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## name: Pacing - Campaign Bulk Sheet
## description:
##  
## 
## author: Jesus Garza
## created: 2024-07-03
## 7/1 Updated version with Daily Budget Adjustment Factor 

today = datetime.datetime.now().date()  # Removed CLIENT_TIMEZONE for simplicity

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_AUTO_PACING_CYCLE_START_DATE = 'Auto. Pacing Cycle Start Date'
RPT_COL_AUTO_PACING_CYCLE_END_DATE = 'Auto. Pacing Cycle End Date'
RPT_COL_AUTO_PACING_CYCLE_DAYS_ELAPSED = 'Auto. Pacing Cycle Days Elapsed'
RPT_COL_AUTO_PACING_CYCLE_DAYS_REMAINING = 'Auto. Pacing Cycle Days Remaining'
RPT_COL_AUTO_PACING_CYCLE_PACING = 'Auto. Pacing Cycle Pacing'
RPT_COL_AUTO_PACING_CYCLE_THRESHOLD = 'Auto. Pacing Cycle Threshold'
RPT_COL_TOTAL_TARGET_SPEND_PER_IMPRVIEWS = 'Total Target (Spend/Impr./Views)'
RPT_COL_TOTAL_DAYS = 'Total Days'
RPT_COL_TOTAL_DAYS_ELAPSED = 'Total Days Elapsed'
RPT_COL_TOTAL_PACING = 'Total Pacing'
RPT_COL_DELIVERY_STATUS = 'Delivery Status'
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PACING_CALCULATION_DATE = 'Pacing Calculation Date'
RPT_COL_SOCIAL_BUDGET = 'Social Budget'
RPT_COL_SOCIAL_BUDGET_UPDATE_STATUS = 'Social Budget Update Status'
RPT_COL_AUTO_PACING_CYCLE_PUB_COST = 'Auto. Pacing Cycle Pub. Cost'
RPT_COL_AUTO_PACING_CYCLE_IMPR = 'Auto. Pacing Cycle Impr.'
RPT_COL_AUTO_PACING_CYCLE_CLICKS = 'Auto. Pacing Cycle Clicks'
RPT_COL_AUTO_PACING_CYCLE_VIEWS = 'Auto. Pacing Cycle Views'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_DAILY_BUDGET_ALERT = 'Daily Budget Alert'

# output columns
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR = 'Daily Budget Adjustment Factor'
BULK_COL_DAILY_BUDGET_ALERT = 'Daily Budget Alert'

# First filter: Exclude campaigns with 'Campaign Ended'
campaigns_not_ended = inputDf[inputDf[RPT_COL_AUTO_PACING_CYCLE_THRESHOLD] != 'Campaign Ended'].copy()

# Second filter: From the remaining, only include those where SBA Traffic is 'Traffic'
filteredDf = campaigns_not_ended[campaigns_not_ended[RPT_COL_SBA_TRAFFIC] == 'Traffic'].copy()

# Apply necessary operations on filteredDf
filteredDf[RPT_COL_DAILY_BUDGET] = np.where(
    filteredDf[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200,
    "",
    filteredDf[RPT_COL_RECOMMENDED_DAILY_BUDGET].apply(lambda x: round(x, 2))
)

# Add the new logic for Daily Budget Adjustment Factor
# Ensure the column exists before setting values
if BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR not in filteredDf.columns:
    filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR] = np.nan

# Now you can safely set the value
filteredDf.loc[(filteredDf[RPT_COL_AUTO_PACING_CYCLE_DAYS_REMAINING] <= 5) & 
               (filteredDf[RPT_COL_AUTO_PACING_CYCLE_THRESHOLD] == 'Underpacing'), 
               BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR] = 0.05

 # Convert the 'RPT_COL_DAILY_BUDGET' column to numeric
filteredDf[RPT_COL_DAILY_BUDGET] = pd.to_numeric(filteredDf[RPT_COL_DAILY_BUDGET], errors='coerce')

# Apply the Daily Budget Adjustment Factor to the Daily Budget column
filteredDf.loc[filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR].notnull(), 
               RPT_COL_DAILY_BUDGET] = filteredDf.loc[filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR].notnull(), 
                                                       RPT_COL_DAILY_BUDGET] * (1 + filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR])                                 
# Convert the 'RPT_COL_DAILY_BUDGET' column to numeric
filteredDf[RPT_COL_DAILY_BUDGET] = pd.to_numeric(filteredDf[RPT_COL_DAILY_BUDGET], errors='coerce')

# Configured 'Daily Budget Alert' and set to 'Flagged' for daily budgets exceeding $200
filteredDf[BULK_COL_DAILY_BUDGET_ALERT] = filteredDf.apply(
    lambda row: 'Checked' if row[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200 and row[RPT_COL_DAILY_BUDGET_ALERT] == 'Checked' else
               'Flagged' if row[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200 else '', axis=1)

# Assign the final filtered DataFrame to outputDf with correct column names
outputDf = filteredDf.rename(columns={
    RPT_COL_ACCOUNT: BULK_COL_ACCOUNT,
    RPT_COL_CAMPAIGN: BULK_COL_CAMPAIGN,
    RPT_COL_DAILY_BUDGET: BULK_COL_DAILY_BUDGET,
    BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR: BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR,
    BULK_COL_DAILY_BUDGET_ALERT: BULK_COL_DAILY_BUDGET_ALERT
})

# Assuming you want to display or utilize outputDf
print(outputDf)