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##
## name: Strategy Target Staging - GSheets - All Campus
## description:
##  Copy Program Budgets from staging GSheets to update Strategy Spend Targets
##  Use GSheets 'Abbrevation' column to match with 'Strategy'
## 
## author: Michael S. Huang, Dana Waidhas
## created: 2024-03-15
## 

########### 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/allcampus_budget_staging_20240507_with_goal.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 ###########

# dial forward to preview next month budgets
# CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=+8))

# 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_CONSTRAINT = 'Constraint Type'
RPT_COL_GOAL = 'Goal'
RPT_COL_STRATEGY_TARGET = 'Target'

# reference data source and columns
gSheetsDf = dataSourceDict["2_1"]  # gsheets dataframe (first sheet)
# To access 10th row of Column C, use gSheetsDf.loc[10, 'C']

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

### Load Current Month Budgets from GSheets and rename

# For debugging, set arbitrary date
# today = datetime.date(2024, 4, 2)

# Construct column key by mapping current month to canonical column name
# Assume column F is January, G is February, H is March, Q is Dec, etc. In terms of integer months, F=1, G=2, H=3,.., Q=12.
column_key = chr(64 + today.month + 5)  
print(f"GSheets column key for current month: {today.strftime(('%B'))} => {column_key}")

# load current month
# - skip first 3 rows via .loc[2:]
# - 'Abbreviation' is column T
# - budgets from January to December are on columns F to Q
current_month_budgets = dataSourceDict['2_1'] \
                        .loc[2:, ['T', column_key]] \
                        .rename(columns={ \
                            'T' : RPT_COL_STRATEGY, \
                            column_key : RPT_COL_STRATEGY_TARGET \
                        })

print("current_month_budgets.shape", current_month_budgets.shape)
print("current_month_budgets.info", current_month_budgets.info())
print("current_month_budgets first 10 rows", current_month_budgets.head(10))

### cleanup Budget values from GSheets
# remove empty strategy rows
current_month_budgets = current_month_budgets.loc[current_month_budgets[RPT_COL_STRATEGY].notnull()]
# make sure Strategy is STR
current_month_budgets[RPT_COL_STRATEGY] = current_month_budgets[RPT_COL_STRATEGY].astype(str)
# make sure target is float; remove prefix if not
# note: can't check for `object` since not imported, so use `0` instead
if current_month_budgets[RPT_COL_STRATEGY_TARGET].dtype == 'O':
    current_month_budgets[RPT_COL_STRATEGY_TARGET] = current_month_budgets[RPT_COL_STRATEGY_TARGET] \
                                    .str.replace('US$', '', case=False, regex=False) \
                                    .str.replace('$', '', case=False, regex=False) \
                                    .str.replace(',', '') \
                                    .str.strip() \
                                    .astype(float)

# remove empty target rows
has_strategy = current_month_budgets[RPT_COL_STRATEGY].notnull() & \
                (current_month_budgets[RPT_COL_STRATEGY].str.len() > 3)
has_strategy_target = current_month_budgets[RPT_COL_STRATEGY_TARGET].notnull() & \
                (current_month_budgets[RPT_COL_STRATEGY_TARGET] > 0.5)
current_month_budgets = current_month_budgets.loc[has_strategy & has_strategy_target]

print("after cleanup gsheets")
print("current_month_budgets.shape", current_month_budgets.shape)
print("current_month_budgets.info", current_month_budgets.info())
print(current_month_budgets.head().to_string())

### 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 Strategy column into string and remove blank or non-numeric rows
inputDf[RPT_COL_STRATEGY] = inputDf[RPT_COL_STRATEGY].astype(str)
valid_abbrev = inputDf[RPT_COL_STRATEGY].str.len() > 3

# 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'

# apply cleanup filters
inputDf = inputDf.loc[valid_abbrev & budget_constraint]

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()

### Merge inputDf with current_month_budgets, and fill in missing target as 0
mergedDf = inputDf.merge(current_month_budgets, on=RPT_COL_STRATEGY, how='left', suffixes=('_old', '')) \
                  .fillna(value={RPT_COL_STRATEGY_TARGET: 0})

print("mergedDf shape", mergedDf.shape)
print("mergedDf", mergedDf.tail(5).to_string())

outputDf, debugDf = select_changed(mergedDf, 
                          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)


change_to_zero_target = (debugDf[RPT_COL_STRATEGY_TARGET + '_new'] < 1) & \
                        (debugDf[RPT_COL_STRATEGY_TARGET + '_orig'] > 1)
print("count of campaigns with target cleared", sum(change_to_zero_target))
print("campaigns with target cleared", tableize(debugDf.loc[change_to_zero_target].head()))

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}")