def jet(dat_dir=king_dat,strength='jsi',Atlantic=True,start=1957,end=2001,a=305,b=90,compress=False,select=True,mnmx='SCmax'): # want to select high/low solar if select == True: from solar import years yrs = years()[mnmx] # call data in from open import open_pkl strength = strength Atlantic = True if Atlantic == True: sea = 'Atlantic' elif Atlantic == False: sea = 'Pacific' # use import pickle extractor database = open_pkl(dat_dir,'era40.year'+str(start)+'-'+str(end)+'.month11-15.'+str(strength)+'.JI_001.'+str(sea)+'.pkl') data = database[str(database['info']['name'])] data.shape = data.shape[-1] # compress days into months, first get the days database days = database['time']['day_of_year'] # firstly create a loop for specific number of years # identify empty data array jdat = np.array([]) # empty arrays jet = np.array([]) tmp = np.array([]) # counters i,dat=1,0 # check if first year is leap year year = start last = b if year % 4 == 0: if year % 100 == 0 and year % 400 != 0: last = b+1 # loop over days sum = 0 for day in days: tmp = np.append(tmp,data[dat]) if i == len(days): # final append before breaking if compress == True: jet = np.append(jet,np.mean(tmp)) else: if year in yrs: jet = np.append(jet,tmp) tmp = np.array([]) break if day == last: #once the winter end has been reached append this data sum += len(tmp) if compress == True: jet = np.append(jet,np.mean(tmp)) else: if year in yrs: jet = np.append(jet,tmp) tmp = np.array([]) # check if next year will be leap year year += 1 last = b if (year+1) % 4 == 0: if (year+1) % 100 == 0 and (year+1) % 400 != 0: pass else: last = b+1 i += 1 dat += 1 return jet
if arr != True
from open import open_pkl
#read in x-y data
opt = []
data_xy = open_pkl(king_dat,'era40.gga'+v+'.year-2002.month-01.b.'+type+'_003.duration_ge_5_day.pkl')
Lon,Lat = data_xy['lon']['lon'],data_xy['lat']['lat']
Lon = np.append(Lon,360+Lon[0])
#print Lon
X,Y = meshgrid(Lon,Lat)
opt.append(X)
opt.append(Y)
# #read in data
from open import stdata
all_data = stdata('[DIR]/era40_blocking_thpv2.list',directory='/media/jonathan/KINGSTON/blocking/data/pkl_files/blocking/',monthly='thpv2')
from solar import years
yrs = np.array(years()['SCmin'])-1957
if high == True:
yrs = np.array(years()['SCmax'])-1957
data = np.mean(all_data[yrs],axis=0)
# data = read_list(king+'high_era40_blocking_'+type+'.list',king_dat)
#generate listname
listnm = 'era40_blocking_'+str(type)+'_high_blk'
stype = 'high'
elif high != True:
yrs = np.array(years()['SCmin'])-1957
data = np.mean(all_data[yrs],axis=0)
# data = read_list(king+'low_era40_blocking_'+type+'.list',king_dat)
#generate listname
listnm = 'era40_blocking_'+str(type)+'_low_blk'
stype = 'low'
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]
