# library structure: {variable: [name_string, color_map, [[species1, [timeseries1]], [species2, [timeseries2]]...]}

# annual emissions fluxes of various species in grams per MJ of fuel created by the supply chain

# color maps from http://matplotlib.org/examples/color/colormaps_reference.html

# or http://matplotlib.org/1.2.1/_images/show_colormaps.png

bioenergy_emissions = {'harvest': ['Feedstock harvest & transport', 'Blues',

[['CO2', [4.83]]]],

'biorefinery': ['Biorefinery energy', 'BuPu',

[['CO2', [13.5]]]],

'misc': ['Misc. inputs & factors', 'YlOrBr',

[['CO2', [14.9]]]],

'ecosystem': ['Ecosystem carbon balance', 'BuGn',

[['CO2', ecosystem_Cflux_timeseries]]]

}

reference_emissions = {'extract': ['Oil extraction/transport/refining', 'Blues',

[['CO2', [19.3]]]],

'tailpipe': ['Tailpipe emissions', 'YlOrBr',

[['CO2', [68.5]]]]

}

test_emissions = {'test': ['Test emissions', 'BuGn',

[['CO2', [1, 1.2, 1.4, 1.6, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]]]]

}

# format: [(a0, t0), (a1, t1)...] for c(t) = a0 * exp((-1.0*t)/t0) + a1 * exp((-1.0*t)/t1) + ...

ghg_decay_params = {'CO2': [(0.217, 0),

(0.259, 172.9),

(0.338, 18.52),

(0.186, 1.186)],

}

# format: (radiative forcing alpha term in W/m2*ppb, emission mass to atmospheric conversion constant in ppbv/Mg)

ghg_forcing_params = {'CO2': (0.000014, 1.29E-7)

}

def decay(species, initial, duration):

param_sets = ghg_decay_params[species]

amounts = []

for year in range(duration):

remaining = 0

for param_set in param_sets:

(a_i, t_i) = param_set

if t_i: # conditional to avoid divide by zero errors

remaining += initial * a_i * exp((-1.0*year)/t_i)

else:

remaining += initial * a_i

amounts.append(remaining)

return amounts

def gradient(figure_object, axis_object, xs, ys, start_year, TWP_length, cmap, key_count):

"""Based on http://matplotlib.org/examples/pylab_examples/multicolored_line.html

and http://stackoverflow.com/questions/19132402/set-a-colormap-under-a-graph

"""

from matplotlib.collections import LineCollection

# plot a color_map line fading to white

points = np.array([xs, ys]).T.reshape(-1, 1, 2)

segments = np.concatenate([points[:-1], points[1:]], axis=1)

lc = LineCollection(segments, cmap=plt.get_cmap('gray'), norm=plt.Normalize(start_year, start_year+TWP_length),

linewidth=0.2, zorder=1) # norm sets the color min:max range

lc.set_array(np.array(xs))

axis_object.add_collection(lc)

# add fading color_map fill as well

xs.append(max(xs))

xs.append(min(xs))

ys.append(0)

ys.append(0)

poly, = axis_object.fill(xs, ys, facecolor='none', edgecolor='none')

img_data = np.arange(0, 100, 1)

img_data = img_data.reshape(1, img_data.size)

im = axis_object.imshow(img_data, aspect='auto', origin='lower', cmap=plt.get_cmap(cmap),

extent=[start_year+TWP_length, start_year, 1000, -1000], vmin=0., vmax=100., zorder=-(start_year+1)*key_count)

im.set_clip_path(poly)

# print start_year*key_count

# main routine

TWP_length = 300 # years

years = range(1, TWP_length+1)

fig, axes = plt.subplots(3, sharex=True)

dictionaries = [bioenergy_emissions, reference_emissions]

y_max = 100

ymins = []

ymaxes = []

nets = []

for e, dictionary in enumerate(dictionaries):

# initialize cumulative radiative forcing arrays

total_additions_timeseries = []

total_subtractions_timeseries = []

for x in range(TWP_length+1):

total_additions_timeseries.append(0)

total_subtractions_timeseries.append(0)

# iterate through all source categories

key_count = 0

for key in dictionary.keys():

key_count += 1

source = dictionary[key][0]

print "Evaluating forcing source ", source

my_cmap = dictionary[key][1]

color = plt.get_cmap(my_cmap)(0.7)

axes[e].plot(0, 0, color=color, marker=None, label=source, linewidth=5)

for emissions in dictionary[key][2]:

species = emissions[0]

alpha, mass_conc = ghg_forcing_params[species]

flux_timeseries = emissions[1]

# adjust flux_timeseries list length for consistency with TWP range

if len(flux_timeseries) > TWP_length:

flux_timeseries = flux_timeseries[TWP_length:]

elif len(flux_timeseries) < TWP_length:

for x in range(TWP_length-len(flux_timeseries)):

flux_timeseries.append(0)

# iterate through flux_timeseries, compute out concentration vs. time and forcing vs. time for each flux,

# add to plot, and add to total_forcing

for i, flux in enumerate(flux_timeseries): # this will always have length = TWP_length

if flux: # don't go through the calculations if there is no flux

time = range(i, TWP_length)

flux_decay_timseries = np.array(decay(species, flux, TWP_length-i))

forcing_timeseries = flux_decay_timseries * mass_conc * alpha * (24*365) * 1E-6 # uWh/m2

plot_forcing_timeseries = []

for j, year in enumerate(time):

if flux > 0:

plot_forcing_timeseries.append(forcing_timeseries[j] + total_additions_timeseries[year])

total_additions_timeseries[year] += forcing_timeseries[j]

else:

plot_forcing_timeseries.append(forcing_timeseries[j] + total_subtractions_timeseries[year])

total_subtractions_timeseries[year] += forcing_timeseries[j]

gradient(fig, axes[e], time, plot_forcing_timeseries, i, TWP_length, my_cmap, key_count)

total_additions_timeseries = np.array(total_additions_timeseries)

total_subtractions_timeseries = np.array(total_subtractions_timeseries)

net_forcing = total_additions_timeseries + total_subtractions_timeseries

axes[e].plot(range(TWP_length), net_forcing[:-1], color='k', label='Net forcing', linewidth=3.0, zorder=1)

ymins.append(min(total_subtractions_timeseries))

ymaxes.append(max(total_additions_timeseries))

nets.append(net_forcing)

bioenergy_net = np.array(nets[0])

reference_net = np.array(nets[1])

TWP = bioenergy_net/reference_net

axes[2].clear()

axes[2].plot(range(TWP_length), TWP[:-1], color='k', linewidth=3.0, zorder=2)

axes[0].set_title('Forcing- biomass harvest, biofuel production & use')

axes[0].set_ylim(min(ymins), max(ymaxes))

axes[0].set_ylabel('uWh/MJ')

l = axes[0].legend(loc=1, prop={'size': 7})

l.set_zorder(3)

axes[0].plot([0, TWP_length-1], [0, 0], marker=None, linestyle='--', color='k', linewidth=2.0, zorder=2)

axes[1].set_title('Forcing- reference gasoline production & use')

axes[1].set_ylim(min(ymins), max(ymaxes))

axes[1].set_ylabel('uWh/MJ')

l = axes[1].legend(loc=1, prop={'size': 7})

l.set_zorder(3)

axes[1].plot([0, TWP_length-1], [0, 0], marker=None, linestyle='--', color='k', linewidth=2.0, zorder=2)

axes[2].set_title('Technology Warming Potential')

axes[2].set_xlim(0, TWP_length)

axes[2].set_ylabel('ratio')

axes[2].set_xlabel('Time elapsed after stand harvest, fuel production & use')

axes[2].plot([0, TWP_length-1], [1, 1], marker=None, linestyle='--', color='k', linewidth=2.0, zorder=2)

axes[2].grid()