#experiment_ids = ['djzny', 'djzns', 'djznw', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq' ]

experiment_ids = ['djzny', 'djzns', 'djznu', 'dkbhu', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkhgu']

#experiment_ids = ['djzns' ]

#experiment_ids = ['dkhgu','dkjxq']

for experiment_id in experiment_ids:

expmin1 = experiment_id[:-1]

pfile = '/projects/cascade/pwille/moose_retrievals/%s/%s/%s.pp' % (expmin1, experiment_id, pp_file)

#pc = iris(pfile)

pcube = iris.load_cube(pfile)

print pcube

#print pc

# Get min and max latitude/longitude and unrotate to get min/max corners to crop plot automatically - otherwise end with blank bits on the edges

lats = pcube.coord('grid_latitude').points

lons = pcube.coord('grid_longitude').points

cs = pcube.coord_system('CoordSystem')

if isinstance(cs, iris.coord_systems.RotatedGeogCS):

print 'Rotated CS %s' % cs

lon_low= np.min(lons)

lon_high = np.max(lons)

lat_low = np.min(lats)

lat_high = np.max(lats)

lon_corners, lat_corners = np.meshgrid((lon_low, lon_high), (lat_low, lat_high))

lon_corner_u,lat_corner_u = unrotate.unrotate_pole(lon_corners, lat_corners, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)

lon_low = lon_corner_u[0,0]

lon_high = lon_corner_u[0,1]

lat_low = lat_corner_u[0,0]

lat_high = lat_corner_u[1,0]

else:

lon_low= np.min(lons)

lon_high = np.max(lons)

lat_low = np.min(lats)

lat_high = np.max(lats)

#lon_low= 62

#lon_high = 102

#lat_low = -7

#lat_high = 33

#lon_high_box = 101.866

#lon_low_box = 64.115

#lat_high_box = 33.

#lat_low_box =-6.79

#lon_high = 101.866

#lon_low = 64.115

#lat_high = 33.

#lat_low =-6.79

lon_low_tick=lon_low -(lon_low%divisor)

lon_high_tick=math.ceil(lon_high/divisor)*divisor

lat_low_tick=lat_low - (lat_low%divisor)

lat_high_tick=math.ceil(lat_high/divisor)*divisor

print lat_high_tick

print lat_low_tick

plt.figure(figsize=(8,8))

cmap=cm.s3pcpn_l

ax = plt.axes(projection=ccrs.PlateCarree(), extent=(lon_low,lon_high,lat_low+degs_crop_bottom,lat_high-degs_crop_top))

#ax = plt.axes(projection=ccrs.PlateCarree(), extent=(lon_low,lon_high,lat_low,lat_high))

#ax = plt.axes(projection=ccrs.PlateCarree())

clevs = np.linspace(min_contour, max_contour,256)

pcubeplot=iris.analysis.maths.multiply(pcube,3600)

cont = iplt.contourf(pcubeplot, clevs, cmap=cmap, extend='both')

#plt.clabel(cont, fmt='%d')

#ax.stock_img()

ax.coastlines(resolution='110m', color='#262626')

gl = ax.gridlines(draw_labels=True,linewidth=0.5, color='#262626', alpha=0.5, linestyle='--')

gl.xlabels_top = False

gl.ylabels_right = False

#gl.xlines = False

dx, dy = 10, 10

gl.xlocator = mticker.FixedLocator(range(int(lon_low_tick),int(lon_high_tick)+dx,dx))

gl.ylocator = mticker.FixedLocator(range(int(lat_low_tick),int(lat_high_tick)+dy,dy))

gl.xformatter = LONGITUDE_FORMATTER

gl.yformatter = LATITUDE_FORMATTER

gl.xlabel_style = {'size': 12, 'color':'#262626'}

#gl.xlabel_style = {'color': '#262626', 'weight': 'bold'}

gl.ylabel_style = {'size': 12, 'color':'#262626'}

cbar = plt.colorbar(cont, orientation='horizontal', pad=0.05, extend='both')

cbar.set_label('mm/h', fontsize=14, color='#262626')

#cbar.set_label(pcube.units, fontsize=10, color='#262626')

cbar.set_ticks(np.arange(min_contour, max_contour+tick_interval,tick_interval))

ticks = (np.arange(min_contour, max_contour+tick_interval,tick_interval))

cbar.set_ticklabels(['%.1f' % i for i in ticks])

cbar.ax.tick_params(labelsize=14, color='#262626')

main_title='Mean Rainfall for EMBRACE Period (smoothed to 24km)'

#main_title=pcube.standard_name.title().replace('_',' ')

model_info=re.sub('(.{68} )', '\\1\n', str(model_name_convert_title.main(experiment_id)), 0, re.DOTALL)

#model_info = re.sub(r'[(\']', ' ', model_info)

#model_info = re.sub(r'[\',)]', ' ', model_info)

#print model_info

if not os.path.exists('%s%s/%s' % (save_path, experiment_id, pp_file)): os.makedirs('%s%s/%s' % (save_path, experiment_id, pp_file))

plt.savefig('%s%s/%s/%s_%s_notitle.png' % (save_path, experiment_id, pp_file, experiment_id, pp_file), format='png', bbox_inches='tight')

plt.title('\n'.join(wrap('%s\n%s' % (main_title, model_info), 1000,replace_whitespace=False)), fontsize=16)

#plt.show()

plt.savefig('%s%s/%s/%s_%s.png' % (save_path, experiment_id, pp_file, experiment_id, pp_file), format='png', bbox_inches='tight')