idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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57,900 | def precmd ( self , line ) : if line . startswith ( 'help' ) : if not q ( "`help in key`.q" ) : try : q ( "\\l help.q" ) except kerr : return '-1"no help available - install help.q"' if line == 'help' : line += "`" return line | Support for help |
57,901 | def onecmd ( self , line ) : if line == '\\' : return True elif line == 'EOF' : print ( '\r' , end = '' ) return True else : try : v = q ( line ) except kerr as e : print ( "'%s" % e . args [ 0 ] ) else : if v != q ( '::' ) : v . show ( ) return False | Interpret the line |
57,902 | def run ( q_prompt = False ) : lines , columns = console_size ( ) q ( r'\c %d %d' % ( lines , columns ) ) if len ( sys . argv ) > 1 : try : q ( r'\l %s' % sys . argv [ 1 ] ) except kerr as e : print ( e ) raise SystemExit ( 1 ) else : del sys . argv [ 1 ] if q_prompt : q ( ) ptp . run ( ) | Run a prompt - toolkit based REPL |
57,903 | def get_unit ( a ) : typestr = a . dtype . str i = typestr . find ( '[' ) if i == - 1 : raise TypeError ( "Expected a datetime64 array, not %s" , a . dtype ) return typestr [ i + 1 : - 1 ] | Extract the time unit from array s dtype |
57,904 | def k2a ( a , x ) : func , scale = None , 1 t = abs ( x . _t ) if 12 <= t <= 15 : unit = get_unit ( a ) attr , shift , func , scale = _UNIT [ unit ] a [ : ] = getattr ( x , attr ) . data a += shift elif 16 <= t <= 19 : unit = get_unit ( a ) func , scale = _SCALE [ unit ] a [ : ] = x . timespan . data else : a [ : ] = l... | Rescale data from a K object x to array a . |
57,905 | def show ( self , start = 0 , geometry = None , output = None ) : if output is None : output = sys . stdout if geometry is None : geometry = q . value ( kp ( "\\c" ) ) else : geometry = self . _I ( geometry ) if start < 0 : start += q . count ( self ) if self . _id ( ) != nil . _id ( ) : r = self . _show ( geometry , s... | pretty - print data to the console |
57,906 | def select ( self , columns = ( ) , by = ( ) , where = ( ) , ** kwds ) : return self . _seu ( 'select' , columns , by , where , kwds ) | select from self |
57,907 | def exec_ ( self , columns = ( ) , by = ( ) , where = ( ) , ** kwds ) : return self . _seu ( 'exec' , columns , by , where , kwds ) | exec from self |
57,908 | def update ( self , columns = ( ) , by = ( ) , where = ( ) , ** kwds ) : return self . _seu ( 'update' , columns , by , where , kwds ) | update from self |
57,909 | def dict ( cls , * args , ** kwds ) : if args : if len ( args ) > 1 : raise TypeError ( "Too many positional arguments" ) x = args [ 0 ] keys = [ ] vals = [ ] try : x_keys = x . keys except AttributeError : for k , v in x : keys . append ( k ) vals . append ( v ) else : keys = x_keys ( ) vals = [ x [ k ] for k in keys ... | Construct a q dictionary |
57,910 | def logical_lines ( lines ) : if isinstance ( lines , string_types ) : lines = StringIO ( lines ) buf = [ ] for line in lines : if buf and not line . startswith ( ' ' ) : chunk = '' . join ( buf ) . strip ( ) if chunk : yield chunk buf [ : ] = [ ] buf . append ( line ) chunk = '' . join ( buf ) . strip ( ) if chunk : y... | Merge lines into chunks according to q rules |
57,911 | def q ( line , cell = None , _ns = None ) : if cell is None : return pyq . q ( line ) if _ns is None : _ns = vars ( sys . modules [ '__main__' ] ) input = output = None preload = [ ] outs = { } try : h = pyq . q ( '0i' ) if line : for opt , value in getopt ( line . split ( ) , "h:l:o:i:12" ) [ 0 ] : if opt == '-l' : pr... | Run q code . |
57,912 | def load_ipython_extension ( ipython ) : ipython . register_magic_function ( q , 'line_cell' ) fmr = ipython . display_formatter . formatters [ 'text/plain' ] fmr . for_type ( pyq . K , _q_formatter ) | Register %q and %%q magics and pretty display for K objects |
57,913 | def get_prompt_tokens ( _ ) : namespace = q ( r'\d' ) if namespace == '.' : namespace = '' return [ ( Token . Generic . Prompt , 'q%s)' % namespace ) ] | Return a list of tokens for the prompt |
57,914 | def cmdloop ( self , intro = None ) : style = style_from_pygments ( BasicStyle , style_dict ) self . preloop ( ) stop = None while not stop : line = prompt ( get_prompt_tokens = get_prompt_tokens , lexer = lexer , get_bottom_toolbar_tokens = get_bottom_toolbar_tokens , history = history , style = style , true_color = T... | A Cmd . cmdloop implementation |
57,915 | def eval ( source , kwd_dict = None , ** kwds ) : kwd_dict = kwd_dict or kwds with ctx ( kwd_dict ) : return handleLine ( source ) | Evaluate a snuggs expression . |
57,916 | def _make_matchers ( self , crontab ) : crontab = _aliases . get ( crontab , crontab ) ct = crontab . split ( ) if len ( ct ) == 5 : ct . insert ( 0 , '0' ) ct . append ( '*' ) elif len ( ct ) == 6 : ct . insert ( 0 , '0' ) _assert ( len ( ct ) == 7 , "improper number of cron entries specified; got %i need 5 to 7" % ( ... | This constructs the full matcher struct . |
57,917 | def next ( self , now = None , increments = _increments , delta = True , default_utc = WARN_CHANGE ) : if default_utc is WARN_CHANGE and ( isinstance ( now , _number_types ) or ( now and not now . tzinfo ) or now is None ) : warnings . warn ( WARNING_CHANGE_MESSAGE , FutureWarning , 2 ) default_utc = False now = now or... | How long to wait in seconds before this crontab entry can next be executed . |
57,918 | def _tostring ( value ) : if value is True : value = 'true' elif value is False : value = 'false' elif value is None : value = '' return unicode ( value ) | Convert value to XML compatible string |
57,919 | def _fromstring ( value ) : if value is None : return None if value . lower ( ) == 'true' : return True elif value . lower ( ) == 'false' : return False try : return int ( value ) except ValueError : pass try : if float ( '-inf' ) < float ( value ) < float ( 'inf' ) : return float ( value ) except ValueError : pass ret... | Convert XML string value to None boolean int or float |
57,920 | def by_col ( cls , df , cols , w = None , inplace = False , pvalue = 'sim' , outvals = None , ** stat_kws ) : if outvals is None : outvals = [ ] outvals . extend ( [ 'bb' , 'p_sim_bw' , 'p_sim_bb' ] ) pvalue = '' return _univariate_handler ( df , cols , w = w , inplace = inplace , pvalue = pvalue , outvals = outvals , ... | Function to compute a Join_Count statistic on a dataframe |
57,921 | def Moran_BV_matrix ( variables , w , permutations = 0 , varnames = None ) : try : import pandas if isinstance ( variables , pandas . DataFrame ) : varnames = pandas . Index . tolist ( variables . columns ) variables_n = [ ] for var in varnames : variables_n . append ( variables [ str ( var ) ] . values ) else : variab... | Bivariate Moran Matrix |
57,922 | def _Moran_BV_Matrix_array ( variables , w , permutations = 0 , varnames = None ) : if varnames is None : varnames = [ 'x{}' . format ( i ) for i in range ( k ) ] k = len ( variables ) rk = list ( range ( 0 , k - 1 ) ) results = { } for i in rk : for j in range ( i + 1 , k ) : y1 = variables [ i ] y2 = variables [ j ] ... | Base calculation for MORAN_BV_Matrix |
57,923 | def by_col ( cls , df , x , y = None , w = None , inplace = False , pvalue = 'sim' , outvals = None , ** stat_kws ) : return _bivariate_handler ( df , x , y = y , w = w , inplace = inplace , pvalue = pvalue , outvals = outvals , swapname = cls . __name__ . lower ( ) , stat = cls , ** stat_kws ) | Function to compute a Moran_BV statistic on a dataframe |
57,924 | def by_col ( cls , df , events , populations , w = None , inplace = False , pvalue = 'sim' , outvals = None , swapname = '' , ** stat_kws ) : if not inplace : new = df . copy ( ) cls . by_col ( new , events , populations , w = w , inplace = True , pvalue = pvalue , outvals = outvals , swapname = swapname , ** stat_kws ... | Function to compute a Moran_Rate statistic on a dataframe |
57,925 | def flatten ( l , unique = True ) : l = reduce ( lambda x , y : x + y , l ) if not unique : return list ( l ) return list ( set ( l ) ) | flatten a list of lists |
57,926 | def weighted_median ( d , w ) : dtype = [ ( 'w' , '%s' % w . dtype ) , ( 'v' , '%s' % d . dtype ) ] d_w = np . array ( list ( zip ( w , d ) ) , dtype = dtype ) d_w . sort ( order = 'v' ) reordered_w = d_w [ 'w' ] . cumsum ( ) cumsum_threshold = reordered_w [ - 1 ] * 1.0 / 2 median_inx = ( reordered_w >= cumsum_threshol... | A utility function to find a median of d based on w |
57,927 | def sum_by_n ( d , w , n ) : t = len ( d ) h = t // n d = d * w return np . array ( [ sum ( d [ i : i + h ] ) for i in range ( 0 , t , h ) ] ) | A utility function to summarize a data array into n values after weighting the array with another weight array w |
57,928 | def crude_age_standardization ( e , b , n ) : r = e * 1.0 / b b_by_n = sum_by_n ( b , 1.0 , n ) age_weight = b * 1.0 / b_by_n . repeat ( len ( e ) // n ) return sum_by_n ( r , age_weight , n ) | A utility function to compute rate through crude age standardization |
57,929 | def direct_age_standardization ( e , b , s , n , alpha = 0.05 ) : age_weight = ( 1.0 / b ) * ( s * 1.0 / sum_by_n ( s , 1.0 , n ) . repeat ( len ( s ) // n ) ) adjusted_r = sum_by_n ( e , age_weight , n ) var_estimate = sum_by_n ( e , np . square ( age_weight ) , n ) g_a = np . square ( adjusted_r ) / var_estimate g_b ... | A utility function to compute rate through direct age standardization |
57,930 | def indirect_age_standardization ( e , b , s_e , s_b , n , alpha = 0.05 ) : smr = standardized_mortality_ratio ( e , b , s_e , s_b , n ) s_r_all = sum ( s_e * 1.0 ) / sum ( s_b * 1.0 ) adjusted_r = s_r_all * smr e_by_n = sum_by_n ( e , 1.0 , n ) log_smr = np . log ( smr ) log_smr_sd = 1.0 / np . sqrt ( e_by_n ) norm_th... | A utility function to compute rate through indirect age standardization |
57,931 | def _univariate_handler ( df , cols , stat = None , w = None , inplace = True , pvalue = 'sim' , outvals = None , swapname = '' , ** kwargs ) : if not inplace : new_df = df . copy ( ) _univariate_handler ( new_df , cols , stat = stat , w = w , pvalue = pvalue , inplace = True , outvals = outvals , swapname = swapname ,... | Compute a univariate descriptive statistic stat over columns cols in df . |
57,932 | def _bivariate_handler ( df , x , y = None , w = None , inplace = True , pvalue = 'sim' , outvals = None , ** kwargs ) : real_swapname = kwargs . pop ( 'swapname' , '' ) if isinstance ( y , str ) : y = [ y ] if isinstance ( x , str ) : x = [ x ] if not inplace : new_df = df . copy ( ) _bivariate_handler ( new_df , x , ... | Compute a descriptive bivariate statistic over two sets of columns x and y contained in df . |
57,933 | def _swap_ending ( s , ending , delim = '_' ) : parts = [ x for x in s . split ( delim ) [ : - 1 ] if x != '' ] parts . append ( ending ) return delim . join ( parts ) | Replace the ending of a string delimited into an arbitrary number of chunks by delim with the ending provided |
57,934 | def is_sequence ( i , include = None ) : return ( hasattr ( i , '__getitem__' ) and iterable ( i ) or bool ( include ) and isinstance ( i , include ) ) | Return a boolean indicating whether i is a sequence in the SymPy sense . If anything that fails the test below should be included as being a sequence for your application set include to that object s type ; multiple types should be passed as a tuple of types . |
57,935 | def as_int ( n ) : try : result = int ( n ) if result != n : raise TypeError except TypeError : raise ValueError ( '%s is not an integer' % n ) return result | Convert the argument to a builtin integer . |
57,936 | def default_sort_key ( item , order = None ) : from sympy . core import S , Basic from sympy . core . sympify import sympify , SympifyError from sympy . core . compatibility import iterable if isinstance ( item , Basic ) : return item . sort_key ( order = order ) if iterable ( item , exclude = string_types ) : if isins... | Return a key that can be used for sorting . |
57,937 | def var ( names , ** args ) : def traverse ( symbols , frame ) : for symbol in symbols : if isinstance ( symbol , Basic ) : frame . f_globals [ symbol . __str__ ( ) ] = symbol else : traverse ( symbol , frame ) from inspect import currentframe frame = currentframe ( ) . f_back try : syms = symbols ( names , ** args ) i... | Create symbols and inject them into the global namespace . |
57,938 | def _combine_attribute_arguments ( self , attr_dict , attr ) : if attr_dict is None : attr_dict = attr else : try : attr_dict . update ( attr ) except AttributeError : raise AttributeError ( "attr_dict argument \ must be a dictionary." ) return attr_dict | Combines attr_dict and attr dictionaries by updating attr_dict with attr . |
57,939 | def remove_node ( self , node ) : if not self . has_node ( node ) : raise ValueError ( "No such node exists." ) for hyperedge_id in self . _star [ node ] : frozen_nodes = self . _hyperedge_attributes [ hyperedge_id ] [ "__frozen_nodes" ] del self . _node_set_to_hyperedge [ frozen_nodes ] del self . _hyperedge_attribute... | Removes a node and its attributes from the hypergraph . Removes every hyperedge that contains this node . |
57,940 | def add_hyperedge ( self , nodes , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) if not nodes : raise ValueError ( "nodes argument cannot be empty." ) frozen_nodes = frozenset ( nodes ) is_new_hyperedge = not self . has_hyperedge ( frozen_nodes ) if is_new_hyperedge... | Adds a hyperedge to the hypergraph along with any related attributes of the hyperedge . This method will automatically add any node from the node set that was not in the hypergraph . A hyperedge without a weight attribute specified will be assigned the default value of 1 . |
57,941 | def add_hyperedges ( self , hyperedges , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) hyperedge_ids = [ ] for nodes in hyperedges : hyperedge_id = self . add_hyperedge ( nodes , attr_dict . copy ( ) ) hyperedge_ids . append ( hyperedge_id ) return hyperedge_ids | Adds multiple hyperedges to the graph along with any related attributes of the hyperedges . If any node of a hyperedge has not previously been added to the hypergraph it will automatically be added here . Hyperedges without a weight attribute specified will be assigned the default value of 1 . |
57,942 | def get_hyperedge_id ( self , nodes ) : frozen_nodes = frozenset ( nodes ) if not self . has_hyperedge ( frozen_nodes ) : raise ValueError ( "No such hyperedge exists." ) return self . _node_set_to_hyperedge [ frozen_nodes ] | From a set of nodes returns the ID of the hyperedge that this set comprises . |
57,943 | def get_hyperedge_attribute ( self , hyperedge_id , attribute_name ) : if not self . has_hyperedge_id ( hyperedge_id ) : raise ValueError ( "No such hyperedge exists." ) elif attribute_name not in self . _hyperedge_attributes [ hyperedge_id ] : raise ValueError ( "No such attribute exists." ) else : return copy . copy ... | Given a hyperedge ID and the name of an attribute get a copy of that hyperedge s attribute . |
57,944 | def get_hyperedge_attributes ( self , hyperedge_id ) : if not self . has_hyperedge_id ( hyperedge_id ) : raise ValueError ( "No such hyperedge exists." ) dict_to_copy = self . _hyperedge_attributes [ hyperedge_id ] . items ( ) attributes = { } for attr_name , attr_value in dict_to_copy : if attr_name != "__frozen_nodes... | Given a hyperedge ID get a dictionary of copies of that hyperedge s attributes . |
57,945 | def get_star ( self , node ) : if node not in self . _node_attributes : raise ValueError ( "No such node exists." ) return self . _star [ node ] . copy ( ) | Given a node get a copy of that node s star that is the set of hyperedges that the node belongs to . |
57,946 | def _F_outdegree ( H , F ) : if not isinstance ( H , DirectedHypergraph ) : raise TypeError ( "Algorithm only applicable to directed hypergraphs" ) return F ( [ len ( H . get_forward_star ( node ) ) for node in H . get_node_set ( ) ] ) | Returns the result of a function F applied to the set of outdegrees in in the hypergraph . |
57,947 | def _F_indegree ( H , F ) : if not isinstance ( H , DirectedHypergraph ) : raise TypeError ( "Algorithm only applicable to directed hypergraphs" ) return F ( [ len ( H . get_backward_star ( node ) ) for node in H . get_node_set ( ) ] ) | Returns the result of a function F applied to the list of indegrees in in the hypergraph . |
57,948 | def _F_hyperedge_tail_cardinality ( H , F ) : if not isinstance ( H , DirectedHypergraph ) : raise TypeError ( "Algorithm only applicable to directed hypergraphs" ) return F ( [ len ( H . get_hyperedge_tail ( hyperedge_id ) ) for hyperedge_id in H . get_hyperedge_id_set ( ) ] ) | Returns the result of a function F applied to the set of cardinalities of hyperedge tails in the hypergraph . |
57,949 | def _F_hyperedge_head_cardinality ( H , F ) : if not isinstance ( H , DirectedHypergraph ) : raise TypeError ( "Algorithm only applicable to directed hypergraphs" ) return F ( [ len ( H . get_hyperedge_head ( hyperedge_id ) ) for hyperedge_id in H . get_hyperedge_id_set ( ) ] ) | Returns the result of a function F applied to the set of cardinalities of hyperedge heads in the hypergraph . |
57,950 | def get_hyperedge_weight_matrix ( H , hyperedge_ids_to_indices ) : hyperedge_weights = { } for hyperedge_id in H . hyperedge_id_iterator ( ) : hyperedge_weights . update ( { hyperedge_ids_to_indices [ hyperedge_id ] : H . get_hyperedge_weight ( hyperedge_id ) } ) hyperedge_weight_vector = [ ] for i in range ( len ( hyp... | Creates the diagonal matrix W of hyperedge weights as a sparse matrix . |
57,951 | def get_hyperedge_degree_matrix ( M ) : degrees = M . sum ( 0 ) . transpose ( ) new_degree = [ ] for degree in degrees : new_degree . append ( int ( degree [ 0 : ] ) ) return sparse . diags ( [ new_degree ] , [ 0 ] ) | Creates the diagonal matrix of hyperedge degrees D_e as a sparse matrix where a hyperedge degree is the cardinality of the hyperedge . |
57,952 | def fast_inverse ( M ) : diags = M . diagonal ( ) new_diag = [ ] for value in diags : new_diag . append ( 1.0 / value ) return sparse . diags ( [ new_diag ] , [ 0 ] ) | Computes the inverse of a diagonal matrix . |
57,953 | def node_iterator ( self ) : return iter ( self . _node_attributes ) def has_hypernode ( self , hypernode ) : return hypernode in self . _hypernode_attributes | Provides an iterator over the nodes . |
57,954 | def add_hypernode ( self , hypernode , composing_nodes = set ( ) , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) if not self . has_hypernode ( hypernode ) : attr_dict [ "__composing_nodes" ] = composing_nodes added_nodes = composing_nodes removed_nodes = set ( ) sel... | Adds a hypernode to the graph along with any related attributes of the hypernode . |
57,955 | def _create_random_starter ( node_count ) : pi = np . zeros ( node_count , dtype = float ) for i in range ( node_count ) : pi [ i ] = random . random ( ) summation = np . sum ( pi ) for i in range ( node_count ) : pi [ i ] = pi [ i ] / summation return pi | Creates the random starter for the random walk . |
57,956 | def _has_converged ( pi_star , pi ) : node_count = pi . shape [ 0 ] EPS = 10e-6 for i in range ( node_count ) : if pi [ i ] - pi_star [ i ] > EPS : return False return True | Checks if the random walk has converged . |
57,957 | def add_element ( self , priority , element , count = None ) : if count is None : count = next ( self . counter ) entry = [ priority , count , element ] self . element_finder [ element ] = entry heapq . heappush ( self . pq , entry ) | Adds an element with a specific priority . |
57,958 | def reprioritize ( self , priority , element ) : if element not in self . element_finder : raise ValueError ( "No such element in the priority queue." ) entry = self . element_finder [ element ] self . add_element ( priority , element , entry [ 1 ] ) entry [ 1 ] = self . INVALID | Updates the priority of an element . |
57,959 | def contains_element ( self , element ) : return ( element in self . element_finder ) and ( self . element_finder [ element ] [ 1 ] != self . INVALID ) | Determines if an element is contained in the priority queue . |
57,960 | def is_empty ( self ) : while self . pq : if self . pq [ 0 ] [ 1 ] != self . INVALID : return False else : _ , _ , element = heapq . heappop ( self . pq ) if element in self . element_finder : del self . element_finder [ element ] return True | Determines if the priority queue has any elements . Performs removal of any elements that were marked - as - invalid . |
57,961 | def is_connected ( H , source_node , target_node ) : visited_nodes , Pv , Pe = visit ( H , source_node ) return target_node in visited_nodes | Checks if a target node is connected to a source node . That is this method determines if a target node can be visited from the source node in the sense of the Visit algorithm . |
57,962 | def is_b_connected ( H , source_node , target_node ) : b_visited_nodes , Pv , Pe , v = b_visit ( H , source_node ) return target_node in b_visited_nodes | Checks if a target node is B - connected to a source node . |
57,963 | def is_f_connected ( H , source_node , target_node ) : f_visited_nodes , Pv , Pe , v = f_visit ( H , source_node ) return target_node in f_visited_nodes | Checks if a target node is F - connected to a source node . |
57,964 | def from_networkx_graph ( nx_graph ) : import networkx as nx if not isinstance ( nx_graph , nx . Graph ) : raise TypeError ( "Transformation only applicable to undirected \ NetworkX graphs" ) G = UndirectedHypergraph ( ) for node in nx_graph . nodes_iter ( ) : G . add_node ( node , copy . copy ( ... | Returns an UndirectedHypergraph object that is the graph equivalent of the given NetworkX Graph object . |
57,965 | def from_networkx_digraph ( nx_digraph ) : import networkx as nx if not isinstance ( nx_digraph , nx . DiGraph ) : raise TypeError ( "Transformation only applicable to directed \ NetworkX graphs" ) G = DirectedHypergraph ( ) for node in nx_digraph . nodes_iter ( ) : G . add_node ( node , copy . c... | Returns a DirectedHypergraph object that is the graph equivalent of the given NetworkX DiGraph object . |
57,966 | def get_tail_incidence_matrix ( H , nodes_to_indices , hyperedge_ids_to_indices ) : if not isinstance ( H , DirectedHypergraph ) : raise TypeError ( "Algorithm only applicable to directed hypergraphs" ) rows , cols = [ ] , [ ] for hyperedge_id , hyperedge_index in hyperedge_ids_to_indices . items ( ) : for node in H . ... | Creates the incidence matrix of the tail nodes of the given hypergraph as a sparse matrix . |
57,967 | def add_node ( self , node , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) if not self . has_node ( node ) : self . _node_attributes [ node ] = attr_dict self . _forward_star [ node ] = set ( ) self . _backward_star [ node ] = set ( ) else : self . _node_attributes ... | Adds a node to the graph along with any related attributes of the node . |
57,968 | def add_nodes ( self , nodes , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) for node in nodes : if type ( node ) is tuple : new_node , node_attr_dict = node new_dict = attr_dict . copy ( ) new_dict . update ( node_attr_dict ) self . add_node ( new_node , new_dict )... | Adds multiple nodes to the graph along with any related attributes of the nodes . |
57,969 | def remove_node ( self , node ) : if not self . has_node ( node ) : raise ValueError ( "No such node exists." ) forward_star = self . get_forward_star ( node ) for hyperedge_id in forward_star : self . remove_hyperedge ( hyperedge_id ) backward_star = self . get_backward_star ( node ) for hyperedge_id in backward_star ... | Removes a node and its attributes from the hypergraph . Removes every hyperedge that contains this node in either the head or the tail . |
57,970 | def get_node_attribute ( self , node , attribute_name ) : if not self . has_node ( node ) : raise ValueError ( "No such node exists." ) elif attribute_name not in self . _node_attributes [ node ] : raise ValueError ( "No such attribute exists." ) else : return copy . copy ( self . _node_attributes [ node ] [ attribute_... | Given a node and the name of an attribute get a copy of that node s attribute . |
57,971 | def get_node_attributes ( self , node ) : if not self . has_node ( node ) : raise ValueError ( "No such node exists." ) attributes = { } for attr_name , attr_value in self . _node_attributes [ node ] . items ( ) : attributes [ attr_name ] = copy . copy ( attr_value ) return attributes | Given a node get a dictionary with copies of that node s attributes . |
57,972 | def add_hyperedge ( self , tail , head , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) if not tail and not head : raise ValueError ( "tail and head arguments \ cannot both be empty." ) frozen_tail = frozenset ( tail ) frozen_head = frozen... | Adds a hyperedge to the hypergraph along with any related attributes of the hyperedge . This method will automatically add any node from the tail and head that was not in the hypergraph . A hyperedge without a weight attribute specified will be assigned the default value of 1 . |
57,973 | def add_hyperedges ( self , hyperedges , attr_dict = None , ** attr ) : attr_dict = self . _combine_attribute_arguments ( attr_dict , attr ) hyperedge_ids = [ ] for hyperedge in hyperedges : if len ( hyperedge ) == 3 : tail , head , hyperedge_attr_dict = hyperedge new_dict = attr_dict . copy ( ) new_dict . update ( hyp... | Adds multiple hyperedges to the graph along with any related attributes of the hyperedges . If any node in the tail or head of any hyperedge has not previously been added to the hypergraph it will automatically be added here . Hyperedges without a weight attribute specified will be assigned the default value of 1 . |
57,974 | def get_hyperedge_id ( self , tail , head ) : frozen_tail = frozenset ( tail ) frozen_head = frozenset ( head ) if not self . has_hyperedge ( frozen_tail , frozen_head ) : raise ValueError ( "No such hyperedge exists." ) return self . _successors [ frozen_tail ] [ frozen_head ] | From a tail and head set of nodes returns the ID of the hyperedge that these sets comprise . |
57,975 | def get_forward_star ( self , node ) : if node not in self . _node_attributes : raise ValueError ( "No such node exists." ) return self . _forward_star [ node ] . copy ( ) | Given a node get a copy of that node s forward star . |
57,976 | def get_backward_star ( self , node ) : if node not in self . _node_attributes : raise ValueError ( "No such node exists." ) return self . _backward_star [ node ] . copy ( ) | Given a node get a copy of that node s backward star . |
57,977 | def get_successors ( self , tail ) : frozen_tail = frozenset ( tail ) if frozen_tail not in self . _successors : return set ( ) return set ( self . _successors [ frozen_tail ] . values ( ) ) | Given a tail set of nodes get a list of edges of which the node set is the tail of each edge . |
57,978 | def get_predecessors ( self , head ) : frozen_head = frozenset ( head ) if frozen_head not in self . _predecessors : return set ( ) return set ( self . _predecessors [ frozen_head ] . values ( ) ) | Given a head set of nodes get a list of edges of which the node set is the head of each edge . |
57,979 | def is_BF_hypergraph ( self ) : for hyperedge_id in self . _hyperedge_attributes : tail = self . get_hyperedge_tail ( hyperedge_id ) head = self . get_hyperedge_head ( hyperedge_id ) if len ( tail ) > 1 and len ( head ) > 1 : return False return True | Indicates whether the hypergraph is a BF - hypergraph . A BF - hypergraph consists of only B - hyperedges and F - hyperedges . See is_B_hypergraph or is_F_hypergraph for more details . |
57,980 | def get_induced_subhypergraph ( self , nodes ) : sub_H = self . copy ( ) sub_H . remove_nodes ( sub_H . get_node_set ( ) - set ( nodes ) ) return sub_H | Gives a new hypergraph that is the subhypergraph of the current hypergraph induced by the provided set of nodes . That is the induced subhypergraph s node set corresponds precisely to the nodes provided and the coressponding hyperedges in the subhypergraph are only those from the original graph consist of tail and head... |
57,981 | def getall ( self , key , default = _marker ) : identity = self . _title ( key ) res = [ v for i , k , v in self . _impl . _items if i == identity ] if res : return res if not res and default is not _marker : return default raise KeyError ( 'Key not found: %r' % key ) | Return a list of all values matching the key . |
57,982 | def extend ( self , * args , ** kwargs ) : self . _extend ( args , kwargs , 'extend' , self . _extend_items ) | Extend current MultiDict with more values . |
57,983 | def setdefault ( self , key , default = None ) : identity = self . _title ( key ) for i , k , v in self . _impl . _items : if i == identity : return v self . add ( key , default ) return default | Return value for key set value to default if key is not present . |
57,984 | def popall ( self , key , default = _marker ) : found = False identity = self . _title ( key ) ret = [ ] for i in range ( len ( self . _impl . _items ) - 1 , - 1 , - 1 ) : item = self . _impl . _items [ i ] if item [ 0 ] == identity : ret . append ( item [ 2 ] ) del self . _impl . _items [ i ] self . _impl . incr_versi... | Remove all occurrences of key and return the list of corresponding values . |
57,985 | def total ( self , xbin1 = 1 , xbin2 = - 2 ) : return self . hist . integral ( xbin1 = xbin1 , xbin2 = xbin2 , error = True ) | Return the total yield and its associated statistical uncertainty . |
57,986 | def iter_sys ( self ) : names = self . sys_names ( ) for name in names : osys = self . GetOverallSys ( name ) hsys = self . GetHistoSys ( name ) yield name , osys , hsys | Iterate over sys_name overall_sys histo_sys . overall_sys or histo_sys may be None for any given sys_name . |
57,987 | def sys_hist ( self , name = None ) : if name is None : low = self . hist . Clone ( shallow = True ) high = self . hist . Clone ( shallow = True ) return low , high osys = self . GetOverallSys ( name ) hsys = self . GetHistoSys ( name ) if osys is None : osys_high , osys_low = 1. , 1. else : osys_high , osys_low = osys... | Return the effective low and high histogram for a given systematic . If this sample does not contain the named systematic then return the nominal histogram for both low and high variations . |
57,988 | def sys_hist ( self , name = None , where = None ) : total_low , total_high = None , None for sample in self . samples : if where is not None and not where ( sample ) : continue low , high = sample . sys_hist ( name ) if total_low is None : total_low = low . Clone ( shallow = True ) else : total_low += low if total_hig... | Return the effective total low and high histogram for a given systematic over samples in this channel . If a sample does not contain the named systematic then its nominal histogram is used for both low and high variations . |
57,989 | def apply_snapshot ( self , argset ) : clone = self . Clone ( ) args = [ var for var in argset if not ( var . name . startswith ( 'binWidth_obs_x_' ) or var . name . startswith ( 'gamma_stat' ) or var . name . startswith ( 'nom_' ) ) ] nargs = [ ] for var in args : is_norm = False name = var . name . replace ( 'alpha_'... | Create a clone of this Channel where histograms are modified according to the values of the nuisance parameters in the snapshot . This is useful when creating post - fit distribution plots . |
57,990 | def printcodelist ( codelist , to = sys . stdout ) : labeldict = { } pendinglabels = [ ] for i , ( op , arg ) in enumerate ( codelist ) : if isinstance ( op , Label ) : pendinglabels . append ( op ) elif op is SetLineno : pass else : while pendinglabels : labeldict [ pendinglabels . pop ( ) ] = i lineno = None islabel ... | Get a code list . Print it nicely . |
57,991 | def recompile ( filename ) : import os import imp import marshal import struct f = open ( filename , 'U' ) try : timestamp = long ( os . fstat ( f . fileno ( ) ) . st_mtime ) except AttributeError : timestamp = long ( os . stat ( filename ) . st_mtime ) codestring = f . read ( ) f . close ( ) if codestring and codestri... | Create a . pyc by disassembling the file and assembling it again printing a message that the reassembled file was loaded . |
57,992 | def recompile_all ( path ) : import os if os . path . isdir ( path ) : for root , dirs , files in os . walk ( path ) : for name in files : if name . endswith ( '.py' ) : filename = os . path . abspath ( os . path . join ( root , name ) ) print >> sys . stderr , filename recompile ( filename ) else : filename = os . pat... | recursively recompile all . py files in the directory |
57,993 | def from_code ( cls , co ) : co_code = co . co_code labels = dict ( ( addr , Label ( ) ) for addr in findlabels ( co_code ) ) linestarts = dict ( cls . _findlinestarts ( co ) ) cellfree = co . co_cellvars + co . co_freevars code = CodeList ( ) n = len ( co_code ) i = 0 extended_arg = 0 while i < n : op = Opcode ( ord (... | Disassemble a Python code object into a Code object . |
57,994 | def effective_sample_size ( h ) : sum = 0 ew = 0 w = 0 for bin in h . bins ( overflow = False ) : sum += bin . value ew = bin . error w += ew * ew esum = sum * sum / w return esum | Calculate the effective sample size for a histogram the same way as ROOT does . |
57,995 | def critical_value ( n , p ) : dn = 1 delta = 0.5 res = ROOT . TMath . KolmogorovProb ( dn * sqrt ( n ) ) while res > 1.0001 * p or res < 0.9999 * p : if ( res > 1.0001 * p ) : dn = dn + delta if ( res < 0.9999 * p ) : dn = dn - delta delta = delta / 2. res = ROOT . TMath . KolmogorovProb ( dn * sqrt ( n ) ) return dn | This function calculates the critical value given n and p and confidence level = 1 - p . |
57,996 | def dump ( obj , root_file , proto = 0 , key = None ) : if isinstance ( root_file , string_types ) : root_file = root_open ( root_file , 'recreate' ) own_file = True else : own_file = False ret = Pickler ( root_file , proto ) . dump ( obj , key ) if own_file : root_file . Close ( ) return ret | Dump an object into a ROOT TFile . |
57,997 | def load ( root_file , use_proxy = True , key = None ) : if isinstance ( root_file , string_types ) : root_file = root_open ( root_file ) own_file = True else : own_file = False obj = Unpickler ( root_file , use_proxy ) . load ( key ) if own_file : root_file . Close ( ) return obj | Load an object from a ROOT TFile . |
57,998 | def dump ( self , obj , key = None ) : if key is None : key = '_pickle' with preserve_current_directory ( ) : self . __file . cd ( ) if sys . version_info [ 0 ] < 3 : pickle . Pickler . dump ( self , obj ) else : super ( Pickler , self ) . dump ( obj ) s = ROOT . TObjString ( self . __io . getvalue ( ) ) self . __io . ... | Write a pickled representation of obj to the open TFile . |
57,999 | def load ( self , key = None ) : if key is None : key = '_pickle' obj = None if _compat_hooks : save = _compat_hooks [ 0 ] ( ) try : self . __n += 1 s = self . __file . Get ( key + ';{0:d}' . format ( self . __n ) ) self . __io . setvalue ( s . GetName ( ) ) if sys . version_info [ 0 ] < 3 : obj = pickle . Unpickler . ... | Read a pickled object representation from the open file . |
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