Descargar y plotear datos de temperatura de NCEP

TNcepnetcdf.py

import urllib
import requests
import xarray as xr
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from matplotlib.cm import get_cmap
import cartopy.crs as ccrs
import cartopy.feature as cf
from cartopy.feature import NaturalEarthFeature
from tqdm import tqdm


def descarga(iniciald, finald):
    for i in range(inicial, final, 1):
        myurl = "https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis/surface_gauss/air.2m.gauss." + str(i) + ".nc"
        # Streaming, so we can iterate over the response.
        response = requests.get(myurl, stream=True)
        total_size_in_bytes = int(response.headers.get('content-length', 0))
        block_size = 1024  # 1 Kibibyte
        print("air.2m.gauss." + str(i) + ".nc")
        progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
        with open("air.2m.gauss." + str(i) + ".nc", 'wb') as file:
            for data in response.iter_content(block_size):
                progress_bar.update(len(data))
                file.write(data)
        progress_bar.close()
        if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
            print("Error, no se pudo descargar")

    # for i in tqdm(range(inicial, final, 1)):
    # myurl = "https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis/surface_gauss/air.2m.gauss." + str(i) + ".nc"
    # filedowloand = "air.2m.gauss." + str(i) + ".nc"
    # listyear.append(filedowloand)
    # tqdm(urllib.request.urlretrieve(myurl, filedowloand))
    # print(listyear[i])


def leerNetcdf(iniciall, finall):

    for i in range(inicial, final + 1, 1):
        datad = 0
        with xr.open_dataset("air.2m.gauss." + str(i) + ".nc") as ds:  # air.2m.gauss. time=8760
            data = ds.air[:]

        for j in range(diames*24):
            datad = datad + data.isel(time=j)

        datad = datad/(diames*24)
        print(datad)
        dataFram = datad.to_dataframe(name=None, dim_order=None)
        dataFram.to_csv('salva.csv')
        print(dataFram)

        # Using geophysical units. `robust` disregards outliers for colour map creation.
        fig = plt.figure(1, figsize=(15., 12.))
        ax = plt.axes(projection=ccrs.PlateCarree())
        ax.coastlines()
        ax.add_feature(cf.LAND)  # utilizar con internet
        datad.plot()
        plt.title("Mean air  " + str(i)+'-'+str(mes))
        ax.gridlines(draw_labels=True)
        #plt.scatter(312.5, 47.5)
        plt.show()

while True:
    print('Entre el año dentro del periodo 1948 - 2019')
    inicial = int(input('Entre el año inicial del periodo que quiere descargar: '))
    if 1948 <= inicial <= 2019:
        print()
        break
    else:
        print('Escriba nuevamente el año inicial\n')

while True:
    print('Entre el año dentro del periodo 1948 - 2019')
    final = int(input('Entre el año final del periodo que quiere descargar: '))
    if 1948 <= final <= 2019:
        print()
        break
    else:
        print('Escriba nuevamente el año final\n')

mes = int(input("Escriba su mes de nacimiento: "))

diames = 0
if mes in [1, 3, 5, 7, 8, 10, 12]:
    diames = 31
elif mes in [4, 6, 9, 11]:
    diames = 30
elif mes == 2:
    if inicial % 4 == 0:
        diames = 29
    else:
        diames = 28

print('cantidad del dia del mes es igual: ', diames)

print('inicial: ', inicial, 'final: ', final)
descarga(inicial,final)
leerNetcdf(inicial, final)