def add_extra_time_coords(cube):
"""
Adds new coordinate for indexing a given simulation based on model and
ensemble and adds additional time coordinates for unit manipulation
"""
if not cube.coords('year'):
icc.add_year(cube, 'time')
if not cube.coords('month'):
icc.add_month(cube, 'time')
if not cube.coords('month_number'):
icc.add_month_number(cube, 'time')
if not cube.coords('day_of_month'):
icc.add_day_of_month(cube, 'time')
if not cube.coords('hour'):
icc.add_hour(cube, 'time')
return cube
def test_basic(self):
cube = self.cube
time_coord = self.time_coord
ccat.add_year(cube, time_coord, 'my_year')
ccat.add_day_of_month(cube, time_coord, 'my_day_of_month')
ccat.add_day_of_year(cube, time_coord, 'my_day_of_year')
ccat.add_month(cube, time_coord, 'my_month')
with warnings.catch_warnings(record=True):
ccat.add_month_shortname(cube, time_coord, 'my_month_shortname')
ccat.add_month_fullname(cube, time_coord, 'my_month_fullname')
ccat.add_month_number(cube, time_coord, 'my_month_number')
ccat.add_weekday(cube, time_coord, 'my_weekday')
ccat.add_weekday_number(cube, time_coord, 'my_weekday_number')
with warnings.catch_warnings(record=True):
ccat.add_weekday_shortname(cube, time_coord,
'my_weekday_shortname')
ccat.add_weekday_fullname(cube, time_coord, 'my_weekday_fullname')
ccat.add_season(cube, time_coord, 'my_season')
ccat.add_season_number(cube, time_coord, 'my_season_number')
with warnings.catch_warnings(record=True):
ccat.add_season_month_initials(cube, time_coord,
'my_season_month_initials')
ccat.add_season_year(cube, time_coord, 'my_season_year')
# also test 'generic' categorisation interface
def _month_in_quarter(coord, pt_value):
date = coord.units.num2date(pt_value)
return (date.month - 1) % 3
ccat.add_categorised_coord(cube,
'my_month_in_quarter',
time_coord,
_month_in_quarter)
# To ensure consistent results between 32-bit and 64-bit
# platforms, ensure all the numeric categorisation coordinates
# are always stored as int64.
for coord in cube.coords():
if coord.long_name is not None and coord.points.dtype.kind == 'i':
coord.points = coord.points.astype(np.int64)
# check values
self.assertCML(cube, ('categorisation', 'quickcheck.cml'))
def test_basic(self):
cube = self.cube
time_coord = self.time_coord
ccat.add_year(cube, time_coord, 'my_year')
ccat.add_day_of_month(cube, time_coord, 'my_day_of_month')
ccat.add_day_of_year(cube, time_coord, 'my_day_of_year')
ccat.add_month(cube, time_coord, 'my_month')
with warnings.catch_warnings(record=True):
ccat.add_month_shortname(cube, time_coord, 'my_month_shortname')
ccat.add_month_fullname(cube, time_coord, 'my_month_fullname')
ccat.add_month_number(cube, time_coord, 'my_month_number')
ccat.add_weekday(cube, time_coord, 'my_weekday')
ccat.add_weekday_number(cube, time_coord, 'my_weekday_number')
with warnings.catch_warnings(record=True):
ccat.add_weekday_shortname(cube, time_coord,
'my_weekday_shortname')
ccat.add_weekday_fullname(cube, time_coord, 'my_weekday_fullname')
ccat.add_season(cube, time_coord, 'my_season')
ccat.add_season_number(cube, time_coord, 'my_season_number')
with warnings.catch_warnings(record=True):
ccat.add_season_month_initials(cube, time_coord,
'my_season_month_initials')
ccat.add_season_year(cube, time_coord, 'my_season_year')
# also test 'generic' categorisation interface
def _month_in_quarter(coord, pt_value):
date = coord.units.num2date(pt_value)
return (date.month - 1) % 3
ccat.add_categorised_coord(cube, 'my_month_in_quarter', time_coord,
_month_in_quarter)
# To ensure consistent results between 32-bit and 64-bit
# platforms, ensure all the numeric categorisation coordinates
# are always stored as int64.
for coord in cube.coords():
if coord.long_name is not None and coord.points.dtype.kind == 'i':
coord.points = coord.points.astype(np.int64)
# check values
self.assertCML(cube, ('categorisation', 'quickcheck.cml'))
def test_basic(self):
#make a series of 'day numbers' for the time, that slide across month boundaries
day_numbers = np.arange(0, 600, 27, dtype=np.int32)
cube = iris.cube.Cube(day_numbers, long_name='test cube', units='metres')
#use day numbers as data values also (don't actually use this for anything)
cube.data = day_numbers
time_coord = iris.coords.DimCoord(
day_numbers, standard_name='time', units=iris.unit.Unit('days since epoch', 'gregorian'))
cube.add_dim_coord(time_coord, 0)
#add test coordinates for examples wanted
ccat.add_year(cube, time_coord)
ccat.add_day_of_month(cube, 'time') #NB test passing coord-name instead of coord itself
ccat.add_month(cube, time_coord)
ccat.add_month_shortname(cube, time_coord, name='month_short')
ccat.add_month_fullname(cube, time_coord, name='month_full')
ccat.add_month_number(cube, time_coord, name='month_number')
ccat.add_weekday(cube, time_coord)
ccat.add_weekday_number(cube, time_coord, name='weekday_number')
ccat.add_weekday_shortname(cube, time_coord, name='weekday_short')
ccat.add_weekday_fullname(cube, time_coord, name='weekday_full')
ccat.add_season(cube, time_coord)
ccat.add_season_number(cube, time_coord, name='season_number')
ccat.add_season_month_initials(cube, time_coord, name='season_months')
ccat.add_season_year(cube, time_coord, name='year_ofseason')
#also test 'generic' categorisation interface
def _month_in_quarter(coord, pt_value):
date = coord.units.num2date(pt_value)
return (date.month - 1) % 3
ccat.add_categorised_coord(cube, 'month_in_quarter', time_coord, _month_in_quarter)
for coord_name in ['month_number', 'month_in_quarter', 'weekday_number', 'season_number', 'year_ofseason', 'year', 'day']:
cube.coord(coord_name).points = cube.coord(coord_name).points.astype(np.int64)
#check values
self.assertCML(cube, ('categorisation', 'quickcheck.cml'))
def test_basic(self):
cube = self.cube
time_coord = self.time_coord
ccat.add_year(cube, time_coord, "my_year")
ccat.add_day_of_month(cube, time_coord, "my_day_of_month")
ccat.add_day_of_year(cube, time_coord, "my_day_of_year")
ccat.add_month(cube, time_coord, "my_month")
ccat.add_month_fullname(cube, time_coord, "my_month_fullname")
ccat.add_month_number(cube, time_coord, "my_month_number")
ccat.add_weekday(cube, time_coord, "my_weekday")
ccat.add_weekday_number(cube, time_coord, "my_weekday_number")
ccat.add_weekday_fullname(cube, time_coord, "my_weekday_fullname")
ccat.add_season(cube, time_coord, "my_season")
ccat.add_season_number(cube, time_coord, "my_season_number")
ccat.add_season_year(cube, time_coord, "my_season_year")
# also test 'generic' categorisation interface
def _month_in_quarter(coord, pt_value):
date = coord.units.num2date(pt_value)
return (date.month - 1) % 3
ccat.add_categorised_coord(
cube, "my_month_in_quarter", time_coord, _month_in_quarter
)
# To ensure consistent results between 32-bit and 64-bit
# platforms, ensure all the numeric categorisation coordinates
# are always stored as int64.
for coord in cube.coords():
if coord.long_name is not None and coord.points.dtype.kind == "i":
coord.points = coord.points.astype(np.int64)
# check values
self.assertCML(cube, ("categorisation", "quickcheck.cml"))
def draw_for_date():
cubes = iris.load(T_FILE_PATH)
sst = cubes[1]
coord_categorisation.add_month_number(sst, "time")
coord_categorisation.add_day_of_month(sst, "time")
sst_sel = sst.extract(iris.Constraint(month_number=7, day_of_month=1))
sst_sel.data = np.ma.masked_where(sst_sel.data == 0, sst_sel.data)
b, lons, lats = nemo_commons.get_basemap_and_coordinates_from_file(T_FILE_PATH)
#Plot the data
fig = plt.figure()
x, y = b(lons, lats)
img = b.pcolormesh(x, y, sst_sel.data)
b.colorbar(img)
b.drawcoastlines()
fname = "sst_1july_1958.jpeg"
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
fig.savefig(os.path.join(NEMO_IMAGES_DIR, fname))
def draw_for_date():
cubes = iris.load(T_FILE_PATH)
sst = cubes[1]
coord_categorisation.add_month_number(sst, "time")
coord_categorisation.add_day_of_month(sst, "time")
sst_sel = sst.extract(iris.Constraint(month_number=7, day_of_month=1))
sst_sel.data = np.ma.masked_where(sst_sel.data == 0, sst_sel.data)
b, lons, lats = nemo_commons.get_basemap_and_coordinates_from_file(
T_FILE_PATH)
#Plot the data
fig = plt.figure()
x, y = b(lons, lats)
img = b.pcolormesh(x, y, sst_sel.data)
b.colorbar(img)
b.drawcoastlines()
fname = "sst_1july_1958.jpeg"
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
fig.savefig(os.path.join(NEMO_IMAGES_DIR, fname))
def read_pr_sm_topo(project_info, model):
"""
;; Arguments
;; project_info: dictionary
;; all info from namelist
;;
;; Return
;; pr: iris cube [time, lat, lon]
;; precipitation time series
;; sm: iris cube [time, lat, lon]
;; soil moisture time series
;; topo: array [lat, lon]
;; topography
;; lon: array [lon]
;; longitude
;; lat: array [lat]
;; latitude
;; time: iris cube coords
;; time info of cube
;; time_bnds_1: float
;; first time_bnd of time series
;;
;;
;; Description
;; Read cmip5 input data for computing the diagnostic
;;
"""
import projects
E = ESMValProject(project_info)
verbosity = E.get_verbosity()
#-------------------------
# Read model info
#-------------------------
currProject = getattr(vars()['projects'], model.split_entries()[0])()
model_info = model.split_entries()
mip = currProject.get_model_mip(model)
exp = currProject.get_model_exp(model)
start_year = currProject.get_model_start_year(model)
end_year = currProject.get_model_end_year(model)
years = range(int(start_year), int(end_year) + 1)
'''
#-------------------------
# Read model info
#-------------------------
model_name = model_info[1]
time_step = model_info[2]
exp_fam = model_info[3]
model_run = model_info[4]
year_start = model_info[5]
year_end = model_info[6]
filedir = model_info[7]
years = range(int(year_start), int(year_end)+1)
'''
#-------------------------
# Input data directories
#-------------------------
currDiag = project_info['RUNTIME']['currDiag']
pr_index = currDiag.get_variables().index('pr')
pr_field = currDiag.get_field_types()[pr_index]
sm_index = currDiag.get_variables().index('mrsos')
sm_field = currDiag.get_field_types()[sm_index]
indir = currProject.get_cf_outpath(project_info, model)
in_file = currProject.get_cf_outfile(project_info, model, pr_field, 'pr', mip, exp)
pr_files = [os.path.join(indir, in_file)]
in_file = currProject.get_cf_outfile(project_info, model, sm_field, 'mrsos', mip, exp)
sm_files = [os.path.join(indir, in_file)]
'''
#-------------------------
# Input data directories
#-------------------------
pr_files = []
sm_files = []
for yy in years:
Patt = filedir+'pr_'+time_step+'_'+model_name+'_'+exp_fam+'_'+\
model_run+'_'+str(yy)+'*.nc'
pr_files.append(glob.glob(Patt))
Patt = filedir+'mrsos_'+time_step+'_'+model_name+'_'+exp_fam+'_'+\
model_run+'_'+str(yy)+'*.nc'
sm_files.append(glob.glob(Patt))
pr_files = [l[0] for l in pr_files if len(l)>0]
pr_files = sorted(pr_files)
sm_files = [l[0] for l in sm_files if len(l)>0]
sm_files = sorted(sm_files)
'''
#----------------------
# Read in precipitation
#----------------------
pr_list = []
for pr_file in pr_files:
info('Reading precipitation from ' + pr_file, verbosity, required_verbosity=1)
pr = iris.load(pr_file)[0]
for at_k in pr.attributes.keys():
pr.attributes.pop(at_k)
pr_list.append(pr)
pr = iris.cube.CubeList(pr_list)
pr = pr.concatenate()[0]
# Convert longitude from 0_360 to -180_180
pr = coord_change([pr])[0]
# Add metadata: day, month, year
add_month(pr, 'time')
add_day_of_month(pr, 'time', name='dom')
add_year(pr, 'time')
# Convert units to kg m-2 hr-1
pr.convert_units('kg m-2 hr-1')
#-----------------------
# Read in soil moisture
#-----------------------
sm_list = []
for sm_file in sm_files:
info('Reading soil moisture from ' + sm_file, verbosity, required_verbosity=1)
sm = iris.load(sm_file)[0]
for at_k in sm.attributes.keys():
sm.attributes.pop(at_k)
sm_list.append(sm)
sm = iris.cube.CubeList(sm_list)
sm = sm.concatenate()[0]
# Convert longitude from 0_360 to -180_180
sm = coord_change([sm])[0]
# Add metadata: day, month, year
add_month(sm, 'time')
add_day_of_month(sm, 'time', name='dom')
add_year(sm, 'time')
#----------------------------------------------
# Constrain pr and sm data to latitude 60S_60N
#----------------------------------------------
latconstraint = iris.Constraint(latitude=lambda cell: -59.0 <= cell <= 59.0)
pr = pr.extract(latconstraint)
sm = sm.extract(latconstraint)
#---------------------------------------------------
# Read in grid info: latitude, longitude, timestamp
#---------------------------------------------------
lon = sm.coords('longitude')[0].points
lat = sm.coords('latitude')[0].points
time = sm.coords('time')
# --------------------------------------
# Convert missing data (if any) to -999.
# --------------------------------------
try:
sm.data.set_fill_value(-999)
sm.data.data[sm.data.mask] = -999.
except:
info('no missing data conversion', verbosity, required_verbosity=1)
#----------------------
# Read in topography
#----------------------
# Topography map specs:
# latitude 60S_60N
# longitude 180W_180E
# model resolution
#ftopo = currProject.get_cf_fx_file(project_info, model)
#dt = '>f4'
#topo = (np.fromfile(ftopo, dtype=dt)).reshape(len(lat), len(lon))
topo = get_topo(project_info, lon, lat, model)
#----------------------
# Read in time bounds
#----------------------
indir, infiles = currProject.get_cf_infile(project_info, model, pr_field, 'pr', mip, exp)
Patt = os.path.join(indir, infiles)
pr_files = sorted(glob.glob(Patt))
ncf = nc4.Dataset(pr_files[0])
time_bnds_1 = ncf.variables['time_bnds'][0][0]
time_bnds_1 = time_bnds_1 - int(time_bnds_1)
ncf.close()
#-----------------------------------------------
# Return input data to compute sm_pr diagnostic
#-----------------------------------------------
return pr, sm, topo, lon, lat, time, time_bnds_1
def read_pr_sm_topo(filedir, years):
"""
;; Arguments
;; filedir: dir
;; directory with input data
;; years: list of int
;; list of years for the analysis
;;
;; Return
;; pr: iris cube [time, lat, lon]
;; precipitation time series
;; sm: iris cube [time, lat, lon]
;; soil moisture time series
;; topo: array [lat, lon]
;; topography
;; lon: array [lon]
;; longitude
;; lat: array [lat]
;; latitude
;; time: iris cube coords
;; time info of cube
;;
;;
;; Description
;; Read cmip5 input data for computing the diagnostic
;;
"""
#-------------------------
# Input data directories
#-------------------------
Patt = filedir + 'pr_3hr_inmcm4_amip_r1i1p1_{}010101-{}123122.nc'
pr_files = [Patt.format(y, y) for y in years]
Patt = filedir + 'mrsos_3hr_inmcm4_amip_r1i1p1_{}010100-{}123121.nc'
sm_files = [Patt.format(y, y) for y in years]
#----------------------
# Read in precipitation
#----------------------
pr_list = []
for pr_file in pr_files:
print 'Reading precipitation from ' + pr_file
pr = iris.load(pr_file)[0]
for at_k in pr.attributes.keys():
pr.attributes.pop(at_k)
pr_list.append(pr)
pr = iris.cube.CubeList(pr_list)
pr = pr.concatenate()[0]
# Convert longitude from 0_360 to -180_180
pr = coord_change([pr])[0]
# Add metadata: day, month, year
add_month(pr, 'time')
add_day_of_month(pr, 'time', name='dom')
add_year(pr, 'time')
# Convert units to kg m-2 hr-1
pr.convert_units('kg m-2 hr-1')
#-----------------------
# Read in soil moisture
#-----------------------
sm_list = []
for sm_file in sm_files:
print 'Reading soil moisture from ' + sm_file
sm = iris.load(sm_file)[0]
for at_k in sm.attributes.keys():
sm.attributes.pop(at_k)
sm_list.append(sm)
sm = iris.cube.CubeList(sm_list)
sm = sm.concatenate()[0]
# Convert longitude from 0_360 to -180_180
sm = coord_change([sm])[0]
# Add metadata: day, month, year
add_month(sm, 'time')
add_day_of_month(sm, 'time', name='dom')
add_year(sm, 'time')
#----------------------------------------------
# Constrain pr and sm data to latitude 60S_60N
#----------------------------------------------
latconstraint = iris.Constraint(
latitude=lambda cell: -59.0 <= cell <= 59.0)
pr = pr.extract(latconstraint)
sm = sm.extract(latconstraint)
#---------------------------------------------------
# Read in grid info: latitude, longitude, timestamp
#---------------------------------------------------
lon = sm.coords('longitude')[0].points
lat = sm.coords('latitude')[0].points
time = sm.coords('time')
# --------------------------------------
# Convert missing data (if any) to -999.
# --------------------------------------
try:
sm.data.set_fill_value(-999)
sm.data.data[sm.data.mask] = -999.
except:
print 'no missing data conversion'
#----------------------
# Read in topography
#----------------------
# Topography map specs:
# latitude 60S_60N
# longitude 180W_180E
# model resolution
ftopo = filedir + 'topo_var_5x5_inmcm4.gra'
dt = '>f4'
topo = (np.fromfile(ftopo, dtype=dt)).reshape(len(lat), len(lon))
#-----------------------------------------------
# Return input data to compute sm_pr diagnostic
#-----------------------------------------------
return pr, sm, topo, lon, lat, time
def add_time_coord_cats(cube):
"""
This function takes in an iris cube, and adds a range of
numeric co-ordinate categorisations to it. Depending
on the data, not all of the coords added will be relevant.
args
----
cube: iris cube that has a coordinate called 'time'
Returns
-------
Cube: cube that has new time categorisation coords added
Notes
-----
test
A simple example:
>>> file = os.path.join(conf.DATA_DIR, 'mslp.daily.rcm.viet.nc')
>>> cube = iris.load_cube(file)
>>> coord_names = [coord.name() for coord in cube.coords()]
>>> print((', '.join(coord_names)))
time, grid_latitude, grid_longitude
>>> ccube = add_time_coord_cats(cube)
>>> coord_names = [coord.name() for coord in ccube.coords()]
>>> print((', '.join(coord_names)))
time, grid_latitude, grid_longitude, day_of_month, day_of_year, month, \
month_number, season, season_number, year
>>> # print every 50th value of the added time cat coords
... for c in coord_names[3:]:
... print(ccube.coord(c).long_name)
... print(ccube.coord(c).points[::50])
...
day_of_month
[ 1 21 11 1 21 11 1 21]
day_of_year
[ 1 51 101 151 201 251 301 351]
month
['Jan' 'Feb' 'Apr' 'Jun' 'Jul' 'Sep' 'Nov' 'Dec']
month_number
[ 1 2 4 6 7 9 11 12]
season
['djf' 'djf' 'mam' 'jja' 'jja' 'son' 'son' 'djf']
season_number
[0 0 1 2 2 3 3 0]
year
[2000 2000 2000 2000 2000 2000 2000 2000]
"""
# most errors pop up when you try to add a coord that has
# previously been added, or the cube doesn't contain the
# necessary attribute.
ccube = cube.copy()
# numeric
try:
iccat.add_day_of_year(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_day_of_month(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_month_number(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_season_number(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_year(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
# strings
try:
iccat.add_month(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_season(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
return ccube
