def phenomenon_bounds(self, time_unit):
"""
Return the phenomenon time bound offsets from the epoch time reference
measured in the appropriate time units.
"""
# TODO #576 Investigate when it's valid to get phenomenon_bounds
time_reference = "%s since epoch" % time_unit
unit = iris.unit.Unit(time_reference, iris.unit.CALENDAR_GREGORIAN)
return [unit.date2num(self._periodStartDateTime), unit.date2num(self._periodEndDateTime)]
def phenomenon_bounds(self, time_unit):
"""
Return the phenomenon time bound offsets from the epoch time reference
measured in the appropriate time units.
"""
# TODO #576 Investigate when it's valid to get phenomenon_bounds
time_reference = '%s since epoch' % time_unit
unit = iris.unit.Unit(time_reference, iris.unit.CALENDAR_GREGORIAN)
return [unit.date2num(self._periodStartDateTime),
unit.date2num(self._periodEndDateTime)]
date_ranges=[[datetime.datetime(2011,8,18,0,0,0), datetime.datetime(2011,8,27,0,0,0)],
[datetime.datetime(2011,8,28,0,0,0),datetime.datetime(2011,9,5,0,0,0)]]
experiment_ids = ['dklyu', 'dkmbq', 'dkmgw']
regrid_model='djznw'
regrid_model_min1=regrid_model[:-1]
#def add_hour_of_day(cube, coord, name='hour'):
# add_categorised_coord(cube, name, coord,
# lambda coord, x: coord.units.num2date(x).hour)
dtmindt = datetime.datetime(2011,8,19,0,0,0)
dtmaxdt = datetime.datetime(2011,9,7,23,0,0)
dtmin = unit.date2num(dtmindt, 'hours since 1970-01-01 00:00:00', unit.CALENDAR_STANDARD)
dtmax = unit.date2num(dtmaxdt, 'hours since 1970-01-01 00:00:00', unit.CALENDAR_STANDARD)
time_constraint = iris.Constraint(time= lambda t: dtmin <= t.point <= dtmax)
fr = '%s%s/%s/%s.pp' % (pp_file_path, regrid_model_min1, regrid_model, diag)
fg = '%sdjzn/djznw/%s.pp' % (pp_file_path, diag)
try:
glob_load = iris.load_cube(fg, ('%s' % cube_names[0]) & time_constraint)
except iris.exceptions.ConstraintMismatchError:
glob_load = iris.load_cube(fg, ('%s' % cube_names2[0]) & time_constraint)
## Get time points from global LAM to use as time constraint when loading other runs
time_list = glob_load.coord('time').points
# Some models have radiation diagnostics that are 10s offset from others so checking int values of time
glob_tc = iris.Constraint(time= lambda t: int(t.point) in time_list.astype(int))
#glob_tc = iris.Constraint(time=time_list)
import pdb
import imp
imp.load_source(
'IrisTimeMeanRoutines',
'/nfs/see-fs-01_users/eepdw/python_scripts/modules/IrisTimeMeanFunctions.py'
)
from IrisTimeMeanRoutines import *
wmo_or_icao = 'wmo'
dtmindt = datetime.datetime(2011, 8, 28, 0, 0, 0)
dtmaxdt = datetime.datetime(2011, 9, 2, 0, 0, 0)
dtmin = unit.date2num(dtmindt, 'hours since 1970-01-01 00:00:00',
unit.CALENDAR_STANDARD)
dtmax = unit.date2num(dtmaxdt, 'hours since 1970-01-01 00:00:00',
unit.CALENDAR_STANDARD)
time_constraint = iris.Constraint(time=lambda t: dtmin <= t.point <= dtmax)
header = np.loadtxt('/nfs/a90/eepdw/Data/Midas/GL_Column_Headers.csv',
dtype=str,
delimiter=',')
header = [h.strip(' ') for h in header]
plot_list = np.load('/nfs/a90/eepdw/Data/Midas/station_plot_list_%s.npy' %
wmo_or_icao)
for stat in station_list:
station_info = plot_list[np.where(plot_list == stat)[0]][0]
def test_handmade(self):
# Test xml output of a handmade cube.
data = numpy.array( [ [1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8],
[5, 6, 7, 8, 9] ], dtype=numpy.int32)
cubes = []
# Different types of test
for ll_dtype in [numpy.float32, numpy.int32]:
for rotated in [False, True]:
for forecast_or_time_mean in ["forecast", "time_mean"]:
for TEST_COMPAT_i in xrange(2): # TODO: remove with TEST_COMPAT purge -
# adds two copies of each cube to cube list
# in line with redundant data first option
cube = iris.cube.Cube(data)
cube.attributes['my_attribute'] = 'foobar'
if rotated == False:
pole_pos = coord_systems.GeoPosition(90, 0)
else:
pole_pos = coord_systems.GeoPosition(30, 150)
lonlat_cs = coord_systems.LatLonCS("datum?", "prime_meridian?", pole_pos, "reference_longitude?")
cube.add_dim_coord(coords.DimCoord(numpy.array([-180, -90, 0, 90, 180], dtype=ll_dtype),
'longitude', units='degrees', coord_system=lonlat_cs), 1)
cube.add_dim_coord(coords.DimCoord(numpy.array([-90, -45, 0, 45, 90], dtype=ll_dtype),
'latitude', units='degrees', coord_system=lonlat_cs), 0)
# height
cube.add_aux_coord(coords.AuxCoord(numpy.array([1000], dtype=numpy.int32),
long_name='pressure', units='Pa'))
# phenom
cube.rename("temperature")
cube.units = "K"
# source
cube.add_aux_coord(coords.AuxCoord(points=["itbb"], long_name='source', units="no_unit"))
# forecast dates
if forecast_or_time_mean == "forecast":
unit = iris.unit.Unit('hours since epoch', calendar=iris.unit.CALENDAR_GREGORIAN)
dt = datetime.datetime(2010, 12, 31, 12, 0)
cube.add_aux_coord(coords.AuxCoord(numpy.array([6], dtype=numpy.int32),
standard_name='forecast_period', units='hours'))
cube.add_aux_coord(coords.AuxCoord(numpy.array([unit.date2num(dt)], dtype=numpy.float64),
standard_name='time', units=unit))
# time mean dates
if forecast_or_time_mean == "time_mean":
unit = iris.unit.Unit('hours since epoch', calendar=iris.unit.CALENDAR_GREGORIAN)
dt1 = datetime.datetime(2010, 12, 31, 6, 0)
dt2 = datetime.datetime(2010, 12, 31, 12, 0)
dt_mid = datetime.datetime(2010, 12, 31, 9, 0)
cube.add_aux_coord(coords.AuxCoord(numpy.array([6], dtype=numpy.int32),
standard_name='forecast_period', units='hours'))
cube.add_aux_coord(coords.AuxCoord(numpy.array(unit.date2num(dt_mid), dtype=numpy.float64),
standard_name='time', units=unit,
bounds=numpy.array([unit.date2num(dt1), unit.date2num(dt2)],
dtype=numpy.float64)))
cube.add_cell_method(coords.CellMethod('mean', cube.coord('forecast_period')))
cubes.append(cube)
# Now we've made all sorts of cube, check the xml...
self.assertCML(cubes, ('xml', 'handmade.cml'))
def main():
# Plot diagnostics, model and pressure levels etc. to plot on for looping through
plot_type='mean_masked'
plot_type_h5py_var='mean'
plot_diags=['temp', 'sp_hum']
plot_levels = [925, 850, 700, 500]
#plot_levels = [925]
experiment_ids = ['djzny', 'djznq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkjxq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq']
#experiment_id = 'djzny'
p_levels = [1000, 950, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10]
dtmindt = datetime.datetime(2011,8,19,0,0,0)
dtmaxdt = datetime.datetime(2011,9,7,23,0,0)
dtmin = unit.date2num(dtmindt, 'hours since 1970-01-01 00:00:00', unit.CALENDAR_STANDARD)
dtmax = unit.date2num(dtmaxdt, 'hours since 1970-01-01 00:00:00', unit.CALENDAR_STANDARD)
time_constraint = iris.Constraint(time= lambda t: dtmin <= t.point <= dtmax)
###### Unrotate global model data ##############################
######### Regrid to global, and difference #######
############################################################################
## Load global wind and orography
fw_global = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/djzn/djznw/30201_mean.pp'
fo_global = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/djzn/djznw/33.pp'
u_global,v_global = iris.load(fw_global)
print u_global
print v_global
oro_global = iris.load_cube(fo_global)
# Unrotate global coordinates
cs_glob = u_global.coord_system('CoordSystem')
cs_glob_v = v_global.coord_system('CoordSystem')
cs_glob_oro = oro_global.coord_system('CoordSystem')
lat_g = u_global.coord('grid_latitude').points
lon_g = u_global.coord('grid_longitude').points
lat_g_oro = oro_global.coord('grid_latitude').points
lon_g_oro = oro_global.coord('grid_longitude').points
if cs_glob!=cs_glob_v:
print 'Global model u and v winds have different poles of rotation'
# Unrotate global winds
if isinstance(cs_glob, iris.coord_systems.RotatedGeogCS):
print ' Global Model - Winds - djznw - Unrotate pole %s' % cs_glob
lons_g, lats_g = np.meshgrid(lon_g, lat_g)
lons_g,lats_g = iris.analysis.cartography.unrotate_pole(lons_g,lats_g, cs_glob.grid_north_pole_longitude, cs_glob.grid_north_pole_latitude)
lon_g=lons_g[0]
lat_g=lats_g[:,0]
for i, coord in enumerate (u_global.coords()):
if coord.standard_name=='grid_latitude':
lat_dim_coord_uglobal = i
if coord.standard_name=='grid_longitude':
lon_dim_coord_uglobal = i
csur_glob=cs_glob.ellipsoid
u_global.remove_coord('grid_latitude')
u_global.remove_coord('grid_longitude')
u_global.add_dim_coord(iris.coords.DimCoord(points=lat_g, standard_name='grid_latitude', units='degrees', coord_system=csur_glob), lat_dim_coord_uglobal)
u_global.add_dim_coord(iris.coords.DimCoord(points=lon_g, standard_name='grid_longitude', units='degrees', coord_system=csur_glob), lon_dim_coord_uglobal)
#print u_global
v_global.remove_coord('grid_latitude')
v_global.remove_coord('grid_longitude')
v_global.add_dim_coord(iris.coords.DimCoord(points=lat_g, standard_name='grid_latitude', units='degrees', coord_system=csur_glob), lat_dim_coord_uglobal)
v_global.add_dim_coord(iris.coords.DimCoord(points=lon_g, standard_name='grid_longitude', units='degrees', coord_system=csur_glob), lon_dim_coord_uglobal)
#print v_global
# Unrotate global model
if isinstance(cs_glob_oro, iris.coord_systems.RotatedGeogCS):
print ' Global Model - Orography - djznw - Unrotate pole %s - Winds and other diagnostics may have different number of grid points' % cs_glob_oro
lons_go, lats_go = np.meshgrid(lon_g_oro, lat_g_oro)
lons_go,lats_go = iris.analysis.cartography.unrotate_pole(lons_go,lats_go, cs_glob_oro.grid_north_pole_longitude, cs_glob_oro.grid_north_pole_latitude)
lon_g_oro=lons_go[0]
lat_g_oro=lats_go[:,0]
for i, coord in enumerate (oro_global.coords()):
if coord.standard_name=='grid_latitude':
lat_dim_coord_og = i
if coord.standard_name=='grid_longitude':
lon_dim_coord_og = i
csur_glob_oro=cs_glob_oro.ellipsoid
oro_global.remove_coord('grid_latitude')
oro_global.remove_coord('grid_longitude')
oro_global.add_dim_coord(iris.coords.DimCoord(points=lat_g_oro, standard_name='grid_latitude', units='degrees', coord_system=csur_glob_oro), lat_dim_coord_og)
oro_global.add_dim_coord(iris.coords.DimCoord(points=lon_g_oro, standard_name='grid_longitude', units='degrees', coord_system=csur_glob_oro), lon_dim_coord_og)
###############################################################################
#################### Load global heights and temp/sp_hum #####################
f_glob_h = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/408_pressure_levels_interp_pressure_djznw_%s' % (plot_type)
######################################################################################
with h5py.File(f_glob_h, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_heights_global = mh[. . .]
######################################################################################
## Loop through experiment id's ######################################################
for pl in plot_diags:
plot_diag=pl
f_glob_d = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/%s_pressure_levels_interp_djznw_%s' % (plot_diag, plot_type)
with h5py.File(f_glob_d, 'r') as i:
mg = i['%s' % plot_type_h5py_var]
mean_var_global = mg[. . .]
for experiment_id in experiment_ids:
expmin1 = experiment_id[:-1]
###############################################################################
#################### Load model heights and temp/sp_hum #####################
fname_h = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/408_pressure_levels_interp_pressure_%s_%s' % (experiment_id, plot_type)
fname_d = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/%s_pressure_levels_interp_%s_%s' % (plot_diag, experiment_id, plot_type)
# print fname_h
# print fname_d
# Height data file
with h5py.File(fname_h, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_heights = mh[. . .]
# print mean_heights.shape
with h5py.File(fname_d, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_var = mh[. . .]
# print mean_var.shape
f_oro = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/33.pp' % (expmin1, experiment_id)
oro = iris.load_cube(f_oro)
#print oro
for i, coord in enumerate (oro.coords()):
if coord.standard_name=='grid_latitude':
lat_dim_coord_oro = i
if coord.standard_name=='grid_longitude':
lon_dim_coord_oro = i
fu = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/30201_mean.pp' % (expmin1, experiment_id)
time_list = u_global.coord('time').points
glob_tc = iris.Constraint(time=time_list)
u_wind,v_wind = iris.load(fu, glob_tc)
# Wind may have different number of grid points so need to do unrotate again for wind grid points
lat_w = u_wind.coord('grid_latitude').points
lon_w = u_wind.coord('grid_longitude').points
p_levs = u_wind.coord('pressure').points
lat = oro.coord('grid_latitude').points
lon = oro.coord('grid_longitude').points
cs_w = u_wind.coord_system('CoordSystem')
cs = oro.coord_system('CoordSystem')
if isinstance(cs_w, iris.coord_systems.RotatedGeogCS):
print ' Wind - %s - Unrotate pole %s' % (experiment_id, cs_w)
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]
csur_w=cs_w.ellipsoid
for i, coord in enumerate (u_wind.coords()):
if coord.standard_name=='grid_latitude':
lat_dim_coord_uwind = i
if coord.standard_name=='grid_longitude':
lon_dim_coord_uwind = i
u_wind.remove_coord('grid_latitude')
u_wind.remove_coord('grid_longitude')
u_wind.add_dim_coord(iris.coords.DimCoord(points=lat_w, standard_name='grid_latitude', units='degrees', coord_system=csur_w),lat_dim_coord_uwind )
u_wind.add_dim_coord(iris.coords.DimCoord(points=lon_w, standard_name='grid_longitude', units='degrees', coord_system=csur_w), lon_dim_coord_uwind)
v_wind.remove_coord('grid_latitude')
v_wind.remove_coord('grid_longitude')
v_wind.add_dim_coord(iris.coords.DimCoord(points=lat_w, standard_name='grid_latitude', units='degrees', coord_system=csur_w), lat_dim_coord_uwind)
v_wind.add_dim_coord(iris.coords.DimCoord(points=lon_w, standard_name='grid_longitude', units='degrees', coord_system=csur_w),lon_dim_coord_uwind )
if isinstance(cs, iris.coord_systems.RotatedGeogCS):
print ' 33.pp - %s - Unrotate pole %s' % (experiment_id, cs)
lons, lats = np.meshgrid(lon, lat)
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))
lons,lats = iris.analysis.cartography.unrotate_pole(lons,lats, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
lon_corner_u,lat_corner_u = iris.analysis.cartography.unrotate_pole(lon_corners, lat_corners, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
#lon_highu,lat_highu = iris.analysis.cartography.unrotate_pole(lon_high, lat_high, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
lon=lons[0]
lat=lats[:,0]
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]
for i, coord in enumerate (oro.coords()):
if coord.standard_name=='grid_latitude':
lat_dim_coord_oro = i
if coord.standard_name=='grid_longitude':
lon_dim_coord_oro = i
csur=cs.ellipsoid
oro.remove_coord('grid_latitude')
oro.remove_coord('grid_longitude')
oro.add_dim_coord(iris.coords.DimCoord(points=lat, standard_name='grid_latitude', units='degrees', coord_system=csur), lat_dim_coord_oro)
oro.add_dim_coord(iris.coords.DimCoord(points=lon, standard_name='grid_longitude', units='degrees', coord_system=csur), lon_dim_coord_oro)
else:
lons, lats = np.meshgrid(lon, lat)
lons_w, lats_w = np.meshgrid(lon_w, lat_w)
lon_low= np.min(lons)
lon_high = np.max(lons)
lat_low = np.min(lats)
lat_high = np.max(lats)
print u_wind
############## Regrid and Difference #################################
# Regrid Height and Temp/Specific humidity to global grid
h_regrid = np.empty((len(lat_g_oro), len(lon_g_oro)))
v_regrid = np.empty((len(lat_g_oro), len(lon_g_oro)))
for p in plot_levels:
### Search for pressure level match
s = np.searchsorted(p_levels[::-1], p)
h_regrid = scipy.interpolate.griddata((lats.flatten(),lons.flatten()),mean_heights[:,:,-(s+1)].flatten() , (lats_go,lons_go),method='linear')
v_regrid = scipy.interpolate.griddata((lats.flatten(),lons.flatten()),mean_var[:,:,-(s+1)].flatten() , (lats_go,lons_go),method='linear')
# Difference heights
plt_h = np.where(np.isnan(h_regrid), np.nan, h_regrid - mean_heights_global[:,:,-(s+1)])
#Difference temperature/specific humidity
plt_v = np.where(np.isnan(v_regrid), np.nan, v_regrid - mean_var_global[:,:,-(s+1)])
# Set u,v for winds, linear interpolate to approx. 2 degree grid
sc = np.searchsorted(p_levs, p)
##### Does not work on iris1.0 as on Leeds computers. Does work on later versions
#u_interp = u_wind[sc,:,:]
#v_interp = v_wind[sc,:,:].
#sample_points = [('grid_latitude', np.arange(lat_low,lat_high,2)), ('grid_longitude', np.arange(lon_low,lon_high,2))]
#u = iris.analysis.interpolate.linear(u_interp, sample_points, extrapolation_mode='linear')
#v = iris.analysis.interpolate.linear(v_interp, sample_points).data
##### Does work on Iris 1.0
# 2 degree lats lon lists for wind regridding on plot
lat_wind_1deg = np.arange(lat_low,lat_high, 2)
lon_wind_1deg = np.arange(lon_low,lon_high, 2)
### Regrid winds to global, difference, and then regrid to 2 degree spacing
fl_la_lo = (lats_w.flatten(),lons_w.flatten())
# print u_wind[sc,:,:].data.flatten().shape
u_wind_rg_to_glob = scipy.interpolate.griddata(fl_la_lo, u_wind[sc,:,:].data.flatten(), (lats_g, lons_g), method='linear')
v_wind_rg_to_glob = scipy.interpolate.griddata(fl_la_lo, v_wind[sc,:,:].data.flatten(), (lats_g, lons_g), method='linear')
u_w=u_wind_rg_to_glob-u_global[sc,:,:].data
v_w=v_wind_rg_to_glob-v_global[sc,:,:].data
#u_interp = u_wind[sc,:,:].data
#v_interp = v_wind[sc,:,:].data
lons_wi, lats_wi = np.meshgrid(lon_wind_1deg, lat_wind_1deg)
fl_la_lo = (lats_g.flatten(),lons_g.flatten())
u = scipy.interpolate.griddata(fl_la_lo, u_w.flatten(), (lats_wi, lons_wi), method='linear')
v = scipy.interpolate.griddata(fl_la_lo, v_w.flatten(), (lats_wi, lons_wi), method='linear')
#######################################################################################
### Plotting #########################################################################
rc('font', family = 'serif', serif = 'cmr10')
rc('text', usetex=True)
#m_title = 'Height of %s-hPa level (m)' % (p)
# Set pressure height contour min/max
if p == 925:
clev_min = -24.
clev_max = 24.
elif p == 850:
clev_min = -24.
clev_max = 24.
elif p == 700:
clev_min = -24.
clev_max = 24.
elif p == 500:
clev_min = -24.
clev_max = 24.
else:
print 'Contour min/max not set for this pressure level'
# Set potential temperature min/max
if p == 925:
clevpt_min = -3.
clevpt_max = 3.
elif p == 850:
clevpt_min = -3.
clevpt_max = 3.
elif p == 700:
clevpt_min = -3.
clevpt_max = 3.
elif p == 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 == 925:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 850:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 700:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 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.linspace(clev_min, clev_max, num=24)
m =\
Basemap(llcrnrlon=lon_low,llcrnrlat=lat_low,urcrnrlon=lon_high,urcrnrlat=lat_high, rsphere = 6371229)
#x, y = m(lons, lats)
x,y = m(lons_go,lats_go)
print x.shape
x_w,y_w = m(lons_wi, lats_wi)
fig=plt.figure(figsize=(8,8))
ax = fig.add_axes([0.05,0.05,0.9,0.85], axisbg='#262626')
m.drawcoastlines(color='#262626')
m.drawcountries(color='#262626')
m.drawcoastlines(linewidth=0.5)
#m.fillcontinents(color='#CCFF99')
m.drawparallels(np.arange(-80,81,10),labels=[1,1,0,0])
m.drawmeridians(np.arange(0,360,10),labels=[0,0,0,1])
cs_lin = m.contour(x,y, plt_h, clevs_lin,colors='#262626',linewidths=0.8)
cmap=plt.cm.RdBu_r
#cmap.set_bad('#262626', 1.)
#cmap.set_over('#262626')
#cmap.set_under('#262626')
if plot_diag=='temp':
plt_v = np.ma.masked_outside(plt_v, clevpt_max+20, clevpt_min-20)
cs_col = m.contourf(x,y, plt_v, np.linspace(clevpt_min, clevpt_max), cmap=cmap, extend='both')
cbar = m.colorbar(cs_col,location='bottom',pad="5%", format = '%d')
cbar.set_ticks(np.arange(clevpt_min,clevpt_max+2,2.))
cbar.set_ticklabels(np.arange(clevpt_min,clevpt_max+2,2.))
cbar.set_label('K')
#plt.suptitle('Difference from global model (Model - global ) of Height, Potential Temperature and Wind Vectors at %s hPa'% (p), fontsize=10)
elif plot_diag=='sp_hum':
plt_v = np.ma.masked_outside(plt_v, clevsh_max+20, clevsh_min-20)
cs_col = m.contourf(x,y, plt_v, np.linspace(clevsh_min, clevsh_max), cmap=cmap, extend='both')
cbar = m.colorbar(cs_col,location='bottom',pad="5%", format = '%.3f')
cbar.set_label('kg/kg')
#plt.suptitle('Difference from global model (Model - Global Model ) of Height, Specific Humidity and Wind Vectors at %s hPa'% (p), fontsize=10)
wind = m.quiver(x_w,y_w, u, v, scale=150,color='#262626' )
qk = plt.quiverkey(wind, 0.1, 0.1, 5, '5 m/s', labelpos='W')
plt.clabel(cs_lin, fontsize=10, fmt='%d', color='black')
#plt.title('%s\n%s' % (m_title, model_name_convert_title.main(experiment_id)), fontsize=10)
plt.savefig('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s/geop_height_difference_globalLAM_%shPa_%s_%s_no_title.png' % (experiment_id, plot_diag, p, experiment_id, plot_diag), format='png', bbox_inches='tight')
plt.title('\n'.join(wrap('%s' % (model_name_convert_title.main(experiment_id)), 75, replace_whitespace=False)), fontsize=16)
#plt.show()
if not os.path.exists('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s' % (experiment_id, plot_diag)): os.makedirs('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s' % (experiment_id, plot_diag))
plt.savefig('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s/geop_height_difference_globalLAM_%shPa_%s_%s.png' % (experiment_id, plot_diag, p, experiment_id, plot_diag), format='png', bbox_inches='tight')
def main():
# Plot diagnostics, model and pressure levels etc. to plot on for looping through
plot_type = 'mean_masked'
plot_type_h5py_var = 'mean'
plot_diags = ['temp', 'sp_hum']
plot_levels = [925, 850, 700, 500]
#plot_levels = [925]
experiment_ids = [
'djzny', 'djznq', 'dklyu', 'dkmbq', 'dklwu', 'dklzq', 'dkjxq', 'dklyu',
'dkmbq', 'dklwu', 'dklzq'
]
#experiment_id = 'djzny'
p_levels = [
1000, 950, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50,
30, 20, 10
]
dtmindt = datetime.datetime(2011, 8, 19, 0, 0, 0)
dtmaxdt = datetime.datetime(2011, 9, 7, 23, 0, 0)
dtmin = unit.date2num(dtmindt, 'hours since 1970-01-01 00:00:00',
unit.CALENDAR_STANDARD)
dtmax = unit.date2num(dtmaxdt, 'hours since 1970-01-01 00:00:00',
unit.CALENDAR_STANDARD)
time_constraint = iris.Constraint(time=lambda t: dtmin <= t.point <= dtmax)
###### Unrotate global model data ##############################
######### Regrid to global, and difference #######
############################################################################
## Load global wind and orography
fw_global = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/djzn/djznw/30201_mean.pp'
fo_global = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/djzn/djznw/33.pp'
u_global, v_global = iris.load(fw_global)
print u_global
print v_global
oro_global = iris.load_cube(fo_global)
# Unrotate global coordinates
cs_glob = u_global.coord_system('CoordSystem')
cs_glob_v = v_global.coord_system('CoordSystem')
cs_glob_oro = oro_global.coord_system('CoordSystem')
lat_g = u_global.coord('grid_latitude').points
lon_g = u_global.coord('grid_longitude').points
lat_g_oro = oro_global.coord('grid_latitude').points
lon_g_oro = oro_global.coord('grid_longitude').points
if cs_glob != cs_glob_v:
print 'Global model u and v winds have different poles of rotation'
# Unrotate global winds
if isinstance(cs_glob, iris.coord_systems.RotatedGeogCS):
print ' Global Model - Winds - djznw - Unrotate pole %s' % cs_glob
lons_g, lats_g = np.meshgrid(lon_g, lat_g)
lons_g, lats_g = iris.analysis.cartography.unrotate_pole(
lons_g, lats_g, cs_glob.grid_north_pole_longitude,
cs_glob.grid_north_pole_latitude)
lon_g = lons_g[0]
lat_g = lats_g[:, 0]
for i, coord in enumerate(u_global.coords()):
if coord.standard_name == 'grid_latitude':
lat_dim_coord_uglobal = i
if coord.standard_name == 'grid_longitude':
lon_dim_coord_uglobal = i
csur_glob = cs_glob.ellipsoid
u_global.remove_coord('grid_latitude')
u_global.remove_coord('grid_longitude')
u_global.add_dim_coord(
iris.coords.DimCoord(points=lat_g,
standard_name='grid_latitude',
units='degrees',
coord_system=csur_glob), lat_dim_coord_uglobal)
u_global.add_dim_coord(
iris.coords.DimCoord(points=lon_g,
standard_name='grid_longitude',
units='degrees',
coord_system=csur_glob), lon_dim_coord_uglobal)
#print u_global
v_global.remove_coord('grid_latitude')
v_global.remove_coord('grid_longitude')
v_global.add_dim_coord(
iris.coords.DimCoord(points=lat_g,
standard_name='grid_latitude',
units='degrees',
coord_system=csur_glob), lat_dim_coord_uglobal)
v_global.add_dim_coord(
iris.coords.DimCoord(points=lon_g,
standard_name='grid_longitude',
units='degrees',
coord_system=csur_glob), lon_dim_coord_uglobal)
#print v_global
# Unrotate global model
if isinstance(cs_glob_oro, iris.coord_systems.RotatedGeogCS):
print ' Global Model - Orography - djznw - Unrotate pole %s - Winds and other diagnostics may have different number of grid points' % cs_glob_oro
lons_go, lats_go = np.meshgrid(lon_g_oro, lat_g_oro)
lons_go, lats_go = iris.analysis.cartography.unrotate_pole(
lons_go, lats_go, cs_glob_oro.grid_north_pole_longitude,
cs_glob_oro.grid_north_pole_latitude)
lon_g_oro = lons_go[0]
lat_g_oro = lats_go[:, 0]
for i, coord in enumerate(oro_global.coords()):
if coord.standard_name == 'grid_latitude':
lat_dim_coord_og = i
if coord.standard_name == 'grid_longitude':
lon_dim_coord_og = i
csur_glob_oro = cs_glob_oro.ellipsoid
oro_global.remove_coord('grid_latitude')
oro_global.remove_coord('grid_longitude')
oro_global.add_dim_coord(
iris.coords.DimCoord(points=lat_g_oro,
standard_name='grid_latitude',
units='degrees',
coord_system=csur_glob_oro), lat_dim_coord_og)
oro_global.add_dim_coord(
iris.coords.DimCoord(points=lon_g_oro,
standard_name='grid_longitude',
units='degrees',
coord_system=csur_glob_oro), lon_dim_coord_og)
###############################################################################
#################### Load global heights and temp/sp_hum #####################
f_glob_h = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/408_pressure_levels_interp_pressure_djznw_%s' % (
plot_type)
######################################################################################
with h5py.File(f_glob_h, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_heights_global = mh[...]
######################################################################################
## Loop through experiment id's ######################################################
for pl in plot_diags:
plot_diag = pl
f_glob_d = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/%s_pressure_levels_interp_djznw_%s' % (
plot_diag, plot_type)
with h5py.File(f_glob_d, 'r') as i:
mg = i['%s' % plot_type_h5py_var]
mean_var_global = mg[...]
for experiment_id in experiment_ids:
expmin1 = experiment_id[:-1]
###############################################################################
#################### Load model heights and temp/sp_hum #####################
fname_h = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/408_pressure_levels_interp_pressure_%s_%s' % (
experiment_id, plot_type)
fname_d = '/nfs/a90/eepdw/Data/EMBRACE/Pressure_level_means/%s_pressure_levels_interp_%s_%s' % (
plot_diag, experiment_id, plot_type)
# print fname_h
# print fname_d
# Height data file
with h5py.File(fname_h, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_heights = mh[...]
# print mean_heights.shape
with h5py.File(fname_d, 'r') as i:
mh = i['%s' % plot_type_h5py_var]
mean_var = mh[...]
# print mean_var.shape
f_oro = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/33.pp' % (
expmin1, experiment_id)
oro = iris.load_cube(f_oro)
#print oro
for i, coord in enumerate(oro.coords()):
if coord.standard_name == 'grid_latitude':
lat_dim_coord_oro = i
if coord.standard_name == 'grid_longitude':
lon_dim_coord_oro = i
fu = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/30201_mean.pp' % (
expmin1, experiment_id)
time_list = u_global.coord('time').points
glob_tc = iris.Constraint(time=time_list)
u_wind, v_wind = iris.load(fu, glob_tc)
# Wind may have different number of grid points so need to do unrotate again for wind grid points
lat_w = u_wind.coord('grid_latitude').points
lon_w = u_wind.coord('grid_longitude').points
p_levs = u_wind.coord('pressure').points
lat = oro.coord('grid_latitude').points
lon = oro.coord('grid_longitude').points
cs_w = u_wind.coord_system('CoordSystem')
cs = oro.coord_system('CoordSystem')
if isinstance(cs_w, iris.coord_systems.RotatedGeogCS):
print ' Wind - %s - Unrotate pole %s' % (experiment_id, cs_w)
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]
csur_w = cs_w.ellipsoid
for i, coord in enumerate(u_wind.coords()):
if coord.standard_name == 'grid_latitude':
lat_dim_coord_uwind = i
if coord.standard_name == 'grid_longitude':
lon_dim_coord_uwind = i
u_wind.remove_coord('grid_latitude')
u_wind.remove_coord('grid_longitude')
u_wind.add_dim_coord(
iris.coords.DimCoord(points=lat_w,
standard_name='grid_latitude',
units='degrees',
coord_system=csur_w),
lat_dim_coord_uwind)
u_wind.add_dim_coord(
iris.coords.DimCoord(points=lon_w,
standard_name='grid_longitude',
units='degrees',
coord_system=csur_w),
lon_dim_coord_uwind)
v_wind.remove_coord('grid_latitude')
v_wind.remove_coord('grid_longitude')
v_wind.add_dim_coord(
iris.coords.DimCoord(points=lat_w,
standard_name='grid_latitude',
units='degrees',
coord_system=csur_w),
lat_dim_coord_uwind)
v_wind.add_dim_coord(
iris.coords.DimCoord(points=lon_w,
standard_name='grid_longitude',
units='degrees',
coord_system=csur_w),
lon_dim_coord_uwind)
if isinstance(cs, iris.coord_systems.RotatedGeogCS):
print ' 33.pp - %s - Unrotate pole %s' % (experiment_id, cs)
lons, lats = np.meshgrid(lon, lat)
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))
lons, lats = iris.analysis.cartography.unrotate_pole(
lons, lats, cs.grid_north_pole_longitude,
cs.grid_north_pole_latitude)
lon_corner_u, lat_corner_u = iris.analysis.cartography.unrotate_pole(
lon_corners, lat_corners, cs.grid_north_pole_longitude,
cs.grid_north_pole_latitude)
#lon_highu,lat_highu = iris.analysis.cartography.unrotate_pole(lon_high, lat_high, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
lon = lons[0]
lat = lats[:, 0]
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]
for i, coord in enumerate(oro.coords()):
if coord.standard_name == 'grid_latitude':
lat_dim_coord_oro = i
if coord.standard_name == 'grid_longitude':
lon_dim_coord_oro = i
csur = cs.ellipsoid
oro.remove_coord('grid_latitude')
oro.remove_coord('grid_longitude')
oro.add_dim_coord(
iris.coords.DimCoord(points=lat,
standard_name='grid_latitude',
units='degrees',
coord_system=csur), lat_dim_coord_oro)
oro.add_dim_coord(
iris.coords.DimCoord(points=lon,
standard_name='grid_longitude',
units='degrees',
coord_system=csur), lon_dim_coord_oro)
else:
lons, lats = np.meshgrid(lon, lat)
lons_w, lats_w = np.meshgrid(lon_w, lat_w)
lon_low = np.min(lons)
lon_high = np.max(lons)
lat_low = np.min(lats)
lat_high = np.max(lats)
print u_wind
############## Regrid and Difference #################################
# Regrid Height and Temp/Specific humidity to global grid
h_regrid = np.empty((len(lat_g_oro), len(lon_g_oro)))
v_regrid = np.empty((len(lat_g_oro), len(lon_g_oro)))
for p in plot_levels:
### Search for pressure level match
s = np.searchsorted(p_levels[::-1], p)
h_regrid = scipy.interpolate.griddata(
(lats.flatten(), lons.flatten()),
mean_heights[:, :, -(s + 1)].flatten(), (lats_go, lons_go),
method='linear')
v_regrid = scipy.interpolate.griddata(
(lats.flatten(), lons.flatten()),
mean_var[:, :, -(s + 1)].flatten(), (lats_go, lons_go),
method='linear')
# Difference heights
plt_h = np.where(
np.isnan(h_regrid), np.nan,
h_regrid - mean_heights_global[:, :, -(s + 1)])
#Difference temperature/specific humidity
plt_v = np.where(np.isnan(v_regrid), np.nan,
v_regrid - mean_var_global[:, :, -(s + 1)])
# Set u,v for winds, linear interpolate to approx. 2 degree grid
sc = np.searchsorted(p_levs, p)
##### Does not work on iris1.0 as on Leeds computers. Does work on later versions
#u_interp = u_wind[sc,:,:]
#v_interp = v_wind[sc,:,:].
#sample_points = [('grid_latitude', np.arange(lat_low,lat_high,2)), ('grid_longitude', np.arange(lon_low,lon_high,2))]
#u = iris.analysis.interpolate.linear(u_interp, sample_points, extrapolation_mode='linear')
#v = iris.analysis.interpolate.linear(v_interp, sample_points).data
##### Does work on Iris 1.0
# 2 degree lats lon lists for wind regridding on plot
lat_wind_1deg = np.arange(lat_low, lat_high, 2)
lon_wind_1deg = np.arange(lon_low, lon_high, 2)
### Regrid winds to global, difference, and then regrid to 2 degree spacing
fl_la_lo = (lats_w.flatten(), lons_w.flatten())
# print u_wind[sc,:,:].data.flatten().shape
u_wind_rg_to_glob = scipy.interpolate.griddata(
fl_la_lo,
u_wind[sc, :, :].data.flatten(), (lats_g, lons_g),
method='linear')
v_wind_rg_to_glob = scipy.interpolate.griddata(
fl_la_lo,
v_wind[sc, :, :].data.flatten(), (lats_g, lons_g),
method='linear')
u_w = u_wind_rg_to_glob - u_global[sc, :, :].data
v_w = v_wind_rg_to_glob - v_global[sc, :, :].data
#u_interp = u_wind[sc,:,:].data
#v_interp = v_wind[sc,:,:].data
lons_wi, lats_wi = np.meshgrid(lon_wind_1deg, lat_wind_1deg)
fl_la_lo = (lats_g.flatten(), lons_g.flatten())
u = scipy.interpolate.griddata(fl_la_lo,
u_w.flatten(),
(lats_wi, lons_wi),
method='linear')
v = scipy.interpolate.griddata(fl_la_lo,
v_w.flatten(),
(lats_wi, lons_wi),
method='linear')
#######################################################################################
### Plotting #########################################################################
rc('font', family='serif', serif='cmr10')
rc('text', usetex=True)
#m_title = 'Height of %s-hPa level (m)' % (p)
# Set pressure height contour min/max
if p == 925:
clev_min = -24.
clev_max = 24.
elif p == 850:
clev_min = -24.
clev_max = 24.
elif p == 700:
clev_min = -24.
clev_max = 24.
elif p == 500:
clev_min = -24.
clev_max = 24.
else:
print 'Contour min/max not set for this pressure level'
# Set potential temperature min/max
if p == 925:
clevpt_min = -3.
clevpt_max = 3.
elif p == 850:
clevpt_min = -3.
clevpt_max = 3.
elif p == 700:
clevpt_min = -3.
clevpt_max = 3.
elif p == 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 == 925:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 850:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 700:
clevsh_min = -0.0025
clevsh_max = 0.0025
elif p == 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.linspace(clev_min, clev_max, num=24)
m =\
Basemap(llcrnrlon=lon_low,llcrnrlat=lat_low,urcrnrlon=lon_high,urcrnrlat=lat_high, rsphere = 6371229)
#x, y = m(lons, lats)
x, y = m(lons_go, lats_go)
print x.shape
x_w, y_w = m(lons_wi, lats_wi)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0.05, 0.05, 0.9, 0.85], axisbg='#262626')
m.drawcoastlines(color='#262626')
m.drawcountries(color='#262626')
m.drawcoastlines(linewidth=0.5)
#m.fillcontinents(color='#CCFF99')
m.drawparallels(np.arange(-80, 81, 10), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(0, 360, 10), labels=[0, 0, 0, 1])
cs_lin = m.contour(x,
y,
plt_h,
clevs_lin,
colors='#262626',
linewidths=0.8)
cmap = plt.cm.RdBu_r
#cmap.set_bad('#262626', 1.)
#cmap.set_over('#262626')
#cmap.set_under('#262626')
if plot_diag == 'temp':
plt_v = np.ma.masked_outside(plt_v, clevpt_max + 20,
clevpt_min - 20)
cs_col = m.contourf(x,
y,
plt_v,
np.linspace(clevpt_min, clevpt_max),
cmap=cmap,
extend='both')
cbar = m.colorbar(cs_col,
location='bottom',
pad="5%",
format='%d')
cbar.set_ticks(np.arange(clevpt_min, clevpt_max + 2, 2.))
cbar.set_ticklabels(
np.arange(clevpt_min, clevpt_max + 2, 2.))
cbar.set_label('K')
#plt.suptitle('Difference from global model (Model - global ) of Height, Potential Temperature and Wind Vectors at %s hPa'% (p), fontsize=10)
elif plot_diag == 'sp_hum':
plt_v = np.ma.masked_outside(plt_v, clevsh_max + 20,
clevsh_min - 20)
cs_col = m.contourf(x,
y,
plt_v,
np.linspace(clevsh_min, clevsh_max),
cmap=cmap,
extend='both')
cbar = m.colorbar(cs_col,
location='bottom',
pad="5%",
format='%.3f')
cbar.set_label('kg/kg')
#plt.suptitle('Difference from global model (Model - Global Model ) of Height, Specific Humidity and Wind Vectors at %s hPa'% (p), fontsize=10)
wind = m.quiver(x_w, y_w, u, v, scale=150, color='#262626')
qk = plt.quiverkey(wind, 0.1, 0.1, 5, '5 m/s', labelpos='W')
plt.clabel(cs_lin, fontsize=10, fmt='%d', color='black')
#plt.title('%s\n%s' % (m_title, model_name_convert_title.main(experiment_id)), fontsize=10)
plt.savefig(
'/nfs/a90/eepdw/Figures/EMBRACE/%s/%s/geop_height_difference_globalLAM_%shPa_%s_%s_no_title.png'
% (experiment_id, plot_diag, p, experiment_id, plot_diag),
format='png',
bbox_inches='tight')
plt.title('\n'.join(
wrap('%s' % (model_name_convert_title.main(experiment_id)),
75,
replace_whitespace=False)),
fontsize=16)
#plt.show()
if not os.path.exists('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s' %
(experiment_id, plot_diag)):
os.makedirs('/nfs/a90/eepdw/Figures/EMBRACE/%s/%s' %
(experiment_id, plot_diag))
plt.savefig(
'/nfs/a90/eepdw/Figures/EMBRACE/%s/%s/geop_height_difference_globalLAM_%shPa_%s_%s.png'
% (experiment_id, plot_diag, p, experiment_id, plot_diag),
format='png',
bbox_inches='tight')