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##
## name: Campaign Budget Dimension Updates
## description:
##  
## 
## author: 
## created: 2023-12-07
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# Function to update campaign information
def update_campaign_info(input_df, campaign_type, target_campaign_name):
    # Filter rows where the Campaign matches the target campaign for the specified type
    target_campaign = input_df[input_df["Campaign"] == target_campaign_name]

    print(f"Number of target_campaigns ({campaign_type}): {len(target_campaign)}")

    # Initialize an empty DataFrame to store the results
    output_df = pd.DataFrame(columns=input_df.columns)

    # Iterate through each row in the target_campaign DataFrame
    for _, row in target_campaign.iterrows():
        # Define the conditions based on campaign type
        if campaign_type == "Performance Max":
            conditions = (
                (input_df["Campaign"].str.contains('PMax', case=False, regex=True)) &
                (~input_df["Campaign"].str.contains('Vendor', case=False, regex=True)) &
                (input_df["Campaign Type"] == "Performance Max") &
                (input_df["Campaign Status"] == "Active") &
                (input_df["Account"] == row["Account"]) &
                (input_df["AVB ID"] == row["AVB ID"])
            )   
        else: 
        # Define the conditions to match other campaigns
            conditions = (
                (input_df["Campaign"].str.contains(row['Campaign'])) &
                (input_df["Campaign Type"] == campaign_type) &
                (input_df["Campaign Status"] == "Active") &
                (input_df["Account"] == row["Account"]) & 
                (input_df["AVB ID"] == row["AVB ID"])
            )

        # Apply conditions and copy values to the output DataFrame
        matching_campaigns = input_df[conditions]

        print(f'Original campaign ({campaign_type}): {row["Campaign"]}')
        print('matching campaigns: ', matching_campaigns["Campaign"].tolist())
        print(f"Number of matching_campaigns: {len(matching_campaigns)}")

        if not matching_campaigns.empty:
            # Retrieve values from the row
            budget_start_date = pd.to_datetime(row[BULK_COL_BUDGET_START_DATE], format='%Y/%m/%d').strftime('%m/%d/%y')
            budget_end_date = pd.to_datetime(row[BULK_COL_BUDGET_END_DATE], format='%Y/%m/%d').strftime('%m/%d/%y')

            pacing_campaign_budget = row[BULK_COL_PACING_CAMPAIGN_BUDGET]
            print('budget start date')
            print(budget_start_date)
            print('budget end date')
            print(budget_end_date)
            print('pacing_campaign_budget')
            print(pacing_campaign_budget)

            # Make a copy to avoid SettingWithCopyWarning
            matching_campaigns = matching_campaigns.copy()

            # Update values in the output DataFrame using .loc
            matching_campaigns.loc[:, BULK_COL_BUDGET_START_DATE] = budget_start_date
            matching_campaigns.loc[:, BULK_COL_BUDGET_END_DATE] = budget_end_date
            matching_campaigns.loc[:, BULK_COL_PACING_CAMPAIGN_BUDGET] = pacing_campaign_budget

            # Concatenate the updated matching campaigns to the output DataFrame
            output_df = pd.concat([output_df, matching_campaigns], ignore_index=True)
            print('output dataframe')
            print(output_df)

    return output_df

# Primary data source and columns
input_df = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_AVB_ID = 'AVB ID'
RPT_COL_BUDGET_START_DATE = 'Budget Start Date'
RPT_COL_BUDGET_END_DATE = 'Budget End Date'
RPT_COL_PACING_CAMPAIGN_BUDGET = 'Pacing - Campaign Budget'
BULK_COL_BUDGET_START_DATE = 'Budget Start Date'
BULK_COL_BUDGET_END_DATE = 'Budget End Date'
BULK_COL_PACING_CAMPAIGN_BUDGET = 'Pacing - Campaign Budget'

# Specify target campaign names for each type
search_target_campaign_name = "SE : Custom :: Appliances"
shopping_target_campaign_name = "SH : Custom :: Appliances"
performance_max_target_campaign_name = "PMax: Test"

# Update information for Search campaigns
output_df_search = update_campaign_info(input_df, "Search", search_target_campaign_name)

# Update information for Shopping campaigns
output_df_shopping = update_campaign_info(input_df, "Shopping", shopping_target_campaign_name)

# Update information for Performance Max campaigns
output_df_performance_max = update_campaign_info(input_df, "Performance Max", performance_max_target_campaign_name)

# Combine the results into one output DataFrame
outputDf = pd.concat([output_df_search, output_df_shopping, output_df_performance_max], ignore_index=True)

# You can now use combined_output_df as the final report