# Using Calculation Setups¶

Calculation setups are a way to efficiently do LCA calculations for multiple functional units and methods at the same time.

This notebook builds on the Getting started with Brightway2 notebook, and won't work if you haven't done that notebook yet.

In [1]:
from brightway2 import *

In [2]:
if "Calculation setups" not in projects:
projects.set_current("BW2 introduction")
projects.copy_project("Calculation setups")
else:
projects.set_current("Calculation setups")


## Defining a calculation setup¶

A calculation setups is defined by three things:

• A name
• A list of functional units
• A list of LCIA methods

In this example, we will choose both activities and methods at random.

In [3]:
functional_units = [{Database('forwast').random(): 1} for _ in range(20)]


We can't choose methods completely at random, though, as the forwast database doesn't have as many biosphere flows as ecoinvent, so many methods will only characterize flows that aren't provided anywhere in forwast. So lets only choose from the methods which will have a non-zero LCA score:

In [4]:
import random

all_forwast_flows = {exc.input for ds in Database("forwast") for exc in ds.biosphere()}
suitable_methods = [method
for method in methods
if {cf[0] for cf in Method(method).load()}.intersection(all_forwast_flows)]

print("Can use {} of {} LCIA methods".format(len(suitable_methods), len(methods)))
chosen_methods = random.sample(suitable_methods, 8)

Can use 306 of 665 LCIA methods


A calculation setup is a normal Python dictionary, with keys inv and ia, for the functional units and LCIA methods, respectively.

In [5]:
my_calculation_setup = {'inv': functional_units, 'ia': chosen_methods}


You define a calculation setup by name in the metadata store calculation_setups, similar to the way that LCIA methods are defined.

In [6]:
calculation_setups['some random stuff'] = my_calculation_setup


We can examine what we have just randomly created:

In [7]:
calculation_setups['some random stuff']

Out[7]:
{'ia': [('ReCiPe Midpoint (H)', 'metal depletion', 'MDP'),
('EDIP w/o LT',
'environmental impact w/o LT',
'human toxicity, via surface water w/o LT'),
('ReCiPe Midpoint (E) w/o LT', 'fossil depletion w/o LT', 'FDP w/o LT'),
('EDIP2003 w/o LT', 'non-renewable resources w/o LT', 'aluminium w/o LT'),
('ecological footprint', 'total', 'total'),
('ReCiPe Endpoint (H,A) w/o LT', 'resources w/o LT', 'total w/o LT'),
('EDIP2003', 'photochemical ozone formation', 'impacts on human health'),
('ecological scarcity 2006', 'total', 'energy resources')],
'inv': [{'_89 Post and telecommunication, EU27' (EUR2003, GLO, ['Input Output', 'EU27 2003']): 1},
{'_73 Furniture and other manufactured goods n.e.c., EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1},
{'__1 Bovine meat and milk, EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1},
{'130 Membership organisations, EU27' (EUR2003, GLO, ['Input Output', 'EU27 2003']): 1},
{'_58 Office machinery and computers, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'111 Waste treatment, Landfill of waste, Alu, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'_30 Wearing apparel and furs, EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1},
{'_61 Instruments, medical, precision, optical, clocks, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'103 Waste treatment, Composting of food waste, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'_54 Recycling of copper basic, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'131 Household use, Meals, DK' (EUR2003, GLO, ['Input Output', 'Denmark 2003']): 1},
{'__8 Recycling of waste wood, EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1},
{'120 Waste treatment, Land application of compost, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'_31 Leather products, footwear, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'_74 Retail  trade and repair services, DK' (EUR2003, GLO, ['Input Output', 'Denmark 2003']): 1},
{'_32 Wood products, except furniture, EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1},
{'128 Waste treatment, Landfill of waste, Slag' (kilogram, ash, EU27, ['Input Output', 'EU27 2003']): 1},
{'_29 Textiles, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'120 Waste treatment, Land application of compost, DK' (kilogram, GLO, ['Input Output', 'Denmark 2003']): 1},
{'__5 Crops n.e.c., EU27' (kilogram, GLO, ['Input Output', 'EU27 2003']): 1}]}

The normal create, update, and delete machanisms apply:

• To create a new calculation setup, assign by name, as in cell above
• To replace a calculation setup, just assign new data, i.e. calculation_setups['some random stuff'] = some_new_stuff.
• To delete a calculation setup, use del, i.e. del calculation_setups['some random stuff'].

## Using a calculation setup¶

Use the MultiLCA class to get LCA results for a calculation setup. Note that this class does all the calculations as soon as you create it.

In [8]:
mlca = MultiLCA('some random stuff')

In [9]:
mlca.results

Out[9]:
array([[  2.01992360e-02,   5.41100722e-06,   8.46148810e-02,
6.61679078e-04,   1.42901040e+00,   1.17617425e-02,
1.28948520e-04,   1.27063423e+01],
[  3.42570244e-01,   7.08964005e-05,   1.07109057e+00,
2.16505158e-02,   1.70176907e+01,   1.52923265e-01,
1.69952888e-03,   1.60852771e+02],
[  8.54603999e-02,   8.20893591e-05,   8.59786378e-01,
2.41886966e-03,   1.35256790e+01,   1.14021466e-01,
1.02473129e-02,   1.29275666e+02],
[  1.38804898e-02,   9.26976794e-06,   1.40957937e-01,
4.54331969e-04,   2.48256765e+00,   1.86521880e-02,
2.23869508e-04,   2.11117955e+01],
[  3.34442558e+00,   3.32505885e-04,   5.29992430e+00,
2.08351234e-01,   7.90777941e+01,   8.33465266e-01,
7.67466305e-03,   7.96400917e+02],
[  2.65436174e-03,   1.79663207e-06,   2.71179542e-02,
4.83997043e-05,   3.35531721e-01,   3.59813477e-03,
3.63611861e-05,   4.08631436e+00],
[  2.98027138e-01,   2.65968682e-04,   4.87916050e+00,
2.02559781e-02,   6.88469431e+01,   6.38612312e-01,
7.05066663e-03,   7.34177367e+02],
[  1.73794610e+00,   3.03233270e-04,   4.42945481e+00,
9.69016355e-02,   6.25006630e+01,   6.47544599e-01,
6.69818467e-03,   6.65724592e+02],
[  4.11230870e-04,   1.87116136e-06,  -3.51387683e-01,
-7.71789294e-05,   2.90108609e-01,  -4.42014831e-02,
1.92414412e-04,  -5.10539989e+01],
[ -1.93323569e+01,   6.99587535e-05,   4.28280943e-01,
3.48533774e-01,   8.00092714e+00,  -8.43024115e-01,
1.50923061e-03,   6.37612809e+01],
[  1.87993088e-02,   2.33919217e-05,   2.80539817e-01,
2.24872423e-04,   4.77588432e+00,   3.67011896e-02,
6.61535727e-04,   4.19915185e+01],
[  8.68322913e-05,  -1.80879514e-06,  -1.44139358e-02,
1.72417862e-05,  -2.19647814e-01,  -1.83291263e-03,
-2.95201063e-05,  -2.14831874e+00],
[ -2.93513576e-03,  -1.94108729e-06,  -4.90487423e-01,
-1.54424488e-04,  -7.66110015e-01,  -6.19688279e-02,
-1.11368000e-04,  -7.15154981e+01],
[  8.49469480e-01,   8.64262824e-04,   1.01367857e+01,
1.67812037e-02,   1.43058798e+02,   1.33767259e+00,
1.97762031e-02,   1.52583574e+03],
[  1.17946656e-02,   9.52604590e-06,   1.09768383e-01,
1.23571916e-04,   1.63443814e+00,   1.45884813e-02,
1.99786699e-04,   1.64821999e+01],
[  1.96479851e-02,   1.80017134e-05,   1.75696168e-01,
8.73941647e-04,   3.12080542e+00,   2.34034028e-02,
3.98288935e-04,   2.64216756e+01],
[  3.30914057e-03,   1.20266327e-06,   1.53790378e-02,
1.69859774e-04,   3.02970929e-01,   2.12255070e-03,
2.70444442e-05,   2.31324601e+00],
[  1.41039455e-01,   1.35668820e-04,   2.04153889e+00,
-9.72030282e-03,   2.75440996e+01,   2.67912963e-01,
2.92791763e-03,   3.07072036e+02],
[ -2.93513576e-03,  -1.94108729e-06,  -4.90487423e-01,
-1.54424488e-04,  -7.66110015e-01,  -6.19688279e-02,
-1.11368000e-04,  -7.15154981e+01],
[  1.44529443e-02,   1.22454377e-05,   2.03155483e-01,
4.51652358e-04,   3.01491530e+00,   2.66933258e-02,
3.51663523e-04,   3.05886931e+01]])

## Visualizing the results¶

There isn't any code built into Brightway2 yet, but Seaborn has some great visualizations for this type of result array.

In [10]:
%matplotlib inline

Warning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')
Warning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')
Warning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')
Warning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')
Warning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')
Warning: InlineBackend._config_changed is deprecated: use @observe and @unobserve instead.
Warning: A parent of InlineBackend._config_changed has adopted the new @observe(change) API


You can ignore any errors that show up here.

In [11]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Warning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.

In [12]:
# From https://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html
cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True)

sns.heatmap(
mlca.results / np.average(mlca.results, axis=0), # Normalize to get relative results
annot=True,
linewidths=.05,
cmap=cmap,
xticklabels=["\n".join(x) for x in mlca.methods]
)
plt.xticks(rotation=70)

Out[12]:
(array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5]),
<a list of 8 Text xticklabel objects>)