y ) if vmin != None and vmin == vmax : plt . pcolor ( mesh_x , mesh_y , z ) else : plt . pcolor ( mesh_x , mesh_y ,...
Create a two - dimension histogram plot or binned map .
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def twoDimensionalScatter ( title , title_x , title_y , x , y , lim_x = None , lim_y = None , color = 'b' , size = 20 , alpha = None ) : plt . figure ( ) plt . scatter ( x , y , c = color , s = size , alpha = alpha , edgecolors = 'none' ) plt . xlabel ( title_x ) plt . ylabel ( title_y ) plt . title ( title ) if type (...
Create a two - dimensional scatter plot .
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def drawHealpixMap ( hpxmap , lon , lat , size = 1.0 , xsize = 501 , coord = 'GC' , * * kwargs ) : ax = plt . gca ( ) x = np . linspace ( - size , size , xsize ) y = np . linspace ( - size , size , xsize ) xx , yy = np . meshgrid ( x , y ) coord = coord . upper ( ) if coord == 'GC' : #Assumes map and (lon,lat) are Gala...
Draw local projection of healpix map .
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def getDSSImage ( ra , dec , radius = 1.0 , xsize = 800 , * * kwargs ) : import subprocess import tempfile service = 'skyview' if service == 'stsci' : url = "https://archive.stsci.edu/cgi-bin/dss_search?" scale = 2.0 * radius * 60. params = dict ( ra = '%.3f' % ra , dec = '%.3f' % dec , width = scale , height = scale ,...
Download Digitized Sky Survey images
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def draw_slices ( hist , func = np . sum , * * kwargs ) : from mpl_toolkits . axes_grid1 import make_axes_locatable kwargs . setdefault ( 'ls' , '-' ) ax = plt . gca ( ) data = hist # Slices vslice = func ( data , axis = 0 ) hslice = func ( data , axis = 1 ) npix = np . array ( data . shape ) #xlim,ylim = plt.array(zip...
Draw horizontal and vertical slices through histogram
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def plotSkymapCatalog ( lon , lat , * * kwargs ) : fig = plt . figure ( ) ax = plt . subplot ( 111 , projection = projection ) drawSkymapCatalog ( ax , lon , lat , * * kwargs )
Plot a catalog of coordinates on a full - sky map .
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def makePath ( x_path , y_path , epsilon = 1.e-10 ) : x_path_closed = np . concatenate ( [ x_path , x_path [ : : - 1 ] ] ) y_path_closed = np . concatenate ( [ y_path , epsilon + y_path [ : : - 1 ] ] ) path = matplotlib . path . Path ( list ( zip ( x_path_closed , y_path_closed ) ) ) return path
Create closed path .
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def drawMask ( self , ax = None , mask = None , mtype = 'maglim' ) : if not ax : ax = plt . gca ( ) if mask is None : mask = ugali . analysis . loglike . createMask ( self . config , roi = self . roi ) mask_map = hp . UNSEEN * np . ones ( hp . nside2npix ( self . nside ) ) if mtype . lower ( ) == 'maglim' : mask_map [ ...
Draw the maglim from the mask .
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def parse ( self , configManager , config ) : parser = ConfigParser . RawConfigParser ( ) configOptions = dict ( ) configFile = self . _getConfigFile ( config ) if configFile : parser . readfp ( configFile ) for section in parser . sections ( ) : if self . sections is None or section in self . sections : configOptions ...
Parse configuration options out of an . ini configuration file .
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def write_chapter ( self ) : self . paragraphs = [ ] self . paragraphs . append ( '\n' ) for x in range ( randint ( 0 , 50 ) ) : p = Paragraph ( self . model ) self . paragraphs . append ( p . get_paragraph ( ) ) self . paragraphs . append ( '\n' ) return self . paragraphs
Create a chapter that contains a random number of paragraphs
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def buildcss ( app , buildpath , imagefile ) : # set default values div = 'body' repeat = 'repeat-y' position = 'center' attachment = 'scroll' if app . config . sphinxmark_div != 'default' : div = app . config . sphinxmark_div if app . config . sphinxmark_repeat is False : repeat = 'no-repeat' if app . config . sphinxm...
Create CSS file .
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def createimage ( app , srcdir , buildpath ) : text = app . config . sphinxmark_text # draw transparent background width = app . config . sphinxmark_text_width height = app . config . sphinxmark_text_spacing img = Image . new ( 'RGBA' , ( width , height ) , ( 255 , 255 , 255 , 0 ) ) d = ImageDraw . Draw ( img ) # set f...
Create PNG image from string .
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def getimage ( app ) : # append source directory to TEMPLATE_PATH so template is found srcdir = os . path . abspath ( os . path . dirname ( __file__ ) ) TEMPLATE_PATH . append ( srcdir ) staticbase = '_static' buildpath = os . path . join ( app . outdir , staticbase ) try : os . makedirs ( buildpath ) except OSError : ...
Get image file .
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def watermark ( app , env ) : if app . config . sphinxmark_enable is True : LOG . info ( 'adding watermark...' , nonl = True ) buildpath , imagefile = getimage ( app ) cssname = buildcss ( app , buildpath , imagefile ) app . add_css_file ( cssname ) LOG . info ( ' done' )
Add watermark .
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def setup ( app ) : app . add_config_value ( 'sphinxmark_enable' , False , 'html' ) app . add_config_value ( 'sphinxmark_div' , 'default' , 'html' ) app . add_config_value ( 'sphinxmark_border' , None , 'html' ) app . add_config_value ( 'sphinxmark_repeat' , True , 'html' ) app . add_config_value ( 'sphinxmark_fixed' ,...
Configure setup for Sphinx extension .
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def gammalnStirling ( z ) : return ( 0.5 * ( np . log ( 2. * np . pi ) - np . log ( z ) ) ) + ( z * ( np . log ( z + ( 1. / ( ( 12. * z ) - ( 1. / ( 10. * z ) ) ) ) ) - 1. ) )
Uses Stirling s approximation for the log - gamma function suitable for large arguments .
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def satellite ( isochrone , kernel , stellar_mass , distance_modulus , * * kwargs ) : mag_1 , mag_2 = isochrone . simulate ( stellar_mass , distance_modulus ) lon , lat = kernel . simulate ( len ( mag_1 ) ) return mag_1 , mag_2 , lon , lat
Wrapping the isochrone and kernel simulate functions .
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def detectability ( self , * * kwargs ) : distance_modulus = kwargs . get ( 'distance_modulus' ) distance = mod2dist ( distance_modulus ) stellar_mass = kwargs . get ( 'stellar_mass' ) extension = kwargs . get ( 'extension' ) # Normalized to 10^3 Msolar at mod=18 norm = 10 ** 3 / mod2dist ( 18 ) ** 2 detect = stellar_m...
An a priori detectability proxy .
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def _create_catalog ( self , catalog = None ) : if catalog is None : catalog = ugali . analysis . loglike . createCatalog ( self . config , self . roi ) cut = self . mask . restrictCatalogToObservableSpace ( catalog ) self . catalog = catalog . applyCut ( cut )
Bundle it .
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def _setup_subpix ( self , nside = 2 ** 16 ) : # Only setup once... if hasattr ( self , 'subpix' ) : return # Simulate over full ROI self . roi_radius = self . config [ 'coords' ] [ 'roi_radius' ] # Setup background spatial stuff logger . info ( "Setup subpixels..." ) self . nside_pixel = self . config [ 'coords' ] [ '...
Subpixels for random position generation .
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def _setup_cmd ( self , mode = 'cloud-in-cells' ) : # Only setup once... if hasattr ( self , 'bkg_lambda' ) : return logger . info ( "Setup color..." ) # In the limit theta->0: 2*pi*(1-cos(theta)) -> pi*theta**2 # (Remember to convert from sr to deg^2) #solid_angle_roi = sr2deg(2*np.pi*(1-np.cos(np.radians(self.roi_rad...
The purpose here is to create a more finely binned background CMD to sample from .
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def toy_background ( self , mc_source_id = 2 , seed = None ) : logger . info ( "Running toy background simulation..." ) size = 20000 nstar = np . random . poisson ( size ) #np.random.seed(0) logger . info ( "Simulating %i background stars..." % nstar ) ### # Random points from roi pixels ### idx = np.random.randint(len...
Quick uniform background generation .
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def satellite ( self , stellar_mass , distance_modulus , mc_source_id = 1 , seed = None , * * kwargs ) : if seed is not None : np . random . seed ( seed ) isochrone = kwargs . pop ( 'isochrone' , self . isochrone ) kernel = kwargs . pop ( 'kernel' , self . kernel ) for k , v in kwargs . items ( ) : if k in kernel . par...
Create a simulated satellite . Returns a catalog object .
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def makeHDU ( self , mag_1 , mag_err_1 , mag_2 , mag_err_2 , lon , lat , mc_source_id ) : if self . config [ 'catalog' ] [ 'coordsys' ] . lower ( ) == 'cel' and self . config [ 'coords' ] [ 'coordsys' ] . lower ( ) == 'gal' : lon , lat = ugali . utils . projector . gal2cel ( lon , lat ) elif self . config [ 'catalog' ]...
Create a catalog fits file object based on input data .
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def inverted_dict ( d ) : return dict ( ( force_hashable ( v ) , k ) for ( k , v ) in viewitems ( dict ( d ) ) )
Return a dict with swapped keys and values
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def inverted_dict_of_lists ( d ) : new_dict = { } for ( old_key , old_value_list ) in viewitems ( dict ( d ) ) : for new_key in listify ( old_value_list ) : new_dict [ new_key ] = old_key return new_dict
Return a dict where the keys are all the values listed in the values of the original dict
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def sort_strings ( strings , sort_order = None , reverse = False , case_sensitive = False , sort_order_first = True ) : if not case_sensitive : sort_order = tuple ( s . lower ( ) for s in sort_order ) strings = tuple ( s . lower ( ) for s in strings ) prefix_len = max ( len ( s ) for s in sort_order ) def compare ( a ,...
Sort a list of strings according to the provided sorted list of string prefixes
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def clean_field_dict ( field_dict , cleaner = str . strip , time_zone = None ) : d = { } if time_zone is None : tz = DEFAULT_TZ for k , v in viewitems ( field_dict ) : if k == '_state' : continue if isinstance ( v , basestring ) : d [ k ] = cleaner ( str ( v ) ) elif isinstance ( v , ( datetime . datetime , datetime . ...
r Normalize field values by stripping whitespace from strings localizing datetimes to a timezone etc
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def generate_tuple_batches ( qs , batch_len = 1 ) : num_items , batch = 0 , [ ] for item in qs : if num_items >= batch_len : yield tuple ( batch ) num_items = 0 batch = [ ] num_items += 1 batch += [ item ] if num_items : yield tuple ( batch )
Iterate through a queryset in batches of length batch_len
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def find_count_label ( d ) : for name in COUNT_NAMES : if name in d : return name for name in COUNT_NAMES : if str ( name ) . lower ( ) in d : return name
Find the member of a set that means count or frequency or probability or number of occurrences .
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def fuzzy_get_value ( obj , approximate_key , default = None , * * kwargs ) : dict_obj = OrderedDict ( obj ) try : return dict_obj [ list ( dict_obj . keys ( ) ) [ int ( approximate_key ) ] ] except ( ValueError , IndexError ) : pass return fuzzy_get ( dict_obj , approximate_key , key_and_value = False , * * kwargs )
Like fuzzy_get but assume the obj is dict - like and return the value without the key
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def joined_seq ( seq , sep = None ) : joined_seq = tuple ( seq ) if isinstance ( sep , basestring ) : joined_seq = sep . join ( str ( item ) for item in joined_seq ) return joined_seq
r Join a sequence into a tuple or a concatenated string
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def dos_from_table ( table , header = None ) : start_row = 0 if not table : return table if not header : header = table [ 0 ] start_row = 1 header_list = header if header and isinstance ( header , basestring ) : header_list = header . split ( '\t' ) if len ( header_list ) != len ( table [ 0 ] ) : header_list = header ....
Produce dictionary of sequences from sequence of sequences optionally with a header row .
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def transposed_lists ( list_of_lists , default = None ) : if default is None or default is [ ] or default is tuple ( ) : default = [ ] elif default is 'None' : default = [ None ] else : default = [ default ] N = len ( list_of_lists ) Ms = [ len ( row ) for row in list_of_lists ] M = max ( Ms ) ans = [ ] for j in range ...
Like numpy . transposed but allows uneven row lengths
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def hist_from_counts ( counts , normalize = False , cumulative = False , to_str = False , sep = ',' , min_bin = None , max_bin = None ) : counters = [ dict ( ( i , c ) for i , c in enumerate ( counts ) ) ] intkeys_list = [ [ c for c in counts_dict if ( isinstance ( c , int ) or ( isinstance ( c , float ) and int ( c ) ...
Compute an emprical histogram PMF or CDF in a list of lists
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def get_similar ( obj , labels , default = None , min_similarity = 0.5 ) : raise NotImplementedError ( "Unfinished implementation, needs to be in fuzzy_get where list of scores & keywords is sorted." ) labels = listify ( labels ) def not_found ( * args , * * kwargs ) : return 0 min_score = int ( min_similarity * 100 ) ...
Similar to fuzzy_get but allows non - string keys and a list of possible keys
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def update_file_ext ( filename , ext = 'txt' , sep = '.' ) : path , filename = os . path . split ( filename ) if ext and ext [ 0 ] == sep : ext = ext [ 1 : ] return os . path . join ( path , sep . join ( filename . split ( sep ) [ : - 1 if filename . count ( sep ) > 1 else 1 ] + [ ext ] ) )
r Force the file or path str to end with the indicated extension
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def transcode ( infile , outfile = None , incoding = "shift-jis" , outcoding = "utf-8" ) : if not outfile : outfile = os . path . basename ( infile ) + '.utf8' with codecs . open ( infile , "rb" , incoding ) as fpin : with codecs . open ( outfile , "wb" , outcoding ) as fpout : fpout . write ( fpin . read ( ) )
Change encoding of text file
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def dict2obj ( d ) : if isinstance ( d , ( Mapping , list , tuple ) ) : try : d = dict ( d ) except ( ValueError , TypeError ) : return d else : return d obj = Object ( ) for k , v in viewitems ( d ) : obj . __dict__ [ k ] = dict2obj ( v ) return obj
Convert a dict to an object or namespace
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def int_pair ( s , default = ( 0 , None ) ) : s = re . split ( r'[^0-9]+' , str ( s ) . strip ( ) ) if len ( s ) and len ( s [ 0 ] ) : if len ( s ) > 1 and len ( s [ 1 ] ) : return ( int ( s [ 0 ] ) , int ( s [ 1 ] ) ) return ( int ( s [ 0 ] ) , default [ 1 ] ) return default
Return the digits to either side of a single non - digit character as a 2 - tuple of integers
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def make_float ( s , default = '' , ignore_commas = True ) : if ignore_commas and isinstance ( s , basestring ) : s = s . replace ( ',' , '' ) try : return float ( s ) except ( IndexError , ValueError , AttributeError , TypeError ) : try : return float ( str ( s ) ) except ValueError : try : return float ( normalize_sc...
r Coerce a string into a float
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def normalize_names ( names ) : if isinstance ( names , basestring ) : names = names . split ( ',' ) names = listify ( names ) return [ str ( name ) . strip ( ) for name in names ]
Coerce a string or nested list of strings into a flat list of strings .
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def normalize_serial_number ( sn , max_length = None , left_fill = '0' , right_fill = str ( ) , blank = str ( ) , valid_chars = ' -0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' , invalid_chars = None , strip_whitespace = True , join = False , na = rex . nones ) : # All 9 kwargs have persistent default...
r Make a string compatible with typical serial number requirements
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def strip_HTML ( s ) : result = '' total = 0 for c in s : if c == '<' : total = 1 elif c == '>' : total = 0 result += ' ' elif total == 0 : result += c return result
Simple clumsy slow HTML tag stripper
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