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#
# AVB Marketing - Budget Pacing - Minimize Lost IS (Budget)
#
# Allocates according to:
# * Only for campaigns within Budget Start/End Date
# * Remaining budget for each Salesforce Item ID Budget Group
# * Remaining days in Budget Period (Budget Start/End Date)
# * Historical spend and spend potential
# * Campaigns with spend in lookback period
# * Minimum daily budget
#
# Author: Michael S. Huang
#
# Created: 2023-09-30
#

##### Configurable Param #####

MINIMUM_DAILY_BUDGET = 10

##############################

########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/avb_marketing_20240303_datasource_dict.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

local_dev = False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    local_dev = True
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        # print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    inputDf = dataSourceDict["1"]
    outputDf = inputDf.copy()

    # setup timezone
    import datetime
    # LA Timezone is GMT-8. Adjust as needed.
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=-8))

    # import pandas
    import pandas as pd
    import numpy as np

    # other imports
    import re
    import urllib

    # import Marin util functions
    from marin_scripts_utils import tableize, select_changed
else:
    print("Running locally but no pickle path defined. dataSourceDict not loaded.")
    exit(1)
########### END - Local Mode Setup ###########



RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_SALESFORCE_ITEM_ID = 'Salesforce Item Id'
RPT_COL_SBA_CAMPAIGN_BUDGET = 'SBA Campaign Budget'
RPT_COL_SBA_ALLOCATION = 'SBA Allocation'
RPT_COL_SBA_REC_DAILY_BUDGET = 'SBA Rec. Daily Budget'
RPT_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_PROGRAM_END_DATE = 'Program End Date'
RPT_COL_BUDGET_START_DATE = 'Budget Start Date'
RPT_COL_BUDGET_END_DATE = 'Budget End Date'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_SBA_ALLOCATION = 'SBA Allocation'
BULK_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
BULK_COL_SBA_REC_DAILY_BUDGET = 'SBA Rec. Daily Budget'

COL_SPEND_FULL_POTENTIAL = 'spend_lookback_full_potential'
COL_SPEND_FULL_POTENTIAL_CAPPED = 'spend_lookback_full_potential_capped'
COL_SPEND_BUDGET_PERIOD = 'spend_budget_period'
COL_SBA_ALLOCATION_NEW_FLOAT = RPT_COL_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_SBA_ALLOCATION + '_new'
COL_SALESFORCE_ITEM_ID_BUDGET_REMAINING = 'Salesforce_Item_ID_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_SBA_BUDGET_PACING + '_new'
COL_CAMPAIGN_BUDGET_REMAINING = 'campaign_budget_remaining'
COL_DAILY_BUDGET_NEW = RPT_COL_DAILY_BUDGET + '_new'
COL_SBA_REC_DAILY_BUDGET_NEW = RPT_COL_SBA_REC_DAILY_BUDGET + '_new'
COL_BUDGET_PERIOD_DAYS_REMAINING= 'budget_period_days_remaining'
COL_BUDGET_PERIOD_DAYS_TOTAL= 'budget_period_days_total'
COL_PACING_CALC = 'pacing_calc'

today = datetime.datetime.now(CLIENT_TIMEZONE).date()
today_pd = pd.to_datetime(today)

print("today", today_pd)
print("inputDf shape", inputDf.shape)
print(inputDf.info())

EMPTY_OUTPUT = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_DAILY_BUDGET, BULK_COL_SBA_REC_DAILY_BUDGET, BULK_COL_SBA_BUDGET_PACING, BULK_COL_SBA_ALLOCATION])

def process(inputDf):

    inputDf = inputDf.set_index([RPT_COL_SALESFORCE_ITEM_ID])
    group_by_salesforce_item_ID = inputDf.groupby(RPT_COL_SALESFORCE_ITEM_ID)

    # ## Calculate Full-Potential Spend
    # * Adjust Historical Spend by _Lost Impression Share due to Budget_ (see [Formula](https://docs.google.com/document/d/1EbCQ5z9Up8TZ6GISEeCaRSB3Fc15vCPCfeIydree23M/edit#bookmark=id.5fsx7jlseze6))
    # 

    adj_ratio = 1 + (inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET] / (1 - inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET]))

    inputDf[COL_SPEND_FULL_POTENTIAL] = round(inputDf[RPT_COL_PUB_COST] * adj_ratio, 2)

    # ## Remove Date Segmentation
    # * Calculate Budget Period Spend
                    
    # give new column values to sum up
    inputDf[COL_SPEND_BUDGET_PERIOD] = inputDf[RPT_COL_PUB_COST]

    # SUM Series but only include dates within Start/End Dates (inclusive)
    def date_range_sum(x, date, start_date, end_date):
        # print('budget_period_sum got: ', start_date.info(), end_date.info(), x.info())
        rows_within_budget_period = x[ (date >= start_date) & (date <= end_date) & (x.values > 0)]
        return rows_within_budget_period.sum()

    groupby_cols = [ \
        RPT_COL_SALESFORCE_ITEM_ID, \
        RPT_COL_STRATEGY, \
        RPT_COL_PUBLISHER_NAME, \
        RPT_COL_ACCOUNT, \
        RPT_COL_CAMPAIGN, \
    ]

    # index by groupby columns so can do inputDf.loc[x.index] in agg spec below
    inputDf = inputDf.reset_index() \
                    .set_index(groupby_cols) \
                    .sort_index()

    agg_spec = {
        RPT_COL_CAMPAIGN_STATUS: 'last', \
        RPT_COL_DAILY_BUDGET: 'last', \
        RPT_COL_SBA_CAMPAIGN_BUDGET: 'last', \
        RPT_COL_SBA_ALLOCATION: 'last', \
        RPT_COL_SBA_REC_DAILY_BUDGET: 'last', \
        RPT_COL_SBA_BUDGET_PACING: 'last', \
        RPT_COL_SBA_TRAFFIC: 'last', \
        RPT_COL_BUDGET_START_DATE: 'last', \
        RPT_COL_BUDGET_END_DATE: 'last', \
        RPT_COL_CLICKS: 'sum', \
        RPT_COL_CONV: 'sum', \
        RPT_COL_PUB_COST: 'sum', \
        COL_SPEND_BUDGET_PERIOD: lambda x: date_range_sum(x, \
                                                            inputDf.loc[x.index[0], RPT_COL_DATE], \
                                                            inputDf.loc[x.index[0], RPT_COL_BUDGET_START_DATE], \
                                                            inputDf.loc[x.index[0], RPT_COL_BUDGET_END_DATE] \
                                                            ), \
        COL_SPEND_FULL_POTENTIAL: 'sum', \
        RPT_COL_PROGRAM_END_DATE: 'last', \
    }


    df_campaign_agg = inputDf \
                            .groupby(groupby_cols) \
                            .agg(agg_spec) \
                            .reset_index() \
                            .set_index(RPT_COL_SALESFORCE_ITEM_ID)


    # ## Only allocate budget for recently trafficking campaigns
    # * Exclude Campaigns that are:
    # ** not ACTIVE 
    # ** without spend in lookback period
    # ** Program Date is in the past
    # ** current date not within Budget Start/End Dates

    inactive_campaigns = (df_campaign_agg[RPT_COL_CAMPAIGN_STATUS] != 'Active') & (df_campaign_agg[RPT_COL_PUB_COST] == 0)
    expired_campaigns = df_campaign_agg[RPT_COL_PROGRAM_END_DATE].notnull() & (df_campaign_agg[RPT_COL_PROGRAM_END_DATE] < pd.to_datetime(today))
    in_budget_period =  df_campaign_agg[RPT_COL_BUDGET_START_DATE].notnull() & \
                        df_campaign_agg[RPT_COL_BUDGET_END_DATE].notnull() & \
                        (today_pd >= df_campaign_agg[RPT_COL_BUDGET_START_DATE]) & \
                        (today_pd <= df_campaign_agg[RPT_COL_BUDGET_END_DATE])

    campaign_count = df_campaign_agg.size

    df_campaign_agg = df_campaign_agg.loc[ ~(inactive_campaigns | expired_campaigns) & in_budget_period]

    eligible_campaign_count = df_campaign_agg.size

    print(f"Out of {campaign_count} Campaigns, {eligible_campaign_count} are eligible for budget allocation")

    if eligible_campaign_count == 0:
        print("No campaigns to allocate. Stopping.")
        return EMPTY_OUTPUT, EMPTY_OUTPUT # Early return from the function, effectively ending the script

    # ## Calculate Budget Allocation Ratio 
    # * Cap full potential spend at 2X (don't spend twice as much as before)
    # * Compare full potential spend for each campaign to total spend within same SALESFORCE_ITEM_ID budget group

    df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] = df_campaign_agg \
        .apply(lambda row: min(row[COL_SPEND_FULL_POTENTIAL], 2 * row[RPT_COL_PUB_COST]), axis=1)


    # use transform to calculate sum for each SALESFORCE_ITEM_ID and make it available on every row
    # note: no need to build aggregate DataFrame and JOIN back to original

    df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] = 100.0 * \
            df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] / \
            df_campaign_agg.groupby(RPT_COL_SALESFORCE_ITEM_ID)[COL_SPEND_FULL_POTENTIAL_CAPPED].transform('sum')

    df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] = df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT].fillna(0)

    df_campaign_agg[COL_SBA_ALLOCATION_NEW] = round(df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT],0).astype(str) + '%'


    # 
    # ## Calculate Remaining Budget
    # * For each SALESFORCE_ITEM_ID budget group, calculate how much Budget is left by substracting SBA Monthly budget from MTD SALESFORCE_ITEM_ID spend

    df_campaign_agg[COL_SALESFORCE_ITEM_ID_BUDGET_REMAINING] =  \
            df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET] - \
            df_campaign_agg.groupby(by=[RPT_COL_SALESFORCE_ITEM_ID])[COL_SPEND_BUDGET_PERIOD].sum()


    # ## Calculate total and remaining days

    df_campaign_agg[COL_BUDGET_PERIOD_DAYS_TOTAL] = (df_campaign_agg[RPT_COL_BUDGET_END_DATE] - df_campaign_agg[RPT_COL_BUDGET_START_DATE]).dt.days + 1

    def calculate_remaining_days(row, today_pd):
        start_date = row[RPT_COL_BUDGET_START_DATE]
        end_date = row[RPT_COL_BUDGET_END_DATE]
        days = None
        if pd.isnull(start_date) or pd.isnull(end_date):
            days = np.nan  # or some default value if null dates are not expected
        if today_pd < start_date:
            days = (end_date - start_date).days + 1
        elif today_pd > end_date:
            days = 0
        else:
            days = (end_date - today_pd).days + 1

        return days

    df_campaign_agg[COL_BUDGET_PERIOD_DAYS_REMAINING] = df_campaign_agg.apply(
        lambda row: calculate_remaining_days(row, today_pd),
        axis=1
    )

    df_campaign_agg[COL_CAMPAIGN_BUDGET_REMAINING] = round(df_campaign_agg[COL_SALESFORCE_ITEM_ID_BUDGET_REMAINING] * df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] / 100.0, 1)


    # ## Calculate SBA Daily Budget
    # 
    # * Calcualte next day Daily Budget by dividing allocated budget by number of Days left in the current budget period
    def calculate_daily_budget(row, today_pd):
        if today_pd < row[RPT_COL_BUDGET_START_DATE] or today_pd > row[RPT_COL_BUDGET_END_DATE]:
            return 0
        else:
            budget = round(row[COL_CAMPAIGN_BUDGET_REMAINING] / row[COL_BUDGET_PERIOD_DAYS_REMAINING], 0)

            # Apply Minimum Rule
            budget = max(MINIMUM_DAILY_BUDGET, budget)

            return budget


    df_campaign_agg[COL_SBA_REC_DAILY_BUDGET_NEW] = df_campaign_agg.apply(
        lambda row: calculate_daily_budget(row, today_pd),
        axis=1
    )




    # ### Traffic Budget
    df_campaign_agg[COL_DAILY_BUDGET_NEW] = np.nan

    # campaigns to traffic
    to_traffic = df_campaign_agg[RPT_COL_SBA_TRAFFIC].notnull() & \
                (df_campaign_agg[RPT_COL_SBA_TRAFFIC].astype(str).str.lower() == 'traffic')
    print("Not weekend. Traffic count", to_traffic.sum())

    # copy budgets over
    df_campaign_agg[COL_DAILY_BUDGET_NEW] = df_campaign_agg[COL_SBA_REC_DAILY_BUDGET_NEW]
    # then blank out budget for non-traffic campaigns
    df_campaign_agg.loc[~to_traffic, COL_DAILY_BUDGET_NEW] = np.nan


    # ## Calculate Salesforece Item ID -level Pacing compliance percentage. Ideally should be 100% each day.

    # calc ratio to prorate monthly budget
    ratio_of_elapsed_days_in_period = (df_campaign_agg[COL_BUDGET_PERIOD_DAYS_TOTAL] - df_campaign_agg[COL_BUDGET_PERIOD_DAYS_REMAINING]) / df_campaign_agg[COL_BUDGET_PERIOD_DAYS_TOTAL]

    # divide budget period spend by prorated total budget
    mask = df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET] > 0
    df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
                                        df_campaign_agg.groupby(by=[RPT_COL_SALESFORCE_ITEM_ID])[COL_SPEND_BUDGET_PERIOD].sum() / \
                                        (ratio_of_elapsed_days_in_period * df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET]), \
                                        0).astype(str) + '%'
    df_campaign_agg.loc[mask, COL_SBA_BUDGET_PACING_NEW] = df_campaign_agg.loc[mask, COL_PACING_CALC]



    # Debug DF with full details
    df_SALESFORCE_ITEM_ID_budget = group_by_salesforce_item_ID[[RPT_COL_SBA_CAMPAIGN_BUDGET]].transform('max').dropna().drop_duplicates()
    print("Salesforce Item ID budgets", df_SALESFORCE_ITEM_ID_budget.head().to_string())


    # ## Generate outputDf

    # Check for changes
    changed = df_campaign_agg[COL_SBA_REC_DAILY_BUDGET_NEW].notnull() & \
        ( \
        (df_campaign_agg[RPT_COL_SBA_REC_DAILY_BUDGET] != df_campaign_agg[COL_SBA_REC_DAILY_BUDGET_NEW]) | \
        (df_campaign_agg[RPT_COL_DAILY_BUDGET] != df_campaign_agg[COL_DAILY_BUDGET_NEW]) | \
        (df_campaign_agg[RPT_COL_SBA_ALLOCATION] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
        (df_campaign_agg[RPT_COL_SBA_BUDGET_PACING] != df_campaign_agg[COL_SBA_BUDGET_PACING_NEW]) \
        )

    print("Changed rows:", changed.sum())

    # Debug
    debugDf = df_campaign_agg.loc[changed] \
        .reset_index() \
        .sort_values(by=[RPT_COL_SALESFORCE_ITEM_ID, COL_DAILY_BUDGET_NEW, COL_SBA_REC_DAILY_BUDGET_NEW], ascending=False) 

    # print("debugDf", tableize(debugDf))

    # Only emit output for changed campaigns
    if changed.sum() > 0:

        # construct outputDf
        outputDf = df_campaign_agg.loc[changed, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, COL_DAILY_BUDGET_NEW, COL_SBA_REC_DAILY_BUDGET_NEW, COL_SBA_BUDGET_PACING_NEW, COL_SBA_ALLOCATION_NEW]] \
                        .copy() \
                        .rename(columns={ \
                                COL_DAILY_BUDGET_NEW: BULK_COL_DAILY_BUDGET, \
                                COL_SBA_REC_DAILY_BUDGET_NEW: BULK_COL_SBA_REC_DAILY_BUDGET, \
                                COL_SBA_BUDGET_PACING_NEW: BULK_COL_SBA_BUDGET_PACING, \
                                COL_SBA_ALLOCATION_NEW: BULK_COL_SBA_ALLOCATION, \
                            }) \
                        .reset_index() \
                        .sort_values(by=[RPT_COL_SALESFORCE_ITEM_ID, BULK_COL_DAILY_BUDGET, BULK_COL_SBA_REC_DAILY_BUDGET], ascending=False) \
                        .drop(RPT_COL_SALESFORCE_ITEM_ID, axis=1)

        print("outputDf shape", outputDf.shape)
        print("outputDf", tableize(outputDf.head()))
    else:
        print("No changes detected, returning an empty dataframe")
        outputDf = EMPTY_OUTPUT

    return outputDf, debugDf

## fix type issues

# change back to percent string
if inputDf[RPT_COL_SBA_ALLOCATION].dtype == "float":
    inputDf[RPT_COL_SBA_ALLOCATION] = round(inputDf[RPT_COL_SBA_ALLOCATION] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_SBA_BUDGET_PACING].dtype == "float":
    inputDf[BULK_COL_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_SBA_BUDGET_PACING] * 100.0, 0).astype(str) + '%'

# coerce Date, Program End Date, Budget Start Date, and Budget End Date into Date type
inputDf[RPT_COL_DATE] = pd.to_datetime(inputDf[RPT_COL_DATE], errors='coerce')
inputDf[RPT_COL_PROGRAM_END_DATE] = pd.to_datetime(inputDf[RPT_COL_PROGRAM_END_DATE], errors='coerce')
inputDf[RPT_COL_BUDGET_START_DATE] = pd.to_datetime(inputDf[RPT_COL_BUDGET_START_DATE], errors='coerce')
inputDf[RPT_COL_BUDGET_END_DATE] = pd.to_datetime(inputDf[RPT_COL_BUDGET_END_DATE], errors='coerce')


## do actual processing
outputDf, debugDf = process(inputDf)

## local debug
if local_dev:
    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")