Depency Graph

A Graph creation, analysis and visualizations library.

Installation

pip install deg

Usage

Can be used directly using the DependencyNode object or by extending the object into some other subjective structure.

A sample usage would look as the following :

from deg import DependencyGraph, DependencyNode

class Table(DependencyNode):
    '''Class for representing each node of the tree.
    This contains every piece of static information that may be required for analysis.
    
    '''
    def __init__(self, name, cols=None):
        super(Table, self).__init__(name)
        self.cols=cols
        self.visited = False
        self.col_relations = None
    
    def add_cols(self, df : pd.DataFrame):
        self.cols = df
    def add_relations(self, df : pd.DataFrame):
        self.col_relations = df
    def generate_search_criteria(self, module=None):
        selection = ''
        if self.is_independent():
            print('Ignoring rules for independent table :\n', self.col_relations.to_string())
            selection = '''TRUNC({tablename}.{datecol}) BETWEEN (SELECT PRG_PERIOD_START FROM PURGE_MODULE_INPUT WHERE REQUEST_ID = PRG_BTCH_ID AND MODULE = '{module}' AND STATUS = 'INITIATED') AND (SELECT PRG_PERIOD_END FROM PURGE_MODULE_INPUT WHERE REQUEST_ID = PRG_BTCH_ID AND MODULE = '{module}' AND STATUS = 'INITIATED')'''.format(tablename=self.name,datecol=self.get_date_col(), module=module)
        else :
            for parent in self.parents:
                criterias = ''
                for _, row in self.col_relations[self.col_relations['r_table'] == parent.name].iterrows():
                    if len(criterias) > 0:
                        criterias += ' AND '
                    criterias += '{0}.{1} = {2}.{3} '.format(row['table_name'], row['column_name'], row['r_table'], row['r_col'])
                criterias += ' AND ' + parent.generate_search_criteria(module=module)
                selection += 'INNER JOIN {parent_name} ON {criterias}'.format(parent_name = parent.name, criterias = criterias)
        return selection
    def get_cols(self) -> pd.DataFrame:
        return self.cols
    def is_visited(self) -> bool :
        return self.visited
    def set_visited(self) :
        self.visited = True
    def get_date_col(self):
        try:
            return self.cols[(self.cols['data_type'] == 'DATE') & (self.cols['nullable'] == 'N')].iloc[-1]['column_name']
        except Exception as e:
            # print(e)
            return None
    def __str__(self):
        return 'Table({0}, {1})'.format(self.name, self.is_independent(), self.children)
    def __repr__(self):
        return 'Table({0}, {1})'.format(self.name, self.is_independent(), self.children)

graph = DependencyGraph(Table)
while True:
    df = get_children(temp)
    if df is None or df.shape == (0, 0) :
        break
    for val in df.itertuples():
        graph.add(val[2], parent=val[1], relation = val[3])
    temp = df.iloc[:, 1]
    execution_limit -= 1
    if execution_limit <= 0 :
        break

Docstrings have been added to detail what functions such as graph.add does.