Plotting aggregate variables

The pyam package offers many great visualisation and analysis tools. In this notebook, we highlight the aggregate and stack_plot methods of an IamDataFrame.

import numpy as np
import pandas as pd

import pyam

%matplotlib inline
import matplotlib.pyplot as plt

Aggregating sectors

Here we provide some sample data for the first part of this tutorial. This data is for a single model-scenario-region combination but provides multiple subsectors of CO\(_2\) emissions. The emissions in the subsectors are both positive and negative and so provide a good test of the flexibility of our aggregation and plotting routines.

df = pyam.IamDataFrame(pd.DataFrame([
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Energy|Oil', 'Mt CO2/yr', 2, 3.2, 2.0, 1.8],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Energy|Gas', 'Mt CO2/yr', 1.3, 1.6, 1.0, 0.7],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Energy|BECCS', 'Mt CO2/yr', 0.0, 0.4, -0.4, 0.3],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Cars', 'Mt CO2/yr', 1.6, 3.8, 3.0, 2.5],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Tar', 'Mt CO2/yr', 0.3, 0.35, 0.35, 0.33],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Agg', 'Mt CO2/yr', 0.5, -0.1, -0.5, -0.7],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|LUC', 'Mt CO2/yr', -0.3, -0.6, -1.2, -1.0]
    ],
    columns=['model', 'scenario', 'region', 'variable', 'unit', 2005, 2010, 2015, 2020],
))
df.head()

	model	scenario	region	variable	unit	year	value
0	IMG	a_scen	World	Emissions|CO2|Agg	Mt CO2/yr	2005	0.5
1	IMG	a_scen	World	Emissions|CO2|Agg	Mt CO2/yr	2010	-0.1
2	IMG	a_scen	World	Emissions|CO2|Agg	Mt CO2/yr	2015	-0.5
3	IMG	a_scen	World	Emissions|CO2|Agg	Mt CO2/yr	2020	-0.7
4	IMG	a_scen	World	Emissions|CO2|Cars	Mt CO2/yr	2005	1.6

Pyam’s stackplot method plots the stacks in the clearest way possible, even when some emissions are negative. The optional total keyword arguments also allows the user to include a total line on their plot.

df.plot.stack();

df.plot.stack(total=True);

The appearance of the stackplot can be simply controlled via kwargs. The appearance of the total line is controlled by passing a dictionary to the total_kwargs keyword argument.

df.plot.stack(alpha=0.5, total={"color": "grey", "ls": "--", "lw": 2.0});

If the user wishes, they can firstly filter their data before plotting.

df.filter(variable="Emissions|CO2|Energy*").plot.stack(total=True);

Using aggregate, it is possible to create arbitrary sums of sub-sectors before plotting.

pdf = df.copy()
afoluluc_vars = ["Emissions|CO2|LUC", "Emissions|CO2|Agg"]
fossil_vars = list(set(pdf.variable) - set(afoluluc_vars))
pdf.aggregate(
    "Emissions|CO2|AFOLULUC",
    components=afoluluc_vars,
    append=True
)
pdf.aggregate(
    "Emissions|CO2|Fossil",
    components=fossil_vars,
    append=True
)
pdf.filter(variable=[
    "Emissions|CO2|AFOLULUC",
    "Emissions|CO2|Fossil"
]).plot.stack(total=True);

Aggregating regions

Here we provide some sample data for the second part of this tutorial. This data is for a single model-scenario combination with a few subsectors of CO\(_2\) emissions. The emissions in the subsectors are both positive and negative and so provide a good test of the flexibility of our aggregation and plotting routines.

df = pyam.IamDataFrame(pd.DataFrame([
    ['IMG', 'a_scen', 'World', 'Emissions|CO2', 'Mt CO2/yr', 4.6, 5.3, 5.5, 4.3],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Fossil', 'Mt CO2/yr', 4.0, 4.6, 4.9, 4.1],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|AFOLU', 'Mt CO2/yr', 0.6, 0.7, 0.6, 0.2],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Fossil|Energy', 'Mt CO2/yr', 3.6, 4.1, 4.3, 3.6],
    ['IMG', 'a_scen', 'World', 'Emissions|CO2|Fossil|Aviation', 'Mt CO2/yr', 0.4, 0.5, 0.6, 0.5],
    ['IMG', 'a_scen', 'R5ASIA', 'Emissions|CO2', 'Mt CO2/yr', 2.3, 2.6, 2.8, 2.6],
    ['IMG', 'a_scen', 'R5ASIA', 'Emissions|CO2|Fossil', 'Mt CO2/yr', 2.0, 2.1, 2.2, 2.3],
    ['IMG', 'a_scen', 'R5ASIA', 'Emissions|CO2|Fossil|Energy', 'Mt CO2/yr', 2.0, 2.1, 2.2, 2.3],
    ['IMG', 'a_scen', 'R5ASIA', 'Emissions|CO2|AFOLU', 'Mt CO2/yr', 0.3, 0.5, 0.6, 0.3],
    ['IMG', 'a_scen', 'R5LAM', 'Emissions|CO2', 'Mt CO2/yr', 1.9, 2.2, 2.1, 1.2],
    ['IMG', 'a_scen', 'R5LAM', 'Emissions|CO2|Fossil', 'Mt CO2/yr', 1.6, 2.0, 2.1, 1.3],
    ['IMG', 'a_scen', 'R5LAM', 'Emissions|CO2|Fossil|Energy', 'Mt CO2/yr', 1.6, 2.0, 2.1, 1.3],
    ['IMG', 'a_scen', 'R5LAM', 'Emissions|CO2|AFOLU', 'Mt CO2/yr', 0.3, 0.2, 0, -0.1],
    ],
    columns=['model', 'scenario', 'region', 'variable', 'unit', 2005, 2010, 2015, 2020],
))
df.head()

	model	scenario	region	variable	unit	year	value
0	IMG	a_scen	R5ASIA	Emissions|CO2	Mt CO2/yr	2005	2.3
1	IMG	a_scen	R5ASIA	Emissions|CO2	Mt CO2/yr	2010	2.6
2	IMG	a_scen	R5ASIA	Emissions|CO2	Mt CO2/yr	2015	2.8
3	IMG	a_scen	R5ASIA	Emissions|CO2	Mt CO2/yr	2020	2.6
4	IMG	a_scen	R5ASIA	Emissions|CO2|AFOLU	Mt CO2/yr	2005	0.3

If we aggregate the regional values for a sector, we get back the world total.

df.aggregate_region("Emissions|CO2|AFOLU")

<class 'pyam.core.IamDataFrame'>
Index:
 * model    : IMG (1)
 * scenario : a_scen (1)
Timeseries data coordinates:
   region   : World (1)
   variable : Emissions|CO2|AFOLU (1)
   unit     : Mt CO2/yr (1)
   year     : 2005, 2010, 2015, 2020 (4)
Meta indicators:
   exclude (bool) False (1)

df.filter(variable="Emissions|CO2|AFOLU", region="World").timeseries()

					2005	2010	2015	2020
model	scenario	region	variable	unit
IMG	a_scen	World	Emissions|CO2|AFOLU	Mt CO2/yr	0.6	0.7	0.6	0.2

We can plot this as shown. The black line shows the World total (which is the same as the total lines shown in the previous part).

ax = plt.figure(figsize=(8, 4.5)).add_subplot(111)
df.filter(
    variable="Emissions|CO2|AFOLU"
).filter(
    region="World", keep=False
).plot.stack(stack="region", ax=ax)
df.filter(variable="Emissions|CO2|AFOLU", region="World").plot(ax=ax, color="black");

Even if there are sectors which are defined only at the world level (e.g. Emissions|CO2|Fossil|Aviation in our example), Pyam will find them and include them when calculating the regional total if we specify components=True when using aggregate_region.

df.aggregate_region("Emissions|CO2|Fossil", components=True).timeseries()

					2005	2010	2015	2020
model	scenario	region	variable	unit
IMG	a_scen	World	Emissions|CO2|Fossil	Mt CO2/yr	4.0	4.6	4.9	4.1

df.filter(variable="Emissions|CO2|Fossil", region="World").timeseries()

					2005	2010	2015	2020
model	scenario	region	variable	unit
IMG	a_scen	World	Emissions|CO2|Fossil	Mt CO2/yr	4.0	4.6	4.9	4.1

If we plot the regions vs. the total, in this case we will see a gap. This gap represents the emissions from variables only defined at the world level.

ax = plt.figure(figsize=(8, 4.5)).add_subplot(111)
df.filter(
    variable="Emissions|CO2|Fossil"
).filter(
    region="World", keep=False
).plot.stack(stack="region", ax=ax)
df.filter(variable="Emissions|CO2|Fossil", region="World").plot(ax=ax, color="black");

We can verify this by making sure that adding the aviation emissions to the regional emissions does indeed give the aggregate total (a nicer way would be to plot these emissions in the stack above, pull requests which do so are welcome :D).

aviation_emms = df.filter(variable="*Aviation*").timeseries()
aggregate_emms = df.aggregate_region("Emissions|CO2|Fossil", components=True).timeseries()
aggregate_emms_region_only = df.filter(region="World", keep=False).aggregate_region("Emissions|CO2|Fossil").timeseries()
np.isclose(aggregate_emms.values, aggregate_emms_region_only.values + aviation_emms.values)

array([[ True,  True,  True,  True]])