Load GRIB file¶
It is necessary the 'pygrib' package. I only could install it using the CONDA environment, so, when I need use it, I need to start the Jupyter by the Conda prompt!
And, reminding that to change the 'disk' by the prompt...
It is necessary the 'pygrib' package. I only could install it using the CONDA environment, so, when I need use it, I need to start the Jupyter by the Conda prompt!
And, reminding that to change the 'disk' by the prompt...
import pygrib
import numpy as np
import matplotlib.pyplot as plt
import os
import datetime
import pickle
path = r'd:\WaveWatch3\Waves\\'
fdir = os.listdir(path)
The data structure of the GRIB files have the results in 3-hours intervals for the model.
First it comes the U component, than comes the V component, and the number changes depending of the month!
f_dp = []
f_hs = []
f_tp = []
for f in fdir:
if '.dp.' in f:
f_dp.append(f)
elif '.hs.' in f:
f_hs.append(f)
elif '.tp.' in f:
f_tp.append(f)
print(len(f_dp), len(f_hs), len(f_tp))
22 22 22
# https://www.youtube.com/watch?v=kEhcJP3G0xw
grbs = pygrib.open(path + f_dp[0])
grbs.seek(0)
c = 0
for grb in grbs:
print(grb)
print(grb.validDate)
print(grb.date)
print(grb.time)
print(grb.name)
print(grb.units)
c+=1
if c == 1:
break
1:Primary wave direction:Degree true (instant):regular_ll:surface:level 1:fcst time 0 hrs:from 201508010000 2015-08-01 00:00:00 20150801 0 Primary wave direction Degree true
# to see the content of 1 grib
grbs.seek(0)
grb=grbs[1]
plt.imshow(grb.values)
plt.xlabel('Pontos de grid X')
plt.ylabel('Pontos de grid Y')
Text(0, 0.5, 'Pontos de grid Y')
# to see the content of 1 grib
grbs.seek(0)
grb=grbs[1]
# get the values of latitude and longitude for all points of the grid (latitude comes first here!)
lalo = grb.latlons()
lalo = np.array(lalo)
la = lalo[0,:,:]
lo = lalo[1,:,:]
extents = [np.min(lo), np.max(lo), np.min(la), np.max(la)]
plt.imshow(grb.values, extent=extents)
plt.xlabel('Longitude +360')
plt.ylabel('Latitude')
Text(0, 0.5, 'Latitude')
# with open('WW3_coordenadas_malha.pkl', 'wb') as io:
# pickle.dump(lalo, io)
# checking the latitude and longitude
latitudes = lalo[0, :, :]
longitudes = lalo[1, :, :]
lats = latitudes[:,0]
lons = longitudes[0,:]
plt.plot(latitudes[:,0], 'g')
plt.plot(longitudes[0,:], 'r')
plt.plot(lats, '.', color='tab:blue', zorder=0)
plt.plot(lons, '.', color='tab:orange', zorder=0)
[<matplotlib.lines.Line2D at 0x23e509d3250>]
latitudes.shape
(311, 720)
# posição do ADCP e da estação meteorológica
adcp_x = -46.564 + 360
adcp_y = -0.672
met_x = -46.6037 + 360
met_y = -0.8301
extends = [lons[0], lons[-1], lats[-1], lats[0]]
plt.imshow(grb.values, extent=extends)
# plt.imshow(grb.values)
# plt.plot(longitudes, latitudes, 'k.')
plt.plot(adcp_x, adcp_y, 'ro')
plt.plot(met_x, met_y, 'ro')
# plt.xlim(310,315)
# plt.ylim(-3, 1)
plt.show()
extends = [lons[0]-360, lons[-1]-360, lats[-1], lats[0]]
plt.imshow(grb.values, extent=extends)
# plt.imshow(grb.values)
# plt.plot(longitudes, latitudes, 'k.')
plt.plot(adcp_x-360, adcp_y, 'ro')
plt.plot(met_x, met_y, 'ro')
# plt.xlim(310,315)
# plt.ylim(-3, 1)
plt.show()
Changing the values of 'linha' (line) and 'coluna' (column) and changing the values of 'xlim' and 'ylim'
linha = 156
coluna = 627
extends = [lons[0], lons[-1], lats[-1], lats[0]]
plt.imshow(grb.values, extent=extends)
plt.plot(longitudes, latitudes, 'k.')
plt.plot(longitudes[linha, coluna], latitudes[linha, coluna], 'bo')
plt.plot(adcp_x, adcp_y, 'ro')
plt.plot(met_x, met_y, 'ro')
plt.xlim(310,320)
plt.ylim(-3, 1)
print(longitudes[linha, coluna]-360, latitudes[linha, coluna])
plt.show()
-46.5 -0.5
# indexes of the nearest point from the ADCP
linha = 156
coluna = 627
# variables to be extracted...
# hs, dp and tp
files = f_tp
var = []
t = []
for i in range(len(files)):
print('Processing ', i, files[i])
grbs = pygrib.open(path + files[i])
grbs.seek(0)
t1 = []
var1 = []
for grb in grbs:
t1.append(grb.validDate)
var1.append(grb.values[linha, coluna])
t = t + t1
var = var + var1
Processando 0 multi_1.glo_30m.tp.201508.grb2 Processando 1 multi_1.glo_30m.tp.201509.grb2 Processando 2 multi_1.glo_30m.tp.201510.grb2 Processando 3 multi_1.glo_30m.tp.201511.grb2 Processando 4 multi_1.glo_30m.tp.201512.grb2 Processando 5 multi_1.glo_30m.tp.201601.grb2 Processando 6 multi_1.glo_30m.tp.201602.grb2 Processando 7 multi_1.glo_30m.tp.201603.grb2 Processando 8 multi_1.glo_30m.tp.201604.grb2 Processando 9 multi_1.glo_30m.tp.201605.grb2 Processando 10 multi_1.glo_30m.tp.201606.grb2 Processando 11 multi_1.glo_30m.tp.201607.grb2 Processando 12 multi_1.glo_30m.tp.201608.grb2 Processando 13 multi_1.glo_30m.tp.201609.grb2 Processando 14 multi_1.glo_30m.tp.201610.grb2 Processando 15 multi_1.glo_30m.tp.201611.grb2 Processando 16 multi_1.glo_30m.tp.201612.grb2 Processando 17 multi_1.glo_30m.tp.201701.grb2 Processando 18 multi_1.glo_30m.tp.201702.grb2 Processando 19 multi_1.glo_30m.tp.201703.grb2 Processando 20 multi_1.glo_30m.tp.201704.grb2 Processando 21 multi_1.glo_30m.tp.201705.grb2
plt.plot(t, var)
[<matplotlib.lines.Line2D at 0x23e530f5400>]
print(t[0:3])
[datetime.datetime(2015, 8, 1, 0, 0), datetime.datetime(2015, 8, 1, 3, 0), datetime.datetime(2015, 8, 1, 6, 0)]
# j = [t, var]
# with open('WW3_periodo_bragranca20152017.pkl', 'wb') as io:
# pickle.dump(j, io)
from ftplib import FTP
from pathlib import Path
ftp = FTP("ftp.gml.noaa.gov")
ftp.login()
ftp.cwd('data/radiation/surfrad/Boulder_CO/2020')
# Get all files
files = ftp.nlst()
# Download all the files to C:\Temp
for file in files:
print("Downloading..." + file)
ftp.retrbinary(f'RETR {file}', open(str(Path(r'C:\Temp') / file), 'wb').write)
ftp.close()
from ftplib import FTP
from pathlib import Path
ftp = FTP("www.ncei.noaa.gov")
ftp.login()
ftp.cwd('data/oceans/ncep/nww3/2015/08/glo_30m')
# Get all files
files = ftp.nlst()
# Download all the files to C:\Temp
for file in files:
if 'wind' in file:
print("Downloading..." + file)
ftp.retrbinary(f'RETR {file}', open(str(Path(r'C:\Temp') / file), 'wb').write)
ftp.close()