def plotwithopts(ifile, method, vars, options = defaultoption):
from PseudoNetCDF.sci_var import getvarpnc
from PseudoNetCDF.pncgen import pncgen
exec(options.pre_txt)
for varkey in vars:
figpath = eval(method)(ifile = ifile, varkey = varkey, options = options, before = options.before_txt, after = options.after_txt)
pncgen(getvarpnc(ifile, list(vars) + ['TFLAG', 'time', 'latitude', 'longitude', 'latitude_bounds', 'longitude_bounds']), figpath + '.nc', verbose = False)
exec(options.post_txt)
def plotwithopts(ifile, method, vars, options=defaultoption):
from PseudoNetCDF.sci_var import getvarpnc
from PseudoNetCDF.pncgen import pncgen
exec(options.pre_txt)
for varkey in vars:
figpath = eval(method)(ifile=ifile, varkey=varkey, options=options,
before=options.before_txt,
after=options.after_txt)
pncgen(getvarpnc(ifile, list(vars) + _coordkeys),
figpath + '.nc', verbose=0)
exec(options.post_txt)
def plotwithopts(ifile, method, vars, options=defaultoption):
from PseudoNetCDF.sci_var import getvarpnc
from PseudoNetCDF.pncgen import pncgen
exec(options.pre_txt)
for varkey in vars:
figpath = eval(method)(ifile=ifile,
varkey=varkey,
options=options,
before=options.before_txt,
after=options.after_txt)
pncgen(getvarpnc(
ifile,
list(vars) + [
'TFLAG', 'time', 'latitude', 'longitude', 'latitude_bounds',
'longitude_bounds'
]),
figpath + '.nc',
verbose=False)
exec(options.post_txt)
def plotwithopts(ifile, method, vars, options=defaultoption):
from PseudoNetCDF.sci_var import getvarpnc
from PseudoNetCDF.pncgen import pncgen
import matplotlib.pyplot as plt
# dummy assignment so that flake8 sees plt as used
# plt is loaded into the environment for exec
varkey = plt
exec(options.pre_txt)
for varkey in vars:
figpath = eval(method)(ifile=ifile,
varkey=varkey,
options=options,
before=options.before_txt,
after=options.after_txt)
pncgen(getvarpnc(ifile,
list(vars) + list(_coordkeys)),
figpath + '.nc',
verbose=0)
exec(options.post_txt)
def get_files(self, date):
"""
Put date into file_templates and return open files,
where all files have been vertically interpolated
and only variables that will be used are present
date - date to use for files
"""
from PseudoNetCDF.sci_var import extract, slice_dim, getvarpnc
#from PseudoNetCDF.cmaqfiles.profile import bcon_profile, icon_profile
import gc
# make quick references to instance variables
sources = self._sources
verbose = self._verbose
coordstr = self._coordstr
# Fill file path templates with date using the time
# function provided
file_paths = [(r, eval(tsf)(date, p)) for r, p, tsf in self._config['file_templates']]
# Return cached files if appropriate
if file_paths == self.last_file_paths:
return self.last_file_objs
# If coordstr is not none, this is
# a data extraction call and the status should be
# updated
if coordstr is not None:
status('')
status('-' * 40)
status("Getting files for " + str(date))
status('-' * 40)
status('')
# For each file, use the reader (r) to open the
# path (p)
for fi, (r, p) in enumerate(file_paths):
print fi, r, p, eval(r)
# If last path is this path
# no need to update
lp = self.last_file_paths[fi]
nf = self.last_file_objs[fi]
if p != lp:
if verbose > 0: timeit('GET_FILE %s' % p, True)
# Close old file to prevent memory leaks
nf.close()
if verbose > 1: status('Opening %s with %s' % (p, r), show = False)
onf = nf = eval(r)(p)
if coordstr is None:
# Coordstr is None, so this call is just for some
# meta-data
with warnings.catch_warnings():
warnings.simplefilter("ignore")
nf = getvarpnc(onf, 'time TFLAG tau0 tau1 latitude longitude latitude_bounds longitude_bounds PRES'.split())
nf = onf
if 'PERIM' in onf.dimensions.keys() and not isinstance(onf, bcon_profile):
nf.createDimension('PERIM', len(onf.dimensions['PERIM']))
nf.createDimension('LAY', len(onf.dimensions['LAY']))
nf.NCOLS = onf.NCOLS
nf.NROWS = onf.NROWS
else:
# If coordstr is not None, this is a real data
# call
#
# Real data calls require vertical interpolation
## Calculate the vertical coordinate of
## the input file
# BCON and ICON file processing
if isinstance(nf, (bcon_profile, icon_profile)):
nf = nf.interptosigma(self.vert_out, sources)
metf = [f for f in self.last_file_objs if 'PERIM' in f.dimensions][0]
## profile files need to be converted
## to METBDY coordinates by repeating
## boundaries
nf = profile_to_ftype(nf, ncols = metf.NCOLS, nrows = metf.NROWS, ftype = metf.FTYPE)
# GEOS-Chem BPCH processing (TPCORE, ND49 BPCH or ND49)
elif isinstance(nf, (bpch, ND49NC)):
## Only extract groups that are used
## in mappings, and only extract variables
## in those groups that are used
nf = nf.tooutcoords(coordstr, self.vert_out, self.vgtop, sources)
elif isinstance(nf, (METBDY3D, METCRO3D)):
# Assuming METBDY and METCRO3D are target coordinates
pass
else:
raise IOError('Unknown type %s; add type to readers' % type(nf))
if verbose > 0: timeit('GET_FILE %s' % p, False)
self.last_file_objs[fi] = nf
self.last_file_paths = file_paths
return self.last_file_objs
print(
'WARNING: %s is newer than %s; not updating. Use -O to overwrite'
% (csvpath, outpath))
if os.path.getmtime(__file__) > os.path.getmtime(outpath):
print(
'WARNING: script is newer than %s; not updating. Use -O to overwrite'
% (outpath, ))
continue
csvfile = pd.read_csv(csvpath)
unique_vars = np.unique(
np.char.replace([v for v in csvfile['POLNAME'].values], 'BEN',
'BENZENE')).tolist()
aggdata = csvfile.pivot_table(index=('POLNAME', 'ROW', 'COL'),
aggfunc=np.sum)
oldpolname = None
outfile = getvarpnc(Dataset(templatepath, 'r+'), ['TFLAG'] + unique_vars)
add_ioapi_from_ioapi(outfile)
# outfile.variables['TFLAG'][:, :, 0] = np.arange(0, 25)[:, None]
# outfile.variables['TFLAG'][:, :, 0] = 2014001
print('Working on: ' + csvpath)
for (polname, rowi, coli), groupdata in aggdata.iterrows():
if args.verbose > 2: print(polname)
if polname != oldpolname:
if not oldpolname is None:
if args.verbose > 0:
print('Writing out ' + polname + ' to ' + var.long_name)
var[0:26, 0, :, :] = temp[0:26, 0, :, :]
if args.verbose > 0: print('Starting ' + polname)
if polname == 'BEN':
var = outfile.variables['BENZENE']
elif polname != 'CO2':
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)