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##
## name: Monthly Spend Target Rollover
## description:
##  - Takes unspent Strategy Spend Target from previous month and add it to current Spend Target
##  - Limited to Strategy using Budget constraint
## 
## author: Michael S. Huang
## created: 2024-05-13
## 

########### 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/gatorworks_strategy_target_rollover.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
    outputDf = dataSourceDict["1"].copy()

    # setup timezone
    import datetime
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=+8))

    # import pandas
    import pandas as pd
    import numpy as np

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

# today in client timezone
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_STRATEGY_TARGET = 'Target'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_GOAL = 'Goal'
RPT_COL_CONSTRAINT = 'Constraint'
RPT_COL_ROLL_OVER_STATUS = 'Roll Over Status'

# output columns and initial values
BULK_COL_GOAL = 'Goal'
BULK_COL_SPEND_TARGET = 'Spend Target'
outputDf[BULK_COL_GOAL] = "<<YOUR VALUE>>"
outputDf[BULK_COL_SPEND_TARGET] = "<<YOUR VALUE>>"

########### User Code Starts Here ###########

## DEBUG: enable rollover flag on a couple of strategies
# inputDf.iloc[0, inputDf.columns.get_loc(RPT_COL_ROLL_OVER_STATUS)] = 'traffic'
# inputDf.iloc[2, inputDf.columns.get_loc(RPT_COL_ROLL_OVER_STATUS)] = 'traffic'

### Cleanup input

# Convert RPT_COL_STRATEGY_TARGET to numeric, coercing errors to NaN
inputDf[RPT_COL_STRATEGY_TARGET] = pd.to_numeric(inputDf[RPT_COL_STRATEGY_TARGET], errors='coerce')
# Replace NaN values with 0.0 if that's the desired behavior
inputDf[RPT_COL_STRATEGY_TARGET].fillna(0.0, inplace=True)

# convert Constraint column into string
# NB: Bulk needs to include Goal column in order to change Spend Target, hence, 
#  remove non-Budget strategies just in case.
inputDf[RPT_COL_CONSTRAINT] = inputDf[RPT_COL_CONSTRAINT].astype(str)
budget_constraint = inputDf[RPT_COL_CONSTRAINT] == 'Budget'

# Create filter for RPT_COL_ROLL_OVER_STATUS where the value is 'traffic'
roll_over_enabled = inputDf[RPT_COL_ROLL_OVER_STATUS].astype(str).str.lower() == 'traffic'

# apply cleanup filters
inputDf = inputDf.loc[budget_constraint & roll_over_enabled].reset_index()

print("after cleanup inputDf")
print("inputDf.shape", inputDf.shape)
print("inputDf.info", inputDf.info())

# fill in missing target as 0 for comparison later
inputDf = inputDf.fillna(value={RPT_COL_STRATEGY_TARGET: 0})

# make copy of input for use with select_changed
originalDf = inputDf.copy()

### calculate unspent budget and add it to current budget target

# Calculate unspent budget by subtracting Pub. Cost $ from Target
inputDf['Unspent Budget'] = inputDf[RPT_COL_STRATEGY_TARGET] - inputDf[RPT_COL_PUB_COST]
# Add unspent budget to current strategy target
inputDf[RPT_COL_STRATEGY_TARGET] = inputDf[RPT_COL_STRATEGY_TARGET] + inputDf['Unspent Budget']

outputDf, debugDf = select_changed(inputDf, 
                          originalDf,
                          diff_cols=[RPT_COL_STRATEGY_TARGET],
                          select_cols=[RPT_COL_STRATEGY, RPT_COL_GOAL, RPT_COL_STRATEGY_TARGET],
                          merged_cols=[RPT_COL_STRATEGY]
                          )

# Bulk column is 'Spend Target', whereas report is 'Target'
outputDf.rename(columns={RPT_COL_STRATEGY_TARGET: BULK_COL_SPEND_TARGET}, inplace=True)

print("outputDf.shape", outputDf.shape)
print("outputDf sample")
print(tableize(outputDf.tail(10)))

## 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}")