Example #1
0
def plot_allregions(ptype='nh'):
    """ plot_allregions(ptype='nh'): plot all defined regions
    """

    regdict = con.get_regiondict()
    nreg = len(regdict)
    rem = np.mod(nreg,2)
    rows = 2
    
    if rem==0:
        cols = nreg/rows
    else:
        cols = nreg/rows + rem # rem will always be 1 if dividing by 2

    if cols>8: 
        rows = 3
        cols = nreg/rows + np.mod(nreg,rows)/2

    print 'nrows: ' + str(rows) + ' ncols: ' + str(cols) # @@@
    
    lat = con.get_t63lat()
    lon = con.get_t63lon()
    
    fig,spax = plt.subplots(rows,cols)
    fig.set_size_inches(cols*2,rows*3)
    
    for aii,ax in enumerate(spax.flat):

        dummy = con.get_t63landmask() # dummy data
        
        regkey = regdict.keys()[aii]
        limsdict = regdict[regkey]
        # mask the dummy data
        dummym,dmask = cutl.mask_region(dummy,lat,lon,regkey,limsdict=limsdict)
                
        kemmap(dummym,lat,lon,ptype=ptype,axis=ax,suppcb=1,cmin=-11,cmax=2,cmap='blue2blue_w10')        
        ax.set_title(regkey)
        ax.set_xlabel(str(limsdict['latlims']) + ',' +str(limsdict['lonlims']) )
        
        if aii==nreg-1: break # get out of loop if done with regions
Example #2
0
fnamec, fnamep = con.build_filepathpair(sim, field)

fldc = cnc.getNCvar(fnamec, ncfield, seas=sea)
fldp = cnc.getNCvar(fnamep, ncfield, seas=sea)

lon = cnc.getNCvar(fnamec, "lon")
lat = cnc.getNCvar(fnamec, "lat")

fldca = np.squeeze(fldc - np.mean(fldc, axis=0))  # remove time mean
fldpa = np.squeeze(fldp - np.mean(fldp, axis=0))


if subset:
    msk1 = con.get_t63regionmask("other", limsdict=limsdict)
    fldca, msk = cutl.mask_region(fldca, lat, lon, "other", limsdict=limsdict)
    fldpa, msk = cutl.mask_region(fldpa, lat, lon, "other", limsdict=limsdict)

    oshape = fldca.shape  # original shape
    print fldca.shape

    fldca1 = fldca[~fldca.mask]  # @@@ why do I have to invert it. so dumb.
    # fldca2=fldca[msk]
    fldpa1 = fldpa[~fldpa.mask]

    nshape = fldca1.shape  # new shape
    print fldca1.shape
    # print fldca2.shape

    # only keep region of interest?
    lato = lat
Example #3
0
def load_ncfield(field, ncfield, zonal=True,conv=1,mo=0,season=None,last='last100',includeyr1=False,verb=False):
    """ 
        If zonal=True, compute zonal mean.
           zonal can also be a region, defined in regiondict in constants.py

        returns dictionaries: full field w/ time, zonal mean w/ time
    """


    timepers,timesels = get_timeinfo(last,includeyr1)


    seasonalizedt = {'mo':mo, 'season': season}
    
    ncflddt={}; ncfldtmdt={}; ncfldzmdt={}
    for casename in casenames:

        fname= basedir + casename +'/ts/' + casename + '_' + field + '_' + timepers[casename] + '_ts.nc'
        timesel=timesels[casename]
        if verb:
            print fname

        try:
            fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv # remlon not operational??? why commented out??
            if verb:
                print fname + ', fld.shape ' + str(fld.shape) # @@@
        except IOError as rte: # previously this was a RuntimeError. Not sure what changed. 10/14/2016
            print rte
            if 'No such file or directory' in rte.args:
                # Here the problem is most likely that pico2ipulse filename fails b/c 
                #   there is no NH version of it (for when last200=True). Remove the NH and try again.
                #   @@ Of course this could be a problem too b/c now we have full globe for just this run.
                if field[-2:] != 'NH':
                    print 'not an NH versus global problem...File really not found'
                    raise
                    
                fname= basedir + casename +'/ts/' + casename + '_' + field[:-2] + '_' + timepers[casename] + '_ts.nc'
                print 'Try again: ' + fname
                fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv 
                tmplat=cnc.getNCvar(fname,'lat')
                if last=='last200': # how to tell which spot lat is?
                    if zonal: # then it is likely time x lat x lon
                        #print 'zonal is true. fld dims? ' + str(fld.shape)
                        fld=fld[...,tmplat>0,:]
                    else: # likely time x lev x lat
                        fld=fld[...,tmplat>0]
            else:
                raise

        # just get the full timeseries, and zonal mean if asked.
        if 1: #timeseries: # do not time average (actually save time average anyway)
            if (season==None and mo==0):
                pass
            else:
                fld = cutl.seasonalize(fld,**seasonalizedt)
            #fldtm = fld
            # zonal average
            if zonal==True:
                fldzm = fld[...,:-1].mean(axis=2) # remove extra lon
            elif zonal in con.get_regiondict().keys():
                print 'lat,lon shapes ' + str(lat.shape),str(lon.shape)
                tempo, _ = cutl.mask_region(fld,lat,lon,region=zonal)
                fldzm = tempo.mean(axis=2)                
            else:
                fldzm = None 
            #fldtm = fldtmzm.mean(axis=0)
#        else:
#            # seasonal & time mean
#            fldtm = np.mean(cutl.seasonalize(fld,**seasonalizedt),axis=0) 
#            # zonal average
#            if zonal:
#                fldtmzm = fldtm[...,:-1].mean(axis=1)
#            elif zonal in con.get_regiondict().keys():                
#                tempo, _ = cutl.mask_region(fldtm,lat,lon,region=zonal)
#                fldtmzm = tempo.mean(axis=1)        
#            else:
#                fldtmzm = fldtm

        print 'fld.shape: ' + str(fld.shape)
        if fldzm!=None:
            print 'fldzm.shape: ' + str(fldzm.shape)

        ncflddt[casename] = fld # keep all dims
        #ncfldtmdt[casename] = fldtm # time mean
        ncfldzmdt[casename] = fldzm # zonal mean  (w/ time dim)

    return ncflddt, ncfldzmdt
def load_ncfield(field, ncfield, zonal=True,conv=1,mo=0,season=None,last='last100',
                     includeyr1=False,verb=False,local=False,seacyc=False,pulse=False):
    """ 
        If zonal=True, compute zonal mean.
           zonal can also be a region, defined in regiondict in constants.py
        If local, use local data (which is limited)
        If seacycle, return the 12 month climo average, which only exists 
                1. locally (as of Mar 24 2017) and 2. is only avail for the last200

        returns dictionaries: full field w/ time, zonal mean w/ time
    """


    timepers,timesels = get_timeinfo(last,includeyr1)

    if local:
        #bd='/Users/kelly/DropboxWork/UWashCanSISE/DATA/relaxationruns/' #limited fields here
        bd='/Users/kelly/DATA/DataDisk/' # limited but not shared so there are more data
        subdir='/'
    else:
        bd=basedir
        subdir='/ts/'
        
    seasonalizedt = {'mo':mo, 'season': season}
    
    ncflddt={}; ncfldtmdt={}; ncfldzmdt={}
    for casename in casenames:
        
        if pulse==False and ('pulse' in casename): continue
            
        if seacyc:
            if last!='last200':
                raise Exception('load_ncfield(): if seacyc=True, last must = last200')
            if local!=True:
                raise Exception('load_ncfield(): if seacyc=True, local must be True (as of Mar 24 2017)')
                
            fname = bd + casename+subdir+casename + '_' + field + '_' + timepers[casename] + '_seacycle.nc'
        else:
            fname= bd + casename +subdir + casename + '_' + field + '_' + timepers[casename] + '_ts.nc'
        timesel=timesels[casename]
        if verb:
            print fname

        try:
            fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv # remlon not operational??? why commented out??
            if verb:
                print fname + ', fld.shape ' + str(fld.shape) # @@@
        except IOError as rte: # previously this was a RuntimeError. Not sure what changed. 10/14/2016
            print rte
            if 'No such file or directory' in rte.args:
                # Here the problem is most likely that pico2ipulse filename fails b/c 
                #   there is no NH version of it (for when last200=True). Remove the NH and try again.
                #   @@ Of course this could be a problem too b/c now we have full globe for just this run.
                if field[-2:] != 'NH':
                    print 'not an NH versus global problem...File really not found'
                    raise
                    
                fname= basedir + casename +'/ts/' + casename + '_' + field[:-2] + '_' + timepers[casename] + '_ts.nc'
                print 'Try again: ' + fname
                fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv 
                tmplat=cnc.getNCvar(fname,'lat')
                if last=='last200': # how to tell which spot lat is?
                    if zonal: # then it is likely time x lat x lon
                        #print 'zonal is true. fld dims? ' + str(fld.shape)
                        fld=fld[...,tmplat>0,:]
                    else: # likely time x lev x lat
                        fld=fld[...,tmplat>0]
            else:
                raise

        # just get the full timeseries, and zonal mean if asked.
        if 1: #timeseries: # do not time average (actually save time average anyway)
            if (season==None and mo==0):
                pass
            else:
                fld = cutl.seasonalize(fld,**seasonalizedt)
            #fldtm = fld
            # zonal average
            if zonal==True:
                fldzm = fld[...,:-1].mean(axis=2) # remove extra lon
            elif zonal in con.get_regiondict().keys():
                print 'lat,lon shapes ' + str(lat.shape),str(lon.shape)
                tempo, _ = cutl.mask_region(fld,lat,lon,region=zonal)
                fldzm = tempo.mean(axis=2)                
            else:
                fldzm = None 
            #fldtm = fldtmzm.mean(axis=0)
#        else:
#            # seasonal & time mean
#            fldtm = np.mean(cutl.seasonalize(fld,**seasonalizedt),axis=0) 
#            # zonal average
#            if zonal:
#                fldtmzm = fldtm[...,:-1].mean(axis=1)
#            elif zonal in con.get_regiondict().keys():                
#                tempo, _ = cutl.mask_region(fldtm,lat,lon,region=zonal)
#                fldtmzm = tempo.mean(axis=1)        
#            else:
#                fldtmzm = fldtm

        print 'fld.shape: ' + str(fld.shape)
        if fldzm!=None:
            print 'fldzm.shape: ' + str(fldzm.shape)

        ncflddt[casename] = fld # keep all dims
        #ncfldtmdt[casename] = fldtm # time mean
        ncfldzmdt[casename] = fldzm # zonal mean  (w/ time dim)

        # try this: add coords # @@@@@@@@@@
        lat,lon = load_nclatlon(field,last=last,includeyr1=includeyr1,verb=verb,local=local)
        ncflddt['lat'] = lat; ncflddt['lon'] = lon;
        try:
            lev = load_nclev(field,last=last,includeyr1=includeyr1,verb=verb)
            ncflddt['lev'] = lev
        except:
            print "No lev coord. Leave it"
        
        ncfldzmdt['lat'] = lat
        
    return ncflddt, ncfldzmdt