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2.989316001086833, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.5, + "cpu_max": 0.5, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.05856204827626546, + "csv_write_time_avg": 0.0 + } + }, + { + "timestamp": "2025-08-16T23:53:48.684260", + "level": "info", + "event_type": "udp_streaming_stopped", + "message": "UDP streaming to PlotJuggler stopped (CSV recording continues)", + "details": {} + }, + { + "timestamp": "2025-08-16T23:53:50.721623", + "level": "info", + "event_type": "csv_recording_stopped", + "message": "🔥 CRITICAL: CSV recording stopped (dataset threads continue for UDP streaming)", + "details": { + "recording_protection": false, + "performance_monitoring": false + } + }, + { + "timestamp": "2025-08-16T23:53:50.751951", + "level": "info", + "event_type": "udp_streaming_stopped", + "message": "UDP streaming to PlotJuggler stopped (CSV recording continues)", + "details": {} + }, + { + "timestamp": "2025-08-16T23:53:51.148411", + "level": "info", + "event_type": "dataset_deactivated", + "message": "Dataset deactivated: Fast", + "details": { + "dataset_id": "Fast" + } + }, + { + "timestamp": "2025-08-16T23:53:51.376355", + "level": "info", + "event_type": "dataset_deactivated", + "message": "Dataset deactivated: DAR", + "details": { + "dataset_id": "DAR" + } + }, + { + "timestamp": "2025-08-16T23:53:51.406408", + "level": "info", + "event_type": "dataset_deactivated", + "message": "Dataset deactivated: test", + "details": { + "dataset_id": "Test" + } + }, + { + "timestamp": "2025-08-16T23:53:51.446547", + "level": "info", + "event_type": "plc_disconnection", + "message": "Disconnected from PLC 10.1.33.11 (application shutdown (will auto-reconnect on restart))", + "details": {} + }, + { + "timestamp": "2025-08-16T23:53:56.108166", + "level": "info", + "event_type": "application_started", + "message": "Application initialization completed successfully", + "details": {} + }, + { + "timestamp": "2025-08-16T23:53:56.188708", + "level": "info", + "event_type": "dataset_activated", + "message": "Dataset activated: DAR", + "details": { + "dataset_id": "DAR", + "variables_count": 3, + "streaming_count": 3, + "prefix": "gateway_phoenix" + } + }, + { + "timestamp": "2025-08-16T23:53:56.216736", + "level": "info", + "event_type": "dataset_activated", + "message": "Dataset activated: Fast", + "details": { + "dataset_id": "Fast", + "variables_count": 2, + "streaming_count": 2, + "prefix": "fast" + } + }, + { + "timestamp": "2025-08-16T23:53:56.245790", + "level": "info", + "event_type": "csv_recording_started", + "message": "🔥 CRITICAL PRIORITY: CSV recording started with MAXIMUM PRIORITY and performance monitoring: 2 datasets activated", + "details": { + "activated_datasets": 2, + "total_datasets": 3, + "priority": "CRITICAL", + "recording_protection": true, + "performance_monitoring": true + } + }, + { + "timestamp": "2025-08-16T23:54:06.189802", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.0% CPU", + "details": { + "duration": 11.097160339355469, + "points_saved": 30, + "points_rate": 2.7033942993151725, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.0, + "cpu_max": 0.0, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.07913546562194824, + "csv_write_time_avg": 0.0008285760879516602 + } + }, + { + "timestamp": "2025-08-16T23:54:16.221775", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 1.1% CPU", + "details": { + "duration": 10.032973527908325, + "points_saved": 30, + "points_rate": 2.9901404520354995, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 1.1, + "cpu_max": 1.1, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.09634163379669189, + "csv_write_time_avg": 3.6875406901041667e-06 + } + }, + { + "timestamp": "2025-08-16T23:54:26.253785", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.2% CPU", + "details": { + "duration": 10.031008005142212, + "points_saved": 30, + "points_rate": 2.9907263541830544, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.2, + "cpu_max": 0.2, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.0821520487467448, + "csv_write_time_avg": 0.0 + } + }, + { + "timestamp": "2025-08-16T23:54:36.289310", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.2% CPU", + "details": { + "duration": 10.035518884658813, + "points_saved": 30, + "points_rate": 2.989382048382238, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.2, + "cpu_max": 0.2, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.07963856856028238, + "csv_write_time_avg": 0.0 + } + }, + { + "timestamp": "2025-08-16T23:54:46.343574", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 31 points saved, 0 lost, 0.0% CPU", + "details": { + "duration": 10.055271863937378, + "points_saved": 31, + "points_rate": 3.0829599059553643, + "variables_saved": 82, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.0, + "cpu_max": 0.0, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.06286808752244519, + "csv_write_time_avg": 3.224803555396295e-05 + } + }, + { + "timestamp": "2025-08-16T23:54:56.378684", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.2% CPU", + "details": { + "duration": 10.034115314483643, + "points_saved": 30, + "points_rate": 2.989800202584557, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.2, + "cpu_max": 0.2, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.07947803338368734, + "csv_write_time_avg": 3.274281819661458e-06 + } + }, + { + "timestamp": "2025-08-16T23:55:06.416743", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.2% CPU", + "details": { + "duration": 10.038043975830078, + "points_saved": 30, + "points_rate": 2.9886300630117737, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.2, + "cpu_max": 0.2, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.067444642384847, + "csv_write_time_avg": 0.0 + } + }, + { + "timestamp": "2025-08-16T23:55:16.451746", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.3% CPU", + "details": { + "duration": 10.036012172698975, + "points_saved": 30, + "points_rate": 2.9892351148805085, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.3, + "cpu_max": 0.3, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.06720998287200927, + "csv_write_time_avg": 0.0 + } + }, + { + "timestamp": "2025-08-16T23:55:26.483755", + "level": "info", + "event_type": "performance_report", + "message": "Performance report: 30 points saved, 0 lost, 0.3% CPU", + "details": { + "duration": 10.03001070022583, + "points_saved": 30, + "points_rate": 2.9910237283520082, + "variables_saved": 80, + "udp_points_sent": 0, + "points_lost": 0, + "cpu_average": 0.3, + "cpu_max": 0.3, + "delay_average": 0.0, + "delay_max": 0.0, + "read_errors": 0, + "csv_errors": 0, + "udp_errors": 0, + "read_time_avg": 0.05762360095977783, + "csv_write_time_avg": 0.0 + } } ], - "last_updated": "2025-08-16T22:35:42.177193", + "last_updated": "2025-08-16T23:55:26.483755", "total_entries": 1000 } \ No newline at end of file diff --git a/frontend/src/pages/Dashboard.jsx b/frontend/src/pages/Dashboard.jsx index f9a05df..3d49af5 100644 --- a/frontend/src/pages/Dashboard.jsx +++ b/frontend/src/pages/Dashboard.jsx @@ -1621,9 +1621,31 @@ function DatasetManager() { ) } -// Events Display Component -function EventsDisplay({ events, loading, onRefresh }) { +// Console Logs Display Component +function ConsoleLogsDisplay({ logs, loading, onRefresh }) { + // All hooks must be called at the top level const cardBg = useColorModeValue('white', 'gray.700') + const logBg = useColorModeValue('gray.50', 'gray.800') + const logColor = useColorModeValue('gray.800', 'gray.100') + const borderColor = useColorModeValue('gray.200', 'gray.600') + const scrollbarBg = useColorModeValue('gray.300', 'gray.600') + const scrollbarThumb = useColorModeValue('gray.500', 'gray.400') + const scrollbarHover = useColorModeValue('gray.600', 'gray.300') + + const exportLogs = () => { + if (!logs || logs.length === 0) { + return + } + + const logText = logs.join('\n') + const blob = new Blob([logText], { type: 'text/plain' }) + const url = URL.createObjectURL(blob) + const link = document.createElement('a') + link.href = url + link.download = `console_logs_${new Date().toISOString().slice(0, 19).replace(/:/g, '-')}.txt` + link.click() + URL.revokeObjectURL(url) + } if (loading) { return ( @@ -1631,7 +1653,7 @@ function EventsDisplay({ events, loading, onRefresh }) { - Loading events... + Loading console logs... @@ -1642,51 +1664,80 @@ function EventsDisplay({ events, loading, onRefresh }) { - 📋 Recent Events + 📋 Console Logs - + + + + - - - - - - - - - - - {events?.map((event, index) => ( - - - - - - ))} - -
TimeTypeMessage
- - {new Date(event.timestamp).toLocaleString()} - - - - {event.level} - - {event.message}
- - {(!events || events.length === 0) && ( + {(!logs || logs.length === 0) ? ( - No events found + No console logs found + + ) : ( + + {logs.map((logLine, index) => ( + + {logLine} + + ))} + + )} + {logs && logs.length > 0 && ( + + Showing {logs.length} most recent log lines )} @@ -1711,8 +1762,8 @@ function DashboardContent() { const [saving, setSaving] = useState(false) const [message, setMessage] = useState('') - const [events, setEvents] = useState([]) - const [eventsLoading, setEventsLoading] = useState(false) + const [consoleLogs, setConsoleLogs] = useState([]) + const [consoleLogsLoading, setConsoleLogsLoading] = useState(false) // Usar polling coordinado para el status const { data: status, isLeader, isConnected } = useCoordinatedPolling( @@ -1751,24 +1802,24 @@ function DashboardContent() { } }, []) - // Load events - const loadEvents = useCallback(async () => { + // Load console logs + const loadConsoleLogs = useCallback(async () => { try { - setEventsLoading(true) - const eventsData = await api.getEvents(50) - setEvents(eventsData.events || []) + setConsoleLogsLoading(true) + const logsData = await api.getConsoleLogs(1000) + setConsoleLogs(logsData.logs || []) } catch (error) { - console.error('Failed to load events:', error) + console.error('Failed to load console logs:', error) } finally { - setEventsLoading(false) + setConsoleLogsLoading(false) } }, []) // Effects useEffect(() => { loadConfig() - loadEvents() - }, [loadConfig, loadEvents]) + loadConsoleLogs() + }, [loadConfig, loadConsoleLogs]) if (!isConnected && !status) { return ( @@ -1807,7 +1858,7 @@ function DashboardContent() { 📈 Plotting 📉 Historical Plots 📁 CSV Files - 📋 Events + 📋 Console Logs @@ -1844,11 +1895,11 @@ function DashboardContent() { - {/* Events Section */} - diff --git a/frontend/src/services/api.js b/frontend/src/services/api.js index acbca09..9f26992 100644 --- a/frontend/src/services/api.js +++ b/frontend/src/services/api.js @@ -35,6 +35,11 @@ export async function getEvents(limit = 50) { return toJsonOrThrow(res) } +export async function getConsoleLogs(limit = 1000) { + const res = await fetch(`${BASE_URL}/api/console-logs?limit=${encodeURIComponent(limit)}`, { headers: { 'Accept': 'application/json' } }) + return toJsonOrThrow(res) +} + // PLC control export async function connectPlc() { const res = await fetch(`${BASE_URL}/api/plc/connect`, { method: 'POST', headers: { 'Accept': 'application/json' } }) diff --git a/i18n.js b/i18n.js new file mode 100644 index 0000000..e69de29 diff --git a/main.py b/main.py index ab95c9b..c1823e1 100644 --- a/main.py +++ b/main.py @@ -20,14 +20,14 @@ from core.rotating_logger import setup_backend_logging # 📝 Setup rotating logger system # This will log to both console and .logs/ directory with automatic rotation backend_logger = setup_backend_logging( - log_dir=".logs", # Directory for log files - max_lines=10000, # Max lines per file - max_files=30 # Max files to retain + log_dir=".logs", # Directory for log files + max_lines=10000, # Max lines per file + max_files=30, # Max files to retain ) # Configure standard logging to use our rotating system # Reduce Flask's request logging to ERROR level only (keep logs clean) -logging.getLogger('werkzeug').setLevel(logging.ERROR) +logging.getLogger("werkzeug").setLevel(logging.ERROR) try: import tkinter as tk @@ -487,7 +487,10 @@ def trigger_csv_cleanup(): # 🔑 PROTECTED: Perform cleanup with recording protection streamer.perform_csv_cleanup_safe() return jsonify( - {"success": True, "message": "CSV cleanup queued in background (recording protection active)"} + { + "success": True, + "message": "CSV cleanup queued in background (recording protection active)", + } ) except Exception as e: @@ -595,15 +598,17 @@ def disconnect_plc(): """Disconnect from PLC""" streamer.disconnect_plc(manual_disconnect=True) # User manually disconnected # Return updated status to ensure frontend gets correct state - return jsonify({ - "success": True, - "message": "Disconnected from PLC", - "status": { - "plc_connected": False, - "streaming": False, - "csv_recording": False + return jsonify( + { + "success": True, + "message": "Disconnected from PLC", + "status": { + "plc_connected": False, + "streaming": False, + "csv_recording": False, + }, } - }) + ) @app.route("/api/variables", methods=["POST"]) @@ -942,11 +947,17 @@ def get_dataset_variable_values(dataset_id): read_result = streamer.get_cached_dataset_values_safe(dataset_id) # Handle rate limiting response - if not read_result.get("success", False) and read_result.get("error_type") == "rate_limited": + if ( + not read_result.get("success", False) + and read_result.get("error_type") == "rate_limited" + ): return jsonify(read_result), 429 # Too Many Requests - # Handle API timeout response - if not read_result.get("success", False) and read_result.get("error_type") == "api_timeout": + # Handle API timeout response + if ( + not read_result.get("success", False) + and read_result.get("error_type") == "api_timeout" + ): return jsonify(read_result), 503 # Service Unavailable # Convert timestamp from ISO format to readable format for consistency @@ -1876,7 +1887,7 @@ def get_plot_variables(): def get_historical_data(): """Get historical data from CSV files for plot initialization with caching""" global historical_cache - + print("🔍 DEBUG: Historical endpoint called") try: data = request.get_json() @@ -1888,7 +1899,7 @@ def get_historical_data(): variables = data.get("variables", []) time_window_seconds = data.get("time_window", 60) - + # Support for explicit start_time and end_time parameters start_time_param = data.get("start_time") end_time_param = data.get("end_time") @@ -1903,11 +1914,13 @@ def get_historical_data(): return jsonify({"error": "No variables specified"}), 400 # Filter out None/null values and empty strings - valid_variables = [var for var in variables if var is not None and str(var).strip()] + valid_variables = [ + var for var in variables if var is not None and str(var).strip() + ] if not valid_variables: print("❌ DEBUG: No valid variables after filtering") return jsonify({"error": "No valid variables provided"}), 400 - + variables = valid_variables print(f"🔍 DEBUG: Valid variables after filtering: {variables}") @@ -1916,6 +1929,7 @@ def get_historical_data(): # print("🔍 DEBUG: Importing modules...") import pandas as pd import glob + # print("🔍 DEBUG: All imports successful") except ImportError as e: print(f"❌ DEBUG: Import failed: {e}") @@ -1926,38 +1940,53 @@ def get_historical_data(): historical_cache = HistoricalDataCache( logger=streamer.logger if streamer and streamer.logger else None, max_cache_entries=50, - buffer_percentage=0.25 + buffer_percentage=0.25, ) # Calculate time range try: # print("🔍 DEBUG: Calculating time range...") - + if start_time_param and end_time_param: # Parse timestamps from frontend (UTC) and convert to local time - + # Parse as UTC timestamps (frontend sends them with 'Z') - start_time_utc = datetime.fromisoformat(start_time_param.replace('Z', '+00:00')) - end_time_utc = datetime.fromisoformat(end_time_param.replace('Z', '+00:00')) - + start_time_utc = datetime.fromisoformat( + start_time_param.replace("Z", "+00:00") + ) + end_time_utc = datetime.fromisoformat( + end_time_param.replace("Z", "+00:00") + ) + # Convert to local time (remove timezone info since CSV data has no timezone) start_time = start_time_utc.astimezone().replace(tzinfo=None) end_time = end_time_utc.astimezone().replace(tzinfo=None) - + print(f"🔍 DEBUG: UTC timestamps: {start_time_utc} to {end_time_utc}") print(f"🔍 DEBUG: Local timestamps: {start_time} to {end_time}") - + # Validate time range if start_time >= end_time: - print(f"❌ DEBUG: Invalid time range - start time must be before end time") - return jsonify({"error": "Invalid time range: start time must be before end time"}), 400 - + print( + f"❌ DEBUG: Invalid time range - start time must be before end time" + ) + return ( + jsonify( + { + "error": "Invalid time range: start time must be before end time" + } + ), + 400, + ) + else: # Legacy behavior: use time_window from now end_time = datetime.now() start_time = end_time - timedelta(seconds=time_window_seconds) - print(f"🔍 DEBUG: Using time window calculation: {start_time} to {end_time}") - + print( + f"🔍 DEBUG: Using time window calculation: {start_time} to {end_time}" + ) + except Exception as e: print(f"❌ DEBUG: Time calculation error: {e}") return jsonify({"error": f"Time calculation failed: {str(e)}"}), 500 @@ -1965,17 +1994,17 @@ def get_historical_data(): # Get relevant CSV files for cache checking records_dir = os.path.join(os.path.dirname(__file__), "records") csv_files = [] - + if os.path.exists(records_dir): # Calculate buffered time range for file discovery (same as cache buffer) duration = end_time - start_time buffer_duration = duration * 0.25 buffer_start = start_time - buffer_duration buffer_end = end_time + buffer_duration - + current_date = buffer_start.date() end_date = buffer_end.date() - + # Collect all relevant CSV files while current_date <= end_date: date_str = current_date.strftime("%d-%m-%Y") @@ -1989,46 +2018,64 @@ def get_historical_data(): # Try to get data from cache first # TEMPORARY: Disable cache to debug data display issues cached_data = None # historical_cache.get_cached_data(variables, start_time, end_time, csv_files) - + if cached_data is not None: print("� DEBUG: Cache hit! Returning cached data") - + # Convert DataFrame to the expected format historical_data = [] for _, row in cached_data.iterrows(): - if 'timestamp' in row and 'variable' in row and 'value' in row: - historical_data.append({ - "timestamp": row['timestamp'].isoformat() if pd.api.types.is_datetime64_any_dtype(row['timestamp']) else str(row['timestamp']), - "variable": str(row['variable']), - "value": float(row['value']) if pd.api.types.is_numeric_dtype(type(row['value'])) else row['value'] - }) - - return jsonify({ - "data": historical_data, - "time_range": { - "start": start_time.isoformat(), - "end": end_time.isoformat(), - }, - "variables_found": list(set([item["variable"] for item in historical_data])), - "total_points": len(historical_data), - "cached": True # Indicate this was from cache - }) + if "timestamp" in row and "variable" in row and "value" in row: + historical_data.append( + { + "timestamp": ( + row["timestamp"].isoformat() + if pd.api.types.is_datetime64_any_dtype( + row["timestamp"] + ) + else str(row["timestamp"]) + ), + "variable": str(row["variable"]), + "value": ( + float(row["value"]) + if pd.api.types.is_numeric_dtype(type(row["value"])) + else row["value"] + ), + } + ) + + return jsonify( + { + "data": historical_data, + "time_range": { + "start": start_time.isoformat(), + "end": end_time.isoformat(), + }, + "variables_found": list( + set([item["variable"] for item in historical_data]) + ), + "total_points": len(historical_data), + "cached": True, # Indicate this was from cache + } + ) # Cache miss - load data from CSV files print("🔍 DEBUG: Cache miss - loading from CSV files") historical_data = [] - + if not os.path.exists(records_dir): print("🔍 DEBUG: Records directory not found, returning empty data") - return jsonify({ - "data": [], - "time_range": { - "start": start_time.isoformat(), - "end": end_time.isoformat(), - }, - "variables_found": [], - "total_points": 0, - }) + return jsonify( + { + "data": [], + "time_range": { + "start": start_time.isoformat(), + "end": end_time.isoformat(), + }, + "variables_found": [], + "total_points": 0, + } + ) # Calculate extended range for cache storage (25% buffer) duration = end_time - start_time @@ -2040,7 +2087,7 @@ def get_historical_data(): current_date = buffered_start.date() end_date = buffered_end.date() date_folders = [] - + while current_date <= end_date: date_str = current_date.strftime("%d-%m-%Y") folder_path = os.path.join(records_dir, date_str) @@ -2054,26 +2101,31 @@ def get_historical_data(): # Use a dictionary to collect data by timestamp, then fill missing values with NaN timestamp_data = {} # {timestamp: {variable: value}} all_timestamps = set() - + for folder_path in date_folders: csv_files_in_folder = glob.glob(os.path.join(folder_path, "*.csv")) - + for csv_file in csv_files_in_folder: try: # Read CSV file with encoding detection df = None for encoding in ["utf-8", "utf-8-sig", "utf-16", "latin-1"]: try: - df = pd.read_csv(csv_file, encoding=encoding, on_bad_lines="skip") + df = pd.read_csv( + csv_file, encoding=encoding, on_bad_lines="skip" + ) break except: continue - + if df is None: continue # Clean column names - df.columns = [col.replace("\ufeff", "").replace("\x00", "").strip() for col in df.columns] + df.columns = [ + col.replace("\ufeff", "").replace("\x00", "").strip() + for col in df.columns + ] # Find timestamp column timestamp_col = None @@ -2086,9 +2138,11 @@ def get_historical_data(): continue # Convert timestamps - df[timestamp_col] = pd.to_datetime(df[timestamp_col], errors='coerce') + df[timestamp_col] = pd.to_datetime( + df[timestamp_col], errors="coerce" + ) df = df.dropna(subset=[timestamp_col]) - + if df.empty: continue @@ -2097,15 +2151,19 @@ def get_historical_data(): df = df.rename(columns={timestamp_col: "timestamp"}) # Filter to buffered time range - mask = (df["timestamp"] >= buffered_start) & (df["timestamp"] <= buffered_end) + mask = (df["timestamp"] >= buffered_start) & ( + df["timestamp"] <= buffered_end + ) filtered_df = df[mask] if filtered_df.empty: continue # Check for matching variables - matching_vars = [var for var in variables if var in filtered_df.columns] - + matching_vars = [ + var for var in variables if var in filtered_df.columns + ] + if not matching_vars: continue @@ -2115,10 +2173,10 @@ def get_historical_data(): for _, row in filtered_df.iterrows(): timestamp = row["timestamp"] all_timestamps.add(timestamp) - + if timestamp not in timestamp_data: timestamp_data[timestamp] = {} - + # Store data for available variables, others will be NaN for var in matching_vars: if var in row: @@ -2141,7 +2199,7 @@ def get_historical_data(): value = float(raw_value) else: value = None - + timestamp_data[timestamp][var] = value except: timestamp_data[timestamp][var] = None @@ -2158,26 +2216,32 @@ def get_historical_data(): for var in variables: # Get value or None if not available value = timestamp_data[timestamp].get(var, None) - all_data_for_cache.append({ - "timestamp": timestamp, - "variable": var, - "value": value, - }) + all_data_for_cache.append( + { + "timestamp": timestamp, + "variable": var, + "value": value, + } + ) - print(f"🔍 DEBUG: Collected {len(all_data_for_cache)} total data points from {len(all_timestamps)} timestamps for {len(variables)} variables") + print( + f"🔍 DEBUG: Collected {len(all_data_for_cache)} total data points from {len(all_timestamps)} timestamps for {len(variables)} variables" + ) # Convert to DataFrame for caching if all_data_for_cache: cache_df = pd.DataFrame(all_data_for_cache) - cache_df = cache_df.sort_values('timestamp') - + cache_df = cache_df.sort_values("timestamp") + # Store in cache historical_cache.store_data(variables, start_time, end_time, cache_df) - + # Filter to actual requested range for response - response_mask = (cache_df["timestamp"] >= start_time) & (cache_df["timestamp"] <= end_time) + response_mask = (cache_df["timestamp"] >= start_time) & ( + cache_df["timestamp"] <= end_time + ) response_df = cache_df[response_mask] - + # Convert to response format historical_data = [] for _, row in response_df.iterrows(): @@ -2186,29 +2250,36 @@ def get_historical_data(): # Convert pandas NaN to None for proper JSON serialization if pd.isna(value): value = None - - historical_data.append({ - "timestamp": row["timestamp"].isoformat(), - "variable": row["variable"], - "value": value - }) + + historical_data.append( + { + "timestamp": row["timestamp"].isoformat(), + "variable": row["variable"], + "value": value, + } + ) print(f"🔍 DEBUG: Loaded {len(historical_data)} data points for response") print(f"🔍 DEBUG: Cached {len(all_data_for_cache)} total data points") - return jsonify({ - "data": historical_data, - "time_range": { - "start": start_time.isoformat(), - "end": end_time.isoformat(), - }, - "variables_found": list(set([item["variable"] for item in historical_data])), - "total_points": len(historical_data), - "cached": False # Indicate this was loaded fresh - }) + return jsonify( + { + "data": historical_data, + "time_range": { + "start": start_time.isoformat(), + "end": end_time.isoformat(), + }, + "variables_found": list( + set([item["variable"] for item in historical_data]) + ), + "total_points": len(historical_data), + "cached": False, # Indicate this was loaded fresh + } + ) except Exception as e: import traceback + traceback.print_exc() return jsonify({"error": f"Internal server error: {str(e)}"}), 500 @@ -2217,25 +2288,24 @@ def get_historical_data(): def get_cache_stats(): """Get cache statistics for monitoring""" global historical_cache - + try: if historical_cache is None: - return jsonify({ - "cache_initialized": False, - "stats": { - "entries": 0, - "total_size_mb": 0, - "max_entries": 0, - "buffer_percentage": 0 + return jsonify( + { + "cache_initialized": False, + "stats": { + "entries": 0, + "total_size_mb": 0, + "max_entries": 0, + "buffer_percentage": 0, + }, } - }) - + ) + stats = historical_cache.get_cache_stats() - return jsonify({ - "cache_initialized": True, - "stats": stats - }) - + return jsonify({"cache_initialized": True, "stats": stats}) + except Exception as e: return jsonify({"error": str(e)}), 500 @@ -2244,14 +2314,14 @@ def get_cache_stats(): def clear_cache(): """Clear the historical data cache""" global historical_cache - + try: if historical_cache is not None: historical_cache.clear_cache() return jsonify({"success": True, "message": "Cache cleared successfully"}) else: return jsonify({"success": False, "message": "Cache not initialized"}) - + except Exception as e: return jsonify({"error": str(e)}), 500 @@ -2262,16 +2332,16 @@ def get_historical_date_range(): try: import pandas as pd import glob - + # Get records directory records_dir = os.path.join(os.path.dirname(__file__), "records") - + if not os.path.exists(records_dir): - return jsonify({ - "success": False, - "error": "No records directory found" - }), 404 - + return ( + jsonify({"success": False, "error": "No records directory found"}), + 404, + ) + # Find all date folders (format: DD-MM-YYYY) date_folders = [] for item in os.listdir(records_dir): @@ -2283,72 +2353,79 @@ def get_historical_date_range(): date_folders.append((date_obj, folder_path)) except ValueError: continue - + if not date_folders: - return jsonify({ - "success": False, - "error": "No valid date folders found" - }), 404 - + return ( + jsonify({"success": False, "error": "No valid date folders found"}), + 404, + ) + # Sort by date date_folders.sort(key=lambda x: x[0]) - + # Get the earliest and latest dates earliest_date = date_folders[0][0] latest_date = date_folders[-1][0] - + # For more precise range, check actual CSV file timestamps min_timestamp = None max_timestamp = None - + # Check files more thoroughly to get precise timestamp range for date_obj, folder_path in date_folders: csv_files = glob.glob(os.path.join(folder_path, "*.csv")) - + for csv_file in csv_files: try: # Try to read timestamp range from CSV with better sampling # Read first and last few rows to get min/max more accurately - df_head = pd.read_csv(csv_file, nrows=100, encoding='utf-8-sig') - df_tail = pd.read_csv(csv_file, encoding='utf-8-sig').tail(100) - + df_head = pd.read_csv(csv_file, nrows=100, encoding="utf-8-sig") + df_tail = pd.read_csv(csv_file, encoding="utf-8-sig").tail(100) + # Combine head and tail for better range detection df_sample = pd.concat([df_head, df_tail]).drop_duplicates() - + # Find timestamp column timestamp_col = None for col in df_sample.columns: - if 'timestamp' in col.lower(): + if "timestamp" in col.lower(): timestamp_col = col break - + if timestamp_col: # Convert timestamp with multiple format attempts - df_sample[timestamp_col] = pd.to_datetime(df_sample[timestamp_col], - errors='coerce') + df_sample[timestamp_col] = pd.to_datetime( + df_sample[timestamp_col], errors="coerce" + ) df_sample = df_sample.dropna(subset=[timestamp_col]) - + if not df_sample.empty: file_min = df_sample[timestamp_col].min() file_max = df_sample[timestamp_col].max() - + # Convert to timezone-naive datetime if needed - if hasattr(file_min, 'tz_localize') and file_min.tz is not None: + if ( + hasattr(file_min, "tz_localize") + and file_min.tz is not None + ): file_min = file_min.tz_localize(None) - if hasattr(file_max, 'tz_localize') and file_max.tz is not None: + if ( + hasattr(file_max, "tz_localize") + and file_max.tz is not None + ): file_max = file_max.tz_localize(None) - + if min_timestamp is None or file_min < min_timestamp: min_timestamp = file_min if max_timestamp is None or file_max > max_timestamp: max_timestamp = file_max - + # print(f"🔍 DEBUG: File {csv_file} - Range: {file_min} to {file_max}") - + except Exception as e: # print(f"🔍 DEBUG: Error reading {csv_file}: {e}") continue - + # Use folder dates as fallback if we couldn't read CSV timestamps if min_timestamp is None: min_timestamp = earliest_date @@ -2356,30 +2433,34 @@ def get_historical_date_range(): if max_timestamp is None: max_timestamp = latest_date + timedelta(days=1) print(f"🔍 DEBUG: Using latest folder date as max: {latest_date}") - + print(f"🔍 DEBUG: Final timestamp range: {min_timestamp} to {max_timestamp}") - - return jsonify({ - "success": True, - "date_range": { - "min_date": min_timestamp.isoformat(), - "max_date": max_timestamp.isoformat(), - "folders_count": len(date_folders), - "earliest_folder": earliest_date.strftime("%d-%m-%Y"), - "latest_folder": latest_date.strftime("%d-%m-%Y") + + return jsonify( + { + "success": True, + "date_range": { + "min_date": min_timestamp.isoformat(), + "max_date": max_timestamp.isoformat(), + "folders_count": len(date_folders), + "earliest_folder": earliest_date.strftime("%d-%m-%Y"), + "latest_folder": latest_date.strftime("%d-%m-%Y"), + }, } - }) - + ) + except ImportError: - return jsonify({ - "success": False, - "error": "pandas is required for date range calculation" - }), 500 + return ( + jsonify( + { + "success": False, + "error": "pandas is required for date range calculation", + } + ), + 500, + ) except Exception as e: - return jsonify({ - "success": False, - "error": str(e) - }), 500 + return jsonify({"success": False, "error": str(e)}), 500 @app.route("/api/plots/historical/data-segments", methods=["GET"]) @@ -2387,17 +2468,17 @@ def get_historical_data_segments(): """Get data availability segments for visualization""" try: import glob - + records_dir = os.path.join(os.path.dirname(__file__), "records") - + if not os.path.exists(records_dir): - return jsonify({ - "success": False, - "error": "No records directory found" - }), 404 - + return ( + jsonify({"success": False, "error": "No records directory found"}), + 404, + ) + segments = [] - + # Find all date folders (format: DD-MM-YYYY) date_folders = [] for item in os.listdir(records_dir): @@ -2409,99 +2490,106 @@ def get_historical_data_segments(): date_folders.append((date_obj, folder_path)) except ValueError: continue - + # Sort by date date_folders.sort(key=lambda x: x[0]) - + # For each date folder, find file segments for date_obj, folder_path in date_folders: csv_files = glob.glob(os.path.join(folder_path, "*.csv")) - + # Group files by dataset name (prefix before _XX.csv) dataset_files = {} for csv_file in csv_files: filename = os.path.basename(csv_file) - if '_' in filename: + if "_" in filename: # Extract dataset name (e.g., "fast" from "fast_00.csv") - dataset_name = filename.split('_')[0] + dataset_name = filename.split("_")[0] if dataset_name not in dataset_files: dataset_files[dataset_name] = [] dataset_files[dataset_name].append(csv_file) - + # For each dataset, create segments based on file sequence for dataset_name, files in dataset_files.items(): # Sort files by sequence number files.sort() - + if not files: continue - + try: # Get time range from first and last file quickly # Read only first few lines of first file and last few lines of last file first_file = files[0] last_file = files[-1] - + # Quick read of timestamps from file headers/footers start_time = None end_time = None - + # Read first file's start time - with open(first_file, 'r', encoding='utf-8-sig') as f: + with open(first_file, "r", encoding="utf-8-sig") as f: lines = f.readlines() if len(lines) > 1: # Skip header first_data_line = lines[1].strip() - if first_data_line and ',' in first_data_line: - timestamp_str = first_data_line.split(',')[0] + if first_data_line and "," in first_data_line: + timestamp_str = first_data_line.split(",")[0] try: - start_time = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00')) + start_time = datetime.fromisoformat( + timestamp_str.replace("Z", "+00:00") + ) if start_time.tzinfo: start_time = start_time.replace(tzinfo=None) except: continue - + # Read last file's end time - with open(last_file, 'r', encoding='utf-8-sig') as f: + with open(last_file, "r", encoding="utf-8-sig") as f: lines = f.readlines() # Find last non-empty line with data for line in reversed(lines): line = line.strip() - if line and ',' in line and not line.startswith('timestamp'): - timestamp_str = line.split(',')[0] + if ( + line + and "," in line + and not line.startswith("timestamp") + ): + timestamp_str = line.split(",")[0] try: - end_time = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00')) + end_time = datetime.fromisoformat( + timestamp_str.replace("Z", "+00:00") + ) if end_time.tzinfo: end_time = end_time.replace(tzinfo=None) break except: continue - + if start_time and end_time: - segments.append({ - "dataset": dataset_name, - "start": start_time.isoformat(), - "end": end_time.isoformat(), - "files_count": len(files), - "date_folder": date_obj.strftime("%d-%m-%Y") - }) - + segments.append( + { + "dataset": dataset_name, + "start": start_time.isoformat(), + "end": end_time.isoformat(), + "files_count": len(files), + "date_folder": date_obj.strftime("%d-%m-%Y"), + } + ) + except Exception as e: # Skip problematic files, don't break the entire response - backend_logger.warning(f"Error processing dataset {dataset_name} in {folder_path}: {e}") + backend_logger.warning( + f"Error processing dataset {dataset_name} in {folder_path}: {e}" + ) continue - - return jsonify({ - "success": True, - "segments": segments, - "total_segments": len(segments) - }) - + + return jsonify( + {"success": True, "segments": segments, "total_segments": len(segments)} + ) + except Exception as e: backend_logger.error(f"Error getting data segments: {e}") - return jsonify({ - "success": False, - "error": str(e) - }), 500 + return jsonify({"success": False, "error": str(e)}), 500 @app.route("/api/plots/sessions/", methods=["GET"]) @@ -2570,11 +2658,13 @@ def get_priority_status(): try: priority_stats = streamer.data_streamer.get_streaming_stats() - return jsonify({ - "success": True, - "priority_protection": priority_stats, - "message": "Priority protection ensures CSV recording has maximum priority over API operations" - }) + return jsonify( + { + "success": True, + "priority_protection": priority_stats, + "message": "Priority protection ensures CSV recording has maximum priority over API operations", + } + ) except Exception as e: return jsonify({"success": False, "error": str(e)}), 500 @@ -2588,11 +2678,13 @@ def get_current_performance(): try: current_stats = streamer.data_streamer.performance_monitor.get_current_stats() - return jsonify({ - "success": True, - "current_performance": current_stats, - "message": "Current performance metrics for active recording operations" - }) + return jsonify( + { + "success": True, + "current_performance": current_stats, + "message": "Current performance metrics for active recording operations", + } + ) except Exception as e: return jsonify({"success": False, "error": str(e)}), 500 @@ -2606,17 +2698,21 @@ def get_historical_performance(): try: # Get query parameters - windows = int(request.args.get('windows', 6)) # Default: last minute (6 * 10s) + windows = int(request.args.get("windows", 6)) # Default: last minute (6 * 10s) windows = min(max(windows, 1), 60) # Limit between 1 and 60 windows - - historical_stats = streamer.data_streamer.performance_monitor.get_historical_stats(windows) - return jsonify({ - "success": True, - "historical_performance": historical_stats, - "windows_requested": windows, - "duration_minutes": windows * 10 / 60, - "message": f"Historical performance metrics for last {windows} windows ({windows*10} seconds)" - }) + + historical_stats = ( + streamer.data_streamer.performance_monitor.get_historical_stats(windows) + ) + return jsonify( + { + "success": True, + "historical_performance": historical_stats, + "windows_requested": windows, + "duration_minutes": windows * 10 / 60, + "message": f"Historical performance metrics for last {windows} windows ({windows*10} seconds)", + } + ) except Exception as e: return jsonify({"success": False, "error": str(e)}), 500 @@ -2695,6 +2791,74 @@ def get_events(): return jsonify({"success": False, "error": str(e)}), 500 +@app.route("/api/console-logs") +def get_console_logs(): + """Get recent console logs from the log files""" + try: + limit = request.args.get("limit", 1000, type=int) + limit = min(limit, 5000) # Maximum 5000 lines per request + + log_dir = ".logs" + if not os.path.exists(log_dir): + return jsonify( + {"success": True, "logs": [], "message": "No log directory found"} + ) + + # Get all log files sorted by modification time (newest first) + log_files = [] + for filename in os.listdir(log_dir): + if filename.startswith("backend_") and filename.endswith(".log"): + filepath = os.path.join(log_dir, filename) + log_files.append((filepath, os.path.getmtime(filepath))) + + log_files.sort(key=lambda x: x[1], reverse=True) + + logs = [] + lines_read = 0 + + # Read logs from newest to oldest files + for filepath, _ in log_files: + if lines_read >= limit: + break + + try: + with open(filepath, "r", encoding="utf-8") as f: + file_lines = f.readlines() + + # Take the last lines from the current file if we haven't reached the limit + remaining_lines = limit - lines_read + if len(file_lines) > remaining_lines: + file_lines = file_lines[-remaining_lines:] + + # Add lines in reverse order (newest first within each file) + for line in reversed(file_lines): + if lines_read >= limit: + break + line = line.strip() + if line: # Skip empty lines + logs.append(line) + lines_read += 1 + + except Exception as e: + backend_logger.error(f"Error reading log file {filepath}: {e}") + continue + + # Don't reverse - logs are already in newest-first order + + return jsonify( + { + "success": True, + "logs": logs, + "total_lines": lines_read, + "files_read": len([f for f, _ in log_files]), + } + ) + + except Exception as e: + backend_logger.error(f"Error getting console logs: {e}") + return jsonify({"success": False, "error": str(e)}), 500 + + @app.route("/api/stream/variables", methods=["GET"]) def stream_variables(): """Stream variable values in real-time using Server-Sent Events""" @@ -2936,9 +3100,9 @@ def graceful_shutdown(): # 🚀 PRESERVE auto-reconnect state: Don't clear reconnection data streamer.disconnect_plc(manual_disconnect=False) # Keep auto-reconnect state # 🔑 PRIORITY: Shutdown priority manager and performance monitor - if hasattr(streamer.data_streamer, 'priority_manager'): + if hasattr(streamer.data_streamer, "priority_manager"): streamer.data_streamer.priority_manager.shutdown() - if hasattr(streamer.data_streamer, 'performance_monitor'): + if hasattr(streamer.data_streamer, "performance_monitor"): streamer.data_streamer.performance_monitor.stop_monitoring() streamer.release_instance_lock() # 📝 Close rotating logger system @@ -2960,7 +3124,7 @@ def main(): # Setup signal handlers for graceful shutdown signal.signal(signal.SIGINT, signal_handler) signal.signal(signal.SIGTERM, signal_handler) - + max_retries = 3 retry_count = 0 @@ -2974,7 +3138,13 @@ def main(): global streamer # Start Flask application - app.run(debug=False, host="0.0.0.0", port=5050, use_reloader=False, threaded=True) + app.run( + debug=False, + host="0.0.0.0", + port=5050, + use_reloader=False, + threaded=True, + ) # If we reach here, the server stopped normally break diff --git a/system_state.json b/system_state.json index 1d5e0b2..826b30c 100644 --- a/system_state.json +++ b/system_state.json @@ -4,11 +4,11 @@ "should_stream": false, "active_datasets": [ "Fast", - "DAR", - "Test" + "Test", + "DAR" ] }, "auto_recovery_enabled": true, - "last_update": "2025-08-16T21:54:24.471700", + "last_update": "2025-08-16T23:53:59.480554", "plotjuggler_path": "C:\\Program Files\\PlotJuggler\\plotjuggler.exe" } \ No newline at end of file diff --git a/translation.json b/translation.json new file mode 100644 index 0000000..e69de29