def testNCF2BPCH(self):
import warnings
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', 'Not scaling variables; good for direct writing'
)
bpchfile = bpch(self.bpchpath, noscale=True)
outpath = self.bpchpath + '.check'
from PseudoNetCDF.pncgen import pncgen
pncgen(bpchfile, outpath, inmode='r',
outmode='w', format='bpch', verbose=0)
orig = open(self.bpchpath, 'rb').read()
new = open(outpath, 'rb').read()
assert(orig == new)
os.remove(outpath)
from PseudoNetCDF.sci_var import reduce_dim, slice_dim
ALD2 = bpchfile.variables['IJ-AVG-$_ALD2']
ALD2_check = np.array(
[1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02,
1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02,
2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02,
3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02,
3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02,
1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02,
1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02,
2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02,
3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02,
3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02,
1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02,
1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02,
2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02,
3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02,
3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02])\
.reshape(ALD2.shape)
slided_reduced_bpchfile = slice_dim(
reduce_dim(bpchfile, 'layer,mean'), 'time,0')
pncgen(slided_reduced_bpchfile, outpath, inmode='r',
outmode='w', format='bpch', verbose=0)
ALD2_check_slided_reduced = ALD2_check[0].mean(0)[None, None]
ALD2 = slided_reduced_bpchfile.variables['IJ-AVG-$_ALD2']
np.testing.assert_allclose(ALD2, ALD2_check_slided_reduced * 1e-9)
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', 'Not scaling variables; good for direct writing'
)
bpchfile = bpch(outpath)
ALD2 = bpchfile.variables['IJ-AVG-$_ALD2']
np.testing.assert_allclose(ALD2, ALD2_check_slided_reduced)
def get_group(file_objs, src, dates):
"""
Return the first file with the SRC group
"""
from PseudoNetCDF.sci_var import extract, slice_dim
for f in file_objs:
if hasattr(f, 'groups'):
if src in f.groups:
grp = f.groups[src]
if 'TFLAG' in grp.variables.keys():
tflag = grp.variables['TFLAG']
thisdate = [datetime.strptime('%d %06d' % (d, t), '%Y%j %H%M%S') for d, t in tflag[:, 0]]
tname = 'TSTEP'
elif 'time' in grp.variables.keys():
time = grp.variables['time']
unit, starttxt = grp.variables['time'].units.split(' since ')
starttxt = starttxt.replace('UTC', '').strip()
start_date = datetime.strptime(starttxt, '%Y-%m-%d %H:%M:%S')
thisdate = [start_date + timedelta(**{unit: t}) for t in time[:]]
tname = 'time'
elif 'tau0' in grp.variables.keys():
time = grp.variables['tau0']
unit, starttxt = time.units.split(' since ')
start_date = datetime.strptime(start_date)
thisdate = [start_date + timedelta(**{unit: t}) for t in time]
tname = 'time'
elif isinstance(f, (icon_profile, bcon_profile, profile_to_bdy, profile_to_grid)):
if len(dates) > 1:
for k in grp.variables.keys():
v = grp.variables[k]
if v.shape[0] == 1:
grp.variables[k] = v.repeat(len(dates), 0)
return grp
else:
return grp
thisdate = np.array(thisdate)
idx, = np.where(np.logical_and(thisdate >= dates[0], thisdate <= dates[-1]))
grpt = slice_dim(grp, '%s,%d,%d' % (tname, idx[0], idx[-1] + 1))
return grpt
else:
raise KeyError('No file provided has the %s group' % src)
def testNCF2BPCH(self):
bpchfile=bpch(self.bpchpath, noscale = True)
outpath = self.bpchpath + '.check'
from PseudoNetCDF.pncgen import pncgen
pncgen(bpchfile,outpath, inmode = 'r', outmode = 'w', format = 'bpch', verbose = False)
orig = open(self.bpchpath, 'rb').read()
new = open(outpath, 'rb').read()
assert(orig == new)
os.remove(outpath)
from PseudoNetCDF.sci_var import reduce_dim, slice_dim
ALD2 = bpchfile.variables['IJ-AVG-$_ALD2']
ALD2_check = np.array([1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02, 1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02, 2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02, 3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02, 3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02, 1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02, 1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02, 2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02, 3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02, 3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02, 1.60520077e-02, 1.82803553e-02, 2.00258084e-02, 2.01461259e-02, 1.84865110e-02, 2.49667447e-02, 2.73083989e-02, 2.87465211e-02, 2.89694592e-02, 2.87686456e-02, 2.87277419e-02, 3.08121163e-02, 3.22086290e-02, 3.35262120e-02, 3.41329686e-02, 3.05218045e-02, 3.30278911e-02, 3.58164124e-02, 3.93186994e-02, 4.15412188e-02]).reshape(ALD2.shape)
bpchfile = slice_dim(reduce_dim(bpchfile, 'layer,mean'), 'time,0')
pncgen(bpchfile,outpath, inmode = 'r', outmode = 'w', format = 'bpch', verbose = False)
ALD2_check = ALD2_check[0].mean(0)[None, None]
ALD2 = bpchfile.variables['IJ-AVG-$_ALD2']
np.testing.assert_allclose(ALD2, ALD2_check * 1e-9)
bpchfile = bpch(outpath)
ALD2 = bpchfile.variables['IJ-AVG-$_ALD2']
np.testing.assert_allclose(ALD2, ALD2_check)
def makemaps(args):
ifiles = args.ifiles
cbar = None
ifile = ifiles[0]
if args.iter != []:
ifile, = ifiles
ifiles = []
for dimk in args.iter:
ifiles += [
slice_dim(getvarpnc(ifile, None), '%s,%d' % (dimk, i))
for i in range(len(ifile.dimensions[dimk]))
]
ax = plt.gca()
map = getmap(ifile, resolution=args.resolution)
if args.coastlines:
map.drawcoastlines(ax=ax)
if args.countries:
map.drawcountries(ax=ax)
if args.states:
map.drawstates(ax=ax)
if args.counties:
map.drawcounties(ax=ax)
for si, shapefile in enumerate(args.shapefiles):
shapeopts = shapefile.split(',')
shapepath = shapeopts[0]
shapeoptdict = eval('dict(' + ','.join(shapeopts[1:]) + ')')
shapename = os.path.basename(shapepath)[:-3] + str(si)
map.readshapefile(shapepath, shapename, ax=ax, **shapeoptdict)
args.map = map
fig = plt.gcf()
if len(args.figure_keywords) > 0:
plt.setp(fig, **args.figure_keywords)
ax = plt.gca()
if len(args.axes_keywords) > 0:
plt.setp(ax, **args.axes_keywords)
map = args.map
nborders = len(ax.collections)
for fi, ifile in enumerate(ifiles):
if map.projection in ('lcc', 'merc'):
lat = ifile.variables['latitude']
lon = ifile.variables['longitude']
latb, latunit = getybnds(ifile)[:]
lonb, lonunit = getxbnds(ifile)[:]
else:
lat = ifile.variables['latitude']
lon = ifile.variables['longitude']
latb, latunit = getlatbnds(ifile)[:]
lonb, lonunit = getlonbnds(ifile)[:]
if latb.ndim == lonb.ndim and lonb.ndim == 2:
LON, LAT = lonb, latb
else:
LON, LAT = np.meshgrid(lonb.view(np.ndarray),
latb.view(np.ndarray))
variables = args.variables
if variables is None:
def isgeo(var):
geo2d = set(['latitude', 'longitude'])
vard = getattr(var, 'coordinates', '').split()
hasgeo2d = len(geo2d.intersection(vard)) == 2
return hasgeo2d
variables = [
key for key, var in ifile.variables.items() if isgeo(var)
]
if len(variables) == 0:
raise ValueError('Unable to heuristically determin plottable ' +
'variables; use -v to specify variables for ' +
'plotting')
for varkey in variables:
ax = plt.gca()
if not args.overlay:
del ax.collections[nborders:]
var = ifile.variables[varkey]
if args.squeeze:
vals = var[:].squeeze()
else:
vals = var[:]
vmin, vmax = vals.min(), vals.max()
if args.normalize is None:
from scipy.stats import normaltest
if normaltest(vals.ravel())[1] < 0.001:
cvals = np.ma.compressed(vals)
boundaries = np.percentile(cvals, np.arange(0, 110, 10))
warn('Autoselect deciles colormap of %s; override ' +
'width --norm' % varkey)
else:
boundaries = np.linspace(vmin, vmax, num=11)
warn(('Autoselect linear colormap of %s; override ' +
'width --norm') % varkey)
ordermag = (boundaries.max() /
np.ma.masked_values(boundaries, 0).min())
if (ordermag) > 10000:
formatter = LogFormatter(labelOnlyBase=False)
else:
formatter = None
norm = BoundaryNorm(boundaries, ncolors=256)
else:
norm = eval(args.normalize)
formatter = None
if args.colorbarformatter is not None:
try:
formatter = eval(args.colorbarformatter)
except Exception:
formatter = args.colorbarformatter
if norm.vmin is not None:
vmin = norm.vmin
if norm.vmax is not None:
vmax = norm.vmax
varunit = getattr(var, 'units', 'unknown').strip()
if args.verbose > 0:
print(varkey, sep='')
if vals.ndim == 1:
notmasked = ~(np.ma.getmaskarray(lon[:]) | np.ma.getmaskarray(
lat[:]) | np.ma.getmaskarray(vals[:]))
scatlon = lon[:][notmasked]
scatlat = lat[:][notmasked]
scatvals = vals[:][notmasked]
patches = map.scatter(scatlon[:],
scatlat[:],
c=scatvals,
edgecolors='none',
s=24,
norm=norm,
ax=ax,
zorder=2)
else:
if vals.ndim != 2:
dimlendictstr = str(dict(zip(var.dimensions, var.shape)))
warn('Maps require 2-d data; values right now %s %s' %
(str(vals.shape), dimlendictstr))
patches = map.pcolor(LON, LAT, vals, norm=norm, ax=ax)
if lonunit == 'x (m)':
ax.xaxis.get_major_formatter().set_scientific(True)
ax.xaxis.get_major_formatter().set_powerlimits((-3, 3))
if latunit == 'y (m)':
ax.yaxis.get_major_formatter().set_scientific(True)
ax.yaxis.get_major_formatter().set_powerlimits((-3, 3))
ax.set_xlabel(lonunit)
ax.set_ylabel(latunit)
height = np.abs(np.diff(ax.get_ylim()))
width = np.abs(np.diff(ax.get_xlim()))
if width >= height:
orientation = 'horizontal'
else:
orientation = 'vertical'
if cbar is None:
cax = None
else:
cax = cbar.ax
cax.cla()
if vals.max() > vmax and vals.min() < vmin:
extend = 'both'
elif vals.max() > vmax:
extend = 'max'
elif vals.min() < vmin:
extend = 'min'
else:
extend = 'neither'
cbar = plt.gcf().colorbar(patches,
orientation=orientation,
cax=cax,
extend=extend,
format=formatter,
spacing='proportional')
del cbar.ax.texts[:]
varminmaxtxt = ('; min=%.3g; max=%.3g)' %
(var[:].min(), var[:].max()))
cbar.set_label(varkey + ' (' + varunit + varminmaxtxt)
# if orientation == 'vertical':
# cbar.ax.text(.5, 1.05, '%.3g' % var[:].max(),
# horizontalalignment = 'center',
# verticalalignment = 'bottom')
# cbar.ax.text(.5, -.06, '%.3g ' % var[:].min(),
# horizontalalignment = 'center',
# verticalalignment = 'top')
# else:
# cbar.ax.text(1.05, .5, ' %.3g' % var[:].max(),
# verticalalignment = 'center',
# horizontalalignment = 'left')
# cbar.ax.text(-.06, .5, '%.3g ' % var[:].min(),
# verticalalignment = 'center',
# horizontalalignment = 'right')
cbar.update_ticks()
fmt = args.figformat
outpath = args.outpath
if len(ifiles) > 1:
lstr = str(fi).rjust(len(str(len(ifiles))), '0')
if args.verbose > 0:
print('adding numeric suffix for file', lstr)
else:
lstr = ''
figpath = os.path.join(outpath + varkey + lstr + '.' + fmt)
if args.interactive:
csl = PNCConsole(locals=globals())
csl.interact()
for cmd in args.plotcommands:
exec(cmd)
plt.savefig(figpath)
if args.verbose > 0:
print('Saved fig', figpath)