import os
import numpy
import datetime
import pickle
import matplotlib
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
start = datetime.datetime(1851, 1, 1, 0, 0)
end = datetime.datetime(2018, 12, 31, 23, 59)
from get_sample import get_sample_cube
(ndata, dts) = get_sample_cube(start, end)
# Plot the resulting array as a 2d colourmap
fig = Figure(
figsize=(19.2, 6), # Width, Height (inches)
dpi=300,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False,
subplotpars=None,
tight_layout=None)
canvas = FigureCanvas(fig)
matplotlib.rc('image', aspect='auto')
# Add a textured grey background
parser.add_argument("--endyear",
help="End Year",
type=int,
required=False,
default=1935)
parser.add_argument("--opdir", help="Directory for output files",
default="%s/20CR/version_3/analyses/Stripes_daily/PRMSL" % \
os.getenv('SCRATCH'),
type=str,required=False)
args = parser.parse_args()
if not os.path.isdir(args.opdir):
os.makedirs(args.opdir)
# Fix dask SPICE bug
import dask
dask.config.set(scheduler='single-threaded')
from get_sample import get_sample_cube
cday = args.day
if args.month == 2 and args.day == 29: cday = 28
cpkl = "%s/20CR/version_3/analyses/daily/PRMSL/clim_%04d-%04d/%02d%02d.pkl" % (
os.getenv('SCRATCH'), args.startyear, args.endyear, args.month, cday)
climatology = pickle.load(open(cpkl, 'rb'))
ndata = get_sample_cube(args.year, args.month, args.day, climatology)
dts = datetime.datetime(args.year, args.month, args.day, 12)
cspf = "%s/%04d%02d%02d.pkl" % (args.opdir, args.year, args.month, args.day)
pickle.dump((ndata, dts), open(cspf, 'wb'))
cell.point.year < (2010+1))
h=iris.load('%s/ERA5/monthly_averaged_ensemble_members/t2m.nc' %
os.getenv('SCRATCH'),
iris.Constraint(name='2 metre temperature') & mc)
# ERA5 data bug - get a masked copy along with the data
# pick the real version.
if numpy.ma.is_masked(h[0].data):
h = h[1]
else:
h = h[0]
n.append(h.extract(mc).collapsed(['time','ensemble_member'],
iris.analysis.MEAN))
# Process in batches or we'll run out of memory.
rst = numpy.random.RandomState(seed=0)
dts=[]
ndata=None
for year in range(1979,end.year+1,5):
ey = min(year+5,end.year)
(ndyr,dtyr) = get_sample_cube(datetime.datetime(year,1,1,0,0),
datetime.datetime(ey,12,31,23,59),
climatology=n,
new_grid=egrid,rstate=rst)
dts.extend(dtyr)
if ndata is None:
ndata = ndyr
else:
ndata = numpy.ma.concatenate((ndata,ndyr))
pickle.dump( (ndata,dts), open( "ERA5.pkl", "wb" ) )
import matplotlib
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
start = datetime.datetime(1851, 1, 1, 0, 0)
end = datetime.datetime(2018, 12, 31, 23, 59)
from get_sample import get_sample_cube
egrid = iris.load_cube("/scratch/hadcc/hadcrut5/build/HadCRUT5/analysis/"+
"HadCRUT.5.0.0.0.analysis.anomalies.%d.nc" % 1,
iris.Constraint(time=lambda cell: cell.point.year==1850 and\
cell.point.month==1))
(ndata, dts) = get_sample_cube(start, end, new_grid=egrid)
# Plot the resulting array as a 2d colourmap
fig = Figure(
figsize=(19.2, 6), # Width, Height (inches)
dpi=600,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False,
subplotpars=None,
tight_layout=None)
canvas = FigureCanvas(fig)
matplotlib.rc('image', aspect='auto')
# Add a textured grey background
