extraction consumption/profit

py/prepare.py

@@ -39,6 +39,66 @@ def add_company_ranks(totals: typing.Mapping[str, CompanyTotals]) -> typing.Mapp
 		company_totals['volumeRank'] = rank
 	return totals
 
+def calc_consumption_profit(prod_data: typing.Mapping[str, ProdData], company_data: CompanyData) -> None:
+	# reddead_solo derived these from apr26 data
+	extraction_cost = {
+		'ALO': 46.469626236277556,
+		'AMM': 64.22413645940814,
+		'AR': 47.70764360662854,
+		'AUO': 54.44708670483523,
+		'BER': 53.972209698989495,
+		'BOR': 83.72543770145344,
+		'BRM': 27.888018032559867,
+		'BTS': 78.06705317448308,
+		'CLI': 35.136976457308684,
+		'CUO': 50.86203030902945,
+		'F': 131.4747740048254,
+		'FEO': 48.650761183540155,
+		'GAL': 57.92699044231113,
+		'H': 22.44162253371223,
+		'H2O': 20.786470813001372,
+		'HAL': 73.71971131519504,
+		'HE': 75.22625975185184,
+		'HE3': 124.06892211329114,
+		'HEX': 42.936879245965734,
+		'KR': 69.26330139617842,
+		'LES': 107.34219811491415,
+		'LIO': 27.095584832287784,
+		'LST': 22.711465634512184,
+		'MAG': 187.27617420211016,
+		'MGS': 58.51507605569296,
+		'N': 27.282314763847143,
+		'NE': 59.13606618175083,
+		'O': 27.57488101890798,
+		'REO': 80.67539173234522,
+		'SCR': 55.37980023917345,
+		'SIO': 31.748301836118316,
+		'TAI': 54.2528323296315,
+		'TCO': 67.4922174225306,
+		'TIO': 40.709175823948684,
+		'TS': 47.68370385324906,
+		'ZIR': 53.627181874429766,
+	}
+	pio_consumable_price_ratios = {
+		'DW': 0.23852885030544477 / 107.1798998000011,
+		'RAT': 0.37223040985862066 / 167.25699210000184,
+		'OVE': 0.03871106977501096 / 139.15460790000103,
+		'COF': 0.302139841269314 / 1086.1015050000133,
+		'PWO': 0.04838982879160972 / 434.86706069999866,
+	}
+
+	universe_consumption: dict[str, float] = collections.defaultdict(float)
+	for co_data in company_data['individual'].values():
+		for mat, mat_data in co_data.items():
+			if (cost := extraction_cost.get(mat)) is not None:
+				total_cost = mat_data['amount'] * cost
+				mat_data['profit'] = mat_data['volume'] - total_cost
+				for consumed_mat, ratio in pio_consumable_price_ratios.items():
+					universe_consumption[consumed_mat] += ratio * total_cost
+
+	for mat, mat_data in prod_data.items():
+		mat_data['consumed'] = universe_consumption.get(mat, 0.0)
+
 def write_data(month: str, data: dict[str, list[Row]],
 		prod_data: typing.Mapping[str, ProdData], company_data: CompanyData) -> None:
 	with open(f'www/data/prod-data-{month}.json', 'w') as f:
@@ -88,6 +148,7 @@ class Price(typing.TypedDict):
 class ProdData(typing.TypedDict):
 	amount: float
 	volume: float
+	consumed: typing.NotRequired[float]
 
 class CompanyData(typing.TypedDict):
 	totals: typing.Mapping[str, CompanyTotals]
@@ -96,6 +157,7 @@ class CompanyData(typing.TypedDict):
 class CompanyTickerData(typing.TypedDict):
 	amount: float
 	volume: float
+	profit: typing.NotRequired[float]
 	rank: int
 
 class CompanyTotals(typing.TypedDict):

py/prepare_from_csv.py

@@ -14,6 +14,7 @@ def main() -> None:
 
 	prices = prepare.get_prices(month)
 	prod_data, company_data = get_prod_and_company_data(data, prices)
+	prepare.calc_consumption_profit(month, prod_data, company_data)
 	prepare.write_data(month, data, prod_data, company_data)
 	prepare.check_missing_tickers(prod_data)
 
@@ -32,11 +33,12 @@ def get_prod_and_company_data(data: dict[str, list[prepare.Row]], prices: typing
 		for row in rows:
 			amount = row.num / 30
 			volume = amount * price
-			individual[row.company_id][ticker] = {
+			co_data: prepare.CompanyTickerData = {
 				'amount': amount,
 				'volume': volume,
 				'rank': row.rank,
 			}
+			individual[row.company_id][ticker] = co_data
 			totals[row.company_id]['volume'] += volume
 
 	company_data: prepare.CompanyData = {'totals': prepare.add_company_ranks(totals), 'individual': dict(individual)}

py/prepare_from_sqlite.py

@@ -24,6 +24,7 @@ def main() -> None:
 
 	with open(f'rawData/{month}.csv', 'r', newline='') as f:
 		data = prepare.read_data(f)
+	prepare.calc_consumption_profit(month, prod_data, company_data)
 	prepare.write_data(month, data, prod_data, company_data)
 	prepare.check_missing_tickers(prod_data)