def test1(self):
# Check conversions do not change original object
c = unit.Unit('deg_c')
f = unit.Unit('deg_f')
orig = np.arange(3, dtype=np.float32)
converted = c.convert(orig, f)
with self.assertRaises(AssertionError):
np.testing.assert_array_equal(orig, converted)
def ConvertHoursSince1970ToDatetime(hours_since):
""" Takes numpy array of 'hours since 1970' and string format
and returns numpy array of date strings"""
u = unit.Unit('hours since 1970-01-01 00:00:00',calendar='gregorian')
return np.array([u.num2date(da) for da in np.array(hours_since)])
degs_crop_top = 1.7
degs_crop_bottom = 2.5
min_contour = 0
max_contour = 3
tick_interval=0.3
figprops = dict(figsize=(8,8), dpi=360)
clevs = np.linspace(min_contour, max_contour,256)
cmap=cm.s3pcpn_l
ticks = (np.arange(min_contour, max_contour+tick_interval,tick_interval))
u = unit.Unit('hours since 1970-01-01 00:00:00',calendar='gregorian')
dx, dy = 10, 10
divisor=10 # for lat/lon rounding
def main():
#experiment_ids = ['djznw', 'djzny', 'djznq', 'djzns', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkbhu', 'djznu', 'dkhgu' ] # All 12
#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', 'djznu', 'dkbhu', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkhgu']
experiment_ids = ['dklzq', 'dkmbq', 'dkjxq', 'dklwu', 'dklyu', 'djzns']
#experiment_ids = ['djzns' ]
#experiment_ids = ['dkhgu','dkjxq']
for experiment_id in experiment_ids:
import numpy as np
import h5py
import datetime
#experiment_ids = ['djzny','djznq', 'djznw']
experiment_ids = ['dklyu', 'dkmbq', 'dklwu', 'dklzq']
#experiment_ids = ['djznq', 'djzny', 'djznw', 'djzns', 'dkbhu','dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq' ]
data_to_mean = ['temp', 'sp_hum']
dset = ['t_on_p', 'sh_on_p']
import iris.unit as unit
# There is some data missing from some of the runs, so cutting out first and last days. Might need more for some, like 8km explicit.
u = unit.Unit('hours since 1970-01-01 00:00:00',
calendar=unit.CALENDAR_STANDARD)
date_min = u.date2num(datetime.datetime(2011, 8, 19))
date_max = u.date2num(datetime.datetime(2011, 9, 7))
for experiment_id in experiment_ids:
print experiment_id
expmin1 = experiment_id[:-1]
time_file_g = '/nfs/a90/eepdw/Model_Run_Time_Lists/djznw_time_list'
with h5py.File(time_file_g, 'r') as i:
dates_global = i['tstamps'][:]
time_file = '/nfs/a90/eepdw/Model_Run_Time_Lists/%s_time_list' % experiment_id
with h5py.File(time_file, 'r') as i:
dates = i['tstamps'][:]
#date_mask = np.ma.masked_outside(dates.flatten(), date_min,date_max)
def main():
save_path = '/nfs/a90/eepdw/Figures/EMBRACE/'
model_name_convert_title = imp.load_source(
'util',
'/nfs/see-fs-01_users/eepdw/python_scripts/modules/model_name_convert_title.py'
)
model_name_convert_legend = imp.load_source(
'util',
'/nfs/see-fs-01_users/eepdw/python_scripts/modules/model_name_convert_legend.py'
)
unrotate = imp.load_source(
'util',
'/nfs/see-fs-01_users/eepdw/python_scripts/modules/unrotate_pole.py')
pp_file_contourf = 'tcwv_mean'
pp_file_contour = '408_on_p_levs_mean'
plot_diag = 'TCWV'
#plot_diags=['sp_hum']
cb_label = 'mm'
min_contour = -5.
max_contour = 5.
tick_interval = 1.
plot_levels = [925]
#experiment_ids = ['dkmbq', 'dklyu']
experiment_ids = ['djzny', 'djznw', 'djznq', 'djzns', 'dklwu',
'dklzq'] # All minus large 2
#Experiment_ids = ['djzny', 'djznq', 'djzns', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkbhu', 'djznu', 'dkhgu' ] # All 12
#experiment_ids = ['djzny', 'djznq', 'djzns', 'dkjxq', 'dklwu', 'dklzq', 'dkbhu',] # All 12
#experiment_ids = ['dkbhu', 'dkjxq']
#experiment_ids = ['dkmbq', 'dklyu', 'djznw', 'djzny', 'djznq', 'djzns', 'dklwu', 'dklzq'] # All minus large 2
#experiment_ids = ['dklyu, dkmgw']
experiment_ids = ['dklyu']
#experiment_ids = ['dklyu']
diff_id = 'dkmbq'
#min_contour = 0
#max_contour = 3
#tick_interval=0.3
clevs = np.linspace(min_contour, max_contour, 16)
#cmap=cm.s3pcpn_l
cmap = plt.cm.RdBu
#ticks = (np.arange(min_contour, max_contour+tick_interval,tick_interval))
pp_file_path = '/nfs/a90/eepdw/Data/EMBRACE/'
degs_crop_top = 1.7
degs_crop_bottom = 2.5
from iris.coord_categorisation import add_categorised_coord
# def add_hour_of_day(cube, coord, name='hour'):
# add_categorised_coord(cube, name, coord,
# lambda coord, x: coord.units.num2date(x).hour)
figprops = dict(figsize=(8, 8), dpi=100)
#cmap=cm.s3pcpn_l
un = unit.Unit('hours since 1970-01-01 00:00:00', calendar='gregorian')
dx, dy = 10, 10
divisor = 10 # for lat/lon rounding
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
for p_level in plot_levels:
# Set pressure height contour min/max
if p_level == 925:
clev_min = -72.
clev_max = 72.
elif p_level == 850:
clev_min = -72.
clev_max = 72.
elif p_level == 700:
clev_min = -72.
clev_max = 72.
elif p_level == 500:
clev_min = -72.
clev_max = 72.
else:
print 'Contour min/max not set for this pressure level'
# Set potential temperature min/max
if p_level == 925:
clevpt_min = -3.
clevpt_max = 3.
elif p_level == 850:
clevpt_min = -3.
clevpt_max = 3.
elif p_level == 700:
clevpt_min = -3.
clevpt_max = 3.
elif p_level == 500:
clevpt_min = -3.
clevpt_max = 3.
else:
print 'Potential temperature min/max not set for this pressure level'
# Set specific humidity min/max
if p_level == 925:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p_level == 850:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p_level == 700:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p_level == 500:
clevsh_min = -0.0025
clevsh_max = 0.0025
else:
print 'Specific humidity min/max not set for this pressure level'
#clevs_col = np.arange(clev_min, clev_max)
clevs_lin = np.arange(clev_min, clev_max, 4.)
p_level_constraint = iris.Constraint(pressure=p_level)
#for plot_diag in plot_diags:
for experiment_id in experiment_ids:
expmin1 = experiment_id[:-1]
diffmin1 = diff_id[:-1]
pfile = '/nfs/a90/eepdw/Data/EMBRACE/%s/%s/%s_%s.pp' % (
expmin1, experiment_id, experiment_id, pp_file_contourf)
pfile_diff = '/nfs/a90/eepdw/Data/EMBRACE/%s/%s/%s_%s.pp' % (
diffmin1, diff_id, diff_id, pp_file_contourf)
#pcube_contourf = iris.load_cube(pfile, p_level_constraint)
pcube_contourf = iris.load_cube(pfile)
#pcube_contourf=iris.analysis.maths.multiply(pcube_contourf,3600)
pcube_contourf_diff = iris.load_cube(pfile_diff)
#pcube_contourf_diff=iris.analysis.maths.multiply(pcube_contourf_diff,3600)
#pdb.set_trace()
height_pp_file = '%s_%s.pp' % (experiment_id, pp_file_contour)
height_pfile = '%s%s/%s/%s' % (pp_file_path, expmin1,
experiment_id, height_pp_file)
height_pp_file_diff = '%s_%s.pp' % (diff_id, pp_file_contour)
height_pfile_diff = '%s%s/%s/%s' % (pp_file_path, diffmin1,
diff_id, height_pp_file_diff)
pcube_contour = iris.load_cube(height_pfile, p_level_constraint)
pcube_contour_diff = iris.load_cube(height_pfile_diff,
p_level_constraint)
#pdb.set_trace()
pcube_contourf = pcube_contourf - pcube_contourf_diff
pcube_contour = pcube_contour - pcube_contour_diff
del pcube_contourf_diff, pcube_contour_diff
#pdb.set_trace()
#time_coords = pcube_contourf.coord('time')
#iris.coord_categorisation.add_day_of_year(pcube_contourf, time_coords, name='day_of_year')
#time_coords = pcube_contour.coord('time')
#iris.coord_categorisation.add_day_of_year(pcube_contour, time_coords, name='day_of_year')
fu = '/nfs/a90/eepdw/Data/EMBRACE/%s/%s/%s_30201_mean.pp' \
% (expmin1, experiment_id, experiment_id)
fu_diff = '/nfs/a90/eepdw/Data/EMBRACE/%s/%s/%s_30201_mean.pp' \
% (diffmin1, diff_id, diff_id)
#pdb.set_trace()
u_wind, v_wind = iris.load(fu, p_level_constraint)
u_wind_diff, v_wind_diff = iris.load(fu_diff, p_level_constraint)
u_wind = u_wind - u_wind_diff
v_wind = v_wind - v_wind_diff
del u_wind_diff, v_wind_diff
for t, time_cube in enumerate(
pcube_contourf.slices(['grid_latitude',
'grid_longitude'])):
#pdb.set_trace()
#height_cube_slice = pcube_contour.extract(iris.Constraint(day_of_year=time_cube.coord('day_of_year').points))
height_cube_slice = pcube_contour
u_wind_slice = u_wind
v_wind_slice = v_wind
#pdb.set_trace()
# Get time of averagesfor plot title
#h = un.num2date(np.array(time_cube.coord('time').points, dtype=float)[0]).strftime('%d%b')
#Convert to India time
# from_zone = tz.gettz('UTC')
# to_zone = tz.gettz('Asia/Kolkata')
# h_utc = un.num2date(np.array(time_cube.coord('day_of_year').points, dtype=float)[0]).replace(tzinfo=from_zone)
# h_local = h_utc.astimezone(to_zone).strftime('%H%M')
### Winds
cs_w = u_wind_slice.coord_system('CoordSystem')
lat_w = u_wind_slice.coord('grid_latitude').points
lon_w = u_wind_slice.coord('grid_longitude').points
lons_w, lats_w = np.meshgrid(lon_w, lat_w)
lons_w, lats_w = iris.analysis.cartography.unrotate_pole(
lons_w, lats_w, cs_w.grid_north_pole_longitude,
cs_w.grid_north_pole_latitude)
lon_w = lons_w[0]
lat_w = lats_w[:, 0]
### Regrid winds to 2 degree spacing
lat_wind_1deg = np.arange(lat_low, lat_high, 2)
lon_wind_1deg = np.arange(lon_low, lon_high, 2)
#pdb.set_trace()
lons_wi, lats_wi = np.meshgrid(lon_wind_1deg, lat_wind_1deg)
fl_la_lo = (lats_w.flatten(), lons_w.flatten())
p_levs = u_wind_slice.coord('pressure').points
sc = np.searchsorted(p_levs, p_level)
u = scipy.interpolate.griddata(fl_la_lo,
u_wind_slice.data.flatten(),
(lats_wi, lons_wi),
method='linear')
v = scipy.interpolate.griddata(fl_la_lo,
v_wind_slice.data.flatten(),
(lats_wi, lons_wi),
method='linear')
################################### # PLOT ##############################################
fig = plt.figure(**figprops)
#cmap=plt.cm.RdBu_r
ax = plt.axes(projection=ccrs.PlateCarree(),
extent=(lon_low, lon_high,
lat_low + degs_crop_bottom,
lat_high - degs_crop_top))
m =\
Basemap(llcrnrlon=lon_low,llcrnrlat=lat_low,urcrnrlon=lon_high,urcrnrlat=lat_high, rsphere = 6371229)
#pdb.set_trace()
# lat = pcube_contourf.coord('grid_latitude').points
# lon = pcube_contourf.coord('grid_longitude').points
# cs = cube.coord_system('CoordSystem')
# lons, lats = np.meshgrid(lon, lat)
# lons, lats = iris.analysis.cartography.unrotate_pole\
# (lons,lats, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
# x,y = m(lons,lats)
#x_w,y_w = m(lons_wi, lats_wi)
if plot_diag == 'temp':
min_contour = clevpt_min
max_contour = clevpt_max
cb_label = 'K'
main_title = '8km Explicit model (dklyu) minus 8km parametrised model geopotential height (grey contours), potential temperature (colours),\
and wind (vectors)'
tick_interval = 2
clev_number = max_contour - min_contour + 1
elif plot_diag == 'sp_hum':
min_contour = clevsh_min
max_contour = clevsh_max
cb_label = 'kg/kg'
main_title = '8km Explicit model (dklyu) minus 8km parametrised model geopotential height (grey contours), specific humidity (colours),\
and wind (vectors)'
tick_interval = 0.002
clev_number = max_contour - min_contour + 0.001
lat = time_cube.coord('grid_latitude').points
lon = time_cube.coord('grid_longitude').points
lons, lats = np.meshgrid(lon, lat)
cs = time_cube.coord_system('CoordSystem')
lons, lats = iris.analysis.cartography.unrotate_pole(
lons, lats, cs.grid_north_pole_longitude,
cs.grid_north_pole_latitude)
cont = plt.contourf(lons,
lats,
time_cube.data * -0.01,
clevs,
cmap=cmap,
extend='both')
#cont = plt.contourf(lons, lats, time_cube.data)
#pdb.set_trace()
cs_lin = plt.contour(lons,
lats,
height_cube_slice.data,
clevs_lin,
colors='#262626',
linewidths=1.)
plt.clabel(cs_lin, fontsize=14, fmt='%d', color='black')
#del time_cube
#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'}
x_w, y_w = m(lons_wi, lats_wi)
wind = m.quiver(x_w, y_w, u, v, scale=75, color='#262626')
qk = plt.quiverkey(wind, 0.1, 0.1, 1, '1 m/s', labelpos='W')
cbar = fig.colorbar(cont,
orientation='horizontal',
pad=0.05,
extend='both')
cbar.set_label('%s' % cb_label, fontsize=10, color='#262626')
# cbar.set_label(time_cube.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=10, color='#262626')
#main_title='Mean Rainfall for EMBRACE Period -%s UTC (%s IST)' % (h, h_local)
#main_title=time_cube.standard_name.title().replace('_',' ')
#model_info = re.sub(r'[(\']', ' ', model_info)
#model_info = re.sub(r'[\',)]', ' ', model_info)
#print model_info
#file_save_name = '%s_%s_and_%s_%s_hPa_geop_height_and_wind' \
# % (experiment_id, pp_file_contour, pp_file_contourf, p_level)
file_save_name = '%s_minus_%s_%s_and_%s_%s_hPa_geop_height_and_wind' \
% (experiment_id, diff_id, pp_file_contour, pp_file_contourf, p_level)
save_dir = '%s%s/%s_and_%s' % (save_path, experiment_id,
pp_file_contour,
pp_file_contourf)
if not os.path.exists('%s' % save_dir):
os.makedirs('%s' % (save_dir))
#plt.show()
plt.title('%s - %s minus %s' %
(plot_diag,
str(model_name_convert_legend.main(experiment_id)),
str(model_name_convert_legend.main(diff_id))))
#plt.title('%s - %s' % (plot_diag, str(model_name_convert_legend.main(experiment_id))))
fig.savefig('%s/%s.png' % (save_dir, file_save_name),
format='png',
bbox_inches='tight')
#fig.savefig('%s/%s_short_title.png' % (save_dir, file_save_name) , format='png', bbox_inches='tight')
#plt.show()
#model_info=re.sub('(.{68} )', '\\1\n', str(model_name_convert_title.main(experiment_id)), 0, re.DOTALL)
#plt.title('\n'.join(wrap('%s\n%s' % (main_title, model_info), 1000,replace_whitespace=False)), fontsize=16)
#fig.savefig('%s/%s_%s.png' % (save_dir, file_save_name), format='png', bbox_inches='tight')
fig.clf()
plt.close()
#del time_cube
gc.collect()
