ncfile = "swan.four.nc"
lon0 = -5.1
lon1 = -4.9
lat0 = 48.35
lat1 = 48.55
# Imports
from vcmq import cdms2, data_sample, N, transect, curve2, code_file_name, os, \
add_map_lines, P, add_shadow
# Read data
f = cdms2.open(data_sample(ncfile))
hs = f('HS', squeeze=1)
f.close()
# Compute transect
hst, lons, lats = transect(hs, (lon0,lon1), (lat0,lat1), getcoords=True)
# Plot along time
s = curve2(hst, figsize=(8,3), title='Spatial transect on curved grid',
show=False, top=0.85)
# Add a small map to show the transect positions
o = add_map_lines(hs, lons, lats, map_bgcolor='w', map_bbox= [.6, .2, .3, .5], map_anchor='W')
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
def plot_scattered_locs(lons,
lats,
depths,
slice_type=None,
interval=None,
plotter=None,
lon=None,
lat=None,
level=None,
label='',
lon_bounds_margin=.1,
lat_bounds_margin=.1,
data=None,
warn=True,
bathy=None,
xybathy=None,
secbathy=None,
size=20,
color='#2ca02c',
linewidth=0.4,
edgecolor='k',
add_profile_line=None,
add_bathy=True,
add_minimap=True,
add_section_bathy=True,
fig=None,
title="{long_name}",
register_sm=True,
depthshade=False,
legend=False,
colorbar=True,
**kwargs):
"""Plot scattered localisations
Parameters
----------
lons: n-D array
lats: n-D array
depths: n-D array
slice_type: one of "3d"/None, "2d", "zonal", "meridional", "horizontal"
The way to slice the observations.
"3d"/"2d" are 3D/2D view of all observations.
Other slices make a selection with a range (``interval``).
interval: None, tuple of float
Interval for selecting valid data
Required if slice_type is not "3d"/None/"2d".
map_<param>:
<param> is passed to :func:`create_map`
section_<param>:
<param> is passed to :func:`vacumm.misc.plot.section2`
minimap_<param>:
<param> is passed to :func:`vacumm.misc.plot.add_map_box`
Todo
----
Add time support.
"""
# Inits
if cdms2.isVariable(data):
data = data.asma()
kwmap = kwfilter(kwargs, 'map_')
kwminimap = kwfilter(kwargs, 'minimap_')
kwsecbat = kwfilter(kwargs, 'section_bathy_')
kwsection = kwfilter(kwargs, 'section_')
kwplt = kwfilter(kwargs, 'plotter_')
dict_check_defaults(kwmap, **kwplt)
dict_check_defaults(kwsection, **kwplt)
kwpf = kwfilter(kwargs, 'add_profile_line')
kwleg = kwfilter(kwargs, 'legend')
kwcb = kwfilter(kwargs, 'colorbar')
long_name = get_long_name(data)
units = getattr(data, 'units', '')
if long_name is None:
long_name = "Locations"
if not title:
title = None
elif title is True:
title = long_name
else:
title = title.format(**locals())
# Slice type
if slice_type is None:
slice_type = "3d"
else:
slice_type = str(slice_type).lower()
valid_slice_types = [
'3d', "2d", 'zonal', 'merid', 'horiz', 'bottom', 'surf'
]
assert slice_type in valid_slice_types, ('Invalid slice type. '
'It must be one of: ' +
', '.join(valid_slice_types))
# Numeric horizontal coordinates
xx = lons[:].copy()
yy = lats[:].copy()
# Profiles?
profiles = (not isinstance(depths, str)
and (isaxis(depths) or N.shape(depths) != N.shape(xx) or
(data is not None and data.ndim == 2)))
# Force some options
if not profiles or slice_type not in ('3d', 'merid', 'zonal'):
add_profile_line = False
elif add_profile_line is None:
add_profile_line = True
# Bathymetry
need_xybathy = int(add_profile_line)
if depths == 'bottom' and slice_type not in ('bottom', '2d'):
need_xybathy = 2
if need_xybathy and xybathy is not None:
if bathy is not None:
xybathy = grid2xy(bathy, lons, lats)
if xybathy.mask.all():
if warn:
sonat_warn(
'Bathymetry is fully masked at bottom locs. Skipping...'
)
if need_xybathy == 2:
return
xybathy = None
elif need_xybathy == 2 and warn: # we really need it
sonat_warn('Bathymetry is needed at obs locations. Skipping...')
return
if xybathy is None:
add_profile_line = False
# Special depths: surf and bottom
indepths = depths
if (depths == 'surf' and slice_type != 'surf'): # surface
depths = N.zeros(len(lons))
elif (depths == 'bottom' and slice_type not in ('bottom', '2d')): # bottom
depths = -xybathy
if interval is not None and N.isscalar(interval[0]):
interval = (depths + interval[0], depths + interval[1])
# Numeric vertical coordinates
strdepths = isinstance(depths, str)
if not strdepths:
zz = N.array(depths[:], copy=True)
# Shape
if data is not None:
dshape = data.shape
elif not profiles or strdepths:
dshape = xx.shape
elif zz.ndim == 2:
dshape = zz.shape
else:
dshape = zz.shape + xx.shape
# Masking outside interval
if (slice_type != '3d' and slice_type != '2d'
and (slice_type != 'surf' or depths != 'surf')
and (slice_type != 'bottom' or depths != 'bottom')):
assert interval is not None, (
'You must provide a valid '
'"interval" for slicing scattered locations')
stype = 'horiz' if slice_type in ('surf', 'bottom') else slice_type
data = mask_scattered_locs(xx, yy, depths, stype, interval, data=data)
if data is None:
return
# Get the full mask: (np), or (nz, np) for profiles
# - mask with data
if data is not None:
if data.dtype.char == '?':
mask = data
data = None
else:
mask = N.ma.getmaskarray(data)
else:
mask = N.zeros(dshape)
# - mask with coordinates
if N.ma.isMA(xx) or N.ma.isMA(yy): # lons/lats
xymask = N.ma.getmaskarray(xx) | N.ma.getmaskarray(yy)
mask |= N.resize(mask, dshape)
if not strdepths and N.ma.isMA(zz): # depths
if profiles:
zmask = N.ma.getmaskarray(zz)
if zz.ndim == 1:
zmask = N.repeat(N.ma.resize(N.ma.getmaskarray(zmask),
(-1, 1)),
xx.size,
axis=1)
mask |= zmask
else:
mask |= N.ma.getmaskarray(zz)
# - check
if mask.all():
if warn:
sonat_warn('All your data are masked')
return
# - mask back
xymask = mask if mask.ndim == 1 else mask.all(axis=0)
xx = N.ma.masked_where(xymask, xx, copy=False)
yy = N.ma.masked_where(xymask, yy, copy=False)
if not strdepths:
if mask.shape == zz.shape:
zz = N.ma.masked_where(mask, zz, copy=False)
elif zz.ndim == 1:
zz = N.ma.masked_where(mask.all(axis=1), zz, copy=False)
if data is not None:
data = N.ma.masked_where(mask, data, copy=0)
# Plotter as Axes
if isinstance(plotter, P.Axes):
ax = plotter
fig = ax.get_figure()
plotter = None
elif plotter is None:
ax = None
elif isinstance(plotter, Plot):
ax = plotter.axes
else:
raise SONATError('Plotter must be matplotlib Axes instance or '
'a vacumm Plot instance')
if slice_type == '3d':
if ax is None:
ax = '3d'
elif not isinstance(ax, Axes3D):
sonat_warn("Requesting 3D plot but provided axes are not 3D."
" Skipping...")
axes = None
# Coordinate bounds
if level is None and slice_type in ['3d', 'zonal', 'merid']:
if strdepths or zz.min() == 0:
level_min = -200 # Fall back to this min depth
else:
level_min = 1.1 * zz.min()
level = (level_min, 0)
if (lon is None and slice_type
in ['3d', "2d", "horiz", 'surf', 'bottom', 'zonal']):
lon = rescale_itv((xx.min(), xx.max()), 1.1)
if (lat is None and slice_type
in ['3d', "2d", "horiz", 'surf', 'bottom', 'merid']):
lat = rescale_itv((yy.min(), yy.max()), 1.1)
# Get the plotter
if slice_type in ['3d', "2d", "horiz", 'surf', 'bottom']: # map
# Map
if plotter is None:
plotter = create_map(lon,
lat,
level=level,
bathy=bathy,
add_bathy=add_bathy,
fig=fig,
axes=ax,
**kwmap)
ax = plotter.axes
# Projection
xx, yy = plotter(xx, yy)
else: # sections
if plotter is None:
# Base plot
kwsection.update(fig=fig, axes=ax, show=False, close=False)
if add_minimap:
dict_check_defaults(kwsection, top=.9, right=.9)
if slice_type == 'merid':
plotter = section(data=None,
xaxis=MV2.array(lat, id='lat'),
yaxis=MV2.array(level, id='dep'),
**kwsection)
else:
plotter = section(data=None,
xaxis=MV2.array(lon, id='lon'),
yaxis=MV2.array(level, id='dep'),
**kwsection)
ax = plotter.axes
# Add minimap
if add_minimap:
if slice_type == 'merid':
xlim = interval
ylim = plotter.axes.get_xlim()
else:
xlim = plotter.axes.get_xlim()
ylim = interval
extents = dict(x=xlim, y=ylim)
dict_check_defaults(kwminimap,
map_square=True,
map_zoom=.5,
map_res=None,
map_arcgisimage="ocean",
map_epsg=3395,
linewidth=.6)
kwminimap['map_fig'] = ax.figure
add_map_box((xx, yy), extents, **kwminimap)
# Bathy profile
if add_section_bathy and bathy is not None or secbathy is not None:
if secbathy is None: # interpolate
if slice_type == 'merid':
secbathy = transect(
bathy, [0.5 * (interval[0] + interval[1])] * 2,
ylim,
outaxis='lat')
else:
secbathy = transect(
bathy,
xlim, [0.5 * (interval[0] + interval[1])] * 2,
outaxis='lon')
tx = secbathy.getAxis(0)[:]
tb = secbathy.asma()
axis_bounds = ax.axis()
dict_check_defaults(kwsecbat, facecolor="0.7")
ax.fill_between(tx, ax.get_ylim()[0], tb, **kwsecbat)
ax.axis(axis_bounds)
axis_bounds = ax.axis()
# Plot params for scatter
kwargs.update(linewidth=linewidth, s=size, edgecolor=edgecolor)
# Data kwargs
if data is not None:
dict_check_defaults(kwargs, vmin=data.min(), vmax=data.max())
# 3D
pp = []
if slice_type == "3d":
kwargs['depthshade'] = depthshade
# Depth labels
zfmtfunc = lambda x, pos: deplab(x, nosign=True)
ax.zaxis.set_major_formatter(FuncFormatter(zfmtfunc))
# Scatter plots
if not profiles: # fully scattered
# Points
if data is not None:
kwargs['c'] = data
else:
kwargs['c'] = color
pp.append(ax.scatter(xx, yy, depths, label=label, **kwargs))
# Profile lines
if add_profile_line:
for ip, (x, y) in enumerate(zip(xx, yy)):
plot_profile_line_3d(ax,
x,
y,
xybathy[ip],
zorder=pp[-1].get_zorder() - 0.01,
**kwpf)
else: # profiles
for ip, (x, y) in enumerate(zip(xx, yy)):
# Skip fully masked
if mask[:, ip].all():
# if warn:
# sonat_warn('Profile fully masked')
continue
# Points
if zz.ndim == 2:
z = depths[:, ip]
else:
z = zz
z = N.ma.masked_where(mask[:, ip], z, copy=False)
if data is not None:
kwargs['c'] = data[..., ip]
else:
kwargs['c'] = color
pp.append(
ax.scatter([x] * len(z), [y] * len(z),
z,
label=label,
**kwargs))
label = '_' + str(label)
# Profile line
if add_profile_line:
plot_profile_line_3d(ax,
x,
y,
-xybathy[ip],
zorder=pp[-1].get_zorder() - 0.01,
**kwpf)
# Horizontal
elif slice_type in ['2d', 'surf', 'bottom', 'horiz']:
# Barotropic case
if data is not None and data.ndim != 1:
data = data.mean(axis=0)
# Scatter plot
if data is not None:
kwargs['c'] = data
else:
kwargs['c'] = color
pp.append(ax.scatter(xx, yy, label=label, **kwargs))
if pp[-1].norm.vmin is None:
pass
# Sections
else:
# X axis data
if slice_type == 'zonal':
xdata = xx
else:
xdata = yy
# Scatter plots
if not profiles: # scattered
if data is not None:
kwargs['c'] = data
else:
kwargs['c'] = color
pp.append(ax.scatter(xdata, depths, label=label, **kwargs))
else: # profiles
for ip, x in enumerate(xdata):
# Skip fully masked
if mask[:, ip].all():
# if warn:
# sonat_warn('Profile fully masked')
continue
# Points
if depths[:].ndim == 2:
z = zz[:, ip]
else:
z = zz
z = N.ma.masked_where(mask[:, ip], z, copy=False)
if data is not None:
kwargs['c'] = data[:, ip]
else:
kwargs['c'] = color
pp.append(ax.scatter([x] * len(z), z, label=label, **kwargs))
label = '_' + str(label)
# Profile line
if add_profile_line:
plot_profile_line_3d(ax,
x,
-xybathy[ip],
zorder=pp[-1].get_zorder() - 0.01,
**kwpf)
# Finalise
ax.axis(axis_bounds)
if title:
ax.set_title(title)
if legend:
plotter.legend(**kwleg)
if colorbar and data is not None:
add_colorbar(plotter, pp, units=units, **kwcb)
if data is not None and register_sm:
register_scalar_mappable(ax, pp, units=units)
register_scatter(ax, pp, label)
return plotter
# Imports
from vcmq import cdms2, data_sample, N, transect, stick2, code_file_name, os, \
transect_specs, add_map_lines, create_time, IterDates, P
# Read data
f = cdms2.open(data_sample(ncfile))
u = f('u')
v = f('v')
f.close()
# Transect specs
tlons, tlats = transect_specs(u.getGrid(), lon0, lat0, lon1, lat1)
ttimes = (time0, time1)
# Compute transect
tu = transect(u, tlons, tlats, times=ttimes)
tv = transect(v, tlons, tlats, times=ttimes)
# Plot along time
s = stick2(tu,
tv,
figsize=(8, 3),
title='Space-time transect of speed',
show=False,
top=0.85,
quiver_width=0.002)
# Add a small map to show the transect positions
add_map_lines(u.getGrid(), tlons, tlats, map_zoom=0.5)
# Save
lon0 = -5.4
lon1 = -4.7
lat0 = 48.1
lat1 = 48.6
time0 = "2008-08-15 07:00"
time1 = "2008-08-15 16:00"
splits = [None, 3, -3, (3, 'hour'), 3600*3.]
# Imports
from vcmq import (cdms2, data_sample, N, transect, stick2, code_file_name, os,
transect_specs, add_map_lines, create_time, IterDates, P)
# Read data
f = cdms2.open(data_sample(ncfile))
u = f('u')
v = f('v')
f.close()
# Transect specs
tlons, tlats = transect_specs(u.getGrid(), lon0, lat0, lon1, lat1)
ttimes = create_time(IterDates((time0, time1), len(tlons), closed=True))
# Compute reference transect
tt = [transect(u, tlons, tlats, times=ttimes, split=split) for split in splits]
# Unittest
for ts in tt[1:]:
N.testing.assert_allclose(tt[0], ts)
# Compute MLD
mld = mixed_layer_depth(dens, depth=depth, mode='deltadens', format_axes=True)
del dens
# Compute transect
tlons = (lon0, lon1)
tlats = (lat0, lat1)
tlons = N.concatenate(
[N.linspace(lon0, lon1, 100.),
N.linspace(lon1, lon1, 100.)])
tlats = N.concatenate(
[N.linspace(lat0, lat1, 100.),
N.linspace(lat1, lat0, 100.)])
xtemp, xlons, xlats = transect(temp,
tlons,
tlats,
getcoords=True,
outaxis='dist')
xdepth = transect(depth, tlons, tlats)
xmld = transect(mld, tlons, tlats)
xmld[:] *= -1
# Plot temperature
s = section2(
xtemp,
yaxis=xdepth,
ymin=-800,
fill='contourf',
nmax=20,
contour_linewidths=0.7,
bgcolor='0.5',
lon0 = -5.1
lon1 = -4.9
lat0 = 48.35
lat1 = 48.55
# Imports
from vcmq import cdms2, data_sample, N, transect, curve2, code_file_name, os, \
add_map_lines, P, add_shadow
# Read data
f = cdms2.open(data_sample(ncfile))
hs = f('HS', squeeze=1)
f.close()
# Compute transect
hst, lons, lats = transect(hs, (lon0, lon1), (lat0, lat1), getcoords=True)
# Plot along time
s = curve2(hst,
figsize=(8, 3),
title='Spatial transect on curved grid',
show=False,
top=0.85)
# Add a small map to show the transect positions
o = add_map_lines(hs,
lons,
lats,
map_bgcolor='w',
map_bbox=[.6, .2, .3, .5],
map_anchor='W')
# Imports
from vcmq import cdms2, data_sample, N, transect, stick2, code_file_name, os, \
transect_specs, add_map_lines, create_time, IterDates, P
# Read data
f = cdms2.open(data_sample(ncfile))
u = f('u')
v = f('v')
f.close()
# Transect specs
tlons, tlats = transect_specs(u.getGrid(), lon0, lat0, lon1, lat1)
ttimes = create_time(IterDates((time0, time1), len(tlons), closed=True))
# Compute transect
tu = transect(u, tlons, tlats, times=ttimes)
tv = transect(v, tlons, tlats, times=ttimes)
# Plot along time
s = stick2(tu, tv, figsize=(8,3), title='Space-time transect of speed',
show=False, top=0.85, quiver_width=0.002)
# Add a small map to show the transect positions
add_map_lines(u.getGrid(), tlons, tlats, map_zoom=0.5)
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
# Inits
ncfile = "mars2d.xyt.nc"
lon0 = -5.4
lon1 = -4.7
lat0 = 48.1
lat1 = 48.6
time0 = "2008-08-15 07:00"
time1 = "2008-08-15 16:00"
splits = [None, 3, -3, (3, 'hour'), 3600 * 3.]
# Imports
from vcmq import (cdms2, data_sample, N, transect, stick2, code_file_name, os,
transect_specs, add_map_lines, create_time, IterDates, P)
# Read data
f = cdms2.open(data_sample(ncfile))
u = f('u')
v = f('v')
f.close()
# Transect specs
tlons, tlats = transect_specs(u.getGrid(), lon0, lat0, lon1, lat1)
ttimes = create_time(IterDates((time0, time1), len(tlons), closed=True))
# Compute reference transect
tt = [transect(u, tlons, tlats, times=ttimes, split=split) for split in splits]
# Unittest
for ts in tt[1:]:
N.testing.assert_allclose(tt[0], ts)
# Read data
ds = DS(data_sample(ncfile), 'mars', logger_level='critical')
temp = ds.get_temp(squeeze=True)
dens = ds.get_dens(squeeze=True)
depth = ds.get_depth(squeeze=True)
# Compute MLD
mld = mixed_layer_depth(dens, depth=depth, mode='deltadens', format_axes=True)
del dens
# Compute transect
tlons = (lon0,lon1)
tlats = (lat0,lat1)
tlons = N.concatenate([N.linspace(lon0,lon1,100.),N.linspace(lon1,lon1,100.)])
tlats = N.concatenate([N.linspace(lat0,lat1,100.),N.linspace(lat1,lat0,100.)])
xtemp, xlons, xlats = transect(temp, tlons, tlats, getcoords=True, outaxis='dist')
xdepth = transect(depth, tlons, tlats)
xmld = transect(mld, tlons, tlats)
xmld[:]*=-1
# Plot temperature
s = section2(xtemp, yaxis=xdepth, ymin=-800, fill='contourf', nmax=20,
contour_linewidths=0.7, bgcolor='0.5', figsize=(7,4),
cmap='vacumm_rnb2_hymex',
title='%(long_name)s (dens) along temp transect', show=False)
# Plot MLD
curve2(xmld, 'w-', linewidth=2, show=False)
# Add a small map to show the transect positions
add_map_lines(temp[-1], xlons, xlats, map_zoom=0.7, color='k')
