def test_timeseries_edit(mp):
scen = TimeSeries(mp, *test_args)
df = {'region': ['World'] * 2, 'variable': ['Testing'] * 2,
'unit': ['???', '???'], 'year': [2010, 2020], 'value': [23.7, 23.8]}
exp = pd.DataFrame.from_dict(df)
obs = scen.timeseries()
npt.assert_array_equal(exp[IDX_COLS], obs[IDX_COLS])
npt.assert_array_almost_equal(exp['value'], obs['value'])
scen.check_out(timeseries_only=True)
df = {'region': ['World'] * 2,
'variable': ['Testing'] * 2,
'unit': ['???', '???'], 'year': [2010, 2020],
'value': [23.7, 23.8]}
df = pd.DataFrame.from_dict(df)
scen.add_timeseries(df)
scen.commit('testing of editing timeseries (same years)')
scen.check_out(timeseries_only=True)
df = {'region': ['World'] * 3,
'variable': ['Testing', 'Testing', 'Testing2'],
'unit': ['???', '???', '???'], 'year': [2020, 2030, 2030],
'value': [24.8, 24.9, 25.1]}
df = pd.DataFrame.from_dict(df)
scen.add_timeseries(df)
scen.commit('testing of editing timeseries (other years)')
mp.close_db()
mp.open_db()
scen = TimeSeries(mp, *test_args)
obs = scen.timeseries().sort_values(by=['year'])
df = df.append(exp.loc[0]).sort_values(by=['year'])
npt.assert_array_equal(df[IDX_COLS], obs[IDX_COLS])
npt.assert_array_almost_equal(df['value'], obs['value'])
def test_new_subannual_timeseries_as_iamc(mp):
mp.add_timeslice('Summer', 'Season', 1.0 / 4)
scen = TimeSeries(mp, *test_args, version='new', annotation='fo')
timeseries = DATA['timeseries'].pivot_table(values='value', index=IDX_COLS)
scen.add_timeseries(timeseries)
scen.commit('adding yearly data')
# add subannual timeseries data
ts_summer = timeseries.copy()
ts_summer['subannual'] = 'Summer'
scen.check_out()
scen.add_timeseries(ts_summer)
scen.commit('adding subannual data')
# generate expected dataframe+
ts_year = timeseries.copy()
ts_year['subannual'] = 'Year'
exp = pd.concat([ts_year, ts_summer]).reset_index()
exp['model'] = 'Douglas Adams'
exp['scenario'] = 'Hitchhiker'
# compare returned dataframe - default behaviour set to 'auto'
assert_timeseries(scen, exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL)
# test behaviour of 'auto' explicitly
assert_timeseries(scen, exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL, subannual='auto')
# test behaviour of 'True' explicitly
assert_timeseries(scen, exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL, subannual=True)
# setting False raises an error because subannual data exists
pytest.raises(ValueError, scen.timeseries, subannual=False)
def populate_test_platform(platform):
"""Populate `platform` with data for testing.
Many of the tests in :mod:`ixmp.tests.core` depend on this set of data.
The data consist of:
- 3 versions of the Dantzig cannery/transport Scenario.
- Version 2 is the default.
- All have :obj:`HIST_DF` and :obj:`TS_DF` as time-series data.
- 1 version of a TimeSeries with model name 'Douglas Adams' and scenario
name 'Hitchhiker', containing 2 values.
"""
s1 = make_dantzig(platform, solve=True, quiet=True)
s2 = s1.clone()
s2.set_as_default()
s2.clone()
s4 = TimeSeries(platform, **models["h2g2"], version="new")
s4.add_timeseries(
pd.DataFrame.from_dict(
dict(
region="World",
variable="Testing",
unit="???",
year=[2010, 2020],
value=[23.7, 23.8],
)))
s4.commit("")
s4.set_as_default()
def test_new_timeseries_as_iamc(self, test_mp):
# TODO rescue use of subannual= here
scen = TimeSeries(test_mp,
*models["h2g2"],
version="new",
annotation="fo")
scen.add_timeseries(DATA["timeseries"].pivot_table(values="value",
index=IDX_COLS))
scen.commit("importing a testing timeseries")
# compare returned dataframe - default behaviour set to 'auto'
assert_timeseries(scen)
# test behaviour of 'auto' explicitly
assert_timeseries(scen, subannual="auto")
# test behaviour of 'False' explicitly
assert_timeseries(scen, subannual=False)
# test behaviour of 'True' explicitly
exp = (DATA["timeseries"].pivot_table(values="value",
index=IDX_COLS).reset_index())
exp["model"] = "Douglas Adams"
exp["scenario"] = "Hitchhiker"
exp["subannual"] = "Year"
assert_timeseries(scen,
exp=exp,
cols=COLS_WITH_SUBANNUAL,
subannual=True)
def test_timeseries_edit(mp):
scen = TimeSeries(mp, *test_args)
df = {
"region": ["World"] * 2,
"variable": ["Testing"] * 2,
"unit": ["???", "???"],
"year": [2010, 2020],
"value": [23.7, 23.8],
}
exp = pd.DataFrame.from_dict(df)
obs = scen.timeseries()
npt.assert_array_equal(exp[IDX_COLS], obs[IDX_COLS])
npt.assert_array_almost_equal(exp["value"], obs["value"])
scen.check_out(timeseries_only=True)
df = {
"region": ["World"] * 2,
"variable": ["Testing"] * 2,
"unit": ["???", "???"],
"year": [2010, 2020],
"value": [23.7, 23.8],
}
df = pd.DataFrame.from_dict(df)
scen.add_timeseries(df)
scen.commit("testing of editing timeseries (same years)")
scen.check_out(timeseries_only=True)
df = {
"region": ["World"] * 3,
"variable": ["Testing", "Testing", "Testing2"],
"unit": ["???", "???", "???"],
"year": [2020, 2030, 2030],
"value": [24.8, 24.9, 25.1],
}
df = pd.DataFrame.from_dict(df)
scen.add_timeseries(df)
scen.commit("testing of editing timeseries (other years)")
mp.close_db()
mp.open_db()
scen = TimeSeries(mp, *test_args)
obs = scen.timeseries().sort_values(by=["year"])
df = df.append(exp.loc[0]).sort_values(by=["year"])
npt.assert_array_equal(df[IDX_COLS], obs[IDX_COLS])
npt.assert_array_almost_equal(df["value"], obs["value"])
def test_new_timeseries_as_iamc(test_mp):
scen = TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_timeseries(DATA['timeseries'].pivot_table(values='value',
index=IDX_COLS))
scen.commit('importing a testing timeseries')
# compare returned dataframe - default behaviour set to 'auto'
assert_timeseries(scen)
# test behaviour of 'auto' explicitly
assert_timeseries(scen, subannual='auto')
# test behaviour of 'False' explicitly
assert_timeseries(scen, subannual=False)
# test behaviour of 'True' explicitly
exp = (DATA['timeseries'].pivot_table(values='value',
index=IDX_COLS).reset_index())
exp['model'] = 'Douglas Adams'
exp['scenario'] = 'Hitchhiker'
exp['subannual'] = 'Year'
assert_timeseries(scen, exp=exp, cols=COLS_WITH_SUBANNUAL, subannual=True)
def test_new_subannual_timeseries_as_iamc(self, mp):
mp.add_timeslice("Summer", "Season", 1.0 / 4)
scen = TimeSeries(mp, *models["h2g2"], version="new", annotation="fo")
timeseries = DATA["timeseries"].pivot_table(values="value",
index=IDX_COLS)
scen.add_timeseries(timeseries)
scen.commit("adding yearly data")
# add subannual timeseries data
ts_summer = timeseries.copy()
ts_summer["subannual"] = "Summer"
scen.check_out()
scen.add_timeseries(ts_summer)
scen.commit("adding subannual data")
# generate expected dataframe+
ts_year = timeseries.copy()
ts_year["subannual"] = "Year"
exp = pd.concat([ts_year, ts_summer]).reset_index()
exp["model"] = "Douglas Adams"
exp["scenario"] = "Hitchhiker"
# compare returned dataframe - default behaviour set to 'auto'
assert_timeseries(scen,
exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL)
# test behaviour of 'auto' explicitly
assert_timeseries(
scen,
exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL,
subannual="auto",
)
# test behaviour of 'True' explicitly
assert_timeseries(scen,
exp=exp[COLS_WITH_SUBANNUAL],
cols=COLS_WITH_SUBANNUAL,
subannual=True)
# setting False raises an error because subannual data exists
pytest.raises(ValueError, scen.timeseries, subannual=False)
def test_new_timeseries_as_year_value(test_mp):
scen = TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_timeseries(DATA['timeseries'])
scen.commit('importing a testing timeseries')
assert_timeseries(scen)
def prepare_scenario(mp, args_all):
scen = TimeSeries(mp, *args_all, version='new', annotation='nk')
scen.add_timeseries(DATA[2010])
scen.commit('updating timeseries in IAMC format')
scen = TimeSeries(mp, *args_all)
return scen
def test_new_timeseries_as_year_value(test_mp):
scen = TimeSeries(test_mp, *test_args, version="new", annotation="fo")
scen.add_timeseries(DATA["timeseries"])
scen.commit("importing a testing timeseries")
assert_timeseries(scen)