I need to generate multiple temperature plots for every month of every year, spanning from 2002 to 2018. I have managed to develop one plot for all of the data in 2002 (about 6 months). I had import my netcdf files into an array and slice the 3d array to a 2d array of lat lon lst. Using pcolormesh I plotted one singular lon, lat, data_2d plot but can't figure out how to define the months so I can plot them separately. The data is not available online but I am looking for a general function or command that can iterate through the months and plot them separately onto a map.
import netCDF4
import numpy as np
import xarray as xr
import pandas as pd
import os
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import cartopy.crs as ccrs
import cartopy.feature as cfeature
print ('All packages imported')
# make an empty np array
data_grid = []
# list of files
days = pd.date_range (start='4/7/2002', end='31/7/2002')
print ('Initiate for loop')
# for loop iterating through %date range to find path files
for day in days:
# defining what paths should look like
# %i = integer, %02d = integer with two characters, for year month and day
path = "/data/atsr/MMDB/AQUA_MODIS_L3E/2.00/%i/%02d/%02d/ESACCI-LST-L3C-LST-MODISA-LONDON_0.01deg_1DAILY_DAY-%i%02d%02d000000-fv2.00.nc" % (day.year, day.month, day.day, day.year, day.month, day.day)
print(path)
# os fetches contents
if os.path.isfile(path):
# open file and define lst daily mean
f = Dataset(path)
lst = f['lst'][:]
# populate numpy array, lst slice ignoring first index and accounting for NaN
data_grid.append(f['lst'][0,:,:].filled(np.nan))
f.close()
else: print ("%s not found" % path)
# stack array into three dimensions (lat, lon, time)
data_3d = np.dstack(data_grid)
# calculate mean of each grid point pixel and ignore NaN values
# make it 2d
data_2d = np.nanmean(data_3d, axis = 2)
# define lat lon from last file
f = netCDF4.Dataset ('/data/atsr/MMDB/AQUA_MODIS_L3E/2.00/2018/12/31/ESACCI-LST-L3C-LST-MODISA-LONDON_0.01deg_1DAILY_DAY-20181231000000-fv2.00.nc')
print (f)
# define lat and lons including all indicies
lat = f['lat'][:]
lon = f['lon'][:]
# plot data
# set up a map and size
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(projection=ccrs.PlateCarree())
# define the coordinate system that the grid lons and grid lats are on
mapped_array = ax.pcolormesh(lon, lat, data_2d)
# add title
plt.title ('Aqua Modis London', fontsize=12)
# set axis labels
plt.xlabel('Latitude', fontsize=10)
plt.ylabel('Longitude',fontsize=10)
# add features
ax.coastlines()
ax.add_feature(cfeature.BORDERS, edgecolor='black')
# add colorbar
divider = make_axes_locatable(ax)
ax_cb = divider.new_horizontal(size="5%", pad=0.1, axes_class=plt.Axes)
fig.add_axes(ax_cb)
plt.colorbar(mapped_array, cax=ax_cb)
#add lat lon grids and ticks
gl = ax.gridlines(draw_labels=True, alpha=0.1)
gl.top_labels = False
gl.right_labels = False
# show and save
plt.show()
plt.close()
I would recommend using the xarray package for collecting all of your data into a single array. First put all of the netCDFs in the same directory, then merge with:
import xarray as xr
f = xr.open_mfdataset('/data/atsr/MMDB/AQUA_MODIS_L3E/2.00/ESACCI-LST-L3C-LST-MODISA-LONDON_0.01deg_1DAILY_DAY*.nc')
From there it looks like you want the monthly mean:
f_monthly = f.resample(time = 'MS', skipna = True).mean()
Then it is pretty straightforward to loop through each of the months/years and plot.
f_monthly.sel(time = '2018-12').plot()