wrfout_画图

from netCDF4 import Dataset
from datetime import timedelta, datetime
import os
import pandas as pd
from wrf import getvar
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt



def get_para_data(para_name, time_range, file):
    para_data = []
    for i in time_range:
        z = getvar(file, para_name, timeidx=i, meta=False)  # meta默认true : cat,false :join
        z = z[:, row, col]
        para_data.extend(z)
    return para_data


def plot_uv(p, u, v, save_path):
    time_ids = [0, 3, 6, 9, 12, 15, 18, 21]
    plt.figure(figsize=(14, 8))
    for k in range(8):
        ax = plt.subplot(2, 4, k + 1)
        pp = p[time_ids[k] * 30:(time_ids[k] + 1) * 30]
        xx = pp * 0
        uu = u[time_ids[k] * 30:(time_ids[k] + 1) * 30]
        vv = v[time_ids[k] * 30:(time_ids[k] + 1) * 30]
        plt.sca(ax)
        Q = plt.quiver(xx, pp, uu, vv, units='x', scale=3.5)
        plt.quiverkey(Q, 0.25, 1.03, 5, '5 m/s', labelpos='W')
        plt.ylim(0, 1500)
        plt.xlim(-10, 10)
        plt.xticks([0], [""])
        plt.text(-8, 150, str(time_ids[k]) + " BJT")
        if k in [0, 4]:
            plt.ylabel("Height (m)")
    plt.tight_layout()
    plt.savefig(save_path)
    plt.close()


def plot_temp(p, t, save_path):
    time_ids = [0, 3, 6, 9, 12, 15, 18, 21]
    plt.figure(figsize=(14, 8))
    for k in range(8):
        ax = plt.subplot(2, 4, k + 1)
        pp = p[time_ids[k] * 30:(time_ids[k] + 1) * 30]
        tt = t[time_ids[k] * 30:(time_ids[k] + 1) * 30]
        plt.sca(ax)
        plt.plot(tt, pp, '-ok')
        plt.ylim(0, 1500)
        plt.xlim(-10, 10)
        plt.text(-8, 150, str(time_ids[k]) + " BJT")
        if k in [0, 4]:
            plt.ylabel("Height (m)")
        if k > 3:
            plt.xlabel("Temp ($^o$C)")
    plt.tight_layout()
    plt.savefig(save_path)
    plt.close()


if __name__ == '__main__':
    # ------------------------------------------显示目录-----------------------------------------------------
    nc_dir = r'/home/share/model/CMAQ_new/output/wrfout'
    save_dir = r'/home/gzblue/xgx/1205/1220/data'
    # -----------------------------------------获取时间信息------------------------------------------------
    tomorrow = datetime.now() + timedelta(days=1)
    after_tomorrow = datetime.now() + timedelta(days=2)
    tomorrow, after_tomorrow = tomorrow.strftime('%Y%m%d'), after_tomorrow.strftime('%Y%m%d')
    today = datetime.now().strftime('%Y%m%d')
    # print(today,tomorrow,after_tomorrow)
    print('Lastest modified time:', today, '!')
    # -----------------------------------------获得nc文件路径------------------------------------------------
    nc_path = os.path.join(nc_dir, today, [i for i in os.listdir(os.path.join(nc_dir, today)) if 'd03' in i][0])
    print(nc_path)
    if not os.path.exists(nc_path):
        print('Today\'s NC file is not found!')
        raise IOError
    # ------------------------------------------获得存图路径------------------------------------------------
    save_png_dir = os.path.join(save_dir, today)
    if not os.path.exists(save_png_dir):
        os.makedirs(save_png_dir)
    # ------------------------------------------当csv不存在时------------------------------------------------
    csv_path = os.path.join(save_png_dir, 'data.csv')
    print(csv_path)
    if not os.path.exists(csv_path):
        data = pd.DataFrame()
        file = Dataset(nc_path)
        row, col = 268, 144  # 固定点的索引
        time_range = list(range(28, 76, 3))
        para_names = ['z', 'ua', 'va', 'tc']
        for para_name in para_names:
            data[para_name] = get_para_data(para_name, time_range, file)
        file.close()
        data.to_csv(csv_path)
        print('CSV is saved!')
    # ------------------------------------------读csv画图------------------------------------------------
    data = pd.read_csv(csv_path)
    data_z = data['z'].values
    data_ua = data['ua'].values
    data_va = data['va'].values
    data_tc = data['tc'].values
    save_uv_path = os.path.join(save_png_dir, tomorrow + '_UV_19.png')
    save_t_path = os.path.join(save_png_dir, tomorrow + '_T_19.png')
    p = data_z[:24 * 30]  # 30层
    u = data_ua[:24 * 30]
    v = data_va[:24 * 30]
    t = data_tc[:24 * 30]
    plot_uv(p, u, v, save_uv_path)
    plot_temp(p, t, save_t_path)
    print(tomorrow, 'is OK!')
    save_uv_path = os.path.join(save_png_dir, after_tomorrow + '_UV_19.png')
    save_t_path = os.path.join(save_png_dir, after_tomorrow + '_T_19.png')
    p = data_z[24 * 30:]
    u = data_ua[24 * 30:]
    v = data_va[24 * 30:]
    t = data_tc[24 * 30:]
    plot_uv(p, u, v, save_uv_path)
    plot_temp(p, t, save_t_path)
    print(after_tomorrow, 'is OK!')