Merge pull request 'prediction' (#9) from prediction into front-2-fix-gen
Reviewed-on: #9
This commit is contained in:
commit
9602a1206c
@ -1,11 +1,10 @@
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from PyWeather.weather.stations.davis import VantagePro
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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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import gc
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import time
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from pprint import pprint
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from PyWeather.weather.stations.davis import VantagePro
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logging.basicConfig(filename="Stations.log",
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format='%(asctime)s %(message)s',
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@ -13,37 +12,10 @@ logging.basicConfig(filename="Stations.log",
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logger = logging.getLogger('davis_api')
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logger.setLevel(logging.DEBUG)
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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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print(data)
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if len(data) < 1:
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return
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else:
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print(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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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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pprint(data)
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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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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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@ -57,23 +29,25 @@ try:
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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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try:
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ports = serial.tools.list_ports.comports()
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available_ports = {}
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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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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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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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devices = [VantagePro(port) for port in available_ports.keys()]
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print(available_ports)
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while True:
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for i in range(len(devices)):
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print(devices[i].fields)
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#write_data(devices[i], 'st' + str(available_ports[list(available_ports.keys())[i]]), True)
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time.sleep(1)
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except Exception as e:
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logger.error('Device_error: ' + str(e))
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raise e
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# todo переписать под influx, для линухи приколы сделать
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185
davisAPI/prediction.py
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185
davisAPI/prediction.py
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@ -0,0 +1,185 @@
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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 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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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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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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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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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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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),
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lon=torch.linspace(0, 360, 32 + 1)[:-1],
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time=(date,),
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atmos_levels=levels,
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),
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)
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def run_model(batch):
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"""Инициализирует модель AuroraSmall и выполняет предсказание."""
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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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model.eval()
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model = model.to("cpu")
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with torch.inference_mode():
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prediction = model.forward(batch)
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return prediction
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def get_wind_speed_and_direction(prediction, batch: Batch, lat: float, lon: float):
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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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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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# 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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def main():
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levels = (100,)
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date = datetime(2024, 11, 5, 12)
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# downloads, download_path = get_download_paths(date)
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# download_data(downloads) # Скачиваем данные, если их нет
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# batch_actual = create_batch(date, levels, downloads, download_path)
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batch_actual = create_batch_random(levels, date)
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prediction_actual = run_model(batch_actual)
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wind_speed_and_direction = get_wind_speed_and_direction(prediction_actual, batch_actual, 50, 20)
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return wind_speed_and_direction
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if __name__ == "__main__":
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main()
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print("Prediction completed!")
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