Anomalien

Code

Importing

import matplotlib
matplotlib.use('Agg')

import numpy as N
import matplotlib.pyplot as P
from scipy import stats as S
from mpl_toolkits.basemap import Basemap
import statsmodels.api as sm
from netCDF4 import Dataset,num2date
import shapefile as shp
import matplotlib.path as mpath
import matplotlib.patches as mpatches
from sklearn.preprocessing import scale
from matplotlib.offsetbox import AnchoredText

P.style.use('bmh')

P.rcParams["font.family"] = "serif"

def ma(a,n=3):
    ret=N.cumsum(a,dtype=float)
    ret[n:]=ret[n:]-ret[:-n]
    return ret[n-1:]/n

Plotting

mods = ['obs-dwd','cnr-clm','ece-clm','had-clm','mpi-clm','mpi-rca','cnr-rca','nor-rca','ips-rca','ece-rca','had-rca','cnr-hir','had-hir','ece-hir','nor-hir','cnr-rac','had-rac','ece-rac']

jo = N.arange(1971,2100,1)
nj = len(jo)
nd = 365

P.figure(figsize=(6,5))

for mod in mods:

    print (mod)

    file = '../data/csv/rcp85/%s/eastbasz.txt'%mod

    dat = N.genfromtxt(file,names=True,dtype=None)

    tmit = N.zeros((nj,nd),float);tmit[:,:] = N.nan

    for j in range(nj):

        id = N.where(dat['jahr']==jo[j])[0]

        if(len(id)>0):

            id = id[0:nd]

            tmit[j,:] = dat['tas'][id]

    basl = N.mean(tmit[0:40,:],0)

    for j in [1,2,3,4,5]:

        if(mod=='obs-dwd'):

            anom = ma(N.ravel(tmit[0:40,:]-basl),5)

            pos = len(N.where(anom>=j)[0])/40.
            neg = len(N.where(anom<=-1*j)[0])/40.

            P.scatter(pos,neg,s=50,c='None',ec='k')
            P.text(pos,neg,'%i K'%j,fontsize=16,weight='bold')

        else:

            anom = ma(N.ravel(tmit[0:40,:]-basl),5)

            pos = len(N.where(anom>=j)[0])/40.
            neg = len(N.where(anom<=-1*j)[0])/40.

            P.scatter(pos,neg,s=50,c='None',ec='y')

            anom = ma(N.ravel(tmit[40:80,:]-basl),5)

            pos = len(N.where(anom>=j)[0])/40.
            neg = len(N.where(anom<=-1*j)[0])/40.

            P.scatter(pos,neg,s=50,c='None',ec='orange')

            anom = ma(N.ravel(tmit[80:120,:]-basl),5)

            pos = len(N.where(anom>=j)[0])/40.
            neg = len(N.where(anom<=-1*j)[0])/40.

            P.scatter(pos,neg,s=50,c='None',ec='r')

P.xlim(0,350)
P.ylim(0,150)

P.xlabel('Tage mit positiven Temperaturanomalien',fontsize=12,weight='bold')
P.ylabel('Tage mit negativen Temperaturanomalien',fontsize=10,weight='bold')

P.tick_params(direction='out')
P.tight_layout()

P.savefig('./img/anomalien.png',dpi=240,bbox_inches='tight')