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This article mainly explains "how to achieve the global temperature map by Python". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to achieve the global temperature map by Python".

Global average air temperature chart for 2012:

Note: this article only takes basemap as an example. Cartopy asks readers to modify it by referring to previous tweets. Import datetime as dt # Python standard library datetime moduleimport numpy as npfrom netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/import matplotlib.pyplot as pltfrom mpl_toolkits.basemap import Basemap, addcyclic, shiftgridnc_f = 'Fjouxan Rpython Your filenamenc_fid lp28 Your filenamenc_fid databank air.sig995.2012.nc' # RpythonUnion = Dataset (nc_f) 'r') # Dataset is the class behavior to open the file # and create an instance of the ncCDF4 class# Extract data from NetCDF filelats = nc_fid.variables [' lat'] [:] # extract/copy the datalons = nc_fid.variables ['lon'] [:] time = nc_fid.variables [' time'] [:] air = nc_fid.variables ['air'] [:] # shape is time, lat Lon as shown abovetime_idx = 237 # some random day in 2018 Python and the renalaysis are slightly off in time so this fixes that problemoffset = dt.timedelta (hours=48) # List of all times in the file as datetime objectsdt_time = [dt.date (1,1,1) + dt.timedelta (hours=t)-offset\ for t in time] cur_time = dt_ time [time _ idx] # Plot of global temperature on our random day#fig = plt.figure () # fig.subplots_adjust (left=0., right=1., bottom=0. Top=0.9) fig=plt.figure (figsize= (16pr 9)) ax=fig.add_subplot (111C) # Setup the map. See http://matplotlib.org/basemap/users/mapsetup.html# for other projections.m=Basemap (llcrnrlat=-90,urcrnrlat=90,llcrnrlon=-180,urcrnrlon=180,resolution='c') m.drawparallels (np.arange (- 90,90 + 1,30), labels = [1,0,0,0], fontsize=14,linewidth='0.2',color='black') m.drawmeridians (np.arange (- 180,180 + 1,60), labels = [0,0,0,1], fontsize=14,linewidth='0.2' Color='black') m.drawcoastlines () m.drawmapboundary () air2= [time _ idx,:,:] # convert 0mm 360 to-180mm 1800Make the plot continuousair_cyclic, lons_cyclic = addcyclic (air2, lons) # Shift the grid so lons go from-180to 180 instead of 0 to 360.air_cyclic, lons_cyclic = shiftgrid (1800.air_cyclic, lons_cyclic, start=False) # False True# Create 2D lat/lon arrays for Basemaplon2d, lat2d = np.meshgrid (lons_cyclic, lats) # Transforms lat/lon into plotting coordinates for projectionx Y = m (lon2d, lat2d) # View array latitude dimen = np.array (x) .shapeprint (dimen) dimen1 = np.array (y) .shapeprint (dimen1) dimen2 = np.array (air_cyclic) .shapeprint (dimen2) # Plot of air temperature with 11 contour intervalsclevs = np.arange cs = m.contourf (cs,shrink=0.75,orientation='vertical',extend='both',pad=0.025) cbar=plt.colorbar (cs,shrink=0.75,orientation='vertical',extend='both',pad=0.025) Aspect=20) # orientation='horizontal'#cbar = plt.colorbar (cs, orientation='horizontal', shrink=0.5) cbar.set_label ("% s (% s)"% (nc_fid.variables ['air'] .var _ desc,nc_fid.variables [' air'] .units)) plt.title ("% s on% s"% (nc_fid.variables ['air'] .var _ desc, cur_time) plt.savefig (' Rpythonapace lp28plot33.pngmmdpian800) plt.show ()

Temperature curve of Darwin, Australia in 2012:

Darwin = {'name':' Darwin, Australia', 'lat':-12.45,' lon': 130.83} # Find the nearest latitude and longitude for Darwinlat_idx = np.abs (lats-darwin ['lat']). Argmin () lon_idx = np.abs (lons-darwin [' lon']). Argmin () # Simple example: temperature profile for the entire year at Darwin.# Open a new NetCDF file to write the data to. For format, you can choose from# 'NETCDF3_CLASSIC',' NETCDF3_64BIT', 'NETCDF4_CLASSIC', and' NETCDF4'w_nc_fid = Dataset ('FGV peg RpythonCharger lp28 RpythonUniverse) w_nc_fid.description = "NCEP/NCAR Reanalysis% s from its value at% s.% s" (nc_fid.variables [' air']. Var_desc.lower ()) Darwin ['name'], nc_fid.description) # Using our previous dimension info, we can create the new time dimension# Even though we know the size, we are going to set the size to unknownw_nc_fid.createDimension (' time', None) w_nc_dim = w_nc_fid.createVariable ('time', nc_fid.variables [' time'] .dtype, ('time') ) # You can do this step yourself but someone else did the work for us.for ncattr in nc_fid.variables ['time'] .ncattrs (): w_nc_dim.setncattr (ncattr, nc_fid.variables [' time'] .getncattr (ncattr)) # Assign the dimension data to the new NetCDF file.w_nc_fid.variables ['time'] [:] = timew_nc_var = w_nc_fid.createVariable (' air', 'f8' ('time') w_nc_var.setncatts ({' long_name': u "mean Daily Air temperature",\ 'units': u "degK",' level_desc': upright surface,\ 'var_desc': u "Air temperature",\' statistic': u'Mean\ nM'}) w_nc_fid.variables ['air'] [:] = air [time _ idx Lat_idx, lon_idx] w_nc_fid.close () # close the new file# A plot of the temperature profile for Darwin in 2012fig = plt.figure () plt.plot (dt_time, air [:, lat_idx, lon_idx], caterpillar r') plt.plot (dt_ time [time _ idx], air [time _ idx, lat_idx, lon_idx], cymbals, marker='o') plt.text (dt_ time [time _ idx], Airport [time _ idx, lat_idx, lon_idx] Cur_time,ha='right') fig.autofmt_xdate () plt.ylabel ("% s (% s)"% (nc_fid.variables ['air'] .var _ desc,nc_fid.variables [' air'] .units) plt.xlabel ("Time") plt.title ("% s from\ n% s for% s"% (nc_fid.variables ['air']. Var_desc,darwin [' name']) Cur_time.year)) plt.savefig ('FRPG Rpython plt.savefig lp28 RPY plot33. 2. Png recording and writing dpirates 800) plt.show ()

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