import mit #Package that loads from the netcdf files to produce the structured array `dgl` import plmit #Package that plots the dgl structured arrays tims = np.arange(0,70) #Set the time points you want to load. This is given in days, #you can change to seconds or years by adjusting set_scales.py levs = np.arange(0,1) #Set the model leves you want to load. This is given in #model level index where 0 is the surface level, 1 is the level below this etc. dfl = 'pv' #This is a pointer to the diagnostic file name in the mnc_tile_xxxx files. #It needs to be a string that would identify the relevant files is you ran a command like #`ls pv*' in the file in linux. The exception is for the state files, where this #field must be 'st', so the code knows to use the different diagnostic names in the state files. diags = ['u','v','rvf'] #List of strings giving the diagnostic names. Diagnostics #that are calculated, such as relative vorticity, should be last. dgl=mit.ldgl(tims, levs, dfl, diags) #Load the diagnostics into the structured array dgl print dgl[0]['tims'] #To check that appropriate times have been loaded print dgl.dtype #To check the size of the loaded files #Now plot this output in the x-y plane clf='rv' #Set the colorfield of the plot clims=[-1,1] #Set the colourbar limits plmit.pxy(dgl,clf,ti='a',clims = clims) #Do the plotting. ti = 'a' means that #the entire time series will be animated automatically.
import matplotlib.pylab as plt
import matplotlib.animation as animation
import mit
import plmit
import os
os.chdir('/work/n01/n01/liamb/pig/tracer/expts/mnc_test_0018_useconv_hydro')
tims = np.arange(0,0.5,1e-2)
levs = np.arange(0,100)
dfile = 'h'
diags = ['t','s']
clims = [1, 3]
dgl = mit.ldgl(tims, levs, dfile, diags)
print dgl.dtype
print dgl[0][0]
time = len(dgl[0]['tims'])
x = dgl[0]['x']
y = dgl[0]['y']
z = dgl[0]['z']
###########
fig = plt.figure(figsize=(14,14),facecolor='white')
ax = plt.subplot()
quad1 = ax.pcolormesh(y,z,np.transpose(np.squeeze(dgl[0]['t'][:,:,:,0])),shading='gouraud',cmap = plt.cm.bwr)
ax.set_xlabel('time')
ax.set_ylabel('amplitude')
