def interp(y, x, xinterp, missing=1e+20):
"""Simple linear interpolation for ordinate with missing values.
Vectors x and y are the data describing a piecewise linear function.
Function returns the interpolated values of the ordinate function
at abscissa values in xinterp. Values of xinterp outside the range
of x are returned as missing. Any elements in the output that uses
missing values in y for the interpolation are set to missing.
Positional Input Arguments:
* y: Ordinate values of data. Rank 1 numeric vector. Required.
Can have missing values. Floating or integer type.
* x: Abscissa values of data. Rank 1 numeric vector. Required.
Can have no missing values. Must be monotonically ascending.
Floating or integer type.
* xinterp: Abscissa values to calculate interpolated ordinate
values at. Rank 1 numeric vector or numeric scalar. Required.
Can have no missing values. Can be in any order. Floating or
integer type.
Keyword Input Arguments:
* missing: If input has missing values, this is the missing value
value. Scalar. Floating or integer type. Default is 1e+20.
Output Result:
* Interpolated ordinate values at xinterp. Rank 1 numeric vector
of same length as xinterp (if xinterp is a numeric scalar,
output is also a numeric scalar). Missing values are set to the
value of argument missing. Type is Float, even if argument
missing and inputs are all integer.
References:
* Lin, J. W.-B.: "Simple Interpolation."
Python/CDAT for Earth Scientists: Tips and Examples.
http://www.johnny-lin.com/cdat_tips/tips_math/interp.html
Example with no missing values (gives same output as function
arrayfns.interp):
>>> from interp import interp
>>> import numpy as N
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 6., 2.,-5.,-3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '4.8']
Example with missing values:
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20']
Example with values out of range of the data:
>>> x = N.array([1., 2.1, 3., 4., 5.1])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, -2.3, 6.])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20', '1e+20']
"""
import arrayfns
import numpy.ma as MA
import numpy as N
from where_close import where_close
#- Check inputs for possible errors:
if (N.rank(y) != 1) or (N.rank(x) != 1):
raise ValueError, "interp: Input(s) not a vector"
if N.rank(xinterp) > 1:
raise ValueError, "interp: xinterp not a vector or scalar"
if x[-1] <= x[0]:
raise ValueError, "interp: x not monotonically increasing"
#- Establish constants and define xint, a rank 1 version of
# xinterp to be used for the rest of the function:
if N.rank(xinterp) == 0:
xint = N.reshape(xinterp, (1, ))
else:
xint = xinterp
num_xint = N.size(xint)
#- Mask as missing values of xint that are outside of the range
# of x:
yint_outrange_mask = N.logical_or( N.less(xint, x[0]) \
, N.greater(xint, x[-1]) )
#- Mask of elements with missing values in y, if there are any
# missing values in y. If xint equals a value in x, missing
# values mask for that xint is the same as the corresponding
# value in x; and mask elements in xint which fall in an interval
# (whose upper bound index is top_idx) where one of the endpoints
# is missing:
y_miss_mask = where_close(y, missing)
yint_miss_mask = N.zeros(num_xint)
if MA.maximum(y_miss_mask) == 1:
for i in xrange(num_xint):
if yint_outrange_mask[i] == 0:
x_eq_xint = where_close(x, xint[i])
if MA.maximum(x_eq_xint) == 1:
yint_miss_mask[i] = y_miss_mask[N.nonzero(x_eq_xint)]
else:
top_idx = N.nonzero(N.greater(x, xint[i]))[0]
yint_miss_mask[i] = y_miss_mask[top_idx] or \
y_miss_mask[top_idx-1]
#- Return interpolated values, set to missing values as
# appropriate, and making a scalar if xinterp is a scalar:
yint = arrayfns.interp(y, x, xint)
N.putmask( yint, N.logical_or(yint_miss_mask, yint_outrange_mask) \
, missing)
if N.rank(xinterp) == 0: yint = yint[0]
return yint
def nice_levels(data, approx_nlev=10, max_nlev=28):
"""Compute a vector of "levels" at "nice" increments.
Returns a 1-D array of "levels" (e.g., contour levels) calculated
to give an aesthetically pleasing and human-readable interval,
if possible. If not, returns levels for approx_nlev levels
between the maximum and minimum of data. In any event, the
function will return no more than max_nlev levels.
Keyword Input Parameter:
* data: Array of values to calculate levels for. Can be of any
size and shape.
Keyword Input Parameter:
* approx_nlev: Integer referring to approximately how many
levels to return. This is the way of adjusting how "coarse"
or "fine" to make the vector of levels.
* max_nlev: The maximum number of levels the function will
permit to be returned. The interval of levels will be adjusted
to keep the number of levels returned under this value. If
approx_nlev is chosen to be greater than or equal to max_nlev,
an exception is raised.
Output:
* This function returns a 1-D array of contour levels.
Function is adaptation of parts of IDL routine contour_plot.pro
by Johnny Lin. This is why the capitalization conventions of
Python are not strictly followed in this function.
Examples:
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z)
>>> ['%g' % out[i] for i in range(len(out))]
['-30', '0', '30', '60', '90', '120', '150', '180', '210']
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z, approx_nlev=5)
>>> ['%g' % out[i] for i in range(len(out))]
['-50', '0', '50', '100', '150', '200']
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z, approx_nlev=10)
>>> ['%g' % out[i] for i in range(len(out))]
['-30', '0', '30', '60', '90', '120', '150', '180', '210']
"""
#- Default settings and error check:
if approx_nlev >= max_nlev:
raise ValueError, 'max_nlev is too small'
MAX_zd_ok = N.max(data)
MIN_zd_ok = N.min(data)
nlevels = N.min([approx_nlev, max_nlev])
tmpcmax = MAX_zd_ok
tmpcmin = MIN_zd_ok
tmpcint = N.abs((tmpcmax - tmpcmin) / float(nlevels))
#- See if the cint can be "even". If not, return alternative
# contour levels vector:
#+ Guess a possible cint. Look for an "even" value that is
# closest to that:
guesscint = N.abs((MAX_zd_ok - MIN_zd_ok) / float(nlevels))
if (guesscint > 1e-10) and (guesscint < 1e+10):
possiblecint = [
1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 0.0001, 0.0002, 0.0005, 0.001,
0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1., 2., 5., 10.,
20., 30., 45., 50., 100., 200., 500., 1000., 2000., 5000., 10000.,
20000., 50000., 1e+5, 1e+6, 1e+7, 1e+8, 1e+9, 1e+10
]
diffcint = N.abs(possiblecint - guesscint)
tempcint = N.compress(diffcint == N.min(diffcint), possiblecint)[0]
tcidx = N.compress(where_close(possiblecint, tempcint),
N.arange(N.size(possiblecint)))[0]
#+ Look around at the "even" values nearby the possible option
# for cint. Calculate how many contours each of those cints
# would give. Dictionary ncon_count is the number of
# contours for a given test cint. test_tcidxs are the indices
# in possiblecint to examine in detail; these index values
# will be the keys in ncon_count.
if tcidx == 0: tcidx = 1
if tcidx == N.size(possiblecint) - 1: tcidx = N.size(possiblecint) - 2
ncon_count = {}
test_tcidxs = [tcidx - 1, tcidx, tcidx + 1]
for i in test_tcidxs:
itcval = possiblecint[i]
positivecon = N.arange(max_nlev + 2, dtype=float) * itcval
negativecon = (N.arange(max_nlev + 2, dtype=float) +
1.0) * itcval * (-1.0)
if (MAX_zd_ok + itcval >= 0) and (MIN_zd_ok - itcval >= 0):
ncon_count[i] = N.sum( \
N.logical_and(positivecon <= MAX_zd_ok + itcval,
positivecon >= MIN_zd_ok - itcval) )
elif (MAX_zd_ok + itcval < 0) and (MIN_zd_ok - itcval < 0):
ncon_count[i] = N.sum( \
N.logical_and(negativecon <= MAX_zd_ok + itcval,
negativecon >= MIN_zd_ok - itcval) )
else:
ncon_count[i] = N.sum(positivecon <= MAX_zd_ok + itcval) \
+ N.sum(negativecon >= MIN_zd_ok - itcval)
#+ Select the cint that has the fewest levels if it has at
# least nlevels-1. Otherwise, try to find the next cint with
# the fewest levels that is below max_nlev. tempcint is what
# you get (changed, if warranted) leaving this section:
min_ncon_count = N.min(ncon_count.values())
current_best_count = max_nlev
for i in test_tcidxs:
if (ncon_count[i] == min_ncon_count) and \
(ncon_count[i] >= nlevels-1):
tempcint = possiblecint[i]
current_best_count = ncon_count[i]
break
elif (ncon_count[i] == min_ncon_count) and \
(ncon_count[i] < nlevels-1):
continue
elif ncon_count[i] > max_nlev:
continue
else:
if N.abs(ncon_count[i]-nlevels) < \
N.abs(current_best_count-nlevels):
tempcint = possiblecint[i]
current_best_count = ncon_count[i]
continue
#+ Create levels for case with neg. and pos. contours. There
# is the case of all pos., all neg., and mixed pos. and neg.
# contours:
positivecon = N.arange(max_nlev + 2, dtype=float) * tempcint
negativecon = (N.arange(max_nlev + 2, dtype=float) +
1.0) * tempcint * (-1.0)
if (MAX_zd_ok + tempcint >= 0) and (MIN_zd_ok - tempcint >= 0):
tmpclevels = N.compress( \
N.logical_and(positivecon <= MAX_zd_ok + tempcint,
positivecon >= MIN_zd_ok - tempcint),
positivecon )
elif (MAX_zd_ok + tempcint < 0) and (MIN_zd_ok - tempcint < 0):
tmpclevels = N.compress( \
N.logical_and(negativecon <= MAX_zd_ok + tempcint,
negativecon >= MIN_zd_ok - tempcint),
negativecon )
else:
uppercon = N.compress(positivecon <= MAX_zd_ok + tempcint,
positivecon)
lowercon = N.compress(negativecon >= MIN_zd_ok - tempcint,
negativecon)
tmpclevels = N.concatenate([lowercon, uppercon])
#+ Sort clevels, reset number of levels, maximum, minimum,
# and interval of contour based on the automatic setting:
tmpclevels = N.sort(tmpclevels)
if (N.size(tmpclevels) <= max_nlev) and (N.size(tmpclevels) > 0):
nlevels = N.size(tmpclevels)
tmpcmax = tmpclevels[-1]
tmpcmin = tmpclevels[0]
tmpcint = tempcint
else:
pass
#- Return output:
return N.arange(tmpcmin, tmpcmax + tmpcint, tmpcint)
def plot_ncdf_output(id, datafn, **kwds):
"""Plot model field id from the data in netCDF file datafn.
Positional Input Parameter:
* id: Name of the id of the field to plot. String.
* datafn: Filename containing the output data to plot. String.
Input keyword parameter descriptions are found in the docstring
for Qtcm methods ploti, plotm, and other methods that call this
private method. In general, those methods take the keyword
parameters they receive and pass it along unchanged as keyword
parameters to this function. In that sense, this function is
seldom used as a stand-alone function, but rather is usually
used coupled with a Qtcm instance.
The data fields read in from the netCDF output file are dimensioned
(time, lat, lon). This is different than how the data is stored
in the compiled QTCM model fields (lon, lat, time), and at the
Python level (lon, lat). The reason this is the case is that
f2py automatically makes the arrays passed between the Python
and Fortran levels match.
For a lat vs. lon plot, the contour plot is superimposed onto
a cylindrical projection map of the Earth with continents drawn
and labeled meridians and parallels. The title also includes
the model time, and x- and y-axis labels are not drawn.
All numerical data used for plotting come from the netCDF output
file for consistency (e.g., the dimensions of u1). Currently
this method only works for 3-D data arrays (two in space, one
in time).
"""
#- Accomodate other ids. The id that this routine will use
# (i.e., iduse corresponds to the name in the netCDF output file)
# is called iduse. Set this to id, except for the case where
# some aliases of ids are entered in which iduse is the alias:
if id == 'Qc': iduse = 'Prec'
elif id == 'FLWut': iduse = 'OLR'
elif id == 'STYPE': iduse = 'stype'
else: iduse = id
#- Set defined keyword defaults. All are set to None except for
# nlatlon which gets an integer:
plotkwds_ids = [
'lat', 'lon', 'time', 'fn', 'levels', 'title', 'xlabel', 'ylabel',
'filled', 'nlatlon', 'tmppreview'
]
plotkwds = {}
for ikey in plotkwds_ids:
if kwds.has_key(ikey):
plotkwds[ikey] = copy.copy(kwds[ikey])
else:
plotkwds[ikey] = None
if not kwds.has_key('nlatlon'):
plotkwds['nlatlon'] = 8
#- Get data and dimensions of iduse to plot:
fileobj = S.NetCDFFile(datafn, mode='r')
data = N.array(fileobj.variables[iduse].getValue())
data_name = fileobj.variables[iduse].long_name
data_units = fileobj.variables[iduse].units
dim = {}
dimname = {}
dimunits = {}
dim['lat'] = N.array(fileobj.variables['lat'].getValue())
dimname['lat'] = fileobj.variables['lat'].long_name
dimunits['lat'] = fileobj.variables['lat'].units
dim['lon'] = N.array(fileobj.variables['lon'].getValue())
dimname['lon'] = fileobj.variables['lon'].long_name
dimunits['lon'] = fileobj.variables['lon'].units
dim['time'] = N.array(fileobj.variables['time'].getValue())
dimname['time'] = fileobj.variables['time'].long_name
dimunits['time'] = fileobj.variables['time'].units
fileobj.close()
#- Alter data long name to remove any units. The definition
# of units as the substring within the [] is the same as used in
# defVar in output.F90 of the compiled QTCM model. Remove
# underscores and extra whitespace in data_name and data_units,
# replacing with a single whitespace character between words:
idx1 = data_name.find('[')
idx2 = data_name.find(']')
if idx1 != -1 and idx2 != -1:
data_name = data_name[:idx1] + data_name[idx2 + 1:]
data_name = data_name.strip()
data_name = ' '.join(data_name.replace('_', ' ').split())
data_units = ' '.join(data_units.replace('_', ' ').split())
#- Alter dimension long name to remove any units. The definition
# of units as the substring within the [] is the same as used in
# defVar in output.F90 of the compiled QTCM model. Remove
# underscores and extra whitespace in name and units, replacing
# with a single whitespace character between words, and
# capitalizing like a title:
for idimkey in dim.keys():
idimname = dimname[idimkey]
idx1 = idimname.find('[')
idx2 = idimname.find(']')
if idx1 != -1 and idx2 != -1:
idimname = idimname[:idx1] + idimname[idx2 + 1:]
dimname[idimkey] = idimname.strip()
dimname[idimkey] = \
' '.join(dimname[idimkey].replace('_',' ').split()).title()
dimunits[idimkey] = \
' '.join(dimunits[idimkey].replace('_',' ').split()).title()
#- Some data checks:
if N.rank(data) != 3:
raise ValueError, '_plot: can only plot lat, lon, time fields'
if not N.allclose(dim['time'], N.sort(dim['time'])):
raise ValueError, '_plot: time not monotonically ascending'
if not N.allclose(dim['lat'], N.sort(dim['lat'])):
raise ValueError, '_plot: lat not monotonically ascending'
if not N.allclose(dim['lon'], N.sort(dim['lon'])):
raise ValueError, '_plot: lon not monotonically ascending'
if N.shape(data)[0] != N.size(dim['time']):
raise ValueError, '_plot: data time dim mismatch'
if N.shape(data)[1] != N.size(dim['lat']):
raise ValueError, '_plot: data lat dim mismatch'
if N.shape(data)[2] != N.size(dim['lon']):
raise ValueError, '_plot: data lon dim mismatch'
#- Choose and describe ranges for lat, lon, and time. The
# section cycles through the dictionary of dimensions. idim is
# the 1-D array of the values of that dimension. rngs is a
# dictionary where each entry corresponds to a dimension, and the
# value of the entry is the values of that dimension that are to
# be plotted. rngs_idxs are the indices in the original
# dimensions array corresponding to the values in rngs.
# keys_rngs_sizes_gt_1 is a list of the keys of ranges that have
# sizes greater than 1:
rngs = {}
rngs_idxs = {}
keys_rngs_sizes_gt_1 = []
for idimkey in dim.keys():
idim = dim[idimkey]
if plotkwds[idimkey] == None:
dim_mask = N.ones(N.size(idim), dtype=int)
elif N.isscalar(plotkwds[idimkey]):
dim_mask = where_close(idim, plotkwds[idimkey])
if N.sum(dim_mask) != 1:
raise ValueError, 'no point chosen'
elif (not N.isscalar(plotkwds[idimkey])) and \
N.size(plotkwds[idimkey]) == 1:
dim_mask = where_close(idim, plotkwds[idimkey][0])
if N.sum(dim_mask) != 1:
raise ValueError, 'no point chosen'
elif N.size(plotkwds[idimkey]) == 2:
dim_mask = N.logical_and(idim >= plotkwds[idimkey][0],
idim <= plotkwds[idimkey][-1])
else:
raise ValueError, 'bad dimension range keyword entry'
rngs[idimkey] = N.compress(dim_mask, idim)
rngs_idxs[idimkey] = N.compress(dim_mask, N.arange(N.size(idim)))
if N.size(rngs[idimkey]) > 1:
keys_rngs_sizes_gt_1.append(idimkey)
#- Set plot types (line or contour):
if len(keys_rngs_sizes_gt_1) == 0:
raise ValueError, 'cannot plot without any fixed dimension'
elif len(keys_rngs_sizes_gt_1) == 1:
plottype = 'line'
elif len(keys_rngs_sizes_gt_1) == 2:
plottype = 'contour'
else:
raise ValueError, 'cannot plot with > 2 varying dimensions'
#- Set plot axis fields and axis names, depending on what sort
# of dimensions will be plotted. If lon is to be plotted, it is
# always the x-axis. If lat is to be plotted, it is always the
# y-axis. In this section and later on in a few places, I
# rely on the count method for a list as a Boolean test: If it
# returns 0, consider that False; > 0 is True. The text for the
# title and axis labels to be passed to the xlabel, etc. methods,
# are called titlename, xname, and yname:
if plottype == 'line': #+ Choose x-axis vector and
x = rngs[keys_rngs_sizes_gt_1[0]] # x/y names for line plot
xname = dimname[keys_rngs_sizes_gt_1[0]] + ' [' \
+ dimunits[keys_rngs_sizes_gt_1[0]] + ']'
yname = data_name + ' [' + data_units + ']'
elif plottype == 'contour': #+ Choose axis vectors and
if keys_rngs_sizes_gt_1.count('lon'): # names for contour plot
x = rngs['lon']
xname = dimname['lon'] + ' [' + dimunits['lon'] + ']'
if keys_rngs_sizes_gt_1.count('lat'):
y = rngs['lat']
yname = dimname['lat'] + ' [' + dimunits['lat'] + ']'
if keys_rngs_sizes_gt_1.count('time'):
if keys_rngs_sizes_gt_1.count('lon'):
y = rngs['time']
yname = dimname['time'] + ' [' + dimunits['time'] + ']'
elif keys_rngs_sizes_gt_1.count('lat'):
x = rngs['time']
xname = dimname['time'] + ' [' + dimunits['time'] + ']'
else:
raise ValueError, 'bad treatment of time'
else:
raise ValueError, 'unrecognized plottype'
#- Override xname, yname, and titlename with keywords, if they
# are not None. titlename receives data_name and data_units
# by default:
if plotkwds['xlabel'] != None:
xname = plotkwds['xlabel']
if plotkwds['ylabel'] != None:
yname = plotkwds['ylabel']
if plotkwds['title'] != None:
titlename = plotkwds['title']
else:
titlename = data_name + ' [' + data_units + ']'
#- Pick data to be plotted and plot:
pylab.clf() #+ Clear any previous figures
pylab.figure(1) #+ Open a pylab figure
if plottype == 'line': #+ Select data for a line plot
y = data[rngs_idxs['time'], # and plot
rngs_idxs['lat'], rngs_idxs['lon']]
pylab.plot(x, y)
elif plottype == 'contour': #+ Select data for a contour
ritim = rngs_idxs['time'] # plot and plot
rilat = rngs_idxs['lat']
rilon = rngs_idxs['lon']
#* Extract subarrays depending on which two dimensions are
# chosen:
if N.size(rngs_idxs['time']) == 1:
zgrid = num.MLab.squeeze(data[ritim[0], rilat[0]:rilat[-1] + 1,
rilon[0]:rilon[-1] + 1])
elif N.size(rngs_idxs['lat']) == 1:
zgrid = num.MLab.squeeze(data[ritim[0]:ritim[-1] + 1, rilat[0],
rilon[0]:rilon[-1] + 1])
elif N.size(rngs_idxs['lon']) == 1:
zgrid = num.MLab.squeeze(data[ritim[0]:ritim[-1] + 1,
rilat[0]:rilat[-1] + 1, rilon[0]])
else:
raise ValueError, 'unrecognized configuration'
#* Change zgrid for special case of a lat. vs. time contour
# plot. Calculate xgrid and ygrid:
if keys_rngs_sizes_gt_1.count('time') and \
keys_rngs_sizes_gt_1.count('lat'):
zgrid = N.transpose(zgrid)
xgrid, ygrid = pylab.meshgrid(x, y)
#* Set contour levels:
if plotkwds['levels'] == None:
levels = nice_levels(zgrid)
else:
levels = plotkwds['levels']
#- Plot (creating continents first if is a lat vs. lon plot)
# and write contour levels/color bar as appropriate:
if keys_rngs_sizes_gt_1.count('lon') and \
keys_rngs_sizes_gt_1.count('lat'):
mapplot = Basemap(projection='cyl',
resolution='l',
llcrnrlon=N.min(xgrid),
llcrnrlat=N.min(ygrid),
urcrnrlon=N.max(xgrid),
urcrnrlat=N.max(ygrid))
mapplot.drawcoastlines()
mapplot.drawmeridians(nice_levels(rngs['lon'],
approx_nlev=plotkwds['nlatlon']),
labels=[1, 0, 0, 1])
mapplot.drawparallels(nice_levels(rngs['lat'],
approx_nlev=plotkwds['nlatlon']),
labels=[1, 0, 0, 1])
if plotkwds['filled']:
plot = mapplot.contourf(xgrid, ygrid, zgrid, levels)
pylab.colorbar(plot, orientation='horizontal', format='%g')
else:
plot = mapplot.contour(xgrid, ygrid, zgrid, levels)
pylab.clabel(plot, inline=1, fontsize=10, fmt='%g')
else:
if plotkwds['filled']:
plot = pylab.contourf(xgrid, ygrid, zgrid, levels)
pylab.colorbar(plot, orientation='horizontal', format='%g')
else:
plot = pylab.contour(xgrid, ygrid, zgrid, levels)
pylab.clabel(plot, inline=1, fontsize=10, fmt='%g')
else:
raise ValueError, 'unrecognized plottype'
#- Add titling. Lat vs. lon plots do not have axis labels because
# the map labels already make it clear, and for those plots the
# title also includes the time value:
if keys_rngs_sizes_gt_1.count('lon') and \
keys_rngs_sizes_gt_1.count('lat'):
titlename = titlename + ' at ' \
+ dimname['time'] + ' ' \
+ str(rngs['time'][0]) + ' ' \
+ dimunits['time']
titlename = mpl_latex_script1(titlename)
pylab.title(titlename)
else:
titlename = mpl_latex_script1(titlename)
xname = mpl_latex_script1(xname)
yname = mpl_latex_script1(yname)
pylab.xlabel(xname)
pylab.ylabel(yname)
pylab.title(titlename)
#- Output plot to PNG file or screen. The show command seems to
# have a problem on my Mac OS X, so save to a temporary file
# and use preview to view for fn == None and tmppreview set to
# True. Note that the temporary file is not deleted by this
# method:
if plotkwds['fn'] == None: #+ Screen display
if plotkwds['tmppreview'] and sys.platform == 'darwin':
outputfn = tempfile.mkstemp('.png', 'qtcm_')
pylab.savefig(outputfn[-1])
os.system('open -a /Applications/Preview.app ' + outputfn[-1])
else:
pylab.show()
elif type(plotkwds['fn']) == type('a'): #+ Write to file
pylab.savefig(plotkwds['fn'])
pylab.close(1)
else:
raise ValueError, 'cannot write to this type of file'
def interp(y, x, xinterp, missing=1e+20):
"""Simple linear interpolation for ordinate with missing values.
Vectors x and y are the data describing a piecewise linear function.
Function returns the interpolated values of the ordinate function
at abscissa values in xinterp. Values of xinterp outside the range
of x are returned as missing. Any elements in the output that uses
missing values in y for the interpolation are set to missing.
Positional Input Arguments:
* y: Ordinate values of data. Rank 1 numeric vector. Required.
Can have missing values. Floating or integer type.
* x: Abscissa values of data. Rank 1 numeric vector. Required.
Can have no missing values. Must be monotonically ascending.
Floating or integer type.
* xinterp: Abscissa values to calculate interpolated ordinate
values at. Rank 1 numeric vector or numeric scalar. Required.
Can have no missing values. Can be in any order. Floating or
integer type.
Keyword Input Arguments:
* missing: If input has missing values, this is the missing value
value. Scalar. Floating or integer type. Default is 1e+20.
Output Result:
* Interpolated ordinate values at xinterp. Rank 1 numeric vector
of same length as xinterp (if xinterp is a numeric scalar,
output is also a numeric scalar). Missing values are set to the
value of argument missing. Type is Float, even if argument
missing and inputs are all integer.
References:
* Lin, J. W.-B.: "Simple Interpolation."
Python/CDAT for Earth Scientists: Tips and Examples.
http://www.johnny-lin.com/cdat_tips/tips_math/interp.html
Example with no missing values (gives same output as function
arrayfns.interp):
>>> from interp import interp
>>> import numpy as N
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 6., 2.,-5.,-3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '4.8']
Example with missing values:
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20']
Example with values out of range of the data:
>>> x = N.array([1., 2.1, 3., 4., 5.1])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, -2.3, 6.])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20', '1e+20']
"""
import arrayfns
import numpy.ma as MA
import numpy as N
from where_close import where_close
#- Check inputs for possible errors:
if (N.rank(y) != 1) or (N.rank(x) != 1):
raise ValueError, "interp: Input(s) not a vector"
if N.rank(xinterp) > 1:
raise ValueError, "interp: xinterp not a vector or scalar"
if x[-1] <= x[0]:
raise ValueError, "interp: x not monotonically increasing"
#- Establish constants and define xint, a rank 1 version of
# xinterp to be used for the rest of the function:
if N.rank(xinterp) == 0:
xint = N.reshape(xinterp, (1,))
else:
xint = xinterp
num_xint = N.size(xint)
#- Mask as missing values of xint that are outside of the range
# of x:
yint_outrange_mask = N.logical_or( N.less(xint, x[0]) \
, N.greater(xint, x[-1]) )
#- Mask of elements with missing values in y, if there are any
# missing values in y. If xint equals a value in x, missing
# values mask for that xint is the same as the corresponding
# value in x; and mask elements in xint which fall in an interval
# (whose upper bound index is top_idx) where one of the endpoints
# is missing:
y_miss_mask = where_close(y, missing)
yint_miss_mask = N.zeros(num_xint)
if MA.maximum(y_miss_mask) == 1:
for i in xrange(num_xint):
if yint_outrange_mask[i] == 0:
x_eq_xint = where_close(x, xint[i])
if MA.maximum(x_eq_xint) == 1:
yint_miss_mask[i] = y_miss_mask[N.nonzero(x_eq_xint)]
else:
top_idx = N.nonzero(N.greater(x, xint[i]))[0]
yint_miss_mask[i] = y_miss_mask[top_idx] or \
y_miss_mask[top_idx-1]
#- Return interpolated values, set to missing values as
# appropriate, and making a scalar if xinterp is a scalar:
yint = arrayfns.interp(y, x, xint)
N.putmask( yint, N.logical_or(yint_miss_mask, yint_outrange_mask) \
, missing)
if N.rank(xinterp) == 0: yint = yint[0]
return yint
def nice_levels(data, approx_nlev=10, max_nlev=28):
"""Compute a vector of "levels" at "nice" increments.
Returns a 1-D array of "levels" (e.g., contour levels) calculated
to give an aesthetically pleasing and human-readable interval,
if possible. If not, returns levels for approx_nlev levels
between the maximum and minimum of data. In any event, the
function will return no more than max_nlev levels.
Keyword Input Parameter:
* data: Array of values to calculate levels for. Can be of any
size and shape.
Keyword Input Parameter:
* approx_nlev: Integer referring to approximately how many
levels to return. This is the way of adjusting how "coarse"
or "fine" to make the vector of levels.
* max_nlev: The maximum number of levels the function will
permit to be returned. The interval of levels will be adjusted
to keep the number of levels returned under this value. If
approx_nlev is chosen to be greater than or equal to max_nlev,
an exception is raised.
Output:
* This function returns a 1-D array of contour levels.
Function is adaptation of parts of IDL routine contour_plot.pro
by Johnny Lin. This is why the capitalization conventions of
Python are not strictly followed in this function.
Examples:
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z)
>>> ['%g' % out[i] for i in range(len(out))]
['-30', '0', '30', '60', '90', '120', '150', '180', '210']
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z, approx_nlev=5)
>>> ['%g' % out[i] for i in range(len(out))]
['-50', '0', '50', '100', '150', '200']
>>> z = N.array([-24.5, 50.3, 183.1, 20.])
>>> out = nice_levels(z, approx_nlev=10)
>>> ['%g' % out[i] for i in range(len(out))]
['-30', '0', '30', '60', '90', '120', '150', '180', '210']
"""
#- Default settings and error check:
if approx_nlev >= max_nlev:
raise ValueError, 'max_nlev is too small'
MAX_zd_ok = N.max(data)
MIN_zd_ok = N.min(data)
nlevels = N.min([approx_nlev, max_nlev])
tmpcmax = MAX_zd_ok
tmpcmin = MIN_zd_ok
tmpcint = N.abs( (tmpcmax-tmpcmin)/float(nlevels) )
#- See if the cint can be "even". If not, return alternative
# contour levels vector:
#+ Guess a possible cint. Look for an "even" value that is
# closest to that:
guesscint = N.abs( (MAX_zd_ok-MIN_zd_ok)/float(nlevels) )
if (guesscint > 1e-10) and (guesscint < 1e+10):
possiblecint = [1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5,
0.0001, 0.0002, 0.0005,
0.001, 0.002, 0.005,
0.01, 0.02, 0.05,
0.1, 0.2, 0.5,
1., 2., 5.,
10., 20., 30., 45., 50.,
100., 200., 500.,
1000., 2000., 5000.,
10000., 20000., 50000.,
1e+5, 1e+6, 1e+7, 1e+8, 1e+9, 1e+10]
diffcint = N.abs(possiblecint-guesscint)
tempcint = N.compress( diffcint == N.min(diffcint), possiblecint )[0]
tcidx = N.compress( where_close( possiblecint, tempcint ),
N.arange(N.size(possiblecint)) )[0]
#+ Look around at the "even" values nearby the possible option
# for cint. Calculate how many contours each of those cints
# would give. Dictionary ncon_count is the number of
# contours for a given test cint. test_tcidxs are the indices
# in possiblecint to examine in detail; these index values
# will be the keys in ncon_count.
if tcidx == 0: tcidx = 1
if tcidx == N.size(possiblecint)-1: tcidx = N.size(possiblecint)-2
ncon_count = {}
test_tcidxs = [tcidx-1, tcidx, tcidx+1]
for i in test_tcidxs:
itcval = possiblecint[i]
positivecon = N.arange(max_nlev+2, dtype=float)*itcval
negativecon = (N.arange(max_nlev+2, dtype=float)+1.0)*itcval*(-1.0)
if (MAX_zd_ok + itcval >= 0) and (MIN_zd_ok - itcval >= 0):
ncon_count[i] = N.sum( \
N.logical_and(positivecon <= MAX_zd_ok + itcval,
positivecon >= MIN_zd_ok - itcval) )
elif (MAX_zd_ok + itcval < 0) and (MIN_zd_ok - itcval < 0):
ncon_count[i] = N.sum( \
N.logical_and(negativecon <= MAX_zd_ok + itcval,
negativecon >= MIN_zd_ok - itcval) )
else:
ncon_count[i] = N.sum(positivecon <= MAX_zd_ok + itcval) \
+ N.sum(negativecon >= MIN_zd_ok - itcval)
#+ Select the cint that has the fewest levels if it has at
# least nlevels-1. Otherwise, try to find the next cint with
# the fewest levels that is below max_nlev. tempcint is what
# you get (changed, if warranted) leaving this section:
min_ncon_count = N.min(ncon_count.values())
current_best_count = max_nlev
for i in test_tcidxs:
if (ncon_count[i] == min_ncon_count) and \
(ncon_count[i] >= nlevels-1):
tempcint = possiblecint[i]
current_best_count = ncon_count[i]
break
elif (ncon_count[i] == min_ncon_count) and \
(ncon_count[i] < nlevels-1):
continue
elif ncon_count[i] > max_nlev:
continue
else:
if N.abs(ncon_count[i]-nlevels) < \
N.abs(current_best_count-nlevels):
tempcint = possiblecint[i]
current_best_count = ncon_count[i]
continue
#+ Create levels for case with neg. and pos. contours. There
# is the case of all pos., all neg., and mixed pos. and neg.
# contours:
positivecon = N.arange(max_nlev+2, dtype=float)*tempcint
negativecon = (N.arange(max_nlev+2, dtype=float)+1.0)*tempcint*(-1.0)
if (MAX_zd_ok + tempcint >= 0) and (MIN_zd_ok - tempcint >= 0):
tmpclevels = N.compress( \
N.logical_and(positivecon <= MAX_zd_ok + tempcint,
positivecon >= MIN_zd_ok - tempcint),
positivecon )
elif (MAX_zd_ok + tempcint < 0) and (MIN_zd_ok - tempcint < 0):
tmpclevels = N.compress( \
N.logical_and(negativecon <= MAX_zd_ok + tempcint,
negativecon >= MIN_zd_ok - tempcint),
negativecon )
else:
uppercon = N.compress( positivecon <= MAX_zd_ok + tempcint,
positivecon )
lowercon = N.compress( negativecon >= MIN_zd_ok - tempcint,
negativecon )
tmpclevels = N.concatenate([lowercon, uppercon])
#+ Sort clevels, reset number of levels, maximum, minimum,
# and interval of contour based on the automatic setting:
tmpclevels = N.sort(tmpclevels)
if (N.size(tmpclevels) <= max_nlev ) and (N.size(tmpclevels) > 0):
nlevels = N.size(tmpclevels)
tmpcmax = tmpclevels[-1]
tmpcmin = tmpclevels[0]
tmpcint = tempcint
else:
pass
#- Return output:
return N.arange(tmpcmin, tmpcmax+tmpcint, tmpcint)
def plot_ncdf_output(id, datafn, **kwds):
"""Plot model field id from the data in netCDF file datafn.
Positional Input Parameter:
* id: Name of the id of the field to plot. String.
* datafn: Filename containing the output data to plot. String.
Input keyword parameter descriptions are found in the docstring
for Qtcm methods ploti, plotm, and other methods that call this
private method. In general, those methods take the keyword
parameters they receive and pass it along unchanged as keyword
parameters to this function. In that sense, this function is
seldom used as a stand-alone function, but rather is usually
used coupled with a Qtcm instance.
The data fields read in from the netCDF output file are dimensioned
(time, lat, lon). This is different than how the data is stored
in the compiled QTCM model fields (lon, lat, time), and at the
Python level (lon, lat). The reason this is the case is that
f2py automatically makes the arrays passed between the Python
and Fortran levels match.
For a lat vs. lon plot, the contour plot is superimposed onto
a cylindrical projection map of the Earth with continents drawn
and labeled meridians and parallels. The title also includes
the model time, and x- and y-axis labels are not drawn.
All numerical data used for plotting come from the netCDF output
file for consistency (e.g., the dimensions of u1). Currently
this method only works for 3-D data arrays (two in space, one
in time).
"""
#- Accomodate other ids. The id that this routine will use
# (i.e., iduse corresponds to the name in the netCDF output file)
# is called iduse. Set this to id, except for the case where
# some aliases of ids are entered in which iduse is the alias:
if id == 'Qc': iduse = 'Prec'
elif id == 'FLWut': iduse = 'OLR'
elif id == 'STYPE': iduse = 'stype'
else: iduse = id
#- Set defined keyword defaults. All are set to None except for
# nlatlon which gets an integer:
plotkwds_ids = ['lat', 'lon', 'time', 'fn', 'levels', 'title',
'xlabel', 'ylabel',
'filled', 'nlatlon', 'tmppreview']
plotkwds = {}
for ikey in plotkwds_ids:
if kwds.has_key(ikey):
plotkwds[ikey] = copy.copy(kwds[ikey])
else:
plotkwds[ikey] = None
if not kwds.has_key('nlatlon'):
plotkwds['nlatlon'] = 8
#- Get data and dimensions of iduse to plot:
fileobj = S.NetCDFFile(datafn, mode='r')
data = N.array(fileobj.variables[iduse].getValue())
data_name = fileobj.variables[iduse].long_name
data_units = fileobj.variables[iduse].units
dim = {}
dimname = {}
dimunits = {}
dim['lat'] = N.array(fileobj.variables['lat'].getValue())
dimname['lat'] = fileobj.variables['lat'].long_name
dimunits['lat'] = fileobj.variables['lat'].units
dim['lon'] = N.array(fileobj.variables['lon'].getValue())
dimname['lon'] = fileobj.variables['lon'].long_name
dimunits['lon'] = fileobj.variables['lon'].units
dim['time'] = N.array(fileobj.variables['time'].getValue())
dimname['time'] = fileobj.variables['time'].long_name
dimunits['time'] = fileobj.variables['time'].units
fileobj.close()
#- Alter data long name to remove any units. The definition
# of units as the substring within the [] is the same as used in
# defVar in output.F90 of the compiled QTCM model. Remove
# underscores and extra whitespace in data_name and data_units,
# replacing with a single whitespace character between words:
idx1 = data_name.find('[')
idx2 = data_name.find(']')
if idx1 != -1 and idx2 != -1:
data_name = data_name[:idx1] + data_name[idx2+1:]
data_name = data_name.strip()
data_name = ' '.join(data_name.replace('_',' ').split())
data_units = ' '.join(data_units.replace('_',' ').split())
#- Alter dimension long name to remove any units. The definition
# of units as the substring within the [] is the same as used in
# defVar in output.F90 of the compiled QTCM model. Remove
# underscores and extra whitespace in name and units, replacing
# with a single whitespace character between words, and
# capitalizing like a title:
for idimkey in dim.keys():
idimname = dimname[idimkey]
idx1 = idimname.find('[')
idx2 = idimname.find(']')
if idx1 != -1 and idx2 != -1:
idimname = idimname[:idx1] + idimname[idx2+1:]
dimname[idimkey] = idimname.strip()
dimname[idimkey] = \
' '.join(dimname[idimkey].replace('_',' ').split()).title()
dimunits[idimkey] = \
' '.join(dimunits[idimkey].replace('_',' ').split()).title()
#- Some data checks:
if N.rank(data) != 3:
raise ValueError, '_plot: can only plot lat, lon, time fields'
if not N.allclose(dim['time'], N.sort(dim['time'])):
raise ValueError, '_plot: time not monotonically ascending'
if not N.allclose(dim['lat'], N.sort(dim['lat'])):
raise ValueError, '_plot: lat not monotonically ascending'
if not N.allclose(dim['lon'], N.sort(dim['lon'])):
raise ValueError, '_plot: lon not monotonically ascending'
if N.shape(data)[0] != N.size(dim['time']):
raise ValueError, '_plot: data time dim mismatch'
if N.shape(data)[1] != N.size(dim['lat']):
raise ValueError, '_plot: data lat dim mismatch'
if N.shape(data)[2] != N.size(dim['lon']):
raise ValueError, '_plot: data lon dim mismatch'
#- Choose and describe ranges for lat, lon, and time. The
# section cycles through the dictionary of dimensions. idim is
# the 1-D array of the values of that dimension. rngs is a
# dictionary where each entry corresponds to a dimension, and the
# value of the entry is the values of that dimension that are to
# be plotted. rngs_idxs are the indices in the original
# dimensions array corresponding to the values in rngs.
# keys_rngs_sizes_gt_1 is a list of the keys of ranges that have
# sizes greater than 1:
rngs = {}
rngs_idxs = {}
keys_rngs_sizes_gt_1 = []
for idimkey in dim.keys():
idim = dim[idimkey]
if plotkwds[idimkey] == None:
dim_mask = N.ones( N.size(idim), dtype=int )
elif N.isscalar(plotkwds[idimkey]):
dim_mask = where_close( idim, plotkwds[idimkey] )
if N.sum(dim_mask) != 1:
raise ValueError, 'no point chosen'
elif (not N.isscalar(plotkwds[idimkey])) and \
N.size(plotkwds[idimkey]) == 1:
dim_mask = where_close( idim, plotkwds[idimkey][0] )
if N.sum(dim_mask) != 1:
raise ValueError, 'no point chosen'
elif N.size(plotkwds[idimkey]) == 2:
dim_mask = N.logical_and( idim >= plotkwds[idimkey][0],
idim <= plotkwds[idimkey][-1] )
else:
raise ValueError, 'bad dimension range keyword entry'
rngs[idimkey] = N.compress( dim_mask, idim )
rngs_idxs[idimkey] = N.compress( dim_mask, N.arange(N.size(idim)) )
if N.size(rngs[idimkey]) > 1:
keys_rngs_sizes_gt_1.append(idimkey)
#- Set plot types (line or contour):
if len(keys_rngs_sizes_gt_1) == 0:
raise ValueError, 'cannot plot without any fixed dimension'
elif len(keys_rngs_sizes_gt_1) == 1:
plottype = 'line'
elif len(keys_rngs_sizes_gt_1) == 2:
plottype = 'contour'
else:
raise ValueError, 'cannot plot with > 2 varying dimensions'
#- Set plot axis fields and axis names, depending on what sort
# of dimensions will be plotted. If lon is to be plotted, it is
# always the x-axis. If lat is to be plotted, it is always the
# y-axis. In this section and later on in a few places, I
# rely on the count method for a list as a Boolean test: If it
# returns 0, consider that False; > 0 is True. The text for the
# title and axis labels to be passed to the xlabel, etc. methods,
# are called titlename, xname, and yname:
if plottype == 'line': #+ Choose x-axis vector and
x = rngs[keys_rngs_sizes_gt_1[0]] # x/y names for line plot
xname = dimname[keys_rngs_sizes_gt_1[0]] + ' [' \
+ dimunits[keys_rngs_sizes_gt_1[0]] + ']'
yname = data_name + ' [' + data_units + ']'
elif plottype == 'contour': #+ Choose axis vectors and
if keys_rngs_sizes_gt_1.count('lon'): # names for contour plot
x = rngs['lon']
xname = dimname['lon'] + ' [' + dimunits['lon'] + ']'
if keys_rngs_sizes_gt_1.count('lat'):
y = rngs['lat']
yname = dimname['lat'] + ' [' + dimunits['lat'] + ']'
if keys_rngs_sizes_gt_1.count('time'):
if keys_rngs_sizes_gt_1.count('lon'):
y = rngs['time']
yname = dimname['time'] + ' [' + dimunits['time'] + ']'
elif keys_rngs_sizes_gt_1.count('lat'):
x = rngs['time']
xname = dimname['time'] + ' [' + dimunits['time'] + ']'
else:
raise ValueError, 'bad treatment of time'
else:
raise ValueError, 'unrecognized plottype'
#- Override xname, yname, and titlename with keywords, if they
# are not None. titlename receives data_name and data_units
# by default:
if plotkwds['xlabel'] != None:
xname = plotkwds['xlabel']
if plotkwds['ylabel'] != None:
yname = plotkwds['ylabel']
if plotkwds['title'] != None:
titlename = plotkwds['title']
else:
titlename = data_name + ' [' + data_units + ']'
#- Pick data to be plotted and plot:
pylab.clf() #+ Clear any previous figures
pylab.figure(1) #+ Open a pylab figure
if plottype == 'line': #+ Select data for a line plot
y = data[rngs_idxs['time'], # and plot
rngs_idxs['lat'],
rngs_idxs['lon']]
pylab.plot(x, y)
elif plottype == 'contour': #+ Select data for a contour
ritim = rngs_idxs['time'] # plot and plot
rilat = rngs_idxs['lat']
rilon = rngs_idxs['lon']
#* Extract subarrays depending on which two dimensions are
# chosen:
if N.size(rngs_idxs['time']) == 1:
zgrid = num.MLab.squeeze(data[ ritim[0],
rilat[0]:rilat[-1]+1,
rilon[0]:rilon[-1]+1 ])
elif N.size(rngs_idxs['lat']) == 1:
zgrid = num.MLab.squeeze(data[ ritim[0]:ritim[-1]+1,
rilat[0],
rilon[0]:rilon[-1]+1 ])
elif N.size(rngs_idxs['lon']) == 1:
zgrid = num.MLab.squeeze(data[ ritim[0]:ritim[-1]+1,
rilat[0]:rilat[-1]+1,
rilon[0] ])
else:
raise ValueError, 'unrecognized configuration'
#* Change zgrid for special case of a lat. vs. time contour
# plot. Calculate xgrid and ygrid:
if keys_rngs_sizes_gt_1.count('time') and \
keys_rngs_sizes_gt_1.count('lat'):
zgrid = N.transpose(zgrid)
xgrid, ygrid = pylab.meshgrid(x, y)
#* Set contour levels:
if plotkwds['levels'] == None:
levels = nice_levels(zgrid)
else:
levels = plotkwds['levels']
#- Plot (creating continents first if is a lat vs. lon plot)
# and write contour levels/color bar as appropriate:
if keys_rngs_sizes_gt_1.count('lon') and \
keys_rngs_sizes_gt_1.count('lat'):
mapplot = Basemap(projection='cyl', resolution='l',
llcrnrlon=N.min(xgrid), llcrnrlat=N.min(ygrid),
urcrnrlon=N.max(xgrid), urcrnrlat=N.max(ygrid))
mapplot.drawcoastlines()
mapplot.drawmeridians(nice_levels(rngs['lon'],
approx_nlev=plotkwds['nlatlon']),
labels=[1,0,0,1])
mapplot.drawparallels(nice_levels(rngs['lat'],
approx_nlev=plotkwds['nlatlon']),
labels=[1,0,0,1])
if plotkwds['filled']:
plot = mapplot.contourf(xgrid, ygrid, zgrid, levels)
pylab.colorbar(plot, orientation='horizontal', format='%g')
else:
plot = mapplot.contour(xgrid, ygrid, zgrid, levels)
pylab.clabel(plot, inline=1, fontsize=10, fmt='%g')
else:
if plotkwds['filled']:
plot = pylab.contourf(xgrid, ygrid, zgrid, levels)
pylab.colorbar(plot, orientation='horizontal', format='%g')
else:
plot = pylab.contour(xgrid, ygrid, zgrid, levels)
pylab.clabel(plot, inline=1, fontsize=10, fmt='%g')
else:
raise ValueError, 'unrecognized plottype'
#- Add titling. Lat vs. lon plots do not have axis labels because
# the map labels already make it clear, and for those plots the
# title also includes the time value:
if keys_rngs_sizes_gt_1.count('lon') and \
keys_rngs_sizes_gt_1.count('lat'):
titlename = titlename + ' at ' \
+ dimname['time'] + ' ' \
+ str(rngs['time'][0]) + ' ' \
+ dimunits['time']
titlename = mpl_latex_script1(titlename)
pylab.title(titlename)
else:
titlename = mpl_latex_script1(titlename)
xname = mpl_latex_script1(xname)
yname = mpl_latex_script1(yname)
pylab.xlabel(xname)
pylab.ylabel(yname)
pylab.title(titlename)
#- Output plot to PNG file or screen. The show command seems to
# have a problem on my Mac OS X, so save to a temporary file
# and use preview to view for fn == None and tmppreview set to
# True. Note that the temporary file is not deleted by this
# method:
if plotkwds['fn'] == None: #+ Screen display
if plotkwds['tmppreview'] and sys.platform == 'darwin':
outputfn = tempfile.mkstemp('.png','qtcm_')
pylab.savefig(outputfn[-1])
os.system('open -a /Applications/Preview.app '+outputfn[-1])
else:
pylab.show()
elif type(plotkwds['fn']) == type('a'): #+ Write to file
pylab.savefig(plotkwds['fn'])
pylab.close(1)
else:
raise ValueError, 'cannot write to this type of file'
