добавление авроры с использованием открытого датасета
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# import gc
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# import logging
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# import time
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# from datetime import datetime, timedelta
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# from pprint import pprint
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# import mariadb
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# import serial.tools.list_ports
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#
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# #from PyWeather.weather.stations.davis import VantagePro
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# from prediction import run_prediction_module
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#
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# logging.basicConfig(filename="Stations.log",
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# format='%(asctime)s %(message)s',
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# filemode='a')
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# logger = logging.getLogger('davis_api')
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# logger.setLevel(logging.DEBUG)
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#
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# console_handler = logging.StreamHandler()
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# console_handler.setLevel(logging.DEBUG)
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# console_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s'))
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# logger.addHandler(console_handler)
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#
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#
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# def write_data(device, station, send=True):
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# try:
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# # device.parse()
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# data = device.fields
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# logger.info(data)
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# if len(data) < 1:
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# return
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# else:
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# logger.info(data)
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# fields = ['BarTrend', 'CRC', 'DateStamp', 'DewPoint', 'HeatIndex', 'ETDay', 'HeatIndex',
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# 'HumIn', 'HumOut', 'Pressure', 'RainDay', 'RainMonth', 'RainRate', 'RainStorm',
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# 'RainYear', 'SunRise', 'SunSet', 'TempIn', 'TempOut', 'WindDir', 'WindSpeed',
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# 'WindSpeed10Min']
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#
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# if send:
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# placeholders = ', '.join(['%s'] * len(fields))
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# field_names = ', '.join(fields)
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# sql = f"INSERT INTO weather_data ({field_names}) VALUES ({placeholders})"
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# values = [data[field] for field in fields]
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# cursor.execute(sql, values)
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# conn.commit()
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# else:
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# logger.info(data)
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#
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# del data
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# del fields
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# gc.collect()
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# except Exception as e:
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# logger.error(str(e))
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# raise e
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#
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#
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# def get_previous_values(cursor):
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# cursor.execute("SELECT SunRise, SunSet, WindDir, DateStamp FROM weather_data ORDER BY DateStamp DESC LIMIT 1")
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# result = cursor.fetchone()
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#
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# if result is None:
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# return None, None, None, None
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#
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# sun_rise, sun_set, wind_dir, datestamp = result
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# return sun_rise, sun_set, wind_dir, datestamp
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#
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#
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# def save_prediction_to_db(predictions):
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# try:
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#
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# sun_rise, sun_set, wind_dir, datestamp = get_previous_values(cursor)
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#
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# fields = ['DateStamp', 'SunRise', 'SunSet', 'WindDir'] + list(predictions.keys())
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# placeholders = ', '.join(['%s'] * len(fields))
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# field_names = ', '.join(fields)
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#
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# values = [datestamp + timedelta(minutes = 1), sun_rise, sun_set, wind_dir] + list(predictions.values())
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# pprint(dict(zip(fields, values)))
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# sql = f"INSERT INTO weather_data ({field_names}) VALUES ({placeholders})"
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# # cursor.execute(sql, values)
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# # conn.commit()
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# logger.info("Save prediction to db success!")
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# except Exception as e:
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# logger.error(str(e))
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# raise e
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#
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#
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# try:
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# conn = mariadb.connect(
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# user="wind",
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# password="wind",
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# host="193.124.203.110",
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# port=3306,
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# database="wind_towers"
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# )
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# cursor = conn.cursor()
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# except mariadb.Error as e:
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# logger.error('DB_ERR: ' + str(e))
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# raise e
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# while True:
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# try:
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# ports = serial.tools.list_ports.comports()
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# available_ports = {}
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#
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# for port in ports:
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# if port.serial_number == '0001':
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# available_ports[port.name] = port.vid
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#
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# devices = [VantagePro(port) for port in available_ports.keys()]
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# while True:
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# for i in range(1):
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# if len(devices) != 0:
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# logger.info(devices)
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# # write_data(devices[i], 'st' + str(available_ports[list(available_ports.keys())[i]]), True)
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# else:
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# raise Exception('Can`t connect to device')
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# time.sleep(60)
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# except Exception as e:
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# logger.error('Device_error' + str(e))
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# predictions = run_prediction_module()
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# #logger.info(predictions)
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# if predictions is not None:
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# save_prediction_to_db(predictions)
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# time.sleep(60)
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import logging
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import time
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import mariadb
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import serial.tools.list_ports
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#todo переписать под influx, для линухи приколы сделать
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import metpy.calc
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from datetime import datetime
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import torch
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from aurora import AuroraSmall, Batch, Metadata
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from metpy.units import units
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from PyWeather.weather.stations.davis import VantagePro
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def get_wind_speed_and_direction(lat:float,lon:float):
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model = AuroraSmall()
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model.load_checkpoint("microsoft/aurora", "aurora-0.25-small-pretrained.ckpt")
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logging.basicConfig(filename="Stations.log",
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format='%(asctime)s %(message)s',
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filemode='a')
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logger = logging.getLogger('davis_api')
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logger.setLevel(logging.DEBUG)
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batch = Batch(
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surf_vars={k: torch.randn(1, 2, 17, 32) for k in ("2t", "10u", "10v", "msl")},
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static_vars={k: torch.randn(17, 32) for k in ("lsm", "z", "slt")},
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atmos_vars={k: torch.randn(1, 2, 4, 17, 32) for k in ("z", "u", "v", "t", "q")},
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metadata=Metadata(
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lat=torch.linspace(90, -90, 17),
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lon=torch.linspace(0, 360, 32 + 1)[:-1],
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time=(datetime(2024, 11, 26, 23, 7),),
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atmos_levels=(100,),
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),
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.DEBUG)
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console_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s'))
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logger.addHandler(console_handler)
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try:
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conn = mariadb.connect(
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user="wind",
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password="wind",
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host="193.124.203.110",
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port=3306,
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database="wind_towers"
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)
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prediction = model.forward(batch)
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cursor = conn.cursor()
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except mariadb.Error as e:
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logger.error('DB_ERR: ' + str(e))
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raise e
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while True:
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try:
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ports = serial.tools.list_ports.comports()
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available_ports = {}
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target_lat = lat
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target_lon = lon
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for port in ports:
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if port.serial_number == '0001':
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available_ports[port.name] = port.vid
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lat_idx = torch.abs(batch.metadata.lat - target_lat).argmin()
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lon_idx = torch.abs(batch.metadata.lon - target_lon).argmin()
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devices = [VantagePro(port) for port in available_ports.keys()]
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while True:
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for i in range(1):
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if len(devices) != 0:
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logger.info(devices)
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else:
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raise Exception('Can`t connect to device')
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time.sleep(60)
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except Exception as e:
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logger.error('Device_error' + str(e))
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time.sleep(60)
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u_values = prediction.atmos_vars["u"][:, :, :, lat_idx, lon_idx]
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v_values = prediction.atmos_vars["v"][:, :, :, lat_idx, lon_idx]
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print("u values at target location:", u_values)
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print("v values at target location:", v_values)
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u_scalar = u_values.item()
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v_scalar = v_values.item()
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print("u value:", u_scalar)
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print("v value:", v_scalar)
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u_with_units = u_scalar * units("m/s")
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v_with_units = v_scalar * units("m/s")
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# Рассчитайте направление и скорость ветра
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wind_dir = metpy.calc.wind_direction(u_with_units, v_with_units)
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wind_speed = metpy.calc.wind_speed(u_with_units, v_with_units)
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wind_dir_text = wind_direction_to_text(wind_dir.magnitude)
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print(type(wind_dir))
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# Вывод результата
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print(f"Направление ветра: {wind_dir_text} ({wind_dir:.2f}°)")
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print(f"Скорость ветра: {wind_speed:.2f} м/с")
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return wind_dir.magnitude.item(),wind_speed.magnitude.item()
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def wind_direction_to_text(wind_dir_deg):
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directions = [
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"север", "северо-восток", "восток", "юго-восток",
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"юг", "юго-запад", "запад", "северо-запад"
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]
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idx = int((wind_dir_deg + 22.5) // 45) % 8
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return directions[idx]
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print(get_wind_speed_and_direction(50,20))
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# todo переписать под influx, для линухи приколы сделать
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@ -1,103 +1,185 @@
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import pandas as pd
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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from sqlalchemy import create_engine
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from datetime import datetime
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from pathlib import Path
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import metpy.calc
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import numpy as np
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import requests
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import torch
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import xarray as xr
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from aurora import AuroraSmall, Batch, Metadata
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from metpy.units import units
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def run_prediction_module():
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engine = create_engine('mysql+pymysql://wind:wind@193.124.203.110:3306/wind_towers')
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def get_download_paths(date):
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"""Создает список путей для загрузки данных."""
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download_path = Path("~/downloads/hres_0.1").expanduser()
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downloads = {}
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var_nums = {
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"2t": "167", "10u": "165", "10v": "166", "msl": "151", "t": "130",
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"u": "131", "v": "132", "q": "133", "z": "129", "slt": "043", "lsm": "172",
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}
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for v in ["2t", "10u", "10v", "msl", "z", "slt", "lsm"]:
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downloads[download_path / date.strftime(f"surf_{v}_%Y-%m-%d.grib")] = (
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f"https://data.rda.ucar.edu/ds113.1/"
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f"ec.oper.an.sfc/{date.year}{date.month:02d}/ec.oper.an.sfc.128_{var_nums[v]}_{v}."
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f"regn1280sc.{date.year}{date.month:02d}{date.day:02d}.grb"
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)
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for v in ["z", "t", "u", "v", "q"]:
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for hour in [0, 6, 12, 18]:
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prefix = "uv" if v in {"u", "v"} else "sc"
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downloads[download_path / date.strftime(f"atmos_{v}_%Y-%m-%d_{hour:02d}.grib")] = (
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f"https://data.rda.ucar.edu/ds113.1/"
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f"ec.oper.an.pl/{date.year}{date.month:02d}/ec.oper.an.pl.128_{var_nums[v]}_{v}."
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f"regn1280{prefix}.{date.year}{date.month:02d}{date.day:02d}{hour:02d}.grb"
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)
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return downloads, download_path
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query = """
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SELECT BarTrend, CRC, DateStamp, DewPoint, HeatIndex, ETDay, HumIn, HumOut,
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Pressure, RainDay, RainMonth, RainRate, RainStorm, RainYear,
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TempIn, TempOut, WindDir, WindSpeed, WindSpeed10Min
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FROM weather_data
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WHERE DateStamp >= '2024-10-14 21:00:00' - INTERVAL 36 HOUR;
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"""
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df = pd.read_sql(query, engine) # Загружаем данные из SQL-запроса в DataFrame
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df['DateStamp'] = pd.to_datetime(df['DateStamp']) # Преобразуем столбец 'DateStamp' в формат datetime
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df.set_index('DateStamp', inplace=True) # Устанавливаем 'DateStamp' как индекс
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df.sort_index(inplace=True) # Сортируем DataFrame по индексу (по дате)
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def download_data(downloads):
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"""Скачивает файлы, если они отсутствуют в целевой директории."""
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for target, source in downloads.items():
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if not target.exists():
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print(f"Downloading {source}")
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target.parent.mkdir(parents=True, exist_ok=True)
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response = requests.get(source)
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response.raise_for_status()
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with open(target, "wb") as f:
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f.write(response.content)
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print("Downloads finished!")
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lags = 3 # Задаем количество временных сдвигов (лагов)
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shifted_dfs = [df] # Создаем список для хранения исходного DataFrame и его лаговых версий
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for lag in range(1, lags + 1):
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shifted_df = df.shift(lag).add_suffix(f'_t-{lag}') # Создаем сдвинутый на lag строк DataFrame
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shifted_dfs.append(shifted_df) # Добавляем его в список
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def load_surf(v, v_in_file, download_path, date):
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"""Загружает переменные поверхностного уровня или статические переменные."""
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ds = xr.open_dataset(download_path / date.strftime(f"surf_{v}_%Y-%m-%d.grib"), engine="cfgrib")
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data = ds[v_in_file].values[:2]
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data = data[None]
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return torch.from_numpy(data)
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df_with_lags = pd.concat(shifted_dfs, axis=1) # Объединяем исходный DataFrame и все лаги по столбцам
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df_with_lags.dropna(inplace=True) # Удаляем строки с пропущенными значениями
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df_with_lags = df_with_lags.copy() # Создаем копию DataFrame (для предотвращения предупреждений)
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def load_atmos(v, download_path, date, levels):
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"""Загружает атмосферные переменные для заданных уровней давления."""
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ds_00 = xr.open_dataset(
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download_path / date.strftime(f"atmos_{v}_%Y-%m-%d_00.grib"), engine="cfgrib"
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)
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ds_06 = xr.open_dataset(
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download_path / date.strftime(f"atmos_{v}_%Y-%m-%d_06.grib"), engine="cfgrib"
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)
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ds_00 = ds_00[v].sel(isobaricInhPa=list(levels))
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ds_06 = ds_06[v].sel(isobaricInhPa=list(levels))
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data = np.stack((ds_00.values, ds_06.values), axis=0)
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data = data[None]
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return torch.from_numpy(data)
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# Преобразуем столбец 'BarTrend' в числовой формат, используя кодировщик категорий
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le = LabelEncoder()
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df_with_lags['BarTrend_encoded'] = le.fit_transform(df_with_lags['BarTrend'])
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# Оставляем в DataFrame только числовые столбцы
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df_with_lags = df_with_lags.select_dtypes(include=['float64', 'int64'])
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def create_batch(date, levels, downloads, download_path):
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"""Создает объект Batch с данными для модели."""
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ds = xr.open_dataset(next(iter(downloads.keys())), engine="cfgrib")
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batch = Batch(
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surf_vars={
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"2t": load_surf("2t", "t2m", download_path, date),
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"10u": load_surf("10u", "u10", download_path, date),
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"10v": load_surf("10v", "v10", download_path, date),
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"msl": load_surf("msl", "msl", download_path, date),
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},
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static_vars={
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"z": load_surf("z", "z", download_path, date)[0, 0],
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"slt": load_surf("slt", "slt", download_path, date)[0, 0],
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"lsm": load_surf("lsm", "lsm", download_path, date)[0, 0],
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},
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atmos_vars={
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"t": load_atmos("t", download_path, date, levels),
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"u": load_atmos("u", download_path, date, levels),
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"v": load_atmos("v", download_path, date, levels),
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"q": load_atmos("q", download_path, date, levels),
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"z": load_atmos("z", download_path, date, levels),
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},
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metadata=Metadata(
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lat=torch.from_numpy(ds.latitude.values),
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lon=torch.from_numpy(ds.longitude.values),
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time=(date.replace(hour=6),),
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atmos_levels=levels,
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),
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)
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return batch.regrid(res=0.1)
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# Создаем словари для хранения моделей и значений MSE
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models = {}
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mse_scores = {}
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# Обучаем модели для каждого целевого столбца
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for target_column in df.columns:
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if target_column not in df_with_lags.columns: # Пропускаем, если столбец отсутствует в df_with_lags
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continue
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def create_batch_random(levels: tuple[int], date: datetime):
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"""Создает объект Batch с рандомными данными для модели."""
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return Batch(
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surf_vars={k: torch.randn(1, 2, 17, 32) for k in ("2t", "10u", "10v", "msl")},
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static_vars={k: torch.randn(17, 32) for k in ("lsm", "z", "slt")},
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atmos_vars={k: torch.randn(1, 2, 4, 17, 32) for k in ("z", "u", "v", "t", "q")},
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metadata=Metadata(
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lat=torch.linspace(90, -90, 17),
|
||||
lon=torch.linspace(0, 360, 32 + 1)[:-1],
|
||||
time=(date,),
|
||||
atmos_levels=levels,
|
||||
),
|
||||
)
|
||||
|
||||
X = df_with_lags.drop(columns=[target_column]).values # Признаки - все столбцы, кроме целевого
|
||||
y = df_with_lags[target_column].values # Целевой столбец
|
||||
|
||||
# Разделяем данные на обучающую и тестовую выборки без перемешивания (временной ряд)
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
|
||||
def run_model(batch):
|
||||
"""Инициализирует модель AuroraSmall и выполняет предсказание."""
|
||||
model = AuroraSmall()
|
||||
model.load_checkpoint("microsoft/aurora", "aurora-0.25-small-pretrained.ckpt")
|
||||
model.eval()
|
||||
model = model.to("cpu")
|
||||
with torch.inference_mode():
|
||||
prediction = model.forward(batch)
|
||||
return prediction
|
||||
|
||||
model = RandomForestRegressor() # Инициализируем модель случайного леса
|
||||
model.fit(X_train, y_train) # Обучаем модель
|
||||
|
||||
y_pred = model.predict(X_test) # Делаем предсказания на тестовой выборке
|
||||
mse = mean_squared_error(y_test, y_pred) # Вычисляем среднеквадратичную ошибку
|
||||
mse_scores[target_column] = mse # Сохраняем MSE для целевого столбца
|
||||
models[target_column] = model # Сохраняем модель для целевого столбца
|
||||
def get_wind_speed_and_direction(prediction, batch: Batch, lat: float, lon: float):
|
||||
target_lat = lat
|
||||
target_lon = lon
|
||||
|
||||
quality = "хорошая" if mse < 1.0 else "плохая" # Определяем качество модели
|
||||
print(f"MSE для {target_column}: {mse} ({quality})") # Выводим MSE и качество
|
||||
lat_idx = torch.abs(batch.metadata.lat - target_lat).argmin()
|
||||
lon_idx = torch.abs(batch.metadata.lon - target_lon).argmin()
|
||||
|
||||
# Обучаем модель для столбца 'BarTrend_encoded' отдельно
|
||||
X_bartrend = df_with_lags.drop(columns=['BarTrend_encoded']).values # Признаки
|
||||
y_bartrend = df_with_lags['BarTrend_encoded'].values # Целевой столбец 'BarTrend_encoded'
|
||||
u_values = prediction.atmos_vars["u"][:, :, :, lat_idx, lon_idx]
|
||||
v_values = prediction.atmos_vars["v"][:, :, :, lat_idx, lon_idx]
|
||||
|
||||
# Разделяем данные на обучающую и тестовую выборки без перемешивания
|
||||
X_train_bartrend, X_test_bartrend, y_train_bartrend, y_test_bartrend = train_test_split(X_bartrend, y_bartrend,
|
||||
test_size=0.2,
|
||||
shuffle=False)
|
||||
u_scalar = u_values.item()
|
||||
v_scalar = v_values.item()
|
||||
|
||||
model_bartrend = RandomForestRegressor() # Инициализируем модель случайного леса
|
||||
model_bartrend.fit(X_train_bartrend, y_train_bartrend) # Обучаем модель
|
||||
print("u value:", u_scalar)
|
||||
print("v value:", v_scalar)
|
||||
u_with_units = u_scalar * units("m/s")
|
||||
v_with_units = v_scalar * units("m/s")
|
||||
|
||||
y_pred_bartrend = model_bartrend.predict(X_test_bartrend) # Предсказания на тестовой выборке для 'BarTrend_encoded'
|
||||
mse_bartrend = mean_squared_error(y_test_bartrend, y_pred_bartrend) # Вычисляем MSE
|
||||
models['BarTrend_encoded'] = model_bartrend # Сохраняем модель для 'BarTrend_encoded'
|
||||
mse_scores['BarTrend_encoded'] = mse_bartrend # Сохраняем MSE для 'BarTrend_encoded'
|
||||
# Рассчитайте направление и скорость ветра
|
||||
wind_dir = metpy.calc.wind_direction(u_with_units, v_with_units)
|
||||
wind_speed = metpy.calc.wind_speed(u_with_units, v_with_units)
|
||||
|
||||
quality_bartrend = "хорошая" if mse_bartrend < 1.0 else "плохая" # Определяем качество модели для 'BarTrend_encoded'
|
||||
print(f"MSE для BarTrend: {mse_bartrend} ({quality_bartrend})") # Выводим MSE и качество
|
||||
wind_dir_text = wind_direction_to_text(wind_dir.magnitude)
|
||||
# Вывод результата
|
||||
print(f"Направление ветра: {wind_dir_text} ({wind_dir:.2f}°)")
|
||||
print(f"Скорость ветра: {wind_speed:.2f} м/с")
|
||||
return wind_dir.magnitude.item(), wind_speed.magnitude.item()
|
||||
|
||||
last_data = X[-1].reshape(1, -1) # Берем последнюю строку данных и преобразуем в формат для предсказания
|
||||
|
||||
predictions = {} # Создаем словарь для хранения предсказаний
|
||||
for target_column, model in models.items():
|
||||
prediction = model.predict(last_data)[0] # Делаем предсказание для последней строки данных
|
||||
if target_column == 'BarTrend_encoded':
|
||||
prediction = le.inverse_transform([int(prediction)])[0] # Декодируем категориальное значение
|
||||
predictions['BarTrend'] = prediction # Сохраняем предсказание для 'BarTrend'
|
||||
#print(f"Предсказание для BarTrend: {prediction}") # Выводим предсказание
|
||||
continue # Продолжаем цикл после предсказания для 'BarTrend_encoded'
|
||||
predictions[target_column] = prediction # Сохраняем предсказание для остальных столбцов
|
||||
#print(f"Предсказание для {target_column}: {prediction}") # Выводим предсказание для столбца
|
||||
def wind_direction_to_text(wind_dir_deg):
|
||||
directions = [
|
||||
"север", "северо-восток", "восток", "юго-восток",
|
||||
"юг", "юго-запад", "запад", "северо-запад"
|
||||
]
|
||||
idx = int((wind_dir_deg + 22.5) // 45) % 8
|
||||
return directions[idx]
|
||||
|
||||
return predictions # Возвращаем словарь с предсказанными значениями и названиями столбцов
|
||||
|
||||
def main():
|
||||
levels = (100,)
|
||||
date = datetime(2024, 11, 5, 12)
|
||||
|
||||
# downloads, download_path = get_download_paths(date)
|
||||
# download_data(downloads) # Скачиваем данные, если их нет
|
||||
# batch_actual = create_batch(date, levels, downloads, download_path)
|
||||
batch_actual = create_batch_random(levels, date)
|
||||
prediction_actual = run_model(batch_actual)
|
||||
wind_speed_and_direction = get_wind_speed_and_direction(prediction_actual, batch_actual, 50, 20)
|
||||
return wind_speed_and_direction
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
print("Prediction completed!")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user