Follow along at:¶
mattgidden.com/presentations/pyam-iamc2017¶
Find us on github:¶
github.com/IAMConsortium/pyam-analysis¶
Diagnostics, analysis and visualization tools
for Integrated Assessment timeseries data¶
First steps with the pyam_analysis package¶
The pyam-analysis package provides a range of diagnostic tools and functions
for analyzing and working with IAMC-style timeseries data.
The package can be used with data that follows the data template convention of the Integrated Assessment Modeling Consortium (IAMC). An illustrative example is shown below; see data.ene.iiasa.ac.at/database for more information.
| model | scenario | region | variable | unit | 2005 | 2010 | 2015 |
|---|---|---|---|---|---|---|---|
| MESSAGE V.4 | AMPERE3-Base | World | Primary Energy | EJ/y | 454.5 | 479.6 | ... |
| ... | ... | ... | ... | ... | ... | ... | ... |
Import package and load data from the AR5 tutorial csv snapshot file¶
First, we import the snapshot timeseries data from the file tutorial_AR5_data.csv in the tutorial folder.
As a first step, we show lists of all models, scenarios, regions, and variables (with units) included in the snapshot.
import pyam_analysis as iam
data = '/home/gidden/work/iiasa/message/pyam-analysis/tutorial/tutorial_AR5_data.csv'
df = iam.IamDataFrame(data=data)
What's in our dataset?¶
df.models()
df.scenarios()
df.regions()
df.variables(include_units=True)
Filtering Data¶
df.scenarios({'model': 'MESSAGE'})
df.scenarios({'model': 'ESSAGE'})
df.variables(filters={'variable': 'Emissions|*'})
df.variables(filters={'variable': 'Emissions|*', 'level': 2})
df.variables(filters={'level': 1})
Working with Timeseries¶
df.timeseries(filters={
'scenario': 'AMPERE3-450',
'variable': 'Primary Energy|Coal',
'region': 'World'
}).head()
df.pivot_table(
index=['year'],
columns=['scenario'],
values='value',
aggfunc='sum',
filters={'variable': 'Primary Energy', 'region': 'World'}
).head()
If you are familiar with the python package pandas, you can access the pd.DataFrame directly.
df.data.head()
Plotting Timeseries¶
df.plot_lines({'variable': 'Emissions|CO2', 'region': 'World'})
Validating and querying timeseries data¶
df.validate('Primary Energy')
df.validate({'Primary Energy': {'up': 515, 'year': 2010}})
df.validate(
{'Primary Energy|Coal': {'up': 400, 'year': 2050}},
filters={'region': 'World'},
exclude=False
)
Categorization of scenarios by timeseries characteristics¶
df.plot_lines({'variable': 'Temperature*'})
We now use the categorization feature of the pyam-analysis package.
By default, each model/scenario is assigned as "uncategorized".
The next function resets all scenarios back to "uncategorized". This may be helpful in this tutorial if you are going back and forth between cells.
df.reset_category()
df.category(
'Below 1.6C',
{'Temperature|Global Mean|MAGICC6|MED': {'up': 1.6, 'year': 2100}},
color='cornflowerblue',
display='list'
)
df.category(
'Below 2.0C',
{'Temperature|Global Mean|MAGICC6|MED': {'up': 2.0, 'year': 2100}},
filters={'category': 'uncategorized'},
color='forestgreen'
)
df.category(
'Below 2.5C',
{'Temperature|Global Mean|MAGICC6|MED': {'up': 2.5, 'year': 2100}},
filters={'category': 'uncategorized'},
color='gold'
)
df.category(
'Below 3.5C',
{'Temperature|Global Mean|MAGICC6|MED': {'up': 3.5, 'year': 2100}},
filters={'category': 'uncategorized'},
color='firebrick'
)
df.category(
'Above 3.5C',
{'Temperature|Global Mean|MAGICC6|MED': {}},
filters={'category': 'uncategorized'},
color='magenta'
)
df.category('uncategorized', display='list')
Now, we again display the median global temperature increase for all scenarios, but we use the colouring by category to illustrate the common charateristics across scenarios.
df.plot_lines({'variable': 'Temperature*'}, color_by_cat=True)
As a last step, we display the aggregate CO2 emissions by category. This allows to highlight alternative pathways within the same category.
df.plot_lines(
{'variable': 'Emissions|CO2', 'region': 'World'},
color_by_cat=True
)
