Getting StartedΒΆ

This is a simple example of the basic capabilities of aneris.

First, model and history data are read in. The model is then harmonized. Finally, output is analyzed.

In [1]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import aneris
from aneris.tutorial import load_data

%matplotlib inline

The driver is used to execute the harmonization. It will handle the data formatting needed to execute the harmonizaiton operation and stores the harmonized results until they are needed.

Some logging output is provided. It can be suppressed with

In [2]:
model, hist, driver = load_data()
for scenario in driver.scenarios():
harmonized, metadata, diagnostics = driver.harmonized_results()
/home/gidden/.local/lib/python2.7/site-packages/pandas/util/ FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead
  return func(*args, **kwargs)
INFO:root:Downselecting prefix|suffix variables
INFO:root:Translating to standard format
INFO:root:Aggregating historical values to native regions
INFO:root:Harmonizing (with example methods):
INFO:root:                                                    method            default  \
region  gas sector                units
regionc BC  prefix|sector1|suffix kt     reduce_ratio_2100  reduce_ratio_2080
            prefix|sector2|suffix kt     reduce_ratio_2100  reduce_ratio_2080

region  gas sector                units
regionc BC  prefix|sector1|suffix kt     reduce_ratio_2100
            prefix|sector2|suffix kt     reduce_ratio_2100
INFO:root:and override methods:
INFO:root:region   gas  sector                 units
regionc  BC   prefix|sector1|suffix  kt       reduce_ratio_2100
              prefix|sector2|suffix  kt       reduce_ratio_2100
Name: method, dtype: object
INFO:root:Harmonizing with reduce_ratio_2100
INFO:root:Translating to IAMC template

All data of interest is combined in order to easily view it. We will specifically investigate output for the World in this example. A few operations are performed in order to get the data into a plotting-friendly format.

In [3]:
data = pd.concat([hist, model, harmonized])
df = data[data.Region.isin(['World'])]
In [4]:
df = pd.melt(df, id_vars=aneris.iamc_idx, value_vars=aneris.numcols(df),
             var_name='Year', value_name='Emissions')
df['Label'] = df['Model'] + ' ' + df['Variable']
In [5]:
Model Scenario Region Variable Year Emissions Label
0 History scen World prefix|Emissions|BC|sector1|suffix 2000 4.0 History prefix|Emissions|BC|sector1|suffix
1 History scen World prefix|Emissions|BC|sector2|suffix 2000 6.0 History prefix|Emissions|BC|sector2|suffix
2 History scen World prefix|Emissions|BC|suffix 2000 10.0 History prefix|Emissions|BC|suffix
3 model sspn World prefix|Emissions|BC|sector1|suffix 2000 NaN model prefix|Emissions|BC|sector1|suffix
4 model sspn World prefix|Emissions|BC|sector2|suffix 2000 NaN model prefix|Emissions|BC|sector2|suffix
In [6]:
sns.tsplot(df, time='Year', condition='Label', value='Emissions', unit='Model')
plt.legend(bbox_to_anchor=(1.05, 1))
/home/gidden/.local/lib/python2.7/site-packages/seaborn/ UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.
  warnings.warn(msg, UserWarning)
<matplotlib.legend.Legend at 0x7fc753e38690>