from netCDF4 import Datasetfrom datetime import timedelta, datetimeimport osimport pandas as pdfrom wrf import getvarimport matplotlib as mplmpl.use('Agg')import matplotlib.pyplot as pltdef 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_datadef 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!')来源：https://www.cnblogs.com/avivi/p/11353838.html