# In[9]:
# plot so we are sure this is going right
ocnmask = da_sic.isel(time=-30).notnull() # take a value near the end when not likely to have missing values
ocnmask.name = 'oceanmask'
#print(ocnmask)
PlotTest = False
if PlotTest:
tmpsic=da_sic.isel(time=30) # save one time at random for plot verification
#tmpsic=da_sic.mean('time')
#print(tmpsic)
# plot one time at random to ensure it is about right Nplots has to be one more than you'd think
(f, axes) = ice_plot.multi_polar_axis(ncols=2, nrows=1, Nplots = 3, sizefcter=3)
tmpsic.plot.pcolormesh(cmap='Reds',ax=axes[0], x='lon', y='lat',transform=ccrs.PlateCarree())
ocnmask.plot.pcolormesh(cmap='Reds',ax=axes[1], x='lon', y='lat',transform=ccrs.PlateCarree())
# # Climatology forecast
# In[ ]:
TestPlot = False
if TestPlot:
# equal to code in mertics.py put here for testing
from scipy import stats
import statsmodels.api as sm
def Update_PanArctic_Maps():
# Plotting Info
runType = 'forecast'
variables = ['sic']
metrics_all = {'sic': ['anomaly', 'mean', 'SIP'], 'hi': ['mean']}
updateAll = False
# Exclude some models
MME_NO = ['hcmr']
# Define Init Periods here, spaced by 7 days (aprox a week)
# Now
cd = datetime.datetime.now()
cd = datetime.datetime(cd.year, cd.month, cd.day) # Set hour min sec to 0.
# Hardcoded start date (makes incremental weeks always the same)
start_t = datetime.datetime(1950, 1, 1) # datetime.datetime(1950, 1, 1)
# Params for this plot
Ndays = 7 # time period to aggregate maps to (default is 7)
Npers = 5 # 5 number of periods to plot (from current date) (default is 14)
NweeksUpdate = 3 # 3 Always update the most recent NweeksUpdate periods
init_slice = np.arange(
start_t, cd, datetime.timedelta(days=Ndays)).astype('datetime64[ns]')
init_slice = init_slice[
-Npers:] # Select only the last Npers of periods (weeks) since current date
# Forecast times to plot
weeks = pd.to_timedelta(np.arange(0, 5, 1), unit='W')
months = pd.to_timedelta(np.arange(2, 12, 1), unit='M')
years = pd.to_timedelta(np.arange(1, 2), unit='Y') - np.timedelta64(
1, 'D') # need 364 not 365
slices = weeks.union(months).union(years).round('1d')
da_slices = xr.DataArray(slices, dims=('fore_time'))
da_slices.fore_time.values.astype('timedelta64[D]')
print(da_slices)
# Help conversion between "week/month" period used for figure naming and the actual forecast time delta value
int_2_days_dict = dict(zip(np.arange(0, da_slices.size), da_slices.values))
days_2_int_dict = {v: k for k, v in int_2_days_dict.items()}
#############################################################
# Load in Data
#############################################################
E = ed.EsioData.load()
# add missing info for climatology
E.model_color['climatology'] = (0, 0, 0)
E.model_linestyle['climatology'] = '--'
E.model_marker['climatology'] = '*'
E.model['climatology'] = {'model_label': 'Clim. Trend'}
E.icePredicted['climatology'] = True
mod_dir = E.model_dir
# Get median ice edge by DOY
median_ice_fill = xr.open_mfdataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'ice_edge.nc')).sic
# Get mean sic by DOY
mean_1980_2010_sic = xr.open_dataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc',
'mean_1980_2010_sic.nc')).sic
# Get average sip by DOY
mean_1980_2010_SIP = xr.open_dataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc',
'hist_SIP_1980_2010.nc')).sip
# Get recent observations
ds_81 = xr.open_mfdataset(E.obs['NSIDC_0081']['sipn_nc'] + '_yearly/*.nc',
concat_dim='time',
autoclose=True,
parallel=True) #,
# Define models to plot
models_2_plot = list(E.model.keys())
models_2_plot = [
x for x in models_2_plot
if x not in ['piomas', 'MME', 'MME_NEW', 'uclsipn']
] # remove some models
models_2_plot = [x for x in models_2_plot
if E.icePredicted[x]] # Only predictive models
models_2_plot = ['MME'] + models_2_plot # Add models to always plot at top
models_2_plot.insert(
1, models_2_plot.pop(-1)) # Move climatology from last to second
# Get # of models and setup subplot dims
Nmod = len(models_2_plot) + 1 #(+3 for obs, MME, and clim)
Nc = int(np.floor(np.sqrt(Nmod)))
# Max number of columns == 5 (plots get too small otherwise)
Nc = 5 #np.min([Nc,5])
Nr = int(np.ceil(Nmod / Nc))
print(Nr, Nc, Nmod)
assert Nc * Nr >= Nmod, 'Need more subplots'
for cvar in variables:
# Load in dask data from Zarr
ds_ALL = xr.open_zarr(
os.path.join(E.data_dir, 'model/zarr', cvar + '.zarr'))
# Define fig dir and make if doesn't exist
fig_dir = os.path.join(E.fig_dir, 'model', 'all_model', cvar,
'maps_weekly_NEW')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
# Make requested dataArray as specified above
ds_status = xr.DataArray(np.ones(
(init_slice.size, da_slices.size)) * np.NaN,
dims=('init_time', 'fore_time'),
coords={
'init_time': init_slice,
'fore_time': da_slices
})
ds_status.name = 'status'
ds_status = ds_status.to_dataset()
# Check what plots we already have
if not updateAll:
print("Removing figures we have already made")
ds_status = update_status(ds_status=ds_status,
fig_dir=fig_dir,
int_2_days_dict=int_2_days_dict,
NweeksUpdate=NweeksUpdate)
print(ds_status.status.values)
# Drop IC/FT we have already plotted (orthoginal only)
ds_status = ds_status.where(
ds_status.status.sum(dim='fore_time') < ds_status.fore_time.size,
drop=True)
print("Starting plots...")
# For each init_time we haven't plotted yet
for it in ds_status.init_time.values:
start_time_cmod = timeit.default_timer()
print(it)
it_start = it - np.timedelta64(Ndays, 'D') + np.timedelta64(
1, 'D'
) # Start period for init period (it is end of period). Add 1 day because when
# we select using slice(start,stop) it is inclusive of end points. So here we are defining the start of the init AND the start of the valid time.
# So we need to add one day, so we don't double count.
# For each forecast time we haven't plotted yet
ft_to_plot = ds_status.sel(init_time=it)
ft_to_plot = ft_to_plot.where(ft_to_plot.isnull(),
drop=True).fore_time
for ft in ft_to_plot.values:
print(ft.astype('timedelta64[D]'))
cs_str = format(days_2_int_dict[ft],
'02') # Get index of current forcast week
week_str = format(
int(ft.astype('timedelta64[D]').astype('int') / Ndays),
'02') # Get string of current week
cdoy_end = pd.to_datetime(it + ft).timetuple(
).tm_yday # Get current day of year end for valid time
cdoy_start = pd.to_datetime(it_start + ft).timetuple(
).tm_yday # Get current day of year end for valid time
it_yr = str(pd.to_datetime(it).year)
it_m = str(pd.to_datetime(it).month)
# Get datetime64 of valid time start and end
valid_start = it_start + ft
valid_end = it + ft
# Loop through variable of interest + any metrics (i.e. SIP) based on that
for metric in metrics_all[cvar]:
# Set up plotting info
if cvar == 'sic':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Blues_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Concentration (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'SIP':
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["white", "orange", "red", "#990000"])
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Probability (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'anomaly':
# cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("coolwarm", 9))
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["red", "white", "blue"])
cmap_c.set_bad(color='lightgrey')
c_label = 'SIC Anomaly to 1980-2010 Mean'
c_vmin = -1
c_vmax = 1
elif cvar == 'hi':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Reds_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Thickness (m)'
c_vmin = 0
c_vmax = None
else:
raise ValueError("cvar not found.")
# New Plot
#start_time_plot = timeit.default_timer()
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc,
nrows=Nr,
Nplots=Nmod)
############################################################################
# OBSERVATIONS #
############################################################################
# Plot Obs (if available)
ax_num = 0
axes[ax_num].set_title('Observed')
try:
da_obs_c = ds_ALL[metric].sel(model='Observed',
init_end=it,
fore_time=ft)
haveObs = True
except KeyError:
haveObs = False
# If obs then plot
if haveObs:
da_obs_c.plot.pcolormesh(ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Observed')
else: # When were in the future (or obs are missing)
if metric == 'SIP': # Plot this historical mean SIP
print("plotting hist obs SIP")
da_obs_c = mean_1980_2010_SIP.isel(
time=slice(cdoy_start, cdoy_end)).mean(
dim='time')
da_obs_c.plot.pcolormesh(
ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Hist. Obs.')
############################################################################
# Plot all models #
############################################################################
p = {}
for (i, cmod) in enumerate(models_2_plot):
#print(cmod)
i = i + 1 # shift for obs
axes[i].set_title(E.model[cmod]['model_label'])
# Select current model to plot
try:
ds_model = ds_ALL[metric].sel(model=cmod,
init_end=it,
fore_time=ft)
haveMod = True
except:
haveMod = False
# Plot
if haveMod:
p[i] = ds_model.plot.pcolormesh(
ax=axes[i],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[i].set_title(E.model[cmod]['model_label'])
# Clean up for current model
ds_model = None
# Make pretty
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
if p: # if its not empty
cbar = f.colorbar(
next(iter(p.values())), cax=cbar_ax,
label=c_label) # use first plot to gen colorbar
if metric == 'anomaly':
cbar.set_ticks(np.arange(-1, 1.1, 0.2))
else:
cbar.set_ticks(np.arange(0, 1.1, 0.1))
# Set title of all plots
init_time_2 = pd.to_datetime(it).strftime('%Y-%m-%d')
init_time_1 = pd.to_datetime(it_start).strftime('%Y-%m-%d')
valid_time_2 = pd.to_datetime(it + ft).strftime('%Y-%m-%d')
valid_time_1 = pd.to_datetime(it_start +
ft).strftime('%Y-%m-%d')
plt.suptitle('Initialization Time: ' + init_time_1 +
' to ' + init_time_2 + '\n Valid Time: ' +
valid_time_1 + ' to ' + valid_time_2,
fontsize=15) # +'\n Week '+week_str
plt.subplots_adjust(top=0.85)
# Save to file
f_out = os.path.join(
fig_dir, 'panArctic_' + metric + '_' + runType + '_' +
init_time_2 + '_' + cs_str + '.png')
f.savefig(f_out, bbox_inches='tight', dpi=200)
print("saved ", f_out)
#print("Figure took ", (timeit.default_timer() - start_time_plot)/60, " minutes.")
# Mem clean up
p = None
plt.close(f)
da_obs_c = None
# Done with current it
print("Took ", (timeit.default_timer() - start_time_cmod) / 60,
" minutes.")
# Update json file
json_format = get_figure_init_times(fig_dir)
json_dict = [{"date": cd, "label": cd} for cd in json_format]
json_f = os.path.join(fig_dir, 'plotdates_current.json')
with open(json_f, 'w') as outfile:
json.dump(json_dict, outfile)
# Make into Gifs
# TODO: make parallel, add &
for cit in json_format:
subprocess.call(
str("/home/disk/sipn/nicway/python/ESIO/scripts/makeGif.sh " +
fig_dir + " " + cit),
shell=True)
print("Finished plotting panArctic Maps.")
def Update_Evaluation_Maps(PaperPlots = False):
# client = Client(n_workers=8)
# client
### Paper Figure Parameters
# Time period options
Last_valid_time = np.datetime64('2018-11-30') # last day in valid time space to include in analysis.
# Use np.datetime64('2018-11-30') to reproduce Paper.
# Domain options to use for calculating metrics over
customDomainName = 'ArcticSeas'
# Options:
# 'ArcticSeas' will use the Arctic Seas domain shown in Supplemental information
# 'X' where X is a model name to use as the domain (i.e. X='rasmesrl' because it is the smallest domain)
### Code
metrics_all = ['anomaly','mean','SIP']
runType = 'forecast'
variables = ['sic']
# Only use 'sic' here
cvar = variables[0]
# Get path data
E = ed.EsioData.load()
grid_dir = E.grid_dir
# Load in regional data
ds_region = xr.open_dataset(os.path.join(grid_dir, 'sio_2016_mask_Update.nc'))
# Define fig dir and make if doesn't exist (Hard coded here)
if PaperPlots:
fig_dir = os.path.join('/home/disk/sipn/nicway/Nic/figures', 'model', 'MME', cvar, 'BSS')
else:
fig_dir = os.path.join('/home/disk/sipn/nicway/public_html/sipn/figures/model/Eval')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
# Load from Zarr
ds_m = xr.open_zarr(os.path.join(E.data_dir,'model/zarr',cvar+'.zarr'))
# Select only initlizations from January 2018 to November 2018 (for GRL paper)
if PaperPlots:
ds_m = ds_m.where(ds_m.valid_end<=Last_valid_time, drop=True)
# print("Warning: using ALL data")
# Drop models we don't want
ds_ALL = ds_m.where(ds_m.model!='dampedAnomaly', drop=True)
def remove_list(X, L):
for cl in L:
X.remove(cl)
# Get recent observations
ds_81 = xr.open_mfdataset(E.obs['NSIDC_0081']['sipn_nc']+'_yearly/*.nc', concat_dim='time', autoclose=True, parallel=True)
# Hack to decode strings
ds_ALL['model'] = [s.decode("utf-8") for s in ds_ALL.model.values]
# # Drop models that we don't evaluate (i.e. monthly means)
exl_mods = ['awispin','nicosipn','szapirosipn']
# Order models to plot in
models_first = ['climatology','dampedAnomalyTrend','MME']
X = list(ds_ALL.model.values)
[X.remove(a) for a in models_first]
model_plot_order = models_first + X
model_plot_order.remove('Observed')
# model_plot_order
# Get custom domain mask if we have defined it (i.e. not None)
if customDomainName=='ArcticSeas':
customDomain = None
fig_ext = '' # Figure extention to separte domain names
else:
customDomain = ds_ALL.sel(model=customDomainName).isel(fore_time=0,init_end=40).SIP.notnull()
# Drop coods
customDomain = customDomain.drop(['fore_time','init_end','init_start','valid_end','valid_start','model'])
if customDomain.sum().values==0:
raise ValueError("Warning: you need to choose a fore_time and init_end that has model data represenative of its extent")
fig_ext = customDomainName+'_' # Figure extention to separte domain names
# Calculate the IIEE (Integrated Ice Edge Error)
l = []
for cmod in ds_ALL.model.values:
c_SIP_IIEE = metrics.IIEE(da_mod=ds_ALL.sel(model=cmod)['mean'],
da_obs=ds_ALL.sel(model='Observed')['mean'],
region=ds_region,
testplots=False,
customDomain=customDomain)
c_SIP_IIEE.coords['model'] = cmod
l.append(c_SIP_IIEE)
SIP_IIEE = xr.concat(l, dim='model')
SIP_IIEE
# For SIP, calculate the Brier Skill Score for panArctic
# BS = (SIP_model - SIP_observed)^2
#
l = []
for cmod in ds_ALL.model.values:
c_SIP_BSS = metrics.BrierSkillScore(da_mod_sip=ds_ALL.sel(model=cmod).SIP,
da_obs_ip=ds_ALL.sel(model='Observed').SIP,
region=ds_region,
testplots=False,
customDomain=customDomain)
c_SIP_BSS.coords['model'] = cmod
l.append(c_SIP_BSS)
SIP_BSS = xr.concat(l, dim='model')
def add_subplot_title(cmod, E, ax=None, BSS_val=''):
if cmod in E.model.keys():
ax.set_title((E.model[cmod]['model_label']).rstrip('*')+'\n('+BSS_val+')')
else:
ax.set_title(cmod)
# add missing info for climatology
E.model_color['climatology'] = (0,0,0)
E.model_linestyle['climatology'] = '--'
E.model_marker['climatology'] = '*'
E.model['climatology'] = {'model_label':'Climatology\nTrend'}
# Aggregate over space (x,y), including all pixels in valid Arctic seas (masked above with BrierSkillScore())
BSS_agg = SIP_BSS.mean(dim=['x','y'])
BSS_agg.load() # Compute and load result into memory
### At what lead time is the MME significantly (95%) better than the Damped Anomaly?
from scipy import stats
model_1 = 'MME'
model_2 = 'dampedAnomalyTrend'
alphaval = 0.05 # 95%
t_all=[]
p_all=[]
cv_all=[]
for ft in np.arange(0,BSS_agg.fore_time.size):
x = BSS_agg.sel(model=model_1).isel(fore_time=ft)
y = BSS_agg.sel(model=model_2).isel(fore_time=ft)
x = x.where(x.notnull() & y.notnull(), drop=True)
y = y.where(x.notnull() & y.notnull(), drop=True)
df = x.size + y.size -2
t, p = stats.ttest_ind(x, y, equal_var=False)
cv = stats.t.ppf(1.0 - alphaval, df)
t_all.append(t)
p_all.append(p)
cv_all.append(cv)
plt.figure()
plt.plot(BSS_agg.fore_time.values.astype('timedelta64[D]').astype(int)/7,
abs(np.array(t_all)),'-k*', label='t-value')
plt.plot(BSS_agg.fore_time.values.astype('timedelta64[D]').astype(int)/7,
cv_all,'-r*', label='critical t-value')
plt.ylabel('t-value')
plt.legend()
plt.figure()
plt.plot(BSS_agg.fore_time.values.astype('timedelta64[D]').astype(int)/7,
p_all,'-k*',label='p value')
plt.plot(BSS_agg.fore_time.values.astype('timedelta64[D]').astype(int)/7,
np.ones(len(p_all))*alphaval,'-r*',label='Alpha p value')
plt.ylabel('p-value')
plt.legend()
# Read off what lead time in weeks where the black line crosses the red line
# central_extent = [-3850000*0.6, 3725000*0.6, -5325000*0.45, 5850000*0.45] # (x0, x1, y0, y1
# (f, axes) = ice_plot.multi_polar_axis(ncols=2, nrows=1, Nplots=2,
# extent=central_extent, central_longitude=0)
# f.set_size_inches(18.5, 10.5)
# # Get Observed mean sea ice edge location mean(SIC in sept) > 0.15
# # obs_SIP_OLD = ds_81['sic'].sel(time=slice(start_date_map,end_date_map)).mean(dim='time')>=0.15
# # Fill in pole hole
# # obs_SIP_OLD = obs_SIP_OLD.where(obs_SIP_OLD.hole_mask==0, other=1)
# p1 = obs_SIP.plot.pcolormesh(ax=axes[0], x='lon', y='lat',
# transform=ccrs.PlateCarree(),
# add_colorbar=False,
# cmap=cmap_c,
# vmin=c_vmin, vmax=c_vmax)
# # ice_plot.remove_small_contours(po, thres=10**6)
# obs_SIP_contour = obs_SIP.where((obs_SIP.lon < 179.1))
# po = obs_SIP_contour.plot.contour(ax=axes[0], x='lon', y='lat',
# transform=ccrs.PlateCarree(), #.NorthPolarStereo(central_longitude=-45),
# colors=('k'),
# linewidths=[1],
# levels=[0.5])
# # ice_plot.remove_small_contours(po, thres=10**6)
### Plot BS spatial plots valid for Summer months (Figure 3)
# Remove some select models
enough_init = model_plot_order.copy()
[enough_init.remove(a) for a in exl_mods]
print(enough_init)
# Define lead time to plot
week_lead_time = 4
period_dict = {'June':[np.datetime64('2018-06-01'),np.datetime64('2018-07-01')],
'July':[np.datetime64('2018-07-01'),np.datetime64('2018-08-01')],
'August':[np.datetime64('2018-08-01'),np.datetime64('2018-09-01')],
'September':[np.datetime64('2018-09-01'),np.datetime64('2018-10-01')]}
for period in period_dict:
print(period)
start_date_map = period_dict[period][0]
end_date_map = period_dict[period][1]
# Select time slice of valid
BS_Sept = SIP_BSS.where( (SIP_BSS.valid_start>=start_date_map) &
(SIP_BSS.valid_start<=end_date_map), drop=True)
# Average over valid time
SIP_BSS_init_avg = BS_Sept.sel(model=enough_init).mean(dim='init_end')
sns.set_context("talk", font_scale=.8, rc={"lines.linewidth": 2.5})
# Set up color maps
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","orange","red","#990000"], N=10)
cmap_c.set_bad(color = 'lightgrey')
c_label = 'BS (0=best, 1=worst)'
c_vmin = 0
c_vmax = 1
for ft in [SIP_BSS_init_avg.fore_time.values[week_lead_time]]:
# Grab current lead time
c_ft_ds = SIP_BSS_init_avg.sel(fore_time=ft)
# Drop models without any data
c_ft_ds = c_ft_ds.where(c_ft_ds.notnull().sum(dim=['x','y'])>0, drop=True)
nrows = np.int(np.ceil(np.sqrt(c_ft_ds.model.size))) - 1
ncols = int(np.ceil(c_ft_ds.model.size/nrows))
Nplots = c_ft_ds.model.size + 1
#print(Nplots)
# New Plot
central_extent = [-3850000*0.6, 3725000*0.6, -5325000*0.45, 5850000*0.45] # (x0, x1, y0, y1
(f, axes) = ice_plot.multi_polar_axis(ncols=ncols, nrows=nrows, Nplots=Nplots,
extent=central_extent, central_longitude=0)
# # Get Observed mean sea ice edge location mean(SIC in sept) > 0.15
# obs_SIP = ds_81['sic'].sel(time=slice(start_date_map,end_date_map)).mean(dim='time')>=0.15
# # Fill in pole hole
# obs_SIP = obs_SIP.where(obs_SIP.hole_mask==0, other=1)
obs_SIP = ds_ALL.sel(model='Observed').SIP
# Select time slice of valid
obs_SIP = obs_SIP.where( (obs_SIP.valid_start>=start_date_map) &
(obs_SIP.valid_start<=end_date_map), drop=True)
# Average over valid time
obs_SIP = obs_SIP.mean(dim='init_end').sel(fore_time=ft)
for (i, cmod) in enumerate(c_ft_ds.model.values):
if cmod in c_ft_ds.model.values:
# Plot
add_subplot_title(cmod, E, ax=axes[i])
p = c_ft_ds.sel(model=cmod).plot.pcolormesh(ax=axes[i], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin, vmax=c_vmax)
# Need to clip obs so contour handles wrap around 180 correctly
obs_SIP_contour = obs_SIP.where((obs_SIP.lon < 179.1))
po = obs_SIP_contour.plot.contour(ax=axes[i], x='lon', y='lat',
transform=ccrs.PlateCarree(), #.NorthPolarStereo(central_longitude=-45),
colors=('k'),
linewidths=[1],
levels=[0.5])
add_subplot_title(cmod, E, ax=axes[i], BSS_val='{0:.3f}'.format(c_ft_ds.sel(model=cmod).mean(dim=['x','y']).load().item()))
# Make pretty
cbar_ax = f.add_axes([0.2, 0.05, .5, 0.04]) # [left, bottom, width, height] w
cbar = f.colorbar(p, cax=cbar_ax, label=c_label, orientation='horizontal')
cbar.set_ticks(np.arange(-1,1.1,0.2))
# Set title of all plots
lead_time_days = str(ft.astype('timedelta64[D]').astype(int))
#print(lead_time_days)
if not PaperPlots: # only add for website plots
cbar_ax.text(0.35, 1.1, 'Wayand et al. (2019)', fontsize=12)
valid_start_str = pd.to_datetime(start_date_map).strftime('%Y-%m-%d')
valid_end_str = pd.to_datetime(end_date_map).strftime('%Y-%m-%d')
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'BSS_Avg_Valid_'+valid_start_str+'_to_'+valid_end_str+'_'+lead_time_days.zfill(3)+'_day_lead_time.png')
f.savefig(f_out,bbox_inches='tight', dpi=300)
### Plot Brier Score vs lead time
min_N_samples = 10 # Min number of samples to allow for mean
BSS_agg_init = BSS_agg.mean(dim='init_end')
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
# Get sample size of for each lead time
for_sample = BSS_agg.sel(model='MME').notnull().sum(dim='init_end')
for_sample
# Use threshold of sample size to cut off lead times
max_lead = for_sample.where(for_sample>=min_N_samples,drop=True).fore_time.max().values.astype('timedelta64[D]').astype(int)/7
f = plt.figure(figsize=(10,10))
NM = 10
ax1 = plt.subplot2grid((NM, NM), (0, 0), colspan=NM, rowspan=NM-1)
ax2 = plt.subplot2grid((NM, NM), (NM-1, 0), colspan=NM, rowspan=1)
for cmod in model_plot_order:
if cmod in exl_mods:
continue
# Get model plotting specs
cc = E.model_color[cmod]
cl = E.model_linestyle[cmod]
cm = E.model_marker[cmod]
if cmod=='rasmesrl':
cflag = '*'
else:
cflag = ''
if cmod in ['MME','dampedAnomalyTrend','climatology']:
lw=5
else:
lw = 2
ax1.plot(BSS_agg_init.fore_time.values.astype('timedelta64[D]').astype(int)/7,
BSS_agg_init.sel(model=cmod).values, label=E.model[cmod]['model_label'].rstrip('*')+cflag,
color=cc,
linestyle=cl,
linewidth=lw,
marker=cm)
ax1.legend(loc='lower right', bbox_to_anchor=(1.4, 0))
ax1.set_ylabel('Pan-Arctic BS (-)')
ax1.set_xlim([-0.5,max_lead])
ax1.set_xticklabels([''])
# second axis
ax2.plot(for_sample.fore_time.values.astype('timedelta64[D]').astype(int)/7,
for_sample.values, '-ko')
ax2.set_ylabel('#\nweeks')
ax2.set_xlabel('Lead time (Weeks)')
ax2.set_xlim(ax1.get_xlim());
ax2.set_ylim([0,for_sample.max()+5]);
ax2.set_yticks(np.arange(0,for_sample.max()+5,15));
if not PaperPlots: # only add for website plots
ax1.text(17, 0.025, 'Wayand et al. (2019)', fontsize=12)
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'BSS_by_lead_time_PanArctic.png')
f.savefig(f_out,bbox_inches='tight', dpi=300)
### Plot Brier Score vs lead time (Figure 1)
print(BSS_agg_init.model.values)
mean_models = []
min_N_samples = 10 # Min number of samples to allow for mean
BSS_agg_init = BSS_agg.mean(dim='init_end')
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
# Get sample size of for each lead time
for_sample = BSS_agg.sel(model='MME').notnull().sum(dim='init_end')
# Use threshold of sample size to cut off lead times
max_lead = for_sample.where(for_sample>=min_N_samples,drop=True).fore_time.max().values.astype('timedelta64[D]').astype(int)/7
f = plt.figure(figsize=(15,10))
NM = 10
ax1 = plt.subplot2grid((NM, NM), (0, 0), colspan=NM, rowspan=NM-1)
ax2 = plt.subplot2grid((NM, NM), (NM-1, 0), colspan=NM, rowspan=1)
for cmod in model_plot_order:
if cmod in exl_mods:
continue
# Get model plotting specs
cc = E.model_color[cmod]
cl = E.model_linestyle[cmod]
cm = E.model_marker[cmod]
if cmod=='rasmesrl':
cflag = '*'
else:
cflag = ''
if cmod in ['MME','dampedAnomalyTrend','climatology']:
lw=5
else:
lw = 2
mean_models.append(cmod)
ax1.plot(BSS_agg_init.fore_time.values.astype('timedelta64[D]').astype(int)/7,
BSS_agg_init.sel(model=cmod).values, label=E.model[cmod]['model_label'].rstrip('*')+cflag,
color=cc,
linestyle=cl,
linewidth=lw,
marker=cm)
# Plot the mean of BS across models
ax1.plot(BSS_agg_init.fore_time.values.astype('timedelta64[D]').astype(int)/7,
BSS_agg_init.sel(model=mean_models).mean(dim='model').values, label='BS Mean',
color='r',
linestyle='-',
linewidth=2,
marker='d')
ax1.legend(loc='lower right', bbox_to_anchor=(1.285, -0.1))
ax1.set_ylabel('Pan-Arctic BS (-)')
ax1.set_xlim([-0.5,max_lead])
ax1.set_xticklabels([''])
ax1.set_ylim([0.02,0.11])
# second axis
ax2.plot(for_sample.fore_time.values.astype('timedelta64[D]').astype(int)/7,
for_sample.values, '-ko')
ax2.set_ylabel('#\nweeks ')
ax2.set_xlabel('Lead time (Weeks)')
ax2.set_xlim(ax1.get_xlim());
ax2.set_ylim([0,for_sample.max()+5]);
ax2.set_yticks(np.arange(0,for_sample.max()+5,15));
if not PaperPlots: # only add for website plots
ax1.text(17, 0.025, 'Wayand et al. (2019)', fontsize=12)
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'BSS_by_lead_time_PanArctic_New.png')
f.savefig(f_out,bbox_inches='tight', dpi=200)
### Plot the IIEE with lead time (SI)
SIP_IIEE.load()
min_N_samples = 10 # Min number of samples to allow for mean
SIP_IIEE_init = SIP_IIEE.mean(dim='init_end')
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
# Get sample size of for each lead time
for_sample = SIP_IIEE.sel(model='MME').notnull().sum(dim='init_end')
# Use threshold of sample size to cut off lead times
max_lead = for_sample.where(for_sample>=min_N_samples,drop=True).fore_time.max().values.astype('timedelta64[D]').astype(int)/7
f = plt.figure(figsize=(10,10))
NM = 10
ax1 = plt.subplot2grid((NM, NM), (0, 0), colspan=NM, rowspan=NM-1)
ax2 = plt.subplot2grid((NM, NM), (NM-1, 0), colspan=NM, rowspan=1)
for cmod in model_plot_order:
if cmod in exl_mods:
continue
# Get model plotting specs
cc = E.model_color[cmod]
cl = E.model_linestyle[cmod]
cm = E.model_marker[cmod]
if cmod=='rasmesrl':
cflag = '*'
else:
cflag = ''
if cmod in ['MME','dampedAnomalyTrend','climatology']:
lw=5
else:
lw = 2
ax1.plot(SIP_IIEE_init.fore_time.values.astype('timedelta64[D]').astype(int)/7,
SIP_IIEE_init.sel(model=cmod).values, label=E.model[cmod]['model_label'].rstrip('*')+cflag,
color=cc,
linestyle=cl,
linewidth=lw,
marker=cm)
ax1.legend(loc='lower right', bbox_to_anchor=(1.4, 0))
ax1.set_ylabel('IIEE (Millions of km$^2$)')
ax1.set_xlim([-0.5,max_lead])
ax1.set_xticklabels([''])
# second axis
ax2.plot(for_sample.fore_time.values.astype('timedelta64[D]').astype(int)/7,
for_sample.values, '-ko')
ax2.set_ylabel('#\nweeks')
ax2.set_xlabel('Lead time (Weeks)')
ax2.set_xlim(ax1.get_xlim());
ax2.set_ylim([0,for_sample.max()+5]);
ax2.set_yticks(np.arange(0,for_sample.max()+5,15));
if not PaperPlots: # only add for website plots
# ax1.text(np.datetime64('2018-01-01'), 0.025, 'Wayand et al. (in review)', fontsize=12)
ax1.text(7, 0.25, 'Wayand et al. (2019)', fontsize=12)
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'IIEE_by_lead_time_PanArctic.png')
f.savefig(f_out,bbox_inches='tight', dpi=200)
### Define DA methods for each model
# copy past info from Table 1
DA_dict = {
'modcansipns_3':'SIC (NG)',
'modcansipns_4':'SIC (NG)',
'ecmwfsipn':'SIC (3DVAR)',
'ecmwf':'SIC (3DVAR)',
'yopp':'SIC (3DVAR)',
'gfdlsipn':'No Sea Ice DA',
'metreofr':'SIC (EnKF)',
'szapirosipn':'No Sea Ice DA',
'ncep-exp-bias-corr':'SIC (NG)',
'noaasipn':'SIC (NG)',
'usnavysipn':'SIC (3DVAR)',
'usnavyncep':'SIC (3DVAR)',
'usnavygofs':'SIC (3DVAR)',
'rasmesrl':'SIC (DI), SIT* (DI)',
'uclsipn':'No Sea Ice DA',
'ukmetofficesipn':'SIC (3DVAR)',
'ukmo':'SIC (3DVAR)',
'ncep':'SIC (NG)',
'kma':'SIC (3DVAR)'
}
# Add models
DA_dict['climatology'] = 'No Sea Ice DA'
DA_dict['dampedAnomalyTrend'] = 'SIC (DI)'
DA_dict['MME'] = 'MME'
DA_dict
DA_options = sorted(list(set(DA_dict.values())))
dict(zip(DA_options,np.arange(len(DA_options))))
#DA_options = [DA_options[1], DA_options[4], DA_options[5], DA_options[7], DA_options[2], DA_options[3], DA_options[6],DA_options[0],] # Reorder from simple to complex
DA_options = [DA_options[1], DA_options[3], DA_options[4], DA_options[6], DA_options[2], DA_options[5],DA_options[0],] # Reorder from simple to complex
DA_options_i = np.arange(len(DA_options))
DA_options_dict = dict(zip(DA_options,DA_options_i))
DA_options_dict
### Plot BS by DA method (Figure 4)
# In place a multi lead times
# Lead times to plot
leads2plot = [0,1,2,3,4] # indices
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1, rc={"lines.linewidth": 2.5})
f, axes = plt.subplots(1, 1, figsize=(9, 5))
for cmod in BSS_agg.model.values:
if cmod in DA_dict.keys():
# Get model plotting specs
cc = E.model_color[cmod]
cl = E.model_linestyle[cmod]
cm = E.model_marker[cmod]
if cmod=='MME':
lw=4
else:
lw=2
BSS_init = BSS_agg.sel(model=cmod).isel(fore_time=leads2plot).mean(dim='init_end').load()
#rand_jit = np.random.randint(-100,100)/1000*2
c_x = np.linspace(DA_options_dict[DA_dict[cmod]],
DA_options_dict[DA_dict[cmod]]+0.75,
len(leads2plot))
#print(c_x)
axes.plot(c_x,
BSS_init.values,
color=cc,
linestyle='-',
linewidth=lw,
marker=cm,
label=E.model[cmod]['model_label'].rstrip('*'))
else:
print(cmod,"not in dict")
axes.set_xticks(DA_options_i)
axes.set_xticklabels(DA_options, rotation='45', ha='right')
plt.legend(loc='lower right', bbox_to_anchor=(1.36, -.25))
plt.ylabel('Pan-Arctic BS (-)')
if not PaperPlots: # only add for website plots
axes.text(4.2, 0.081, 'Based on Wayand et al. (2019)', fontsize=12)
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'BSS_week_Multi_by_DA_Type.png')
f.savefig(f_out,bbox_inches='tight', dpi=200)
### Plot BS by initialization time (Figure 2)
sns.set_style("whitegrid")
for ft_i in [4]:
BSS_agg_fore = BSS_agg.isel(fore_time=ft_i)
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
f = plt.figure(figsize=(10,10))
for cmod in model_plot_order:
# Get model plotting specs
cc = E.model_color[cmod]
cl = E.model_linestyle[cmod]
cm = E.model_marker[cmod]
if cmod in ['MME','dampedAnomalyTrend','climatology']:
lw=5
else:
lw = 2
if BSS_agg_fore.sel(model=cmod).notnull().sum().values==0:
continue # Don't plot
plt.plot(BSS_agg_fore.init_end.values,
BSS_agg_fore.sel(model=cmod).values, label=E.model[cmod]['model_label'].rstrip('*'),
color=cc,
linestyle=cl,
linewidth=lw,
marker=cm)
plt.legend(loc='lower right', bbox_to_anchor=(1.4, -0.1))
plt.ylabel('Pan-Arctic BS (-)')
plt.xlabel('Initialization date')
#plt.title(BSS_agg_fore.fore_time.values.astype('timedelta64[D]').astype(int))
f.autofmt_xdate()
if not PaperPlots: # only add for website plots
plt.text(np.datetime64('2018-01-01'), 0.163, 'Based on Wayand et al. (2019)', fontsize=12)
# Save to file
f_out = os.path.join(fig_dir,fig_ext+'BSS_by_init_time_'+str(BSS_agg_fore.fore_time.values.astype('timedelta64[D]').astype(int))+'_days.png')
f.savefig(f_out,bbox_inches='tight', dpi=200)
print("Finished Eval_Weekly")
# For each region
for cR in ds_region.ocean_regions.values:
# Get regional extent to plot
crExt = ds_region.where(ds_region.mask == cR,
drop=True)[['xm', 'ym']]
crExt = [
crExt.xm.min().values,
crExt.xm.max().values,
crExt.ym.min().values,
crExt.ym.max().values
]
# New Plot
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc,
nrows=Nr,
Nplots=Nmod,
extent=crExt)
p = None # initlaize to know if we found any data
for (i, cmod) in enumerate(models_2_plot):
i = i + 1 # shift for obs
axes[i].set_title(E.model[cmod]['model_label'])
# Load in Model
# TODO: try filtering file list by year and month
model_forecast = os.path.join(
E.model[cmod][runType]['sipn_nc'], '*.nc')
# Check we have files
files = glob.glob(model_forecast)
if not files:
continue # Skip this model
def Update_Regional_Maps():
# Make requested dataArray as specified above
ds_status = xr.DataArray(np.ones(
(init_slice.size, da_slices.size)) * np.NaN,
dims=('init_time', 'fore_time'),
coords={
'init_time': init_slice,
'fore_time': da_slices
})
ds_status.name = 'status'
ds_status = ds_status.to_dataset()
# Check what plots we already have
if not updateAll:
print("Removing figures we have already made")
ds_status = update_status(ds_status=ds_status,
fig_dir=fig_dir,
int_2_days_dict=int_2_days_dict,
NweeksUpdate=NweeksUpdate)
print(ds_status.status.values)
# Drop IC/FT we have already plotted (orthoginal only)
ds_status = ds_status.where(
ds_status.status.sum(dim='fore_time') < ds_status.fore_time.size,
drop=True)
print("Starting plots...")
# For each init_time we haven't plotted yet
for it in ds_status.init_time.values:
start_time_cmod = timeit.default_timer()
print(it)
it_start = it - np.timedelta64(Ndays, 'D') + np.timedelta64(
1, 'D'
) # Start period for init period (it is end of period). Add 1 day because when
# we select using slice(start,stop) it is inclusive of end points. So here we are defining the start of the init AND the start of the valid time.
# So we need to add one day, so we don't double count.
# For each forecast time we haven't plotted yet
ft_to_plot = ds_status.sel(init_time=it)
ft_to_plot = ft_to_plot.where(ft_to_plot.isnull(), drop=True).fore_time
print('all forecast times to be plotted, ft_to_plot ',
ft_to_plot.values)
for ft in ft_to_plot.values:
print('Processing forecast time: ', ft.astype('timedelta64[D]'))
ift = days_2_int_dict[ft] # index of ft
cs_str = format(days_2_int_dict[ft],
'02') # Get index of current forcast week
week_str = format(iweek, '02') # Get string of current week
cdoy_end = pd.to_datetime(it + ft).timetuple(
).tm_yday # Get current day of year end for valid time
cdoy_start = pd.to_datetime(it_start + ft).timetuple(
).tm_yday # Get current day of year end for valid time
it_yr = str(pd.to_datetime(it).year)
it_m = str(pd.to_datetime(it).month)
# Get datetime64 of valid time start and end
valid_start = it_start + ft
valid_end = it + ft
print(ift)
#if ift<=5:
# continue
models_2_plot = models_2_plot_master[ift]
print('models to plot ', models_2_plot)
# Get # of models and setup subplot dims
Nmod = len(models_2_plot) + 2 #(+3 for obs, MME, and clim)
Nc = int(np.floor(np.sqrt(Nmod)))
# Max number of columns == 5 (plots get too small otherwise)
Nc = 5 #np.min([Nc,5])
Nr = int(np.ceil((Nmod - 1) / Nc))
print(Nr, Nc, Nmod)
assert Nc * Nr >= Nmod - 1, 'Need more subplots'
# Loop through variable of interest + any metrics (i.e. SIP) based on that
for metric in metrics_all[cvar]:
# Set up plotting info
if cvar == 'sic':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Blues_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Concentration (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'SIP':
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["white", "orange", "red", "#990000"])
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Probability (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'anomaly':
# cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("coolwarm", 9))
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["red", "white", "blue"])
cmap_c.set_bad(color='lightgrey')
c_label = 'SIC Anomaly to 1980-2010 Mean'
c_vmin = -1
c_vmax = 1
elif cvar == 'hi':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Reds_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Thickness (m)'
c_vmin = 0
c_vmax = None
else:
raise ValueError("cvar not found.")
for cR in np.arange(0, len(reg2plot), 1):
# New Plot
#start_time_plot = timeit.default_timer()
# Get regional extent to plot
crExt = ds_region.where(ds_region.mask.isin(reg2plot[cR]),
drop=True)[['xm', 'ym']]
crExt = [
crExt.xm.min().values,
crExt.xm.max().values,
crExt.ym.min().values,
crExt.ym.max().values
]
crExt[2] = np.max([crExt[2], -3700000])
# New Plot
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc,
nrows=Nr,
Nplots=Nmod,
extent=crExt)
############################################################################
# OBSERVATIONS #
############################################################################
# Plot Obs (if available)
ax_num = 0
axes[ax_num].set_title('Observed', fontsize=10)
try:
da_obs_c = ds_ALL[metric].sel(model=b'Observed',
init_end=it,
fore_time=ft)
#print('da_obs_c',da_obs_c)
haveObs = True # we think there are obs...
except KeyError:
haveObs = False
rightnow = datetime.datetime.now()
if valid_start > np.datetime64(rightnow):
haveObs = False # but we know there are no obs in the future...
# If obs then plot
if haveObs:
#da_obs_c = da_obs_c.where(ds_region.mask.isin(reg2plot[cR]))
da_obs_c.plot.pcolormesh(ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Observed', fontsize=10)
else: # When in the future (or obs are missing)
#print('no obs avail yet')
if metric == 'SIP': # Plot this historical mean SIP
print("plotting hist obs SIP")
da_obs_c = mean_1980_2010_SIP.isel(
time=slice(cdoy_start, cdoy_end)).mean(
dim='time')
da_obs_c.plot.pcolormesh(
ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Hist. Obs.', fontsize=10)
else:
textstr = 'Not Available'
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round',
facecolor='white',
alpha=0.5)
# place a text box in upper left in axes coords
axes[ax_num].text(0.05,
0.55,
textstr,
transform=axes[ax_num].transAxes,
fontsize=8,
verticalalignment='top',
bbox=props)
############################################################################
# Plot all models #
############################################################################
p = {}
for (i, cmod) in enumerate(models_2_plot):
#print(cmod)
i = i + 1 # shift for obs
axes[i].set_title(E.model[cmod]['model_label'],
fontsize=10)
# Select current model to plot
try:
ds_model = ds_ALL[metric].sel(
model=cmod.encode('utf-8'),
init_end=it,
fore_time=ft)
haveMod = True
except:
haveMod = False
# Plot
if haveMod:
# Select region
# Lat and Long feilds have round off differences, so set to same here
ds_model['lat'] = ds_region.lat
ds_model['lon'] = ds_region.lon
#ds_model = ds_model.where(ds_region.mask.isin(reg2plot[cR]))
p[i] = ds_model.plot.pcolormesh(
ax=axes[i],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[i].set_title(E.model[cmod]['model_label'],
fontsize=10)
# Clean up for current model
ds_model = None
# Make pretty
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
if p: # if its not empty
cbar = f.colorbar(
next(iter(p.values())), cax=cbar_ax,
label=c_label) # use first plot to gen colorbar
if metric == 'anomaly':
cbar.set_ticks(np.arange(-1, 1.1, 0.2))
else:
cbar.set_ticks(np.arange(0, 1.1, 0.1))
# Set title of all plots
init_time_2 = pd.to_datetime(it).strftime('%Y-%m-%d')
init_time_1 = pd.to_datetime(it_start).strftime('%Y-%m-%d')
valid_time_2 = pd.to_datetime(it + ft).strftime('%Y-%m-%d')
valid_time_1 = pd.to_datetime(it_start +
ft).strftime('%Y-%m-%d')
if ift < 3:
titlesize = 15
elif ift < 5:
titlesize = 13
else:
titlesize = 11
plt.suptitle('Initialization Time: ' + init_time_1 +
' to ' + init_time_2 + '\n Valid Time: ' +
valid_time_1 + ' to ' + valid_time_2,
fontsize=titlesize) # +'\n Week '+week_str
if (ift > 3):
plt.subplots_adjust(top=0.75)
else:
plt.subplots_adjust(top=0.85)
# Save to file
f_out = os.path.join(
fig_dir, 'Region_' + str(cR) + '_' + metric + '_' +
runType + '_' + init_time_2 + '_' + cs_str + '.png')
f.savefig(f_out, bbox_inches='tight', dpi=200)
print("saved ", f_out)
#print("Figure took ", (timeit.default_timer() - start_time_plot)/60, " minutes.")
# Mem clean up
p = None
plt.close(f)
da_obs_c = None
#diehere
# loop over regions
# Done with current it
print("Took ", (timeit.default_timer() - start_time_cmod) / 60,
" minutes.")
# Update json file
json_format = get_figure_init_times(fig_dir)
json_dict = [{"date": cd, "label": cd} for cd in json_format]
json_f = os.path.join(fig_dir, 'plotdates_current.json')
with open(json_f, 'w') as outfile:
json.dump(json_dict, outfile)
# Make into Gifs
# TODO: make parallel, add &
# for cit in json_format:
# subprocess.call(str("/home/disk/sipn/nicway/python/ESIO/scripts/makeGif.sh " + fig_dir + " " + cit), shell=True)
print("Finished plotting panArctic Maps.")
c_vmax = 1
elif cvar=='hi':
if metric=='mean':
cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("Reds_r", 10))
cmap_c.set_bad(color = 'lightgrey')
c_label = 'Sea Ice Thickness (m)'
c_vmin = 0
c_vmax = None
else:
raise ValueError("cvar not found.")
MME_list = []
# New Plot
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc, nrows=Nr, Nplots=Nmod)
# Plot Obs (if available)
ax_num = 0
if ((it + ft) in ds_81.time.values):
if metric=='mean':
da_obs_c = ds_81.sic.sel(time=(it + ft))
elif metric=='SIP':
da_obs_c = (ds_81.sic.sel(time=(it + ft)) >=0.15).astype('int').where(ds_81.sic.sel(time=(it + ft)).notnull())
elif metric=='anomaly':
da_obs_VT = ds_81.sic.sel(time=(it + ft))
da_obs_mean = mean_1980_2010_sic.isel(time=cdoy)
da_obs_c = da_obs_VT - da_obs_mean
else:
raise ValueError('Not implemented')
da_obs_c.plot.pcolormesh(ax=axes[ax_num], x='lon', y='lat',
def Update_PanArctic_Maps():
# Plotting Info
runType = 'forecast'
variables = ['sic']
metrics_all = {'sic': ['anomaly', 'mean', 'SIP'], 'hi': ['mean']}
#metrics_all = {'sic':['SIP']}
updateAll = False
# Some models are terrible/have serious issues, so don't include in MME
MME_NO = ['hcmr']
# Define Init Periods here, spaced by 7 days (aprox a week)
# Now
cd = datetime.datetime.now()
cd = datetime.datetime(cd.year, cd.month, cd.day) # Set hour min sec to 0.
# Hardcoded start date (makes incremental weeks always the same)
start_t = datetime.datetime(1950, 1, 1) # datetime.datetime(1950, 1, 1)
# Params for this plot
Ndays = 7 # time period to aggregate maps to (default is 7)
Npers = 29 # number of periods to plot (from current date) (default is 14)
init_slice = np.arange(
start_t, cd, datetime.timedelta(days=Ndays)).astype('datetime64[ns]')
init_slice = init_slice[
-Npers:] # Select only the last Npers of periods (weeks) since current date
# Forecast times to plot
weeks = pd.to_timedelta(np.arange(0, 5, 1), unit='W')
months = pd.to_timedelta(np.arange(2, 12, 1), unit='M')
years = pd.to_timedelta(np.arange(1, 2), unit='Y') - np.timedelta64(
1, 'D') # need 364 not 365
slices = weeks.union(months).union(years).round('1d')
da_slices = xr.DataArray(slices, dims=('fore_time'))
da_slices.fore_time.values.astype('timedelta64[D]')
print(da_slices)
# Help conversion between "week/month" period used for figure naming and the actual forecast time delta value
int_2_days_dict = dict(zip(np.arange(0, da_slices.size), da_slices.values))
days_2_int_dict = {v: k for k, v in int_2_days_dict.items()}
#############################################################
# Load in Data
#############################################################
E = ed.EsioData.load()
mod_dir = E.model_dir
# Get median ice edge by DOY
median_ice_fill = xr.open_mfdataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'ice_edge.nc')).sic
# Get mean sic by DOY
mean_1980_2010_sic = xr.open_dataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc',
'mean_1980_2010_sic.nc')).sic
# Get average sip by DOY
mean_1980_2010_SIP = xr.open_dataset(
os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc',
'hist_SIP_1980_2010.nc')).sip
# Climatology model
cmod = 'climatology'
all_files = os.path.join(mod_dir, cmod, runType, 'sipn_nc',
str(cd.year) + '*.nc')
files = glob.glob(all_files)
obs_clim_model = xr.open_mfdataset(sorted(files),
chunks={
'time': 30,
'x': 304,
'y': 448
},
concat_dim='time',
autoclose=True,
parallel=True)
obs_clim_model = obs_clim_model['sic']
# Get recent observations
ds_81 = xr.open_mfdataset(E.obs['NSIDC_0081']['sipn_nc'] + '_yearly/*.nc',
concat_dim='time',
autoclose=True,
parallel=True) #,
# Define models to plot
models_2_plot = list(E.model.keys())
models_2_plot = [
x for x in models_2_plot
if x not in ['piomas', 'MME', 'MME_NEW', 'uclsipn']
] # remove some models
models_2_plot = [x for x in models_2_plot
if E.icePredicted[x]] # Only predictive models
# Get # of models and setup subplot dims
Nmod = len(models_2_plot) + 4 #(+3 for obs, MME, and clim)
Nc = int(np.floor(np.sqrt(Nmod)))
# Max number of columns == 5 (plots get too small otherwise)
Nc = np.min([Nc, 5])
Nr = int(np.ceil(Nmod / Nc))
print(Nr, Nc, Nmod)
assert Nc * Nr >= Nmod, 'Need more subplots'
for cvar in variables:
# Define fig dir and make if doesn't exist
fig_dir = os.path.join(E.fig_dir, 'model', 'all_model', cvar,
'maps_weekly')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
# Make requested dataArray as specified above
ds_status = xr.DataArray(np.ones(
(init_slice.size, da_slices.size)) * np.NaN,
dims=('init_time', 'fore_time'),
coords={
'init_time': init_slice,
'fore_time': da_slices
})
ds_status.name = 'status'
ds_status = ds_status.to_dataset()
# Check what plots we already have
if not updateAll:
print("Removing figures we have already made")
ds_status = update_status(ds_status=ds_status,
fig_dir=fig_dir,
int_2_days_dict=int_2_days_dict)
print(ds_status.status.values)
# Drop IC/FT we have already plotted (orthoginal only)
ds_status = ds_status.where(
ds_status.status.sum(dim='fore_time') < ds_status.fore_time.size,
drop=True)
print("Starting plots...")
# For each init_time we haven't plotted yet
start_time_cmod = timeit.default_timer()
for it in ds_status.init_time.values:
print(it)
it_start = it - np.timedelta64(Ndays, 'D') + np.timedelta64(
1, 'D'
) # Start period for init period (it is end of period). Add 1 day because when
# we select using slice(start,stop) it is inclusive of end points. So here we are defining the start of the init AND the start of the valid time.
# So we need to add one day, so we don't double count.
# For each forecast time we haven't plotted yet
ft_to_plot = ds_status.sel(init_time=it)
ft_to_plot = ft_to_plot.where(ft_to_plot.isnull(),
drop=True).fore_time
for ft in ft_to_plot.values:
print(ft.astype('timedelta64[D]'))
cs_str = format(days_2_int_dict[ft],
'02') # Get index of current forcast week
week_str = format(
int(ft.astype('timedelta64[D]').astype('int') / Ndays),
'02') # Get string of current week
cdoy_end = pd.to_datetime(it + ft).timetuple(
).tm_yday # Get current day of year end for valid time
cdoy_start = pd.to_datetime(it_start + ft).timetuple(
).tm_yday # Get current day of year end for valid time
it_yr = str(pd.to_datetime(it).year)
it_m = str(pd.to_datetime(it).month)
# Get datetime64 of valid time start and end
valid_start = it_start + ft
valid_end = it + ft
# Loop through variable of interest + any metrics (i.e. SIP) based on that
for metric in metrics_all[cvar]:
# Set up plotting info
if cvar == 'sic':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Blues_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Concentration (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'SIP':
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["white", "orange", "red", "#990000"])
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Probability (-)'
c_vmin = 0
c_vmax = 1
elif metric == 'anomaly':
# cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("coolwarm", 9))
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["red", "white", "blue"])
cmap_c.set_bad(color='lightgrey')
c_label = 'SIC Anomaly to 1980-2010 Mean'
c_vmin = -1
c_vmax = 1
elif cvar == 'hi':
if metric == 'mean':
cmap_c = matplotlib.colors.ListedColormap(
sns.color_palette("Reds_r", 10))
cmap_c.set_bad(color='lightgrey')
c_label = 'Sea Ice Thickness (m)'
c_vmin = 0
c_vmax = None
else:
raise ValueError("cvar not found.")
MME_list = []
# New Plot
start_time_plot = timeit.default_timer()
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc,
nrows=Nr,
Nplots=Nmod)
############################################################################
# OBSERVATIONS #
############################################################################
# Plot Obs (if available)
ax_num = 0
axes[ax_num].set_title('Observed')
#ds_model = ds_model.sel(init_time=slice(it_start, it))
da_obs_c = ds_81.sic.sel(
time=slice(valid_start, valid_end))
# Check we found any times in target valid time range
if da_obs_c.time.size > 0:
#if ((it + ft) in ds_81.time.values):
if metric == 'mean':
da_obs_c = da_obs_c.mean(
dim='time') #ds_81.sic.sel(time=(it + ft))
elif metric == 'SIP':
da_obs_c = (da_obs_c >= 0.15).mean(
dim='time').astype('int').where(
da_obs_c.isel(time=0).notnull())
elif metric == 'anomaly':
da_obs_VT = da_obs_c.mean(dim='time')
da_obs_mean = mean_1980_2010_sic.isel(
time=slice(cdoy_start, cdoy_end)).mean(
dim='time')
da_obs_c = da_obs_VT - da_obs_mean
else:
raise ValueError('Not implemented')
da_obs_c.plot.pcolormesh(ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Observed')
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[ax_num], x='xm', y='ym',
#=('#bc0f60'),
#linewidths=[0.5],
#levels=[0.5])
#remove_small_contours(po, thres=10)
else: # When were in the future (or obs are missing)
if metric == 'anomaly': # Still get climatological mean for model difference
da_obs_mean = mean_1980_2010_sic.isel(
time=slice(cdoy_start, cdoy_end)).mean(
dim='time')
elif metric == 'SIP': # Plot this historical mean SIP
da_obs_c = mean_1980_2010_SIP.isel(
time=slice(cdoy_start, cdoy_end)).mean(
dim='time')
da_obs_c.plot.pcolormesh(
ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[ax_num].set_title('Hist. Obs.')
############################################################################
# Plot climatology trend #
############################################################################
i = 2
cmod = 'climatology'
axes[i].set_title('clim. trend')
# Check if we have any valid times in range of target dates
ds_model = obs_clim_model.where(
(obs_clim_model.time >= valid_start) &
(obs_clim_model.time <= valid_end),
drop=True)
if 'time' in ds_model.lat.dims:
ds_model.coords['lat'] = ds_model.lat.isel(
time=0).drop(
'time'
) # Drop time from lat/lon dims (not sure why?)
# If we have any time
if ds_model.time.size > 0:
# Average over time
ds_model = ds_model.mean(dim='time')
if metric == 'mean': # Calc ensemble mean
ds_model = ds_model
elif metric == 'SIP': # Calc probability
# Issue of some ensemble members having missing data
ocnmask = ds_model.notnull()
ds_model = (ds_model >= 0.15).where(ocnmask)
elif metric == 'anomaly': # Calc anomaly in reference to mean observed 1980-2010
ds_model = ds_model - da_obs_mean
# Add back lat/long (get dropped because of round off differences)
ds_model['lat'] = da_obs_mean.lat
ds_model['lon'] = da_obs_mean.lon
else:
raise ValueError('metric not implemented')
if 'doy' in ds_model.coords:
ds_model = ds_model.drop(['doy'])
if 'xm' in ds_model.coords:
ds_model = ds_model.drop(['xm'])
if 'ym' in ds_model.coords:
ds_model = ds_model.drop(['ym'])
# Build MME
if cmod not in MME_NO: # Exclude some models (bad) from MME
ds_model.coords['model'] = cmod
MME_list.append(ds_model)
# Plot
p = ds_model.plot.pcolormesh(
ax=axes[i],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
axes[i].set_title('clim. trend')
# Clean up for current model
ds_model = None
###########################################################
# Plot Models in SIPN format #
###########################################################
# Plot all Models
p = None # initlaize to know if we found any data
for (i, cmod) in enumerate(models_2_plot):
print(cmod)
i = i + 3 # shift for obs, MME, and clim
axes[i].set_title(E.model[cmod]['model_label'])
# Load in Model
# Find only files that have current year and month in filename (speeds up loading)
all_files = os.path.join(
E.model[cmod][runType]['sipn_nc'],
'*' + it_yr + '*' + it_m + '*.nc')
# Check we have files
files = glob.glob(all_files)
if not files:
continue # Skip this model
# Load in model
ds_model = xr.open_mfdataset(sorted(files),
chunks={
'fore_time': 1,
'init_time': 1,
'nj': 304,
'ni': 448
},
concat_dim='init_time',
autoclose=True,
parallel=True)
ds_model.rename({'nj': 'x', 'ni': 'y'}, inplace=True)
# Select init period and fore_time of interest
ds_model = ds_model.sel(init_time=slice(it_start, it))
# Check we found any init_times in range
if ds_model.init_time.size == 0:
print('init_time not found.')
continue
# Select var of interest (if available)
if cvar in ds_model.variables:
# print('found ',cvar)
ds_model = ds_model[cvar]
else:
print('cvar not found.')
continue
# Get Valid time
ds_model = import_data.get_valid_time(ds_model)
# Check if we have any valid times in range of target dates
ds_model = ds_model.where(
(ds_model.valid_time >= valid_start) &
(ds_model.valid_time <= valid_end),
drop=True)
if ds_model.fore_time.size == 0:
print("no fore_time found for target period.")
continue
# Average over for_time and init_times
ds_model = ds_model.mean(
dim=['fore_time', 'init_time'])
if metric == 'mean': # Calc ensemble mean
ds_model = ds_model.mean(dim='ensemble')
elif metric == 'SIP': # Calc probability
# Issue of some ensemble members having missing data
# ds_model = ds_model.where(ds_model>=0.15, other=0).mean(dim='ensemble')
ok_ens = (
(ds_model.notnull().sum(dim='x').sum(dim='y'))
> 0) # select ensemble members with any data
ds_model = ((ds_model.where(ok_ens, drop=True) >=
0.15)).mean(dim='ensemble').where(
ds_model.isel(
ensemble=0).notnull())
elif metric == 'anomaly': # Calc anomaly in reference to mean observed 1980-2010
ds_model = ds_model.mean(
dim='ensemble') - da_obs_mean
# Add back lat/long (get dropped because of round off differences)
ds_model['lat'] = da_obs_mean.lat
ds_model['lon'] = da_obs_mean.lon
else:
raise ValueError('metric not implemented')
#print("Calc metric took ", (timeit.default_timer() - start_time), " seconds.")
# drop ensemble if still present
if 'ensemble' in ds_model:
ds_model = ds_model.drop('ensemble')
# Build MME
if cmod not in MME_NO: # Exclude some models (bad) from MME
ds_model.coords['model'] = cmod
if 'xm' in ds_model:
ds_model = ds_model.drop(
['xm', 'ym']) #Dump coords we don't use
MME_list.append(ds_model)
# Plot
#start_time = timeit.default_timer()
p = ds_model.plot.pcolormesh(
ax=axes[i],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
#print("Plotting took ", (timeit.default_timer() - start_time), " seconds.")
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[i], x='xm', y='ym',
#colors=('#bc0f60'),
#linewidths=[0.5],
#levels=[0.5]) #, label='Median ice edge 1981-2010')
#remove_small_contours(po, thres=10)
axes[i].set_title(E.model[cmod]['model_label'])
# Clean up for current model
ds_model = None
# MME
ax_num = 1
if MME_list: # If we had any models for this time
# Concat over all models
ds_MME = xr.concat(MME_list, dim='model')
# Set lat/lon to "model" lat/lon (round off differences)
if 'model' in ds_MME.lat.dims:
ds_MME.coords['lat'] = ds_MME.lat.isel(
model=0).drop('model')
# Plot average
# Don't include some models (i.e. climatology and dampedAnomaly)
mod_to_avg = [
m for m in ds_MME.model.values
if m not in ['climatology', 'dampedAnomaly']
]
pmme = ds_MME.sel(model=mod_to_avg).mean(
dim='model').plot.pcolormesh(
ax=axes[ax_num],
x='lon',
y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin,
vmax=c_vmax)
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[ax_num], x='xm', y='ym',
# colors=('#bc0f60'),
# linewidths=[0.5],
# levels=[0.5])
#remove_small_contours(po, thres=10)
# Save all models for given target valid_time
out_metric_dir = os.path.join(
E.model['MME'][runType]['sipn_nc'], metric)
if not os.path.exists(out_metric_dir):
os.makedirs(out_metric_dir)
out_init_dir = os.path.join(
out_metric_dir,
pd.to_datetime(it).strftime('%Y-%m-%d'))
if not os.path.exists(out_init_dir):
os.makedirs(out_init_dir)
out_nc_file = os.path.join(
out_init_dir,
pd.to_datetime(it + ft).strftime('%Y-%m-%d') +
'.nc')
# Add observations
# Check if exits (if we have any observations for this valid period)
# TODO: require ALL observations to be present, otherwise we could only get one day != week avg
if ds_81.sic.sel(time=slice(valid_start,
valid_end)).time.size > 0:
da_obs_c.coords['model'] = 'Observed'
# Drop coords we don't need
da_obs_c = da_obs_c.drop(['hole_mask', 'xm', 'ym'])
if 'time' in da_obs_c:
da_obs_c = da_obs_c.drop('time')
if 'xm' in ds_MME:
ds_MME = ds_MME.drop(['xm', 'ym'])
# Add obs
ds_MME_out = xr.concat([ds_MME, da_obs_c],
dim='model')
else:
ds_MME_out = ds_MME
# Add init and valid times
ds_MME_out.coords['init_start'] = it_start
ds_MME_out.coords['init_end'] = it
ds_MME_out.coords['valid_start'] = it_start + ft
ds_MME_out.coords['valid_end'] = it + ft
ds_MME_out.coords['fore_time'] = ft
ds_MME_out.name = metric
# Save
ds_MME_out.to_netcdf(out_nc_file)
axes[ax_num].set_title('MME')
# Make pretty
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
if p:
cbar = f.colorbar(p, cax=cbar_ax, label=c_label)
if metric == 'anomaly':
cbar.set_ticks(np.arange(-1, 1.1, 0.2))
else:
cbar.set_ticks(np.arange(0, 1.1, 0.1))
#cbar.set_ticklabels(np.arange(0,1,0.05))
# Set title of all plots
init_time_2 = pd.to_datetime(it).strftime('%Y-%m-%d')
init_time_1 = pd.to_datetime(it_start).strftime('%Y-%m-%d')
valid_time_2 = pd.to_datetime(it + ft).strftime('%Y-%m-%d')
valid_time_1 = pd.to_datetime(it_start +
ft).strftime('%Y-%m-%d')
plt.suptitle('Initialization Time: ' + init_time_1 +
' to ' + init_time_2 + '\n Valid Time: ' +
valid_time_1 + ' to ' + valid_time_2,
fontsize=15) # +'\n Week '+week_str
plt.subplots_adjust(top=0.85)
# Save to file
f_out = os.path.join(
fig_dir, 'panArctic_' + metric + '_' + runType + '_' +
init_time_2 + '_' + cs_str + '.png')
f.savefig(f_out, bbox_inches='tight', dpi=200)
print("saved ", f_out)
print("Figure took ",
(timeit.default_timer() - start_time_plot) / 60,
" minutes.")
# Mem clean up
plt.close(f)
p = None
ds_MME = None
da_obs_c = None
da_obs_mean = None
# Done with current it
print("Took ", (timeit.default_timer() - start_time_cmod) / 60,
" minutes.")
# Update json file
json_format = get_figure_init_times(fig_dir)
json_dict = [{"date": cd, "label": cd} for cd in json_format]
json_f = os.path.join(fig_dir, 'plotdates_current.json')
with open(json_f, 'w') as outfile:
json.dump(json_dict, outfile)
# Make into Gifs
for cit in json_format:
subprocess.call(
str("/home/disk/sipn/nicway/python/ESIO/scripts/makeGif.sh " +
fig_dir + " " + cit),
shell=True)
print("Finished plotting panArctic Maps.")