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##
## name: Epicor Budget Staging - GSheets
## description:
##  Copy Epicor budget from staging GSheets on month start
## 
## author: Michael S. Huang
## created: 2023-11-10
## 

########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=True
# 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/heartland_dental_staging_20240131_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

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.")
    # 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_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_EPICORID = 'epicorID'
RPT_COL_EPICORID_MONTHLY_BUDGET = 'epicorID - Monthly Budget'
RPT_COL_EPICORID_MONTHLY_BUDGET__STAGING = 'epicorID - Monthly Budget - Staging'
RPT_COL_EPICORID_MONTHLY_BUDGET__STAGING_MONTH = 'epicorID - Monthly Budget - Staging Month'

# 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_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_EPICORID__MONTHLY_BUDGET = 'epicorID - Monthly Budget'
outputDf[BULK_COL_EPICORID__MONTHLY_BUDGET] = "<<YOUR VALUE>>"

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

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

### Load current month Search Budget from GSheets 
key = f"2_{today.month}"

print(f"GSheets key for current month: {key}")
# get columns A and C from current month tab of GSheets
current_month_budgets = dataSourceDict[key] \
                        .loc[:, ['A', 'C']] \
                        .rename(columns={ \
                            'A' : RPT_COL_EPICORID, \
                            'C' : RPT_COL_EPICORID_MONTHLY_BUDGET \
                        })

print("current_month_budgets.shape", current_month_budgets.shape)
print("current_month_budgets.dtypes", current_month_budgets.dtypes)

### cleanup Budget values from GSheets
# remove empty epicorID rows
current_month_budgets = current_month_budgets.loc[current_month_budgets[RPT_COL_EPICORID].notnull()]
# make sure EpicorID is STR of Integers
current_month_budgets[RPT_COL_EPICORID] = current_month_budgets[RPT_COL_EPICORID].astype(str).str.extract('(\d+)')[0]
# make sure budget 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_EPICORID_MONTHLY_BUDGET].dtype == 'O':
    current_month_budgets[RPT_COL_EPICORID_MONTHLY_BUDGET] = current_month_budgets[RPT_COL_EPICORID_MONTHLY_BUDGET] \
                                    .str.replace('US$', '', case=False, regex=False) \
                                    .str.replace(',', '') \
                                    .str.strip() \
                                    .astype(float)

# remove empty budget rows
has_epicor_id = current_month_budgets[RPT_COL_EPICORID].notnull() & \
                (current_month_budgets[RPT_COL_EPICORID].str.len() > 3)
has_epicor_budget = current_month_budgets[RPT_COL_EPICORID_MONTHLY_BUDGET].notnull() & \
                (current_month_budgets[RPT_COL_EPICORID_MONTHLY_BUDGET] > 0.5)
current_month_budgets = current_month_budgets.loc[ has_epicor_id & has_epicor_budget]

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

### Cleanup report

### Cleanup report

### convert EpicorID column into string and remove blank or non-numeric rows
inputDf[RPT_COL_EPICORID] = inputDf[RPT_COL_EPICORID].astype(str)
valid_epicor = inputDf[RPT_COL_EPICORID].str.isdigit() & inputDf[RPT_COL_EPICORID] != ''
inputDf = inputDf.loc[valid_epicor]

print("after cleanup inputDf")
print("inputDf.shape", inputDf.shape)
print("intpuDf.dtypes", inputDf.dtypes)

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

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

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

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

outputDf, debugDf = select_changed(mergedDf, 
                          originalDf,
                          diff_cols=[RPT_COL_EPICORID_MONTHLY_BUDGET],
                          select_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_EPICORID, RPT_COL_EPICORID_MONTHLY_BUDGET],
                          # HACK: comment out next line on server
                          merged_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN]
                          )

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

print("outputDf.shape", outputDf.shape)
print("outputDf head")
print(tableize(outputDf.head()))