190 lines
6.5 KiB
Python
190 lines
6.5 KiB
Python
# 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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#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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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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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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)
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prediction = model.forward(batch)
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target_lat = lat
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target_lon = lon
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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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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)) |