Spaces:
Sleeping
Sleeping
| import pandas as pd | |
| async def machine_metrics(raw_data): | |
| df = pd.DataFrame(raw_data) | |
| datetime_cols = ['Timestamp', 'Downtime Start', 'Downtime End'] | |
| for col in datetime_cols: | |
| df[col] = pd.to_datetime(df[col], errors='coerce', format="%Y-%m-%d %H:%M:%S") | |
| opening_time = df['Timestamp'].max() - df['Timestamp'].min() | |
| required_time = opening_time | |
| # planned_stop_time = 0 non implémenté | |
| downtime_df = df.dropna(subset=['Downtime Start', 'Downtime End']) | |
| unplanned_stop_time = (downtime_df['Downtime End'] - downtime_df['Downtime Start']).sum() | |
| operating_time = required_time - unplanned_stop_time | |
| net_time = operating_time | |
| # cadency_variance = 0 non implémenté | |
| nok_count = (df['Compliance'] != 'OK').sum() | |
| useful_time = net_time - pd.Timedelta(seconds=nok_count) | |
| operating_sec = operating_time.total_seconds() | |
| net_sec = net_time.total_seconds() | |
| required_sec = required_time.total_seconds() | |
| quality_rate = (useful_time / net_time) * 100 if net_time else 0 | |
| operating_rate = (net_sec / operating_sec) * 100 if operating_sec > 0 else 0 | |
| availability_rate = (operating_sec / required_sec) * 100 if required_sec > 0 else 0 | |
| OEE = (quality_rate / 100) * (operating_rate / 100) * (availability_rate / 100) * 100 | |
| downtime_count = len(downtime_df) | |
| mtbf = operating_time / downtime_count if downtime_count > 0 else pd.Timedelta(0) | |
| mttr = unplanned_stop_time / downtime_count if downtime_count > 0 else pd.Timedelta(0) | |
| # Quality rate per tool ID | |
| quality_by_tool = {} | |
| for tool_id in [1, 2]: | |
| tool_df = df[df["Tool ID"] == tool_id] | |
| total = len(tool_df) | |
| ok_count = (tool_df["Compliance"] == "OK").sum() | |
| quality_by_tool[f"quality_rate_tool_{tool_id}"] = round(((ok_count / total) * 100), 2) if total > 0 else 0 | |
| return { | |
| "opening_time": str(opening_time), | |
| "required_time": str(required_time), | |
| "unplanned_stop_time": str(unplanned_stop_time), | |
| "operating_time": str(operating_time), | |
| "net_time": str(net_time), | |
| "useful_time": str(useful_time), | |
| "quality_rate": round(quality_rate, 2), | |
| **quality_by_tool, | |
| "operating_rate": round(operating_rate, 2), | |
| "availability_rate": round(availability_rate, 2), | |
| "OEE": round(OEE, 2), | |
| "MTBF": str(mtbf), | |
| "MTTR": str(mttr) | |
| } | |
| async def fetch_issues(raw_data): | |
| df = pd.DataFrame(raw_data) | |
| issues = df[df["Event"] == "Machine Error"] | |
| selected_issues = issues[ | |
| ["Timestamp", "Event", "Error Code", "Error Description", "Downtime Start", "Downtime End"] | |
| ] | |
| return selected_issues |