out_res = options.out_res
outfile = options.outfile
out_formats = options.out_formats.split(',')
print_mode = options.print_mode
variables = options.variables.split(',')
dashes = ['-', '--', '-.', ':', '-', '--', '-.', ':']
output_order = ('station', 'time', 'z', 'profile')
alpha = 0.5
my_colors = colorList()
try:
cdict = plt.cm.datad[colormap]
except:
# import and convert colormap
cdict = gmtColormap(colormap)
cmap = colors.LinearSegmentedColormap('my_colormap', cdict)
# Init Unit system
sys = System()
# Plotting styles
axisbg = '0.9'
shadow_color = '0.25'
numpoints = 1
aspect_ratio = golden_mean
# set the print mode
lw, pad_inches = set_mode(print_mode, aspect_ratio=aspect_ratio)
vmax = options.vmax
reverse = options.reverse
colorbar_label = options.colorbar_label
cb_extend = options.cb_extend
# experimental
log_color = False
orientation = options.orientation
# read in CPT colormap
cmap_file = args[0]
try:
cmap = getattr(plt.cm, cmap_file)
prefix = cmap_file
except:
# import and convert colormap
cdict = gmtColormap(cmap_file, log_color=log_color, reverse=reverse)
prefix = ".".join(cmap_file.split(".")[0:-1])
suffix = cmap_file.split(".")[-1]
cmap = mpl.colors.LinearSegmentedColormap("my_colormap", cdict)
class nlcmap(object):
def __init__(self, cmap, levels):
self.cmap = cmap
self.levels = np.asarray(levels, dtype="float64")
self._x = self.levels
self.levmax = self.levels.max()
self.transformed_levels = np.linspace(0.0, self.levmax,
len(self.levels))
def __call__(self, xi, alpha=1.0, **kw):
reverse = options.reverse
colorbar_label = options.colorbar_label
cb_extend = options.cb_extend
# experimental
log_color = False
orientation = options.orientation
# read in CPT colormap
cmap_file = args[0]
try:
cdict = plt.cm.datad[cmap_file]
prefix = cmap_file
except:
# import and convert colormap
cdict = gmtColormap(cmap_file, log_color=log_color, reverse=reverse)
prefix = '.'.join(cmap_file.split('.')[0:-1])
suffix = cmap_file.split('.')[-1]
if colorbar_type in ('linear'):
data_values = np.linspace(vmin, vmax, N)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cb_extend = cb_extend
format = '%2.1f'
elif colorbar_type in ('gris_bath_topo'):
vmin = -800
vmax = 3000
data_values = np.linspace(vmin, vmax, N)
N = len(data_values)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cb_extend = 'both'
