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# columns for output
OUT_COL_MD_DEBUG_PERF_CALC = 'Performance Calc Raw Numbers'
OUT_COL_MD_DEBUG_PERF_CALC_LAST_RUN = 'Performance Calc Last Run Date'

# columns for input
IN_COL_METRIC_CVR = 'Conv. Rate %'
IN_COL_METRIC_CPA = 'Cost/Conv. $'
IN_COL_METRIC_CTR = 'CTR %'
IN_COL_METRIC_CPC = 'Avg. CPC $'


# columns for both input and output
IN_OUT_COL_CAMPAIGN = 'Campaign'
IN_OUT_COL_MD_PERF_CVR = 'Conv Rate % - Performance'
IN_OUT_COL_MD_PERF_CPA = 'Cost/Conv $ - Performance'
IN_OUT_COL_MD_PERF_CTR = 'CTR % - Performance'
IN_OUT_COL_MD_PERF_CPC = 'CPC $ - Performance'

# setup empty output columns
outputDf[OUT_COL_MD_DEBUG_PERF_CALC] = np.nan
#outputDf[OUT_COL_MD_DEBUG_PERF_CALC_LAST_RUN] = datetime.date.today().isoformat()
outputDf[IN_OUT_COL_MD_PERF_CVR] = np.nan
outputDf[IN_OUT_COL_MD_PERF_CPA] = np.nan
outputDf[IN_OUT_COL_MD_PERF_CTR] = np.nan
outputDf[IN_OUT_COL_MD_PERF_CPC] = np.nan

# Ensure that columns with percentage values (e.g., 'Conv. Rate %', 'CTR %') are properly cleaned and converted to numeric
# We need to handle both numeric and percentage values
def clean_and_convert_to_numeric(value):
    if isinstance(value, str):
        cleaned_value = value.replace(',', '')  # Remove commas (e.g., for numbers like '1,000')
        if cleaned_value.endswith('%'):
            numeric_value = float(cleaned_value.rstrip('%')) / 100  # Convert percentage to float
            print(f"Converted {value} to {numeric_value}")
            return numeric_value
        else:
            try:
                numeric_value = float(cleaned_value)
                print(f"Converted {value} to {numeric_value}")
                return numeric_value
            except ValueError:
                print(f"Unable to convert {value} to numeric, setting to np.nan")
                return np.nan
    elif isinstance(value, (float, int)):
        return float(value)
    else:
        return np.nan



# constructs tuple of 4 column names with names of marin dimensions columns based on convention used
def get_col_set_tuple(col_name):
	col_name_clean = col_name.replace('.','')
	return (
		col_name,
		col_name_clean + ' - Avg - Benchmark',
		col_name_clean + ' - High - Criteria',
		col_name_clean + ' - Low - Criteria'
	)


# set col_tag column on outDf with performance tag by comparing col_metric with col_benchmark
# returns array of boolean indicating changed rows in inDf
def tag_by_performance(inDf, outDf, col_set_tuple, col_tag):

	(col_metric, col_benchmark, col_over_margin, col_under_margin) = col_set_tuple

	# setup tmp columns for intrim values
	col_tag_name_new_value = col_tag + "_new"
	col_perf_ratio = col_tag + "_perf_ratio"

	inDf[col_tag_name_new_value] = np.nan

	# Ensure that columns with percentage values (e.g., 'Conv. Rate %', 'CTR %') are properly cleaned and converted to numeric
	inDf[col_metric] = inDf[col_metric].apply(clean_and_convert_to_numeric)
	inDf[col_benchmark] = inDf[col_benchmark].apply(clean_and_convert_to_numeric)
	inDf[col_over_margin] = inDf[col_over_margin].apply(clean_and_convert_to_numeric)
	inDf[col_under_margin] = inDf[col_under_margin].apply(clean_and_convert_to_numeric)

	# calculate benchmark if none provided
	median_metric = inputDf[col_metric].median()
	print(col_metric, "median", median_metric)
	inDf[col_benchmark].fillna({col_benchmark: median_metric}, inplace=True)
		
	# calc relative performance to benchmark
	inDf[col_perf_ratio] = inputDf[col_metric] / inputDf[col_benchmark]

	# separate into different performance groups
	array_benchmark_not_defined = inDf[col_benchmark] == np.nan
	array_benchmark_zero = inDf[col_benchmark] <= 0.0
	array_no_benchmark =  np.logical_or(array_benchmark_not_defined, array_benchmark_zero)

	array_over_perf = (inDf[col_perf_ratio] - 1.0) > inDf[col_over_margin]
	array_under_perf = (1.0 - inDf[col_perf_ratio]) > inDf[col_under_margin]
	array_on_target = np.logical_not(np.logical_or(array_no_benchmark, np.logical_or(array_over_perf, array_under_perf)))

	inDf.loc[ array_over_perf, col_tag_name_new_value ] = 'Over Target'
	inDf.loc[ array_under_perf, col_tag_name_new_value ] = 'Under Target'
	inDf.loc[ array_on_target, col_tag_name_new_value ] = 'On Target'
	#inDf.loc[ array_no_benchmark, col_tag_name_new_value ] = 'No Benchmark'

	# find changed perf tags
	changed = inDf[col_tag_name_new_value].notnull() & (inDf[col_tag] != inDf[col_tag_name_new_value])

	outDf[col_tag] = inDf[col_tag_name_new_value]

	return changed


# tag by performance for each of the 4 metrics

changed1 = tag_by_performance(
				inputDf, 
				outputDf,
				get_col_set_tuple(IN_COL_METRIC_CVR), 
				IN_OUT_COL_MD_PERF_CVR)

changed2 = tag_by_performance(
				inputDf, 
				outputDf,
				get_col_set_tuple(IN_COL_METRIC_CPA), 
				IN_OUT_COL_MD_PERF_CPA)

changed3 = tag_by_performance(
				inputDf, 
				outputDf,
				get_col_set_tuple(IN_COL_METRIC_CTR), 
				IN_OUT_COL_MD_PERF_CTR)

changed4 = tag_by_performance(
				inputDf, 
				outputDf,
				(IN_COL_METRIC_CPC,
				'CPC $ - Avg - Benchmark',
				'CPC $ - High - Criteria',
				'CPC $ - Low - Criteria'
				),
				IN_OUT_COL_MD_PERF_CPC)

combined_changes = np.logical_or(changed4, np.logical_or(changed3, np.logical_or(changed1, changed2)))

# debug print what's changed
print("== Campaigns with CVR Perf Tag changes ==")
in_cols = [IN_OUT_COL_CAMPAIGN, IN_OUT_COL_MD_PERF_CVR] + list(get_col_set_tuple(IN_COL_METRIC_CVR))
out_cols = [IN_OUT_COL_CAMPAIGN, IN_OUT_COL_MD_PERF_CVR]
inDf_changed = inputDf.loc[changed1, in_cols]
outDf_changed = outputDf.loc[changed1, out_cols]
debug_df = inDf_changed.join(outDf_changed, rsuffix="_out")
print(debug_df.to_string())


# only include campaigns with changed perf tag in bulk file
outputDf = outputDf[ combined_changes ]