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added cost reports for SPL contracts (certain actions)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| import glob | ||
| import json | ||
| import os | ||
| import pathlib | ||
| import re | ||
| from collections import Counter | ||
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| import pandas as pd | ||
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| from deploy.cli.dapps import NETWORK_MANAGER | ||
| from deploy.cli.infrastructure import get_solana_accounts_transactions_compute_units | ||
| from utils.web3client import NeonChainWeb3Client | ||
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| def prepare_report_data(directory: str) -> pd.DataFrame: | ||
| proxy_url = NETWORK_MANAGER.get_network_param(os.environ.get("NETWORK"), "proxy_url") | ||
| web3_client = NeonChainWeb3Client(proxy_url) | ||
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| reports = {} | ||
| for path in glob.glob(str(pathlib.Path(directory) / "*-report.json")): | ||
| with open(path, "r") as f: | ||
| rep = json.load(f) | ||
| if isinstance(rep, list): | ||
| for r in rep: | ||
| if "actions" in r: | ||
| reports[r["name"]] = r["actions"] | ||
| else: | ||
| if "actions" in rep: | ||
| reports[rep["name"]] = rep["actions"] | ||
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| data = [] | ||
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| for app, actions in reports.items(): | ||
| counts = Counter([action["name"].lower().strip() for action in actions]) | ||
| duplicate_actions = [action for action, count in counts.items() if count > 1] | ||
| added_numbers = {dup_action: 1 for dup_action in duplicate_actions} | ||
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| for action in actions: | ||
| # Ensure action name is unique by appending a counter if necessary | ||
| base_action_name = action["name"].lower().strip() | ||
| if base_action_name in duplicate_actions: | ||
| added_number = added_numbers[base_action_name] | ||
| unique_action_name = f"{base_action_name} {added_number}" | ||
| added_numbers[base_action_name] += 1 | ||
| else: | ||
| unique_action_name = base_action_name | ||
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| accounts, trx, compute_units = get_solana_accounts_transactions_compute_units(action["tx"]) | ||
| # accounts, trx, compute_units = (2, 12, 8946) | ||
| tx = web3_client.get_transaction_by_hash(action["tx"]) | ||
| estimated_gas = int(tx.gas) if tx and tx.gas else None | ||
| # estimated_gas = 122879 | ||
| used_gas = int(action["usedGas"]) | ||
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| data.append( | ||
| { | ||
| "dapp_name": app.lower().strip(), | ||
| "action": unique_action_name, | ||
| "acc_count": accounts, | ||
| "trx_count": trx, | ||
| "gas_estimated": estimated_gas, | ||
| "gas_used": used_gas, | ||
| "compute_units": compute_units, | ||
| } | ||
| ) | ||
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| df = pd.DataFrame(data) | ||
| if df.empty: | ||
| raise Exception(f"no reports found in {directory}") | ||
| return df | ||
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| def report_data_to_markdown(df: pd.DataFrame) -> str: | ||
| report_content = "" | ||
| dapp_names = df['dapp_name'].unique() | ||
| df.columns = [col.upper() for col in df.columns] | ||
| df['GAS_USED_%'] = df['GAS_USED_%'].apply(lambda x: f"{x:.2f}") | ||
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| for dapp_name in dapp_names: | ||
| dapp_df = df[df['DAPP_NAME'] == dapp_name].drop(columns='DAPP_NAME') | ||
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| # sort by ACTION (to mitigate [action 1, action 10, action 2, ...]) | ||
| dapp_df[['ACTION_TEXT', 'ACTION_NUM']] = dapp_df['ACTION'].apply(split_action).apply(pd.Series) | ||
| dapp_df = dapp_df.sort_values(by=['ACTION_TEXT', 'ACTION_NUM']) | ||
| dapp_df = dapp_df.drop(columns=['ACTION_TEXT', 'ACTION_NUM']) | ||
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| report_content += f'\n## Cost Report for "{dapp_name.title()}" dApp\n\n' | ||
| report_content += dapp_df.to_markdown(index=False) + "\n" | ||
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| return report_content | ||
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| def split_action(action) -> tuple[str, int]: | ||
| match = re.match(r"(.+?)\s*(\d*)$", action) | ||
| text_ = match.group(1) | ||
| number = int(match.group(2)) if match.group(2).isdigit() else 0 | ||
| return text_, number |
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