def spatiofreq_noplt(chs, lat, lon, yrs, per='year'):
'''Get grid-cell frequencies for no. of times a grid-cell falls within a
contour describing a feature from metblobs.
spatiofreq_nolt is new version by RJ designed to read in subsets of events
designed for plotting within a multipanel plot
AND to just output the values of "allmask" rather than plotting
so that you can do plotting in the wrapper
& calculate biases
Need to first run SubSet_Events.evset_info to get chs
per is used to determine if it's plotting per year or per CBs in this model
If a subset of month or season is required, input only those cbs'''
allmask = np.zeros((lat.shape[0], lon.shape[0]), dtype=np.float32)
nblobs = len(chs)
print 'Running spatiofreq on ' + str(nblobs) + ' CBs '
for bl in range(nblobs):
this_ch = chs[bl]
mask = my.poly2mask(lon, lat, this_ch)
allmask = allmask + np.float32(mask)
if per == 'year':
std_mask = allmask / len(yrs)
elif per == 'cbs':
std_mask = allmask / nblobs * 100
print 'Dividing by number of blobs'
print nblobs
return std_mask
print 'Selecting rain under TTTs'
masked_rain = np.ma.zeros((nttt4rain, nlat, nlon), dtype=np.float32)
rcnt=0
for rdt in range(nttt):
this_dt=dates_ln[rdt]
ix = my.ixdtimes(ttt_rain_dates, [this_dt[0]], \
[this_dt[1]], [this_dt[2]], [0])
if len(ix) >= 1:
ind=ix[0]
this_dt_rain=ttt_rain_dates[ind]
print 'Checking dates correspond'
print this_dt
print this_dt_rain
print 'Running poly to mask'
chmask = my.poly2mask(rlon, rlat, chs_ln[rdt])
print 'Finished running poly to mask'
r = np.ma.MaskedArray(rainsel[ind, :, :], mask=~chmask)
masked_rain[rcnt, :, :] = r
rcnt+=1
elif under_of=='dayof':
masked_rain=rainsel[:]
# Get a timeseries of mean TTT rain from each event
print 'Getting a rain value for each TTT event'
reg_ttt_sum = np.zeros((nttt4rain), dtype=np.float32)
reg_ttt_mean = np.zeros((nttt4rain), dtype=np.float32)
if weightlats:
latr = np.deg2rad(rlat)
indices = indices
if under_of == 'under':
chs_4rain = np.squeeze(np.asarray(chs_4rain))
nttt = len(indices)
print 'Selecting rain on TTT days'
rainsel = rain[indices, :, :]
ttt_rain_dates = rdtime[indices]
ndt = nttt
if under_of == 'under':
print 'Selecting rain under TTTs'
masked_rain = np.ma.zeros((ndt, nlat, nlon), dtype=np.float32)
for rdt in range(ndt):
chmask = my.poly2mask(rlon, rlat, chs_4rain[rdt])
r = np.ma.MaskedArray(rainsel[rdt, :, :], mask=~chmask)
masked_rain[rdt, :, :] = r
elif under_of=='dayof':
masked_rain=rainsel[:]
# Get a timeseries of mean TTT rain from each event
print 'Getting a rain value for each TTT event'
reg_ttt_sum = np.zeros((len(ttt_rain_dates)), dtype=np.float32)
reg_ttt_mean = np.zeros((len(ttt_rain_dates)), dtype=np.float32)
for st in range(len(ttt_rain_dates)):
reg_ttt_mean[st] = np.ma.mean(masked_rain[st,:,:])
# Getting a long term sum or mean
tottttrain=np.nansum(reg_ttt_mean)
def spatiofreq6(m,chs,modname,lat,lon,yrs,per='year',\
clim=(4,36,4),savefig=False,\
col='col',cbar='ind',title=''):
'''Get grid-cell frequencies for no. of times a grid-cell falls within a
contour describing a feature from metblobs.
spatiofreq6 is new version by RJ designed to read in subsets of events
designed for plotting within a multipanel plot
Need to first run SubSet_Events.evset_info to get chs
per is used to determine if it's plotting per year or per CBs in this model
If a subset of month or season is required, input only those cbs
USAGE: if cbar='ind' will make it for just this plot
if cbar='set' will make it at right assuming multipanel
title can be string or variable e.g. modname '''
allmask = np.zeros((lat.shape[0], lon.shape[0]), dtype=np.float32)
nblobs = len(chs)
print 'Running spatiofreq on ' + str(nblobs) + ' CBs '
for bl in range(nblobs):
this_ch = chs[bl]
mask = my.poly2mask(lon, lat, this_ch)
allmask = allmask + np.float32(mask)
if col == 'col':
cm = plt.cm.magma
elif col == 'bw':
cm = plt.cm.gist_gray_r
if per == 'year':
std_mask = allmask / len(yrs)
elif per == 'cbs':
std_mask = allmask / nblobs * 100
print 'Dividing by number of blobs'
print nblobs
## NEED TO DO THIS SINCE PCOLOR IS NOT SHADING VALUES OUTSIDE OF THE CLIMS
cstd_mask = np.where(std_mask > clim[1], clim[1], std_mask)
cstd_mask = np.where(cstd_mask < clim[0], clim[0], cstd_mask)
# Plot pcolor
pcolmap = m.pcolormesh(lon, lat, cstd_mask, cmap=cm, zorder=1)
img = plt.gci() # gets a reference for the image
plt.clim(clim[0], clim[1]) # sets color limits of current image
bounds = np.arange(clim[0], clim[1] + clim[2], clim[2])
if savefig:
f, ax = plt.gcf(), plt.gca()
axcol = f.add_axes([0.93, 0.2, 0.02, 0.6])
plt.colorbar(mappable=img, cax=axcol, boundaries=bounds)
my.ytickfonts()
plt.ylabel('grid-point count / year', fontdict=fd)
plt.axes(ax)
fname = '/FootprintFreqencygray-' + modname + '.png'
plt.savefig(fname, dpi=150)
else:
f, ax = plt.gcf(), plt.gca() # get reference and set axes
if cbar == 'set':
axcol = f.add_axes([0.91, 0.15, 0.01, 0.6])
plt.colorbar(cax=axcol, boundaries=bounds)
elif cbar == 'ind':
plt.colorbar()
my.ytickfonts(fontsize=10, fontweight='demibold')
if per == 'year':
if cbar == 'set':
plt.ylabel('grid-point count / year', fontsize=10)
elif per == 'cbs':
if cbar == 'set':
plt.ylabel('% of cbs covering gridbox', fontsize=10)
plt.axes(ax)
plt.title(title, fontsize=8, fontweight='demibold')
return std_mask, img
smp_edts = smp_edts[keyinds]
smp_degs = smp_degs[keyinds]
smp_mons = smp_mons[keyinds]
smp_chs = smp_chs[keyinds]
smp_ekeys = smp_ekeys[keyinds]
smp_olrmns = smp_olrmns[keyinds]
print 'Printing data for OLR means stored in mbs files'
print smp_olrmns
# then use the outline to cut out data from sample
masked_var=np.ma.zeros((nsamp,this_nlat,this_nlon),dtype=np.float32)
cbmeans=np.zeros(nsamp,dtype=np.float32)
for rdt in range(nsamp):
chmask = my.poly2mask(lon, lat, smp_chs[rdt])
r = np.ma.MaskedArray(chosedata[rdt, :, :], mask=~chmask)
masked_var[rdt, :, :] = r
cbmeans[rdt]= np.ma.mean(r)
print 'Printing data for OLR means calculated here'
print cbmeans
if which_olr=='calc':
new_cbmeans=cbmeans
elif which_olr=='store':
new_cbmeans=smp_olrmns
# Adjust if thresh adjustment
if th_offset:
new_cbmeans=new_cbmeans+thdiff
for edt in range(len(edts)):
ix = my.ixdtimes(smpdtime, [edts[edt][0]], \
[edts[edt][1]], [edts[edt][2]], [0])
if len(ix) >= 1:
date_check.append(edts[edt])
chs_smpl.append(chs[edt])
date_check = np.squeeze(np.asarray(date_check))
chs_smpl = np.squeeze(np.asarray(chs_smpl))
# then use the outline to cut out data from sample
masked_var = np.ma.zeros(
(nsamp, this_nlat, this_nlon),
dtype=np.float32)
cbmeans = np.zeros(nsamp, dtype=np.float32)
for rdt in range(nsamp):
chmask = my.poly2mask(lon, lat, chs_smpl[rdt])
r = np.ma.MaskedArray(chosedata[rdt, :, :],
mask=~chmask)
masked_var[rdt, :, :] = r
cbmeans[rdt] = np.ma.mean(r)
# Collect this model's data for anova
collect[cnt - 1] = cbmeans
print collect[cnt - 1]
# Make boxplot
plt.boxplot(cbmeans,
positions=[cnt],
notch=0,
sym='+',
vert=1,
