Setup and Load Data¶

In [1]:
import cufflinks as cf

from plotly.offline import iplot

cf.set_config_file(offline=True)
%matplotlib inline
In [2]:
import utils
from utils import beccs, fuel, hydrogen, supply
from utils import capacity, activity
from utils import capvars, actvars 
from utils import Analysis
In [3]:
a = Analysis()
a.generate_file()
a.load_file()
a.df.head()
Out[3]:
case scenario region variable tech policy model unit year value
0 lo R3-B-lo-ready2050 World Primary Energy|Biomass|Modern ready2050 B EMF33_V3 EJ/yr 1990 13.505208
1 lo R3-B-lo-ready2050 World Secondary Energy|Electricity|Biomass|w/ CCS ready2050 B EMF33_V3 EJ/yr 1990 0.000000
2 lo R3-B-lo-ready2050 World Secondary Energy|Hydrogen|Biomass ready2050 B EMF33_V3 EJ/yr 1990 0.000000
3 lo R3-B-lo-ready2050 World Secondary Energy|Liquids|Biomass|w/ CCS ready2050 B EMF33_V3 EJ/yr 1990 0.000000
4 lo R3-B-lo-ready2050 World Secondary Energy|Liquids|Biomass|w/o CCS ready2050 B EMF33_V3 EJ/yr 1990 0.183657

Analysis Cases¶

In [4]:
sorted(a.df.tech.unique())
Out[4]:
['cost100', 'full', 'limbio', 'nobeccs', 'nofuel', 'none', 'ready2050']
  1. cost100: 2x cost for technologies
  2. full: all technologies available
  3. limbio: supply under criteria
  4. nobeccs: no elec biomass capacity, small or no activity
  5. nofuel: no liquids biomass capacity, small or no activity
  6. none: no capacity variables, small or no activity variables
  7. ready2050: no capacity before 2050, small or no activity before 2050

none Cases¶

In [5]:
filters = {'tech': ['none']}
for var in beccs + fuel + hydrogen:
    a.make_traces(var, filters=filters).plot()

nofuel Cases¶

In [6]:
filters = {'tech': ['nofuel']}
for var in beccs + fuel + hydrogen:
    a.make_traces(var, filters=filters).plot()

nobeccs Cases¶

In [7]:
filters = {'tech': ['nobeccs']}
for var in beccs + fuel + hydrogen:
    a.make_traces(var, filters=filters).plot()

ready2050 Cases¶

In [8]:
filters = {'tech': ['ready2050']}
for var in beccs + fuel + hydrogen:
    a.make_traces(var, filters=filters).plot()

limbio Cases¶

In [9]:
filters = {'tech': ['limbio']}
for var in supply:
    a.make_traces(var, filters=filters).plot()
In [10]:
filters = {'tech': ['limbio']}
for var in beccs + fuel + hydrogen:
    a.make_traces(var, filters=filters).plot()