def plot_climate_indices(E,
index_name,
copies_to_plot,
climatology_option='NODA',
hostname='taurus',
verbose=False):
"""
This subroutine computes a bunch of simple climate indices
for a given experiment, and plots them for all copies given by 'copies_to_plot'
INPUTS:
copies_to_plot: list containing keywords for what copies to plot. Here are the options:
+ any valid copystring in DART output data (e.g. "ensemble member 1")
+ 'ensemble' = plot the entire ensemble
+ 'ensemble mean' = plot the ensemble mean
+ 'operational' = plot the operational value of this index
"""
# create a list of copies to load
copy_list = []
if "copystring" in copies_to_plot:
copy_list.append(E['copystring'])
if ("ensemble" in copies_to_plot):
N = dart.get_ensemble_size_per_run(E['exp_name'])
for iens in np.arange(1, N + 1):
if iens < 10:
spacing = ' '
else:
spacing = ' '
copy_list.append("ensemble member" + spacing + str(iens))
if ("ensemble mean" in copies_to_plot):
copy_list.append("ensemble mean")
# retrieve desired index for all the copies in the list
L = []
for copy in copy_list:
E['copystring'] = copy
x = compute_climate_indices(E, index_name, climatology_option,
hostname, verbose)
L.append(x)
# plot it
for copy, climate_index in zip(copy_list, L):
# define a color for the ensemble mean - depending on experiment
lcolor = "#000000"
if E['exp_name'] == 'W0910_NODA':
bmap = brewer2mpl.get_map('Dark2', 'qualitative', 7)
lcolor = bmap.hex_colors[1]
if E['exp_name'] == 'W0910_GLOBAL':
# make this one black, since it's sort of the reference
lcolor = "#000000"
if E['exp_name'] == 'W0910_TROPICS':
bmap = brewer2mpl.get_map('Dark2', 'qualitative', 7)
lcolor = bmap.hex_colors[0]
# make the ensemble a lighter version of their original color
ensemble_color = plot_tools.colorscale(lcolor, 1.6)
# here is the plot
if (copy == 'ensemble mean'):
plt.plot(E['daterange'], climate_index, color=lcolor, linewidth=2)
if "ensemble member" in copy:
plt.plot(E['daterange'],
climate_index,
color=ensemble_color,
linewidth=1)
def plot_climate_indices(E,index_name,copies_to_plot,climatology_option = 'NODA',hostname='taurus',verbose=False):
"""
This subroutine computes a bunch of simple climate indices
for a given experiment, and plots them for all copies given by 'copies_to_plot'
INPUTS:
copies_to_plot: list containing keywords for what copies to plot. Here are the options:
+ any valid copystring in DART output data (e.g. "ensemble member 1")
+ 'ensemble' = plot the entire ensemble
+ 'ensemble mean' = plot the ensemble mean
+ 'operational' = plot the operational value of this index
"""
# create a list of copies to load
copy_list = []
if "copystring" in copies_to_plot:
copy_list.append(E['copystring'])
if ("ensemble" in copies_to_plot):
N = dart.get_ensemble_size_per_run(E['exp_name'])
for iens in np.arange(1,N+1):
if iens < 10:
spacing = ' '
else:
spacing = ' '
copy_list.append("ensemble member"+spacing+str(iens))
if ("ensemble mean" in copies_to_plot):
copy_list.append("ensemble mean")
# retrieve desired index for all the copies in the list
L = []
for copy in copy_list:
E['copystring'] = copy
x = compute_climate_indices(E,index_name,climatology_option,hostname,verbose)
L.append(x)
# plot it
for copy,climate_index in zip(copy_list,L):
# define a color for the ensemble mean - depending on experiment
lcolor = "#000000"
if E['exp_name'] == 'W0910_NODA':
bmap = brewer2mpl.get_map('Dark2', 'qualitative', 7)
lcolor = bmap.hex_colors[1]
if E['exp_name'] == 'W0910_GLOBAL':
# make this one black, since it's sort of the reference
lcolor = "#000000"
if E['exp_name'] == 'W0910_TROPICS':
bmap = brewer2mpl.get_map('Dark2', 'qualitative', 7)
lcolor = bmap.hex_colors[0]
# make the ensemble a lighter version of their original color
ensemble_color = plot_tools.colorscale(lcolor, 1.6)
# here is the plot
if (copy == 'ensemble mean'):
plt.plot(E['daterange'],climate_index,color=lcolor,linewidth=2)
if "ensemble member" in copy:
plt.plot(E['daterange'],climate_index,color=ensemble_color,linewidth=1)
