def main(trop_limit=True, res='4x5', debug=False):
"""
Get prod loss output for a family and print this to screen
"""
# --- Get family from Command line (and other vars)
wd = sys.argv[1]
spec = sys.argv[2]
# version?
ver = AC.iGEOSChem_ver(wd)
# --- Get all tags for this family (through dictionary route)
# ( e.g. 'PIOx', 'LIOx', 'P_Iy', 'L_Iy' )
nums, rxns, tags, Coe = AC.prod_loss_4_spec(wd, spec, ver=ver)
# beatify reaction strings
rxnstr_l = [''.join(i[4:]) for i in rxns]
# one consider one tag per reaction and tagged reactions
try:
tags = [i[0] for i in tags] # just consider first tag
except:
print 'WARNING! - attempting to process just tagged reactions'
detail_zip = zip(rxnstr_l, zip(nums, tags))
untagged = [n for n, i in enumerate(tags) if (len(i) < 1)]
print 'Untagged reactions: ', [detail_zip[i] for i in untagged]
tags = [i for n, i in enumerate(tags) if (n not in untagged)]
tags = [i[0] for i in tags] # just consider first tag
# tags.pop( tags.index('LR71') ) # rm tag for ClOO loss...
# --- Extract prod loss for these tracers
# get prod loss IDs
PDs = [AC.PLO3_to_PD(i, ver=ver, wd=wd, fp=True) for i in tags]
# extract en mass
fam_loss = AC.get_GC_output( wd, vars=['PORL_L_S__'+i for i in PDs], \
trop_limit=trop_limit, r_list=True)
# print [ ( i.shape, i.sum() ) for i in fam_loss ]
# Get reference species for family ( e.g. so output is in X g of Y )
ref_spec = AC.get_ref_spec(spec)
# get shared variable arrrays
s_area = get_surface_area(res=res)[..., 0] # m2 land map
# convert to mass terms ( in g X )
fam_loss = convert_molec_cm3_s_2_g_X_s( ars=fam_loss, \
ref_spec=ref_spec, wd=wd, conbine_ars=False, \
rm_strat=True, month_eq=True )
print[i.shape for i in fam_loss]
# sum and convert to Gg
p_l = [i.sum() / 1E9 for i in fam_loss]
# --- print output as: reaction, magnitude, percent of family
pcent = [np.sum(i) / np.sum(p_l) * 100 for i in p_l]
d = dict(zip(tags, zip(rxnstr_l, p_l, pcent)))
df = pd.DataFrame(d).T
df.columns = ['rxn', 'Gg X', '% of total']
# sort
df = df.sort_values(['% of total'], ascending=False)
print df
def main( trop_limit=True, res='4x5', debug=False):
"""
Get prod loss output for a family and print this to screen
"""
# --- Get family from Command line (and other vars)
wd = sys.argv[1]
spec = sys.argv[2]
# version?
ver = AC.iGEOSChem_ver( wd)
# --- Get all tags for this family (through dictionary route)
# ( e.g. 'PIOx', 'LIOx', 'P_Iy', 'L_Iy' )
nums, rxns, tags, Coe = AC.prod_loss_4_spec( wd, spec, ver=ver )
# beatify reaction strings
rxnstr_l = [ ''.join( i[4:] ) for i in rxns ]
# one consider one tag per reaction and tagged reactions
try:
tags = [ i[0] for i in tags ] # just consider first tag
except:
print 'WARNING! - attempting to process just tagged reactions'
detail_zip = zip( rxnstr_l, zip( nums, tags) )
untagged = [n for n,i in enumerate(tags) if (len(i)<1) ]
print 'Untagged reactions: ', [ detail_zip[i] for i in untagged ]
tags = [ i for n, i in enumerate( tags ) if (n not in untagged) ]
tags = [ i[0] for i in tags ] # just consider first tag
# tags.pop( tags.index('LR71') ) # rm tag for ClOO loss...
# --- Extract prod loss for these tracers
# get prod loss IDs
PDs = [ AC.PLO3_to_PD(i, ver=ver, wd=wd, fp=True) for i in tags ]
# extract en mass
fam_loss = AC.get_GC_output( wd, vars=['PORL_L_S__'+i for i in PDs], \
trop_limit=trop_limit, r_list=True)
# print [ ( i.shape, i.sum() ) for i in fam_loss ]
# Get reference species for family ( e.g. so output is in X g of Y )
ref_spec = AC.get_ref_spec( spec )
# get shared variable arrrays
s_area = get_surface_area(res=res)[...,0] # m2 land map
# convert to mass terms ( in g X )
fam_loss = convert_molec_cm3_s_2_g_X_s( ars=fam_loss, \
ref_spec=ref_spec, wd=wd, conbine_ars=False, \
rm_strat=True, month_eq=True )
print [ i.shape for i in fam_loss ]
import AC_tools as AC
# Download the example data if it is not already downloaded.
from AC_tools.Scripts import get_data_files
# Specify the working directory
wd = "../data"
# Get the GeosChem species data from the wd
my_data = AC.get_GC_output(wd, species='O3')
# Get a 2d slice from the 3d array
my_data = my_data[:, :, 0, 0]
# Turn from part per part to part per billion
my_data = my_data * 1E9
# Create the plot
AC.map_plot(my_data)
# Save the plot and show it.
AC.save_plot("my_plot")
AC.show_plot()
unit, scale = AC.tra_unit( species, scale=True)
# debug/print verbose output?
debug=True
# Only consider GEOS-Chem chemical troposphere
trop_limit=True
calc_burden=False#True
try: # chcck if a directory was given ad command line
wd = sys.argv[1]
except: # Otherwise use path below
wd = '<insert GEOS-Chem run direcotory path here>'
# get data as 4D array ( lon, lat, alt, time )
mixing_ratio = AC.get_GC_output( wd, species=species, category='IJ-AVG-$', \
trop_limit=trop_limit )
print mixing_ratio.shape
# Get data to calculate burden
if calc_burden:
# Get air mass as numpy array
air_mass = AC.get_GC_output( wd, vars=['BXHGHT_S__AD'], \
trop_limit=trop_limit )
# get time in troposphere as fraction from ctm.bpchh diagnostics
time_in_trop = AC.get_GC_output( wd, vars=['TIME_TPS__TIMETROP'],
trop_limit=trop_limit )
# print shapes of array to screen
print [i.shape for i in mixing_ratio, air_mass, time_in_trop ]
# calc the total & mean mass of a speices - select data you want to calc & print
unit, scale = AC.tra_unit(species, scale=True)
# debug/print verbose output?
debug = True
# Only consider GEOS-Chem chemical troposphere
trop_limit = True
calc_burden = False #True
try: # chcck if a directory was given ad command line
wd = sys.argv[1]
except: # Otherwise use path below
wd = '<insert GEOS-Chem run direcotory path here>'
# get data as 4D array ( lon, lat, alt, time )
mixing_ratio = AC.get_GC_output( wd, species=species, category='IJ-AVG-$', \
trop_limit=trop_limit )
print mixing_ratio.shape
# Get data to calculate burden
if calc_burden:
# Get air mass as numpy array
air_mass = AC.get_GC_output( wd, vars=['BXHGHT_S__AD'], \
trop_limit=trop_limit )
# get time in troposphere as fraction from ctm.bpchh diagnostics
time_in_trop = AC.get_GC_output(wd,
vars=['TIME_TPS__TIMETROP'],
trop_limit=trop_limit)
# print shapes of array to screen
print[i.shape for i in mixing_ratio, air_mass, time_in_trop]
import AC_tools as AC
# Download the example data if it is not already downloaded.
from AC_tools.Scripts import get_data_files
# Specify the working directory
wd = "../data"
# Get the GeosChem species data from the wd
my_data = AC.get_GC_output( wd, species='O3')
# Get a 2d slice from the 3d array
my_data = my_data[:,:,0,0]
# Turn from part per part to part per billion
my_data = my_data*1E9
# Create the plot
AC.map_plot( my_data)
# Save the plot and show it.
AC.save_plot("my_plot")
AC.show_plot()