javalang_parser_V2.py

import pandas as pd
import numpy as np
import json
f_path = "ContactsPage.java"
with open(f_path,'r') as f:
    content = f.read()
#def flattern_json(d):
#    if len(d) == 0:
#        return {}
#    from collections import deque
#    q = deque()
#    res = dict()
#    for key, val in d.items(): # This loop push the top most keys and values into queue.
#        if not isinstance(val, dict):  # If it's not dict
#            if isinstance(val, list):  # If it's list then check list values if it contains dict object.
#                temp = list()  # Creating temp list for storing the values that we will need which are not dict.
#                for v in val:
#                    if not isinstance(v, dict):
#                        temp.append(v)
#                    else:
#                        q.append((key, v))  # if it's value is dict type then we push along with parent which is key.
#                if len(temp) > 0:
#                    res[key] = temp
#            else:
#                res[key] = val
#        else:
#            q.append((key, val))
#    while q:
#        k, v = q.popleft()  # Taking parent and the value out of queue
#        for key, val in v.items():
#            new_parent = k + "_" + key  # New parent will be old parent_currentval
#            if isinstance(val, list):
#                temp = list()
#                for v in val:
#                    if not isinstance(v, dict):
#                        temp.append(v)
#                    else:
#                        q.append((new_parent, v))
#                if len(temp) >= 0:
#                    res[new_parent] = temp
#            elif not isinstance(val, dict):
#                res[new_parent] = val
#            else:
#                q.append((new_parent, val))
#    return res
#
def flatten_json(y):
    out = {}

    def flatten(x, name=''):
        if type(x) is dict:
            for a in x:
                flatten(x[a], name + a + '_')
        elif type(x) is list:
            i = 0
            for a in x:
                flatten(a, name + str(i) + '_')
                i += 1
        else:
            out[name[:-1]] = x

    flatten(y)
    return out
def json_ast_encoder(o):
    if type(o) is set and len(o) == 0:
        return []
    if hasattr(o, "__dict__"):
        return o.__dict__
    return ""
#def find_end_line_number(node):
#    """Finds end line of a node."""
#    max_line = node.position.line
#
#    def traverse(node):
#        for child in node.children:
#            if isinstance(child, list) and (len(child) > 0):
#                for item in child:
#                    traverse(item)
#            else:
#                if hasattr(child, '_position'):
#                    nonlocal max_line
#                    if child._position.line > max_line:
#                        max_line = child._position.line
#                        return
#
#    traverse(node)
#    return max_line
with open(f_path,'r') as f:
    lines = f.readlines()
import javalang as jl
tree = jl.parse.parse(content)
##jl.tree.BlockStatement
#
#docs = []
#failed = False
#index = 0
#for path, node in tree.filter(jl.tree.MethodDeclaration):
#    d = {}
#    if failed == False:
#        failed_method = None
#    start_line = node.position.line
#    try:
#        
##        print(node.name)
#        d['method'] = node.name
#        d['index'] = index
#        index += 1
#        d['start_line'] = start_line
#        end_line = find_end_line_number(node)
#        d['code']  = lines[start_line:end_line]
#        d['end_line'] = end_line
#    except Exception:
#        failed_method = node.name
#        d['end_line'] = 0
#        failed = True
#        print(f"Error in {node.name}")
#    docs.append(d)
#
#
#    
#df = pd.DataFrame(docs)
#
#def get_new_end_line(index,df,lines_count,end_line):
#    if end_line == 0:
#        max_index= df['index'].max()
##        print(max_index,index)
#        if index == max_index:
#            return lines_count
#        else:
#            next_index = index + 1
#            next_df = df[(df['index']==next_index)]
#            new_end_line = next_df['start_line'].iloc[0] - 1
#            return new_end_line
#    else:
#        return end_line
#lines_count = len(lines)
#
#df['new_end_line'] = df.apply(lambda row: get_new_end_line(row['index'],df,lines_count,row['end_line']),axis=1)
#result = df.to_dict(orient="records")


v = json.dumps(tree, sort_keys=True, default=json_ast_encoder)
y = json.loads(v)['types'][0]['body']
z = json.dumps(y)
#docs = []
#for i in y:
#    m = flatten_json(i)
#    docs.append(m)
#df = pd.DataFrame(docs)
#df_1 = df.dropna(axis=1, how='all')
#df_2 = df_1.select_dtypes(include=['object'])
#df_3 = df_2.dropna(subset=['name'])

def get_parameters(param_list:list)->str:
    params = []
    if param_list:
        for pl in param_list:
            params.append(pl.get('name','NA'))
        return ','.join(params)
    else:
        return 'No Param'

def get_operator(ops):
    
    pass
def get_actions(body_list:list)->str:
    actions = []
    object_argument = []
    if body_list:
        for bl in body_list:
            expression = bl.get('expression',{})
            action = expression.get('member','NA')
            _object = expression.get('qualifier','NA')
            indirect_actions = expression.get('selectors',[])
            if indirect_actions:
                for ic in indirect_actions:
                    actions.append({"action":ic.get('member','NA')})
                    pos = ic['_position']
#                    print(pos)
#                    line = lines[pos[0]-1]
                    last_line = pos[0]-1
                    print("last line:",last_line)
                    first_line = last_line
#                    print(last_line,lines[last_line])
#                    print(lines[last_line-1])
                    while lines[first_line].strip().startswith("+"):
                        first_line -= 1
#                        print(first_line)
                    line = ' '.join(lines[first_line:last_line+1]).strip().replace("\t","")
                    object_argument.append({'line': line})
                    print(first_line,last_line+1)
            if not(action == 'NA' and _object == 'NA'):
                actions.append({"action":action,"object":_object})
        return actions,object_argument
    else:
        return "no object arg",'No Actions'
            
methods = []
docs = []
for i in y:
#    if not i['annotations']:
        d = {}
        methods.append(i)
        d['method_name'] = i.get('name','NA')
        d['parameters'] = get_parameters(i.get('parameters',[]))
        d['actions'],d['object_argument'] = get_actions(i.get('body',[]))
        docs.append(d)
#y1 = methods[-2]['body']
#x1 = methods[-2].get('body')[0]['expression']['arguments'][0]['arguments']


df = pd.DataFrame(docs)