def compile(self, compiler):
""" Writes kernel code to file and compiles it."""
with open(self.src_file, 'w') as f:
f.write(self.ccode)
compiler.compile(self.src_file, self.lib_file, self.log_file)
logger.info("Compiled %s ==> %s" % (self.name, self.lib_file))
self._cleanup_files = finalize(self, cleanup_remove_files, self.delete_cfiles, self.src_file, self.lib_file, self.log_file)
def create_parcelsfig_axis(spherical, land=True, projection=None, central_longitude=0):
try:
import matplotlib.pyplot as plt
except:
logger.info("Visualisation is not possible. Matplotlib not found.")
return None, None, None, None # creating axes was not possible
if projection is not None and not spherical:
raise RuntimeError('projection not accepted when Field doesn''t have geographic coordinates')
if spherical:
try:
import cartopy
except:
logger.info("Visualisation of field with geographic coordinates is not possible. Cartopy not found.")
return None, None, None, None # creating axes was not possible
projection = cartopy.crs.PlateCarree(central_longitude) if projection is None else projection
fig, ax = plt.subplots(1, 1, subplot_kw={'projection': projection})
try: # gridlines not supported for all projections
gl = ax.gridlines(crs=projection, draw_labels=True)
gl.xlabels_top, gl.ylabels_right = (False, False)
gl.xformatter = cartopy.mpl.gridliner.LONGITUDE_FORMATTER
gl.yformatter = cartopy.mpl.gridliner.LATITUDE_FORMATTER
except:
pass
if land:
ax.coastlines()
else:
cartopy = None
fig, ax = plt.subplots(1, 1)
ax.grid()
return plt, fig, ax, cartopy
def lib(self, compiler=GNUCompiler()):
if self._lib is None:
with open(self.src_file, 'w') as f:
f.write(self.ccode)
compiler.compile(self.src_file, self.lib_file, self.log_file)
logger.info("Compiled %s ==> %s" % ("random", self.lib_file))
self._lib = npct.load_library(self.lib_file, '.')
return self._lib
def test_plotting(pset_mode, mode, tmpdir):
if mode == '3d' and sys.platform in ['linux', 'linux2']:
logger.info(
'Skipping 3d test in linux Travis, since it fails to find display to connect'
)
return
fp = create_outputfiles(tmpdir, pset_mode)
plotTrajectoriesFile(fp, mode=mode, show_plt=False)
def remove_deleted(self, pset, output_file, endtime):
"""
Utility to remove all particles that signalled deletion.
This version is generally applicable to all structures and collections
"""
# Indices marked for deletion.
indices = [i for i, p in enumerate(pset) if p.state == OperationCode.Delete]
if len(indices) > 0:
logger.info("Deleted {} particles.".format(len(indices)))
if len(indices) > 0 and output_file is not None:
output_file.write(pset, endtime, deleted_only=indices)
pset.remove_indices(indices)
def write(self, filename):
"""Write FieldSet to NetCDF file using NEMO convention
:param filename: Basename of the output fileset"""
logger.info("Generating NEMO FieldSet output with basename: %s" % filename)
if hasattr(self, 'U'):
self.U.write(filename, varname='vozocrtx')
if hasattr(self, 'V'):
self.V.write(filename, varname='vomecrty')
for v in self.get_fields():
if (v.name != 'U') and (v.name != 'V'):
v.write(filename)
def compile(self, compiler):
""" Writes kernel code to file and compiles it."""
all_files_array = []
if self.src_file is None:
for dyn_src in self.dyn_srcs:
with open(dyn_src, 'w') as f:
f.write(self.ccode)
all_files_array.append(dyn_src)
compiler.compile(self.dyn_srcs, self.lib_file, self.log_file)
else:
with open(self.src_file, 'w') as f:
f.write(self.ccode)
all_files_array.append(self.src_file)
compiler.compile(self.src_file, self.lib_file, self.log_file)
logger.info("Compiled %s ==> %s" % (self.name, self.lib_file))
all_files_array.append(self.log_file)
self._cleanup_files = finalize(self, BaseKernel.cleanup_remove_files, self.lib_file, all_files_array, self.delete_cfiles)
def compile(self, compiler=None):
if self.src_file is None or self.lib_file is None or self.log_file is None:
basename = 'parcels_random_%s' % uuid.uuid4()
lib_filename = "lib" + basename
basepath = path.join(get_cache_dir(), "%s" % basename)
libpath = path.join(get_cache_dir(), "%s" % lib_filename)
self.src_file = "%s.c" % basepath
self.lib_file = "%s.so" % libpath
self.log_file = "%s.log" % basepath
ccompiler = compiler
if ccompiler is None:
cppargs = []
incdirs = [path.join(get_package_dir(), 'include'), ]
ccompiler = GNUCompiler(cppargs=cppargs, incdirs=incdirs)
if self._lib is None:
with open(self.src_file, 'w+') as f:
f.write(self.ccode)
ccompiler.compile(self.src_file, self.lib_file, self.log_file)
logger.info("Compiled %s ==> %s" % ("ParcelsRandom", self.lib_file))
def compile(self, compiler):
""" Writes kernel code to file and compiles it."""
all_files_array = []
if self.src_file is None:
if self.dyn_srcs is not None:
for dyn_src in self.dyn_srcs:
with open(dyn_src, 'w') as f:
f.write(self.ccode)
all_files_array.append(dyn_src)
compiler.compile(self.dyn_srcs, self.lib_file, self.log_file)
else:
if self.src_file is not None:
with open(self.src_file, 'w') as f:
f.write(self.ccode)
if self.src_file is not None:
all_files_array.append(self.src_file)
compiler.compile(self.src_file, self.lib_file, self.log_file)
if len(all_files_array) > 0:
logger.info("Compiled %s ==> %s" % (self.name, self.lib_file))
if self.log_file is not None:
all_files_array.append(self.log_file)
def execute(self,
pyfunc=AdvectionRK4,
endtime=None,
runtime=None,
dt=1.,
moviedt=None,
recovery=None,
output_file=None,
movie_background_field=None,
verbose_progress=None,
postIterationCallbacks=None,
callbackdt=None):
"""Execute a given kernel function over the particle set for
multiple timesteps. Optionally also provide sub-timestepping
for particle output.
:param pyfunc: Kernel function to execute. This can be the name of a
defined Python function or a :class:`parcels.kernel.Kernel` object.
Kernels can be concatenated using the + operator
:param endtime: End time for the timestepping loop.
It is either a datetime object or a positive double.
:param runtime: Length of the timestepping loop. Use instead of endtime.
It is either a timedelta object or a positive double.
:param dt: Timestep interval to be passed to the kernel.
It is either a timedelta object or a double.
Use a negative value for a backward-in-time simulation.
:param moviedt: Interval for inner sub-timestepping (leap), which dictates
the update frequency of animation.
It is either a timedelta object or a positive double.
None value means no animation.
:param output_file: :mod:`parcels.particlefile.ParticleFile` object for particle output
:param recovery: Dictionary with additional `:mod:parcels.tools.error`
recovery kernels to allow custom recovery behaviour in case of
kernel errors.
:param movie_background_field: field plotted as background in the movie if moviedt is set.
'vector' shows the velocity as a vector field.
:param verbose_progress: Boolean for providing a progress bar for the kernel execution loop.
:param postIterationCallbacks: (Optional) Array of functions that are to be called after each iteration (post-process, non-Kernel)
:param callbackdt: (Optional, in conjecture with 'postIterationCallbacks) timestep inverval to (latestly) interrupt the running kernel and invoke post-iteration callbacks from 'postIterationCallbacks'
"""
# check if pyfunc has changed since last compile. If so, recompile
if self.kernel is None or (self.kernel.pyfunc is not pyfunc
and self.kernel is not pyfunc):
# Generate and store Kernel
if isinstance(pyfunc, Kernel):
self.kernel = pyfunc
else:
self.kernel = self.Kernel(pyfunc)
# Prepare JIT kernel execution
if self.ptype.uses_jit:
self.kernel.remove_lib()
cppargs = ['-DDOUBLE_COORD_VARIABLES'
] if self.lonlatdepth_dtype == np.float64 else None
self.kernel.compile(compiler=GNUCompiler(cppargs=cppargs))
self.kernel.load_lib()
# Convert all time variables to seconds
if isinstance(endtime, delta):
raise RuntimeError('endtime must be either a datetime or a double')
if isinstance(endtime, datetime):
endtime = np.datetime64(endtime)
if isinstance(endtime, np.datetime64):
if self.time_origin.calendar is None:
raise NotImplementedError(
'If fieldset.time_origin is not a date, execution endtime must be a double'
)
endtime = self.time_origin.reltime(endtime)
if isinstance(runtime, delta):
runtime = runtime.total_seconds()
if isinstance(dt, delta):
dt = dt.total_seconds()
outputdt = output_file.outputdt if output_file else np.infty
if isinstance(outputdt, delta):
outputdt = outputdt.total_seconds()
if isinstance(moviedt, delta):
moviedt = moviedt.total_seconds()
if isinstance(callbackdt, delta):
callbackdt = callbackdt.total_seconds()
assert runtime is None or runtime >= 0, 'runtime must be positive'
assert outputdt is None or outputdt >= 0, 'outputdt must be positive'
assert moviedt is None or moviedt >= 0, 'moviedt must be positive'
mintime, maxtime = self.fieldset.gridset.dimrange(
'time_full') if self.fieldset is not None else (0, 1)
if np.any(np.isnan(self.particle_data['time'])):
self.particle_data['time'][np.isnan(
self.particle_data['time'])] = mintime if dt >= 0 else maxtime
# Derive _starttime and endtime from arguments or fieldset defaults
if runtime is not None and endtime is not None:
raise RuntimeError(
'Only one of (endtime, runtime) can be specified')
_starttime = self.particle_data['time'].min(
) if dt >= 0 else self.particle_data['time'].max()
if self.repeatdt is not None and self.repeat_starttime is None:
self.repeat_starttime = _starttime
if runtime is not None:
endtime = _starttime + runtime * np.sign(dt)
elif endtime is None:
mintime, maxtime = self.fieldset.gridset.dimrange('time_full')
endtime = maxtime if dt >= 0 else mintime
execute_once = False
if abs(endtime - _starttime) < 1e-5 or dt == 0 or runtime == 0:
dt = 0
runtime = 0
endtime = _starttime
logger.warning_once(
"dt or runtime are zero, or endtime is equal to Particle.time. "
"The kernels will be executed once, without incrementing time")
execute_once = True
self.particle_data['dt'][:] = dt
# First write output_file, because particles could have been added
if output_file:
output_file.write(self, _starttime)
if moviedt:
self.show(field=movie_background_field,
show_time=_starttime,
animation=True)
if moviedt is None:
moviedt = np.infty
if callbackdt is None:
interupt_dts = [np.infty, moviedt, outputdt]
if self.repeatdt is not None:
interupt_dts.append(self.repeatdt)
callbackdt = np.min(np.array(interupt_dts))
time = _starttime
if self.repeatdt:
next_prelease = self.repeat_starttime + (
abs(time - self.repeat_starttime) // self.repeatdt +
1) * self.repeatdt * np.sign(dt)
else:
next_prelease = np.infty if dt > 0 else -np.infty
next_output = time + outputdt if dt > 0 else time - outputdt
next_movie = time + moviedt if dt > 0 else time - moviedt
next_callback = time + callbackdt if dt > 0 else time - callbackdt
next_input = self.fieldset.computeTimeChunk(
time, np.sign(dt)) if self.fieldset is not None else np.inf
tol = 1e-12
if verbose_progress is None:
walltime_start = time_module.time()
if verbose_progress:
pbar = self.__create_progressbar(_starttime, endtime)
while (time < endtime and dt > 0) or (time > endtime
and dt < 0) or dt == 0:
if verbose_progress is None and time_module.time(
) - walltime_start > 10:
# Showing progressbar if runtime > 10 seconds
if output_file:
logger.info('Temporary output files are stored in %s.' %
output_file.tempwritedir_base)
logger.info(
'You can use "parcels_convert_npydir_to_netcdf %s" to convert these '
'to a NetCDF file during the run.' %
output_file.tempwritedir_base)
pbar = self.__create_progressbar(_starttime, endtime)
verbose_progress = True
if dt > 0:
time = min(next_prelease, next_input, next_output, next_movie,
next_callback, endtime)
else:
time = max(next_prelease, next_input, next_output, next_movie,
next_callback, endtime)
self.kernel.execute(self,
endtime=time,
dt=dt,
recovery=recovery,
output_file=output_file,
execute_once=execute_once)
if abs(time - next_prelease) < tol:
pset_new = ParticleSet(
fieldset=self.fieldset,
time=time,
lon=self.repeatlon,
lat=self.repeatlat,
depth=self.repeatdepth,
pclass=self.repeatpclass,
lonlatdepth_dtype=self.lonlatdepth_dtype,
partitions=False,
pid_orig=self.repeatpid,
**self.repeatkwargs)
p = pset_new.data_accessor()
for i in range(pset_new.size):
p.set_index(i)
p.dt = dt
self.add(pset_new)
next_prelease += self.repeatdt * np.sign(dt)
if abs(time - next_output) < tol:
if output_file:
output_file.write(self, time)
next_output += outputdt * np.sign(dt)
if abs(time - next_movie) < tol:
self.show(field=movie_background_field,
show_time=time,
animation=True)
next_movie += moviedt * np.sign(dt)
# ==== insert post-process here to also allow for memory clean-up via external func ==== #
if abs(time - next_callback) < tol:
if postIterationCallbacks is not None:
for extFunc in postIterationCallbacks:
extFunc()
next_callback += callbackdt * np.sign(dt)
if time != endtime:
next_input = self.fieldset.computeTimeChunk(time, dt)
if dt == 0:
break
if verbose_progress:
pbar.update(abs(time - _starttime))
if output_file:
output_file.write(self, time)
if verbose_progress:
pbar.finish()
def plotfield(field, show_time=None, domain=None, depth_level=0, projection=None, land=True,
vmin=None, vmax=None, savefile=None, **kwargs):
"""Function to plot a Parcels Field
:param show_time: Time at which to show the Field
:param domain: Four-vector (latN, latS, lonE, lonW) defining domain to show
:param depth_level: depth level to be plotted (default 0)
:param projection: type of cartopy projection to use (default PlateCarree)
:param land: Boolean whether to show land. This is ignored for flat meshes
:param vmin: minimum colour scale (only in single-plot mode)
:param vmax: maximum colour scale (only in single-plot mode)
:param savefile: Name of a file to save the plot to
:param animation: Boolean whether result is a single plot, or an animation
"""
if type(field) is VectorField:
spherical = True if field.U.grid.mesh == 'spherical' else False
field = [field.U, field.V]
plottype = 'vector'
elif type(field) is Field:
spherical = True if field.grid.mesh == 'spherical' else False
field = [field]
plottype = 'scalar'
else:
raise RuntimeError('field needs to be a Field or VectorField object')
plt, fig, ax, cartopy = create_parcelsfig_axis(spherical, land, projection=projection)
if plt is None:
return None, None, None, None # creating axes was not possible
data = {}
plotlon = {}
plotlat = {}
for i, fld in enumerate(field):
show_time = fld.grid.time[0] if show_time is None else show_time
if fld.grid.defer_load:
fld.fieldset.computeTimeChunk(show_time, 1)
(idx, periods) = fld.time_index(show_time)
show_time -= periods * (fld.grid.time[-1] - fld.grid.time[0])
if show_time > fld.grid.time[-1] or show_time < fld.grid.time[0]:
raise TimeExtrapolationError(show_time, field=fld, msg='show_time')
latN, latS, lonE, lonW = parsedomain(domain, fld)
if isinstance(fld.grid, CurvilinearGrid):
plotlon[i] = fld.grid.lon[latS:latN, lonW:lonE]
plotlat[i] = fld.grid.lat[latS:latN, lonW:lonE]
else:
plotlon[i] = fld.grid.lon[lonW:lonE]
plotlat[i] = fld.grid.lat[latS:latN]
if i > 0 and not np.allclose(plotlon[i], plotlon[0]):
raise RuntimeError('VectorField needs to be on an A-grid for plotting')
if fld.grid.time.size > 1:
if fld.grid.zdim > 1:
data[i] = np.squeeze(fld.temporal_interpolate_fullfield(idx, show_time))[depth_level, latS:latN, lonW:lonE]
else:
data[i] = np.squeeze(fld.temporal_interpolate_fullfield(idx, show_time))[latS:latN, lonW:lonE]
else:
if fld.grid.zdim > 1:
data[i] = np.squeeze(fld.data)[depth_level, latS:latN, lonW:lonE]
else:
data[i] = np.squeeze(fld.data)[latS:latN, lonW:lonE]
if plottype is 'vector':
spd = data[0] ** 2 + data[1] ** 2
speed = np.sqrt(spd, where=spd > 0)
vmin = speed.min() if vmin is None else vmin
vmax = speed.max() if vmax is None else vmax
if isinstance(field[0].grid, CurvilinearGrid):
x, y = plotlon[0], plotlat[0]
else:
x, y = np.meshgrid(plotlon[0], plotlat[0])
nonzerospd = speed != 0
u, v = (np.zeros_like(data[0]) * np.nan, np.zeros_like(data[1]) * np.nan)
np.place(u, nonzerospd, data[0][nonzerospd] / speed[nonzerospd])
np.place(v, nonzerospd, data[1][nonzerospd] / speed[nonzerospd])
if cartopy:
cs = ax.quiver(x, y, u, v, speed, cmap=plt.cm.gist_ncar, clim=[vmin, vmax], scale=50, transform=cartopy.crs.PlateCarree())
else:
cs = ax.quiver(x, y, u, v, speed, cmap=plt.cm.gist_ncar, clim=[vmin, vmax], scale=50)
else:
vmin = data[0].min() if vmin is None else vmin
vmax = data[0].max() if vmax is None else vmax
if cartopy:
cs = ax.pcolormesh(plotlon[0], plotlat[0], data[0], transform=cartopy.crs.PlateCarree())
else:
cs = ax.pcolormesh(plotlon[0], plotlat[0], data[0])
if cartopy is None or projection is None:
ax.set_xlim(np.nanmin(plotlon[0]), np.nanmax(plotlon[0]))
ax.set_ylim(np.nanmin(plotlat[0]), np.nanmax(plotlat[0]))
elif domain is not None:
ax.set_extent([np.nanmin(plotlon[0]), np.nanmax(plotlon[0]), np.nanmin(plotlat[0]), np.nanmax(plotlat[0])])
cs.cmap.set_over('k')
cs.cmap.set_under('w')
cs.set_clim(vmin, vmax)
cartopy_colorbar(cs, plt, fig, ax)
timestr = parsetimestr(field[0].grid.time_origin, show_time)
titlestr = kwargs.pop('titlestr', '')
if field[0].grid.zdim > 1:
if field[0].grid.gtype in [GridCode.CurvilinearZGrid, GridCode.RectilinearZGrid]:
gphrase = 'depth'
depth_or_level = field[0].grid.depth[depth_level]
else:
gphrase = 'level'
depth_or_level = depth_level
depthstr = ' at %s %g ' % (gphrase, depth_or_level)
else:
depthstr = ''
if plottype is 'vector':
ax.set_title(titlestr + 'Velocity field' + depthstr + timestr)
else:
ax.set_title(titlestr + field[0].name + depthstr + timestr)
if not spherical:
ax.set_xlabel('Zonal distance [m]')
ax.set_ylabel('Meridional distance [m]')
plt.draw()
if savefile:
plt.savefig(savefile)
logger.info('Plot saved to ' + savefile + '.png')
plt.close()
return plt, fig, ax, cartopy
def plotparticles(particles, with_particles=True, show_time=None, field=None, domain=None, projection=None,
land=True, vmin=None, vmax=None, savefile=None, animation=False):
"""Function to plot a Parcels ParticleSet
:param show_time: Time at which to show the ParticleSet
:param with_particles: Boolean whether particles are also plotted on Field
:param field: Field to plot under particles (either None, a Field object, or 'vector')
:param domain: Four-vector (latN, latS, lonE, lonW) defining domain to show
:param projection: type of cartopy projection to use (default PlateCarree)
:param land: Boolean whether to show land. This is ignored for flat meshes
:param vmin: minimum colour scale (only in single-plot mode)
:param vmax: maximum colour scale (only in single-plot mode)
:param savefile: Name of a file to save the plot to
:param animation: Boolean whether result is a single plot, or an animation
"""
show_time = particles[0].time if show_time is None else show_time
if isinstance(show_time, datetime):
show_time = np.datetime64(show_time)
if isinstance(show_time, np.datetime64):
if not particles.time_origin:
raise NotImplementedError(
'If fieldset.time_origin is not a date, showtime cannot be a date in particleset.show()')
show_time = particles.time_origin.reltime(show_time)
if isinstance(show_time, delta):
show_time = show_time.total_seconds()
if np.isnan(show_time):
show_time, _ = particles.fieldset.gridset.dimrange('time_full')
if field is None:
spherical = True if particles.fieldset.U.grid.mesh == 'spherical' else False
plt, fig, ax, cartopy = create_parcelsfig_axis(spherical, land, projection)
if plt is None:
return # creating axes was not possible
ax.set_title('Particles' + parsetimestr(particles.fieldset.U.grid.time_origin, show_time))
latN, latS, lonE, lonW = parsedomain(domain, particles.fieldset.U)
if cartopy is None or projection is None:
if domain is not None:
if isinstance(particles.fieldset.U.grid, CurvilinearGrid):
ax.set_xlim(particles.fieldset.U.grid.lon[latS, lonW], particles.fieldset.U.grid.lon[latN, lonE])
ax.set_ylim(particles.fieldset.U.grid.lat[latS, lonW], particles.fieldset.U.grid.lat[latN, lonE])
else:
ax.set_xlim(particles.fieldset.U.grid.lon[lonW], particles.fieldset.U.grid.lon[lonE])
ax.set_ylim(particles.fieldset.U.grid.lat[latS], particles.fieldset.U.grid.lat[latN])
else:
ax.set_xlim(np.nanmin(particles.fieldset.U.grid.lon), np.nanmax(particles.fieldset.U.grid.lon))
ax.set_ylim(np.nanmin(particles.fieldset.U.grid.lat), np.nanmax(particles.fieldset.U.grid.lat))
elif domain is not None:
if isinstance(particles.fieldset.U.grid, CurvilinearGrid):
ax.set_extent([particles.fieldset.U.grid.lon[latS, lonW], particles.fieldset.U.grid.lon[latN, lonE],
particles.fieldset.U.grid.lat[latS, lonW], particles.fieldset.U.grid.lat[latN, lonE]])
else:
ax.set_extent([particles.fieldset.U.grid.lon[lonW], particles.fieldset.U.grid.lon[lonE],
particles.fieldset.U.grid.lat[latS], particles.fieldset.U.grid.lat[latN]])
else:
if field is 'vector':
field = particles.fieldset.UV
elif not isinstance(field, Field):
field = getattr(particles.fieldset, field)
plt, fig, ax, cartopy = plotfield(field=field, animation=animation, show_time=show_time, domain=domain,
projection=projection, land=land, vmin=vmin, vmax=vmax, savefile=None,
titlestr='Particles and ')
if plt is None:
return # creating axes was not possible
if with_particles:
plon = np.array([p.lon for p in particles])
plat = np.array([p.lat for p in particles])
if cartopy:
ax.scatter(plon, plat, s=20, color='black', zorder=20, transform=cartopy.crs.PlateCarree())
else:
ax.scatter(plon, plat, s=20, color='black', zorder=20)
if animation:
plt.draw()
plt.pause(0.0001)
elif savefile is None:
plt.show()
else:
plt.savefig(savefile)
logger.info('Plot saved to ' + savefile + '.png')
plt.close()
def plotfield(field,
show_time=None,
domain=None,
depth_level=0,
projection=None,
land=True,
vmin=None,
vmax=None,
savefile=None,
**kwargs):
"""Function to plot a Parcels Field
:param show_time: Time at which to show the Field
:param domain: dictionary (with keys 'N', 'S', 'E', 'W') defining domain to show
:param depth_level: depth level to be plotted (default 0)
:param projection: type of cartopy projection to use (default PlateCarree)
:param land: Boolean whether to show land. This is ignored for flat meshes
:param vmin: minimum colour scale (only in single-plot mode)
:param vmax: maximum colour scale (only in single-plot mode)
:param savefile: Name of a file to save the plot to
:param animation: Boolean whether result is a single plot, or an animation
"""
if type(field) is VectorField:
spherical = True if field.U.grid.mesh == 'spherical' else False
field = [field.U, field.V]
plottype = 'vector'
elif type(field) is Field:
spherical = True if field.grid.mesh == 'spherical' else False
field = [field]
plottype = 'scalar'
else:
raise RuntimeError('field needs to be a Field or VectorField object')
if field[0].grid.gtype in [
GridCode.CurvilinearZGrid, GridCode.CurvilinearSGrid
]:
logger.warning(
'Field.show() does not always correctly determine the domain for curvilinear grids. '
'Use plotting with caution and perhaps use domain argument as in the NEMO 3D tutorial'
)
plt, fig, ax, cartopy = create_parcelsfig_axis(spherical,
land,
projection=projection)
if plt is None:
return None, None, None, None # creating axes was not possible
data = {}
plotlon = {}
plotlat = {}
for i, fld in enumerate(field):
show_time = fld.grid.time[0] if show_time is None else show_time
if fld.grid.defer_load:
fld.fieldset.computeTimeChunk(show_time, 1)
(idx, periods) = fld.time_index(show_time)
show_time -= periods * (fld.grid.time_full[-1] - fld.grid.time_full[0])
if show_time > fld.grid.time[-1] or show_time < fld.grid.time[0]:
raise TimeExtrapolationError(show_time, field=fld, msg='show_time')
latN, latS, lonE, lonW = parsedomain(domain, fld)
if isinstance(fld.grid, CurvilinearGrid):
plotlon[i] = fld.grid.lon[latS:latN, lonW:lonE]
plotlat[i] = fld.grid.lat[latS:latN, lonW:lonE]
else:
plotlon[i] = fld.grid.lon[lonW:lonE]
plotlat[i] = fld.grid.lat[latS:latN]
if i > 0 and not np.allclose(plotlon[i], plotlon[0]):
raise RuntimeError(
'VectorField needs to be on an A-grid for plotting')
if fld.grid.time.size > 1:
if fld.grid.zdim > 1:
data[i] = np.squeeze(
fld.temporal_interpolate_fullfield(idx,
show_time))[depth_level,
latS:latN,
lonW:lonE]
else:
data[i] = np.squeeze(
fld.temporal_interpolate_fullfield(idx,
show_time))[latS:latN,
lonW:lonE]
else:
if fld.grid.zdim > 1:
data[i] = np.squeeze(fld.data)[depth_level, latS:latN,
lonW:lonE]
else:
data[i] = np.squeeze(fld.data)[latS:latN, lonW:lonE]
if plottype == 'vector':
if field[0].interp_method == 'cgrid_velocity':
logger.warning_once(
'Plotting a C-grid velocity field is achieved via an A-grid projection, reducing the plot accuracy'
)
d = np.empty_like(data[0])
d[:-1, :] = (data[0][:-1, :] + data[0][1:, :]) / 2.
d[-1, :] = data[0][-1, :]
data[0] = d
d = np.empty_like(data[0])
d[:, :-1] = (data[0][:, :-1] + data[0][:, 1:]) / 2.
d[:, -1] = data[0][:, -1]
data[1] = d
spd = data[0]**2 + data[1]**2
speed = np.where(spd > 0, np.sqrt(spd), 0)
vmin = speed.min() if vmin is None else vmin
vmax = speed.max() if vmax is None else vmax
if isinstance(field[0].grid, CurvilinearGrid):
x, y = plotlon[0], plotlat[0]
else:
x, y = np.meshgrid(plotlon[0], plotlat[0])
u = np.where(speed > 0., data[0] / speed, 0)
v = np.where(speed > 0., data[1] / speed, 0)
if cartopy:
cs = ax.quiver(np.asarray(x),
np.asarray(y),
np.asarray(u),
np.asarray(v),
speed,
cmap=plt.cm.gist_ncar,
clim=[vmin, vmax],
scale=50,
transform=cartopy.crs.PlateCarree())
else:
cs = ax.quiver(x,
y,
u,
v,
speed,
cmap=plt.cm.gist_ncar,
clim=[vmin, vmax],
scale=50)
else:
vmin = data[0].min() if vmin is None else vmin
vmax = data[0].max() if vmax is None else vmax
assert len(data[0].shape) == 2
if field[0].interp_method == 'cgrid_tracer':
d = data[0][1:, 1:]
elif field[0].interp_method == 'cgrid_velocity':
if field[0].fieldtype == 'U':
d = np.empty_like(data[0])
d[:-1, :-1] = (data[0][1:, :-1] + data[0][1:, 1:]) / 2.
elif field[0].fieldtype == 'V':
d = np.empty_like(data[0])
d[:-1, :-1] = (data[0][:-1, 1:] + data[0][1:, 1:]) / 2.
else: # W
d = data[0][1:, 1:]
else: # if A-grid
d = (data[0][:-1, :-1] + data[0][1:, :-1] + data[0][:-1, 1:] +
data[0][1:, 1:]) / 4.
d = np.where(data[0][:-1, :-1] == 0, 0, d)
d = np.where(data[0][1:, :-1] == 0, 0, d)
d = np.where(data[0][1:, 1:] == 0, 0, d)
d = np.where(data[0][:-1, 1:] == 0, 0, d)
if cartopy:
cs = ax.pcolormesh(plotlon[0],
plotlat[0],
d,
transform=cartopy.crs.PlateCarree())
else:
cs = ax.pcolormesh(plotlon[0], plotlat[0], d)
if cartopy is None:
ax.set_xlim(np.nanmin(plotlon[0]), np.nanmax(plotlon[0]))
ax.set_ylim(np.nanmin(plotlat[0]), np.nanmax(plotlat[0]))
elif domain is not None:
ax.set_extent([
np.nanmin(plotlon[0]),
np.nanmax(plotlon[0]),
np.nanmin(plotlat[0]),
np.nanmax(plotlat[0])
],
crs=cartopy.crs.PlateCarree())
cs.cmap.set_over('k')
cs.cmap.set_under('w')
cs.set_clim(vmin, vmax)
cartopy_colorbar(cs, plt, fig, ax)
timestr = parsetimestr(field[0].grid.time_origin, show_time)
titlestr = kwargs.pop('titlestr', '')
if field[0].grid.zdim > 1:
if field[0].grid.gtype in [
GridCode.CurvilinearZGrid, GridCode.RectilinearZGrid
]:
gphrase = 'depth'
depth_or_level = field[0].grid.depth[depth_level]
else:
gphrase = 'level'
depth_or_level = depth_level
depthstr = ' at %s %g ' % (gphrase, depth_or_level)
else:
depthstr = ''
if plottype == 'vector':
ax.set_title(titlestr + 'Velocity field' + depthstr + timestr)
else:
ax.set_title(titlestr + field[0].name + depthstr + timestr)
if not spherical:
ax.set_xlabel('Zonal distance [m]')
ax.set_ylabel('Meridional distance [m]')
plt.draw()
if savefile:
plt.savefig(savefile)
logger.info('Plot saved to ' + savefile + '.png')
plt.close()
return plt, fig, ax, cartopy
def compile(self, compiler):
""" Writes kernel code to file and compiles it."""
with open(self.src_file, 'w') as f:
f.write(self.ccode)
compiler.compile(self.src_file, self.lib_file, self.log_file)
logger.info("Compiled %s ==> %s" % (self.name, self.lib_file))
