def test_cumulative_out_of_range():
# set logger level to exclude warnings in unit test output
logging.getLogger("pyam").setLevel('ERROR')
# note that the series is not order and the index is defined as float
y = pd.Series(data=[np.nan, 1, 3, 1], index=[2002., 2005., 2007., 2013.])
assert cumulative(y, 2008, 2015) is np.nan
logging.getLogger("pyam").setLevel('NOTSET')
def overshoot_severity(x, meta):
exceedance_yr = meta.loc[x.name[0:2]]['exceedance year|1.5°C']
return_yr = meta.loc[x.name[0:2]]['return year|1.5°C'] - 1
# do not include year in which mean temperature returns to below 1.5
if exceedance_yr > 0 and return_yr > 0:
return pyam.cumulative(
x, exceedance_yr,
return_yr) - (return_yr - exceedance_yr + 1) * 1.5
def test_cumulative():
# note that the series is not order and the index is defined as float
y = pd.Series(data=[np.nan, 1, 3, 1],
index=[2002.0, 2007.0, 2003.0, 2013.0])
assert cumulative(y, 2008, 2013) == 6
# ### Cumulative CO2 emissions until peak warming
#%%
def get_from_meta_column(df, x, col):
val = df.meta.loc[x.name[0:2], col]
return val if val < np.inf else max(x.index)
#%%
name = 'cumulative CO2 emissions ({} to peak warming, {})'.format(
baseyear, cumulative_unit)
sr1p5.set_meta(
co2.apply(lambda x: pyam.cumulative(x,
first_year=baseyear,
last_year=get_from_meta_column(
sr1p5, x,
'year of peak warming (MAGICC6)')),
raw=False,
axis=1), name)
meta_docs[
name] = 'cumulative net CO2 emissions from {} until the year of peak warming as computed by MAGICC6 (including the year of peak warming, {})'.format(
baseyear, cumulative_unit)
#%%
(sr1p5.filter(category=cats).scatter(
x='cumulative CO2 emissions (2016 to peak warming, {})'.format(
cumulative_unit),
y='median warming at peak (MAGICC6)',
color='category'))