Lon = np.append(Lon,360+Lon[0]) X,Y = meshgrid(Lon,Lat) opt.append(X) opt.append(Y) # obtain final value for diff so values overlap a = zeros((len(diff),1)) diff = np.concatenate((diff,a),axis=-1) for lat in range(0, len(diff)): diff[lat][-1] = diff[lat][0] opt.append(diff) if sig == True: try: from sig_test import ttest regions = ttest(test_dat1,test_dat2,tcrit=tcrit,sig_lvl=sig_lvl,diff=mnmx) extra = regions[:,0] extra.shape = (regions[:,0].shape[0],1) regions = np.concatenate((regions,extra),1) except ValueError as err: print "Value Error: " + str(err) if graph == True: # generate output filename sgtype = 'ttest' output = str(output)+'_blk_'+str(typ)+'_'+str(sgtype)+'_'+str(int(100*(1-cutoff)+1)) from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt # use low resolution coastlines.
def plots(SC='SCmax',compress=False,select=True,two_hist=True,wrttime=False,hist=False,strength='jsi',start=1957,end='2001',sig=False,sea='Atlantic'):
# get data
data = jet(strength=strength)
# plot data wrt time to compare to blocking cycle
import matplotlib.pyplot as plt
from solar import years
yrs = np.array(years()[SC])-start
all_yrs = np.array(range(1957,2002))-start
if wrttime == True:
fig = plt.figure()
x = all_yrs+start#linspace(0, 5, 10)
y = data
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)
axes.plot(x, y, 'r')
axes.set_xlabel('time (winters)')
axes.set_ylabel(strength)
axes.set_title('Jet '+strength+' wrt time');
fig.savefig(king_gra+'jet_'+strength+'_wrt_time')
if two_hist == True:
if select == False:
from open import open_pkl
database = open_pkl(king_dat,'era40.year'+str(start)+'-'+str(end)+'.month11-15.jsi.JI_001.'+str(sea)+'.pkl')
sdata = database[str(database['info']['name'])]
sdata.shape = sdata.shape[-1]
database = open_pkl(king_dat,'era40.year'+str(start)+'-'+str(end)+'.month11-15.jli.JI_001.'+str(sea)+'.pkl')
ldata = database[str(database['info']['name'])]
ldata.shape = ldata.shape[-1]
elif compress == False:
sdata = jet(strength='jsi',mnmx=SC)
ldata = jet(strength='jli',mnmx=SC)
else:
sdata = jet(strength='jsi',compress=True)
ldata = jet(strength='jli',compress=True)
from matplotlib.colors import LogNorm
fig = plt.figure()
# plt.subplots
# plt.scatter(ldata,sdata)
plt.hist2d(ldata,sdata, bins=40, cmap='Greens', norm=LogNorm())
ax = plt.gca()
ax.set_xlabel('jli')
ax.set_ylabel('jsi')
plt.title("Jet jsi v jli for "+SC)
plt.colorbar()
# plt.xlim(-15, 15)
# plt.ylim(-15, 15)
plt.show()
fig.savefig(king_gra+'jet_jsi_v_jli_'+SC)
if hist == True:
# plot a histogram of values based on high/low solar
max = np.array(years()['SCmax'])-start
min = np.array(years()['SCmin'])-start
# years()
# # print years
# print data[yrs]
# print np.mean(data[yrs]),np.mean(data[max]),np.mean(data[min])
plt.subplots()
plt.hist(data[all_yrs],20)
plt.subplots()
plt.hist(data[max],20)
plt.subplots()
plt.hist(data[min],20)
#try a ttest for the means
if sig == True:
import sig_test
data.shape = (data.shape[-1],1,1)
print sig_test.ttest(data,data[min],tcrit=1.34)[0][0]
