Su Template de datos para el proceso de registro a informe, cierre del período y conciliación

WITH Config AS (
    -- Configuration: Set the start date, end date, and company code filter for the extraction.
    SELECT
        CAST('2023-01-01' AS DATETIME) AS StartDate,
        CAST('2023-12-31' AS DATETIME) AS EndDate,
        'USMF' AS CompanyFilter -- Replace 'USMF' with your target company code (DataAreaId)
),
AllEvents AS (
    -- 1. Period Close Process Initiated
    -- Inferred from the first recorded action on a fiscal period module within the date range.
    SELECT
        FORMAT(lpmac.MODIFIEDDATETIME, 'yyyy-MM') AS FinancialPeriod,
        'Period Close Process Initiated' AS ActivityName,
        MIN(lpmac.MODIFIEDDATETIME) AS EventTime,
        lpmac.MODIFIEDBY AS ResponsibleUser,
        lpmac.DATAREAID AS CompanyCode,
        NULL AS Department,
        NULL AS GlAccount
    FROM LedgerPeriodModuleAccessControl lpmac
    JOIN Config ON lpmac.DATAREAID = Config.CompanyFilter AND lpmac.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    GROUP BY FORMAT(lpmac.MODIFIEDDATETIME, 'yyyy-MM'), lpmac.MODIFIEDBY, lpmac.DATAREAID

    UNION ALL

    -- 2. Subledgers Placed On Hold
    -- Captures when modules like Accounts Payable (Vend) and Accounts Receivable (Cust) are put on hold.
    SELECT
        FORMAT(lpmac.MODIFIEDDATETIME, 'yyyy-MM') AS FinancialPeriod,
        'Subledgers Placed On Hold' AS ActivityName,
        lpmac.MODIFIEDDATETIME AS EventTime,
        lpmac.MODIFIEDBY AS ResponsibleUser,
        lpmac.DATAREAID AS CompanyCode,
        NULL AS Department,
        NULL AS GlAccount
    FROM LedgerPeriodModuleAccessControl lpmac
    JOIN Config ON lpmac.DATAREAID = Config.CompanyFilter AND lpmac.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE lpmac.ACCESSLEVEL = 2 -- 2 corresponds to 'On hold'
      AND lpmac.MODULENUMBER IN (2, 3) -- 2 for 'Cust', 3 for 'Vend'. Adjust if needed.

    UNION ALL

    -- 3. Accrual Journals Posted
    -- Captured from General Journal Entries with a specific journal name for accruals.
    SELECT 
        FORMAT(gje.ACCOUNTINGDATE, 'yyyy-MM') AS FinancialPeriod,
        'Accrual Journals Posted' AS ActivityName,
        gje.CREATEDDATETIME AS EventTime,
        gje.CREATEDBY AS ResponsibleUser,
        gje.LEDGER_DATAREAID AS CompanyCode,
        NULL AS Department,
        gjae.LEDGERACCOUNT AS GlAccount
    FROM GeneralJournalEntry gje
    JOIN GeneralJournalAccountEntry gjae ON gje.RECID = gjae.GENERALJOURNALENTRY
    JOIN Config ON gje.LEDGER_DATAREAID = Config.CompanyFilter AND gje.ACCOUNTINGDATE BETWEEN Config.StartDate AND Config.EndDate
    WHERE gje.JOURNALNAME = '[Your Accrual Journal Name]' -- !! IMPORTANT: Replace with your actual journal name for accruals

    UNION ALL

    -- 4. Intercompany Journals Posted
    -- Captured from General Journal Entries with a specific journal name for intercompany transactions.
    SELECT 
        FORMAT(gje.ACCOUNTINGDATE, 'yyyy-MM') AS FinancialPeriod,
        'Intercompany Journals Posted' AS ActivityName,
        gje.CREATEDDATETIME AS EventTime,
        gje.CREATEDBY AS ResponsibleUser,
        gje.LEDGER_DATAREAID AS CompanyCode,
        NULL AS Department,
        gjae.LEDGERACCOUNT AS GlAccount
    FROM GeneralJournalEntry gje
    JOIN GeneralJournalAccountEntry gjae ON gje.RECID = gjae.GENERALJOURNALENTRY
    JOIN Config ON gje.LEDGER_DATAREAID = Config.CompanyFilter AND gje.ACCOUNTINGDATE BETWEEN Config.StartDate AND Config.EndDate
    WHERE gje.JOURNALNAME = '[Your Intercompany Journal Name]' -- !! IMPORTANT: Replace with your actual journal name for intercompany postings

    UNION ALL

    -- 5. Bank Reconciliation Started
    -- The creation of a bank reconciliation statement record.
    SELECT 
        FORMAT(bs.CREATEDDATETIME, 'yyyy-MM') AS FinancialPeriod,
        'Bank Reconciliation Started' AS ActivityName,
        bs.CREATEDDATETIME AS EventTime,
        bs.CREATEDBY AS ResponsibleUser,
        bs.DATAREAID AS CompanyCode,
        NULL AS Department,
        bs.ACCOUNTID AS GlAccount
    FROM BankCStmt bs
    JOIN Config ON bs.DATAREAID = Config.CompanyFilter AND bs.CREATEDDATETIME BETWEEN Config.StartDate AND Config.EndDate

    UNION ALL

    -- 6. Bank Reconciliation Completed
    -- When a bank reconciliation is marked as reconciled.
    SELECT 
        FORMAT(bs.RECONCILEDATE, 'yyyy-MM') AS FinancialPeriod,
        'Bank Reconciliation Completed' AS ActivityName,
        bs.MODIFIEDDATETIME AS EventTime,
        bs.MODIFIEDBY AS ResponsibleUser,
        bs.DATAREAID AS CompanyCode,
        NULL AS Department,
        bs.ACCOUNTID AS GlAccount
    FROM BankCStmt bs
    JOIN Config ON bs.DATAREAID = Config.CompanyFilter AND bs.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE bs.RECONCILE = 1 -- 1 typically means 'Reconciled'

    UNION ALL

    -- 7. Account Reconciliation Started & 8. Account Reconciliation Approved
    -- Captured from the Financial Close Workspace task statuses.
    SELECT 
        FORMAT(cst.DUEDATE, 'yyyy-MM') AS FinancialPeriod,
        CASE cst.STATUS
            WHEN 1 THEN 'Account Reconciliation Started'   -- 1 for 'In progress', adjust status value if needed
            WHEN 3 THEN 'Account Reconciliation Approved'  -- 3 for 'Completed' or 'Approved', adjust status value
        END AS ActivityName,
        cst.MODIFIEDDATETIME AS EventTime,
        cst.MODIFIEDBY AS ResponsibleUser,
        cst.DATAREAID AS CompanyCode,
        cst.RESPONSIBLEDEPARTMENT AS Department,
        cst.ACCOUNTDISPLAYVALUE AS GlAccount
    FROM ClosingSheetTask cst
    JOIN Config ON cst.DATAREAID = Config.CompanyFilter AND cst.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE cst.TASKDESCRIPTION LIKE '%Reconcile%'
      AND cst.STATUS IN (1, 3)

    UNION ALL

    -- 9. Adjusting Journal Entries Posted
    -- Captured from General Journal Entries with a specific journal name for adjustments.
    SELECT 
        FORMAT(gje.ACCOUNTINGDATE, 'yyyy-MM') AS FinancialPeriod,
        'Adjusting Journal Entries Posted' AS ActivityName,
        gje.CREATEDDATETIME AS EventTime,
        gje.CREATEDBY AS ResponsibleUser,
        gje.LEDGER_DATAREAID AS CompanyCode,
        NULL AS Department,
        gjae.LEDGERACCOUNT AS GlAccount
    FROM GeneralJournalEntry gje
    JOIN GeneralJournalAccountEntry gjae ON gje.RECID = gjae.GENERALJOURNALENTRY
    JOIN Config ON gje.LEDGER_DATAREAID = Config.CompanyFilter AND gje.ACCOUNTINGDATE BETWEEN Config.StartDate AND Config.EndDate
    WHERE gje.JOURNALNAME = '[Your Adjustment Journal Name]' -- !! IMPORTANT: Replace with your actual journal name for adjustments

    UNION ALL

    -- 10. Trial Balance Prepared
    -- Inferred as the time of the last general journal posting for the period before the close.
    SELECT 
        FORMAT(gje.ACCOUNTINGDATE, 'yyyy-MM') AS FinancialPeriod,
        'Trial Balance Prepared' AS ActivityName,
        MAX(gje.CREATEDDATETIME) AS EventTime,
        MAX(gje.CREATEDBY) AS ResponsibleUser,
        gje.LEDGER_DATAREAID AS CompanyCode,
        NULL AS Department,
        NULL AS GlAccount
    FROM GeneralJournalEntry gje
    JOIN Config ON gje.LEDGER_DATAREAID = Config.CompanyFilter AND gje.ACCOUNTINGDATE BETWEEN Config.StartDate AND Config.EndDate
    GROUP BY FORMAT(gje.ACCOUNTINGDATE, 'yyyy-MM'), gje.LEDGER_DATAREAID

    UNION ALL

    -- 11. Financial Statements Generated & 12. Financial Statements Approved
    -- Captured from tasks in the Financial Close Workspace.
    SELECT 
        FORMAT(cst.DUEDATE, 'yyyy-MM') AS FinancialPeriod,
        CASE
            WHEN cst.TASKDESCRIPTION LIKE '%Generate Financial Statement%' AND cst.STATUS = 3 THEN 'Financial Statements Generated'
            WHEN cst.TASKDESCRIPTION LIKE '%Approve Financial Statement%' AND cst.STATUS = 3 THEN 'Financial Statements Approved'
        END AS ActivityName,
        cst.MODIFIEDDATETIME AS EventTime,
        cst.MODIFIEDBY AS ResponsibleUser,
        cst.DATAREAID AS CompanyCode,
        cst.RESPONSIBLEDEPARTMENT AS Department,
        NULL AS GlAccount
    FROM ClosingSheetTask cst
    JOIN Config ON cst.DATAREAID = Config.CompanyFilter AND cst.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE cst.TASKDESCRIPTION LIKE '%Financial Statement%'
      AND cst.STATUS = 3 -- 3 for 'Completed', assumes completion implies generation/approval

    UNION ALL

    -- 13. GL Period Closed
    -- The final closing of the General Ledger period.
    SELECT
        FORMAT(lfp.ENDDATE, 'yyyy-MM') AS FinancialPeriod,
        'GL Period Closed' AS ActivityName,
        lfp.MODIFIEDDATETIME AS EventTime,
        lfp.MODIFIEDBY AS ResponsibleUser,
        lfp.DATAREAID AS CompanyCode,
        NULL AS Department,
        NULL AS GlAccount
    FROM LedgerFiscalPeriod lfp
    JOIN Config ON lfp.DATAREAID = Config.CompanyFilter AND lfp.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE lfp.STATUS = 2 -- 2 corresponds to 'Closed'
      AND lfp.TYPE = 1 -- 1 corresponds to 'Operating'

    UNION ALL

    -- 14. GL Period Re-Opened
    -- When a previously closed period is set back to 'On Hold' or 'Open'.
    SELECT
        FORMAT(lfp.ENDDATE, 'yyyy-MM') AS FinancialPeriod,
        'GL Period Re-Opened' AS ActivityName,
        lfp.MODIFIEDDATETIME AS EventTime,
        lfp.MODIFIEDBY AS ResponsibleUser,
        lfp.DATAREAID AS CompanyCode,
        NULL AS Department,
        NULL AS GlAccount
    FROM LedgerFiscalPeriod lfp
    JOIN Config ON lfp.DATAREAID = Config.CompanyFilter AND lfp.MODIFIEDDATETIME BETWEEN Config.StartDate AND Config.EndDate
    WHERE lfp.STATUS IN (1, 3) -- 1 is 'On Hold', 3 is 'Open'
      AND lfp.TYPE = 1 -- 'Operating'
      AND EXISTS (
          -- This logic is complex; a simpler check is to assume any modification to Open/On Hold after a close is a reopen.
          -- A more robust solution requires change tracking, which is outside a simple SQL query.
          SELECT 1 FROM LedgerFiscalPeriod prev_status
          WHERE prev_status.RECID = lfp.RECID AND prev_status.MODIFIEDDATETIME < lfp.MODIFIEDDATETIME
      )
)
SELECT
    FinancialPeriod,
    ActivityName,
    EventTime,
    'Microsoft Dynamics 365' AS SourceSystem,
    GETUTCDATE() AS LastDataUpdate,
    ResponsibleUser,
    CompanyCode,
    Department,
    GlAccount
FROM AllEvents
WHERE ActivityName IS NOT NULL;

import requests
import pandas as pd
from datetime import datetime
import time

class=class="hl-string">"hl-comment"># --- CONFIGURATION --- 
D365_BASE_URL = class="hl-string">"https://[your-d365-environment].operations.dynamics.com"
TENANT_ID = class="hl-string">"[your-tenant-id]"
CLIENT_ID = class="hl-string">"[your-client-id]"
CLIENT_SECRET = class="hl-string">"[your-client-secret]"

class=class="hl-string">"hl-comment"># --- PARAMETERS ---
START_DATE = class="hl-string">"2023-01-01"
END_DATE = class="hl-string">"2023-12-31"
COMPANY_CODE = class="hl-string">"[YourCompanyCode]" class=class="hl-string">"hl-comment"># e.g., class="hl-string">'USMF'

class=class="hl-string">"hl-comment"># --- Function to get Access Token ---
def get_access_token():
    url = fclass="hl-string">"https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token"
    payload = {
        class="hl-string">'grant_type': class="hl-string">'client_credentials',
        class="hl-string">'client_id': CLIENT_ID,
        class="hl-string">'client_secret': CLIENT_SECRET,
        class="hl-string">'scope': fclass="hl-string">"{D365_BASE_URL}/.default"
    }
    response = requests.post(url, data=payload)
    response.raise_for_status()
    return response.json()[class="hl-string">'access_token']

class=class="hl-string">"hl-comment"># --- Generic function to get data from D365 OData ---
def get_d365_data(entity_name, select_fields, filter_query, access_token):
    all_records = []
    api_url = fclass="hl-string">"{D365_BASE_URL}/data/{entity_name}?$select={select_fields}&$filter={filter_query}&cross-company=true"
    headers = {
        class="hl-string">'Authorization': fclass="hl-string">'Bearer {access_token}',
        class="hl-string">'Content-Type': class="hl-string">'application/json'
    }
    
    while api_url:
        try:
            response = requests.get(api_url, headers=headers)
            response.raise_for_status()
            data = response.json()
            all_records.extend(data[class="hl-string">'value'])
            api_url = data.get(class="hl-string">'@odata.nextLink')
            time.sleep(0.1) class=class="hl-string">"hl-comment"># Small delay to be respectful to the API
        except requests.exceptions.HTTPError as err:
            print(fclass="hl-string">"HTTP Error fetching {entity_name}: {err}")
            print(fclass="hl-string">"Response content: {err.response.text}")
            break

    return pd.DataFrame(all_records)

class=class="hl-string">"hl-comment"># --- Activity Extraction Functions ---

def extract_gl_period_events(token):
    print(class="hl-string">"Extracting GL Period status changes...")
    entity = class="hl-string">"LedgerFiscalCalendarPeriods"
    fields = class="hl-string">"LedgerFiscalCalendarId,PeriodName,Status,ModifiedDateTime,ModifiedBy,LegalEntityId"
    filters = fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and LegalEntityId eq '{COMPANY_CODE}'"
    df = get_d365_data(entity, fields, filters, token)
    if df.empty:
        return pd.DataFrame()

    df.rename(columns={class="hl-string">'ModifiedDateTime': class="hl-string">'EventTime', class="hl-string">'ModifiedBy': class="hl-string">'ResponsibleUser', class="hl-string">'LegalEntityId': class="hl-string">'CompanyCode'}, inplace=True)
    df[class="hl-string">'FinancialPeriod'] = pd.to_datetime(df[class="hl-string">'EventTime']).dt.strftime(class="hl-string">'%Y-%m')
    
    class=class="hl-string">"hl-comment"># Map status to activities
    activity_map = {
        class="hl-string">'OnHold': [class="hl-string">'Period Close Process Initiated', class="hl-string">'Subledgers Placed On Hold'],
        class="hl-string">'Closed': [class="hl-string">'GL Period Closed'],
        class="hl-string">'Open': [class="hl-string">'GL Period Re-Opened'] class=class="hl-string">"hl-comment"># Assuming re-open moves from Closed to Open
    }
    
    event_dfs = []
    for status, activities in activity_map.items():
        for activity in activities:
            temp_df = df[df[class="hl-string">'Status'] == status].copy()
            temp_df[class="hl-string">'ActivityName'] = activity
            event_dfs.append(temp_df)

    result_df = pd.concat(event_dfs, ignore_index=True)
    return result_df[[class="hl-string">'FinancialPeriod', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'ResponsibleUser', class="hl-string">'CompanyCode']]

def extract_journal_postings(token):
    print(class="hl-string">"Extracting Journal Postings...")
    entity = class="hl-string">"GeneralJournalAccountEntryEntities"
    fields = class="hl-string">"JournalNumber,JournalCategory,PostingLayer,AccountingDate,CreatedBy,CompanyCode,LedgerAccount"
    filters = fclass="hl-string">"AccountingDate ge {START_DATE} and AccountingDate le {END_DATE} and CompanyCode eq '{COMPANY_CODE}'"
    df = get_d365_data(entity, fields, filters, token)
    if df.empty:
        return pd.DataFrame()
    
    df.rename(columns={class="hl-string">'AccountingDate': class="hl-string">'EventTime', class="hl-string">'CreatedBy': class="hl-string">'ResponsibleUser', class="hl-string">'LedgerAccount': class="hl-string">'GlAccount'}, inplace=True)
    df[class="hl-string">'FinancialPeriod'] = pd.to_datetime(df[class="hl-string">'EventTime']).dt.strftime(class="hl-string">'%Y-%m')

    class=class="hl-string">"hl-comment"># Placeholder journal categories, update based on your setup
    journal_map = {
        class="hl-string">'Allocation': class="hl-string">'Accrual Journals Posted', class=class="hl-string">"hl-comment"># Example mapping
        class="hl-string">'Intercompany': class="hl-string">'Intercompany Journals Posted',
        class="hl-string">'Adjustment': class="hl-string">'Adjusting Journal Entries Posted' class=class="hl-string">"hl-comment"># Example mapping
    }
    
    df[class="hl-string">'ActivityName'] = df[class="hl-string">'JournalCategory'].map(journal_map)
    result_df = df.dropna(subset=[class="hl-string">'ActivityName'])
    return result_df[[class="hl-string">'FinancialPeriod', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'ResponsibleUser', class="hl-string">'CompanyCode', class="hl-string">'GlAccount']]

def extract_bank_reconciliations(token):
    print(class="hl-string">"Extracting Bank Reconciliations...")
    entity = class="hl-string">"BankReconciliationHeaders"
    fields = class="hl-string">"ReconciliationDate,ReconciliationStatus,CreatedDateTime,CreatedBy,DataAreaId,BankAccount"
    filters = fclass="hl-string">"CreatedDateTime ge {START_DATE}T00:00:00Z and CreatedDateTime le {END_DATE}T23:59:59Z and DataAreaId eq '{COMPANY_CODE}'"
    df = get_d365_data(entity, fields, filters, token)
    if df.empty:
        return pd.DataFrame()
    
    df.rename(columns={class="hl-string">'DataAreaId': class="hl-string">'CompanyCode', class="hl-string">'BankAccount': class="hl-string">'GlAccount'}, inplace=True)

    class=class="hl-string">"hl-comment"># Started event
    started_df = df.copy()
    started_df[class="hl-string">'ActivityName'] = class="hl-string">'Bank Reconciliation Started'
    started_df[class="hl-string">'EventTime'] = started_df[class="hl-string">'CreatedDateTime']
    started_df[class="hl-string">'ResponsibleUser'] = started_df[class="hl-string">'CreatedBy']
    started_df[class="hl-string">'FinancialPeriod'] = pd.to_datetime(started_df[class="hl-string">'EventTime']).dt.strftime(class="hl-string">'%Y-%m')

    class=class="hl-string">"hl-comment"># Completed event
    completed_df = df[df[class="hl-string">'ReconciliationStatus'] == class="hl-string">'Reconciled'].copy()
    completed_df[class="hl-string">'ActivityName'] = class="hl-string">'Bank Reconciliation Completed'
    completed_df[class="hl-string">'EventTime'] = completed_df[class="hl-string">'ReconciliationDate'] class=class="hl-string">"hl-comment"># Using ReconciliationDate as completion time
    completed_df[class="hl-string">'ResponsibleUser'] = completed_df[class="hl-string">'CreatedBy']
    completed_df[class="hl-string">'FinancialPeriod'] = pd.to_datetime(completed_df[class="hl-string">'EventTime']).dt.strftime(class="hl-string">'%Y-%m')
    
    result_df = pd.concat([started_df, completed_df], ignore_index=True)
    return result_df[[class="hl-string">'FinancialPeriod', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'ResponsibleUser', class="hl-string">'CompanyCode', class="hl-string">'GlAccount']]

def extract_close_workspace_tasks(token):
    print(class="hl-string">"Extracting Close Workspace Tasks (placeholder)...")
    class=class="hl-string">"hl-comment"># This is a placeholder. The entity name class="hl-string">'ClosingSheetTasks' might be different in your system.
    class=class="hl-string">"hl-comment"># Common entities: ClosingSheet, FinancialPeriodCloseWorkspace, ClosingSheetTasks
    entity = class="hl-string">"[FinancialCloseTaskEntity]"
    fields = class="hl-string">"TaskDescription,Status,ModifiedDateTime,ModifiedBy,DataAreaId,MainAccount"
    filters = fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and DataAreaId eq '{COMPANY_CODE}'"
    class=class="hl-string">"hl-comment"># df = get_d365_data(entity, fields, filters, token)
    class=class="hl-string">"hl-comment"># if df.empty:
    class=class="hl-string">"hl-comment">#     return pd.DataFrame()
    class=class="hl-string">"hl-comment"># For demonstration, creating a dummy dataframe
    print(fclass="hl-string">"SKIPPING: Entity '{entity}' not queried. This requires system-specific entity name.")
    return pd.DataFrame()

class=class="hl-string">"hl-comment"># --- Main Execution Logic ---
if __name__ == class="hl-string">"__main__":
    print(class="hl-string">"Starting D365 Period Close data extraction...")
    access_token = get_access_token()
    all_events = []

    all_events.append(extract_gl_period_events(access_token))
    all_events.append(extract_journal_postings(access_token))
    all_events.append(extract_bank_reconciliations(access_token))
    all_events.append(extract_close_workspace_tasks(access_token)) class=class="hl-string">"hl-comment"># Placeholder call

    event_log = pd.concat([df for df in all_events if not df.empty], ignore_index=True)
    
    class=class="hl-string">"hl-comment"># --- Handle Inferred and Missing Events ---
    if not event_log.empty and class="hl-string">'Adjusting Journal Entries Posted' in event_log[class="hl-string">'ActivityName'].values:
        print(class="hl-string">"Inferring 'Trial Balance Prepared' events...")
        adj_journals = event_log[event_log[class="hl-string">'ActivityName'] == class="hl-string">'Adjusting Journal Entries Posted'].copy()
        adj_journals[class="hl-string">'EventTime'] = pd.to_datetime(adj_journals[class="hl-string">'EventTime'])
        class=class="hl-string">"hl-comment"># Infer as last adjusting entry per period
        trial_balance_events = adj_journals.loc[adj_journals.groupby(class="hl-string">'FinancialPeriod')[class="hl-string">'EventTime'].idxmax()]
        trial_balance_events[class="hl-string">'ActivityName'] = class="hl-string">'Trial Balance Prepared'
        event_log = pd.concat([event_log, trial_balance_events], ignore_index=True)

    class=class="hl-string">"hl-comment"># Placeholder for class="hl-string">'Financial Statements Generated' and class="hl-string">'Approved'
    class=class="hl-string">"hl-comment"># These are often manual or hard to track. A common approach is to infer them based on the final closing date.

    class=class="hl-string">"hl-comment"># --- Final Formatting ---
    if not event_log.empty:
        event_log[class="hl-string">'SourceSystem'] = class="hl-string">'Microsoft Dynamics 365'
        event_log[class="hl-string">'LastDataUpdate'] = datetime.utcnow().isoformat() + class="hl-string">'Z'
        
        class=class="hl-string">"hl-comment"># Standardize columns
        final_cols = [class="hl-string">'FinancialPeriod', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'SourceSystem', class="hl-string">'LastDataUpdate', 
                        class="hl-string">'ResponsibleUser', class="hl-string">'CompanyCode', class="hl-string">'Department', class="hl-string">'GlAccount']
        for col in final_cols:
            if col not in event_log.columns:
                event_log[col] = None

        event_log = event_log[final_cols]
        event_log.sort_values(by=[class="hl-string">'FinancialPeriod', class="hl-string">'EventTime'], inplace=True)

        class=class="hl-string">"hl-comment"># Save to CSV
        output_filename = class="hl-string">'d365_period_close_event_log.csv'
        event_log.to_csv(output_filename, index=False, date_format=class="hl-string">'%Y-%m-%dT%H:%M:%SZ')
        print(fclass="hl-string">"Extraction complete. Event log saved to {output_filename}")
    else:
        print(class="hl-string">"No events found for the specified criteria.")