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205 | def getMongoDB(**context):
token = context.get("ti").xcom_pull(key="token")
response_s01 = requests.get(
url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/60cac3736e0eeb6af04a3884?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
response_s02 = requests.get(
url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/611f840fe5ba2a51bf4c524d?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
RISC_Data_01 = json.loads(response_s01.text)
RISC_Data_02 = json.loads(response_s02.text)
host = 'drowdatewarehouse.crlwwhgepgi7.ap-east-1.rds.amazonaws.com'
# User name of the database server
dbUserName = 'dRowAdmin'
# Password for the database user
dbUserPassword = 'drowsuper'
# Name of the database
database = 'drowDateWareHouse'
# Character set
charSet = "utf8mb4"
port = "5432"
conn_string = ('postgres://' +
dbUserName + ':' +
dbUserPassword +
'@' + host + ':' + port +
'/' + database)
db = create_engine(conn_string)
conn = db.connect()
contract_title = "1wsd19"
dailies_tables = [
"1. Arc Welding",
"2. Excavation",
"3. Formwork",
"4. Temporary support to Pressure Test and Connection Pipe Works",
"5. Work in Manholes"
]
weekly_tables = [
"1. Earth Moving Plant (e.g. Excavators, Backhoes)",
"2. Lifting Operation",
"3. Mechanical Material Handling (Load-shifting Machinery)",
"4. Excavation",
"5. Transportation",
"6. Mechanical Plant and Equipment",
"7. Working at height",
"8. ladder & Staircase",
"9. Protection against Falling Objects",
"10. Welding / Cutting Operations and Equipment",
"11. Hand tools",
"12. Compressed Air Tools",
"13. Concrete Formworks",
"14. Woodworking Machines",
"15. Electrical Supply System, Electrical Works & Electric Hand Tools",
"16. Personal protective equipments and related facilities",
"17. Storage of dangerous goods (e.g. fuels, gas cylinders and other hazardous chemicals, paints)",
"18. Fire prevention and protection (e.g. fire extinguishers, escape routes)",
"19. Housekeeping",
"20. Noise control",
"21. Confined Spaces",
"22. Welfare Facilities",
"23. Traffic diversion and control (e.g. lighting, signing & guarding)",
"24. Other"
]
full_df = pd.DataFrame()
monthly_summary = {}
with conn:
for entry in RISC_Data_01:
df_nested_list = json_normalize(entry['data'])
# List to hold object for each table
df_list = []
# Inspection date
date_of_inspection = df_nested_list['Date & Time'][0]
# Process each table dynamically
for key in dailies_tables:
if key not in df_nested_list:
continue
df_table = df_nested_list[key]
[group_key, group_name] = key.split(". ")
for i in range(len(df_table)):
record = df_table[i]
dict_record = {
'Date of Inspection': date_of_inspection,
'Month': date_of_inspection[:7],
'Contract Title': contract_title,
'Group No.': group_key,
'Group': group_name,
'Item No.': str(i),
'Description': record[group_key + ". Behaviour to be monitored"],
'Result': record[group_key + ". Result"],
'Template': 'A. Daily Inspection Checklist',
}
df_list.append(dict_record)
# if 'Date completed' not in dict_record or 'Agreed date for completion' not in dict_record:
# dict_record['On Time'] = None
# elif not dict_record['Date completed'] or not dict_record['Agreed date for completion']:
# dict_record['On Time'] = None
# elif dict_record['Date completed'] <= dict_record['Agreed date for completion']:
# dict_record['On Time'] = "On-Time"
# else:
# dict_record['On Time'] = "Late"
if date_of_inspection[:7] in monthly_summary:
monthly_summary[date_of_inspection[:7]]['items'] += 1
if record[group_key + ". Result"] == 'At Risk':
monthly_summary[date_of_inspection[:7]]['concern'] += 1
else:
monthly_summary[date_of_inspection[:7]] = {
'items': 1,
'concern': 1 if record[group_key + ". Result"] == 'At Risk' else 0
}
df_combined = pd.DataFrame(data=df_list)
# Append non-compliant records
if not full_df.empty and not df_combined.empty:
full_df = pd.concat([full_df, df_combined], ignore_index=True)
elif not df_combined.empty:
full_df = df_combined
for entry in RISC_Data_02:
df_nested_list = json_normalize(entry['data'])
# List to hold object for each table
df_list = []
# Inspection date
date_of_inspection = df_nested_list['A03 Inspection Date time']
if 'A - Follow-up Summary' in df_nested_list and df_nested_list['A - Follow-up Summary'] > 0:
total_late_retification = 0
for summaryData in df_nested_list['A - Follow-up Summary']:
if ("A11 Agreed Date" in summaryData and "A11 Agreed Date" in summaryData and not (summaryData["A11 Agreed Date"]!='') and (not (summaryData["A12 Date Completed"]!='')) and (summaryData["A11 Agreed Date"].astype('datetime64[ns]') < summaryData["A12 Date Completed"].astype('datetime64[ns]')).bool()):
total_late_retification += 1
else:
total_late_retification = 0
for key in df_nested_list:
group_key = key.replace(".", "").split(" ")[0]
if 'checklist' not in group_name.lower():
continue
group_name = weekly_tables[int(group_key) - 1]
for i in range(len(df_nested_list[key])):
record = df_nested_list[key][i]
item_desc = ""
result = False
for record_key in record:
if "item" in record_key.lower():
item_desc = record[record_key]
if "result" in record_key.lower():
result = record[record_key]
dict_record = {
'Date of Inspection': date_of_inspection,
'Month': date_of_inspection[:7],
'Contract Title': contract_title,
'Group No.': group_key,
'Group': group_name,
'Item No.': item_desc.split(" ")[0],
'Description': item_desc,
'Result': result,
'Template': 'B. Weekly Safety Inspection',
}
df_list.append(dict_record)
# Sort by date of inspection
non_compliant_df = full_df[full_df['Safety Compliance'] == 'No']
non_compliant_df.sort_values(by=['Date of Inspection', 'Contract Title', 'Item No.'], inplace=True)
# Clean up column names for SQL
non_compliant_df.columns = non_compliant_df.columns.str.replace(' ', '_').str.replace(r'[().%]', '', regex=True).str.replace('/', '_')
# Retrieve only relevant columns
final_df = non_compliant_df[['Date_of_Inspection', 'Month', 'Contract_Title', 'Template', 'Group_No', 'Group', 'Item_No', 'Description', 'Location', 'Safety_Compliance', 'Date_completed', 'Agreed_date_for_completion', 'On_Time']]
# Write to SQL database
final_df.to_sql('safety_inspection_1wsd19', con=conn, if_exists='replace', index=False)
# Create a summary df
summary_dict = []
for k, v in monthly_summary.items():
summary_dict.append({
'Month': k,
'Items': v['items'],
'Concerns': v['concern']
})
summary_df = pd.DataFrame(data=summary_dict)
summary_df.to_sql('safety_inspection_summary_1wsd19', con=conn, if_exists='replace', index=False)
|