from netCDF4 import Dataset as dst
from matplotlib import pyplot as plt
nc = dst('air.mon.mean.nc',mode='r')
time = 120
limit = 852
init = limit-time
#nc.variables['level'][2] --> 850 millibar
#air --> time --> level --> latitude --> longitude
count = 0
#print(nc.variables['level'][:])
def latitudinal(level):
iterator = -120
while iterator < 0:
profile = []
for latitude in list(nc.variables['air'])[iterator][level]:
item = []
for longitude in latitude:
item.append(longitude)
iterator += 1
average = sum(item)/len(item)
profile.append(average)
return profile
profile850 = latitudinal(2)
profile250 = latitudinal(8)
a = plt.plot(nc.variables['lat'][:],profile850, color='r', label='850mbar')
b = plt.plot(nc.variables['lat'][:],profile250, color='g', label='250mbar')
plt.xlabel("Latitude (°)")
plt.ylabel("Temperature (m/s)")
from netCDF4 import Dataset as dst
from numpy import mean, array
from matplotlib.pyplot import contourf, plot, show, savefig, colorbar, figure, setp, xlabel, ylabel
def slice_per(source, step):
return [source[i::step] for i in range(step)]
nc = dst("../vwnd.mon.mean.nc", mode='r')
months = range(853)[-61:-1]
avgs = []
levels = range(17)[1:12]
for l in levels:
item = []
for m in months:
for latitude in nc.variables['vwnd'][m][l]:
item.append(mean(latitude))
item = slice_per(item, 73)
for elem in item:
average = mean(elem)
avgs.append(average)
avgs = array(slice_per(avgs, 73))
x = nc.variables['lat'][:]
levels = [
'925', '850', '700', '600', '500', '400', '300', '250', '200', '150', '100'
]
avgs = avgs.T
cs = contourf(x, levels, avgs, 15, cmap='nipy_spectral')
colorbar()
from netCDF4 import Dataset as dst
from matplotlib import pyplot as plt
nc = dst('../uwnd.mon.mean.nc', mode='r')
iterator = -120
while iterator < 0:
profile850 = []
for latitude in list(nc.variables['uwnd'])[iterator][2]:
item = []
for longitude in latitude:
item.append(longitude)
iterator += 1
average = sum(item) / len(item)
profile850.append(average)
iterator_a = -120
while iterator_a < 0:
profile250 = []
for latitude in list(nc.variables['uwnd'])[iterator_a][8]:
item2 = []
for longitude in latitude:
item2.append(longitude)
iterator_a += 1
average = sum(item2) / len(item2)
profile250.append(average)
print(nc.variables['level'][:])
a = plt.plot(nc.variables['lat'][:], profile850, color='r', label='850 mbar')
b = plt.plot(nc.variables['lat'][:], profile250, color='g', label='250 mbar')
plt.xlabel("Latitude (°)")
plt.ylabel("Wind (m/s)")
plt.legend()
plt.savefig("plot_250v850.png")
from netCDF4 import Dataset as dst
from matplotlib import pyplot as plt
import numpy as np
from pathlib import Path
import matplotlib.patheffects as pe
nc = dst(str(Path.home()) + '/Desktop/vwnd.mon.mean.nc', mode='r')
def slice_per(source, step):
return [source[i::step] for i in range(step)]
def seasonalLatprof(month_index, alt):
a = range(858)[-128 - month_index:-8 - month_index:12]
res = []
for month in a:
#YEARLY PROFILE
profileMonthly = [] #np.empty(73,0)
for latitude in nc.variables['vwnd'][month][alt]:
profileMonthly.append(np.average(latitude))
res.append(profileMonthly)
print(np.array(res).shape)
res = np.average(res, axis=0)
print(np.array(res).shape)
return res
if __name__ == "__main__":
jun250 = seasonalLatprof(6, 8)
jul250 = seasonalLatprof(7, 8)
from netCDF4 import Dataset as dst
from matplotlib import pyplot as plt
import numpy as np
nc = dst("../air.mon.mean.nc", mode="r")
time_range = range(-12, 0)
months = []
for month in time_range:
m = []
for lvl in nc.variables['air'][month]:
l = np.sum(lvl) / (73 * 144)
m.append(l)
months.append(m)
levels = np.array(months).mean(axis=0)
print(nc.variables['level'][:])
print((levels[8] - levels[2]))
plt.xlabel("Temperature (°C)")
plt.ylabel("Pressure (mbar)")
plt.plot(levels, nc.variables['level'])
plt.gca().invert_yaxis()
plt.savefig("img_meanTemp.png")
plt.show()
from netCDF4 import Dataset as dst
import cartopy.crs as ccrs
from numpy import mean, empty, shape, array, arange, sqrt
from matplotlib import pyplot as plt
u = dst('../../Zonal/uwnd.mon.mean.nc', mode='r')
v = dst('../../Meridional/vwnd.mon.mean.nc', mode='r')
months = range(853)[-68:-8]
level = 8
def slice_per(source, step):
return [source[i::step] for i in range(step)]
def zonal_timeMean(v, dstname, months, level):
avgs = []
for month in months:
for latitude in v.variables[dstname][month][8]:
avgs.append(latitude)
avgs = array(avgs).flatten()
avgs = slice_per(avgs, 10512)
avg = []
for elem in avgs:
avg.append(mean(elem))
avg = array(avg).reshape(73, 144)
return avg
vwnd = zonal_timeMean(v, 'vwnd', months, level)
from netCDF4 import Dataset as dst
import numpy as np
from matplotlib import pyplot as plt
tempnc = dst("../air.mon.mean.nc", mode='r')
zonalnc = dst("../uwnd.mon.mean.nc", mode='r')
#Datasets downloaded 21 Aug 2020
#Time length- 871 ==> 871/12 --> 7/12
def slice_per(source, step):
return [source[i::step] for i in range(step)]
def zonal_data():
months = range(871)[-68:-8]
avgs = []
levels = range(17)[0:12]
for l in levels:
item = []
for m in months:
for latitude in zonalnc.variables['uwnd'][m][l]:
item.append(np.mean(latitude))
item = slice_per(item, 73)
for elem in item:
average = np.mean(elem)
avgs.append(average)
avgs = np.array(slice_per(avgs, 73))
return avgs.T