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#
# Update, Status, tCPA, Bid Strategy, and or Campaign Maturity for New Launch Campaigns
#  
#  
#
#
# Author: Byron Porter
# Date: 2023-10-16
#


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
RPT_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_REVENUE = 'Revenue $'
RPT_COL_ROAS = 'CLICKS ROAS %'
RPT_COL_RPC = 'Rev./Click $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_MATURITY = 'Campaign Maturity'
RPT_COL_COST_PER_CONV = 'CLICKS Cost/Conv.'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STATUS = 'Status'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
BULK_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
BULK_COL_MATURITY = 'Campaign Maturity'


#set maturity parameter value so that it can be used in tuple
current_strategy = inputDf[RPT_COL_PUBLISHER_BIDSTRATEGY]

# temp columns to house new values and make sure all values are cleared out
TMP_STATUS = RPT_COL_CAMPAIGN_STATUS + '_'
inputDf[TMP_STATUS] = np.nan

TMP_PUB_STRATEGY = RPT_COL_PUBLISHER_BIDSTRATEGY + '_'
inputDf[TMP_PUB_STRATEGY] = np.nan

TMP_BUDGET = RPT_COL_DAILY_BUDGET + '_'
inputDf[TMP_BUDGET] = np.nan

TMP_MATURITY = RPT_COL_MATURITY + '_'
inputDf[TMP_MATURITY] = np.nan

TMP_TARGETCPA = RPT_COL_PUBLISHER_TARGETCPA + '_'
inputDf[TMP_TARGETCPA] = np.nan

# define temp dataframes
moreDf = pd.DataFrame()
lessDf = pd.DataFrame()

# Check if campaigns have 5 or more clicks
clicks_check = inputDf[RPT_COL_CLICKS] >= 5

# Use check to create DataFrames for campaigns with and without more than 5 clicks during reporting period
after_fiveDf = inputDf.loc[clicks_check, :].copy()
before_fiveDf = inputDf.loc[~clicks_check, :].copy()


# use boolean expressions to set new values based on Rev/Click and spend thresholds
after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] < 2.75, TMP_STATUS] = 'Pause'
after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] < 2.75, TMP_PUB_STRATEGY] = after_fiveDf[RPT_COL_PUBLISHER_BIDSTRATEGY]
after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] < 2.75, TMP_TARGETCPA] = after_fiveDf[RPT_COL_PUBLISHER_TARGETCPA]

after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] >= 2.75, TMP_STATUS] = after_fiveDf[RPT_COL_CAMPAIGN_STATUS]
after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] >= 2.75, TMP_PUB_STRATEGY] = 'TargetCpa'
after_fiveDf.loc[after_fiveDf[RPT_COL_RPC] >= 2.75, TMP_TARGETCPA] = after_fiveDf[RPT_COL_COST_PER_CONV]

before_fiveDf.loc[before_fiveDf[RPT_COL_PUB_COST] > 50, TMP_STATUS] = 'Pause'
before_fiveDf.loc[before_fiveDf[RPT_COL_PUB_COST] > 50, TMP_PUB_STRATEGY] = before_fiveDf[RPT_COL_PUBLISHER_BIDSTRATEGY]
before_fiveDf.loc[before_fiveDf[RPT_COL_PUB_COST] > 50, TMP_TARGETCPA] = before_fiveDf[RPT_COL_PUBLISHER_TARGETCPA]


print("== Current After Dataframe ==", tableize(after_fiveDf))
print("== Current Before Dataframe ==", tableize(before_fiveDf))

# find changed campaigns for campaigns with 5 or more clicks
changed = (after_fiveDf[TMP_STATUS].notnull() & (after_fiveDf[RPT_COL_CAMPAIGN_STATUS] != after_fiveDf[TMP_STATUS])) | \
          (after_fiveDf[TMP_PUB_STRATEGY].notnull() & (after_fiveDf[RPT_COL_PUBLISHER_BIDSTRATEGY] != after_fiveDf[TMP_PUB_STRATEGY])) | \
          (after_fiveDf[TMP_TARGETCPA].notnull() & (after_fiveDf[RPT_COL_PUBLISHER_TARGETCPA] != after_fiveDf[TMP_TARGETCPA]))

if sum(changed) > 0:

    print("== Campaigns with Changed Adj ==", tableize(after_fiveDf.loc[changed]))

    # only select changed rows
    cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_STATUS, TMP_PUB_STRATEGY, TMP_TARGETCPA]
    moreDf = after_fiveDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                        TMP_STATUS: BULK_COL_STATUS, \
                        TMP_PUB_STRATEGY: BULK_COL_PUBLISHER_BIDSTRATEGY, \
                        TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
                    })

else:
    
    print("Empty moreDf")
    moreDf = moreDf.iloc[0:0]

# find changed campaigns for campaigns with less than 5 clicks
changed = (before_fiveDf[TMP_STATUS].notnull() & (before_fiveDf[RPT_COL_CAMPAIGN_STATUS] != before_fiveDf[TMP_STATUS])) | \
          (before_fiveDf[TMP_PUB_STRATEGY].notnull() & (before_fiveDf[RPT_COL_PUBLISHER_BIDSTRATEGY] != before_fiveDf[TMP_PUB_STRATEGY])) | \
          (before_fiveDf[TMP_TARGETCPA].notnull() & (before_fiveDf[RPT_COL_PUBLISHER_TARGETCPA] != before_fiveDf[TMP_TARGETCPA]))

if sum(changed) > 0:

    print("== Campaigns with Changed Adj ==", tableize(before_fiveDf.loc[changed]))

    # only select changed rows
    cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_STATUS, TMP_PUB_STRATEGY, TMP_TARGETCPA]
    lessDf = before_fiveDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                        TMP_STATUS: BULK_COL_STATUS, \
                        TMP_PUB_STRATEGY: BULK_COL_PUBLISHER_BIDSTRATEGY, \
                        TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
                    })

else:
    
    print("Empty lessDf")
    lessDf = lessDf.iloc[0:0]    

# Merge defined data, exlcuding those that are empty, and print the resulting outputDf
dataframes = [moreDf, lessDf]
non_empty_dataframes = [df for df in dataframes if not df.empty]

if non_empty_dataframes:
    outputDf = pd.concat(non_empty_dataframes)
    print("outputDf", tableize(outputDf))
else:
    print("Empty outputDf")
    outputDf = outputDf.iloc[0:0]