def test_timeseries_edit(test_mp):
scen = ixmp.TimeSeries(test_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[cols_str], obs[cols_str])
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)')
test_mp.close_db()
test_mp.open_db()
scen = ixmp.TimeSeries(test_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[cols_str], obs[cols_str])
npt.assert_array_almost_equal(df['value'], obs['value'])
def test_timeseries_edit_with_region_synonyms(test_mp):
args_all = ('Douglas Adams 1', 'test_remove_all')
test_mp.set_log_level('DEBUG')
test_mp.add_region_synonym('Hell', 'World')
scen = ixmp.TimeSeries(test_mp, *args_all, version='new', annotation='nk')
df = pd.DataFrame.from_dict({
'region': ['World'],
'variable': ['Testing'],
'unit': ['???'],
'2010': [23.7],
'2020': [23.8]
})
scen.add_timeseries(df)
scen.commit('updating timeseries in IAMC format')
scen = ixmp.TimeSeries(test_mp, *args_all)
obs = scen.timeseries()
exp = pd.DataFrame.from_dict({
'region': ['World'] * 2,
'variable': ['Testing'] * 2,
'unit': ['???', '???'],
'year': [2010, 2020],
'value': [23.7, 23.8]
})
npt.assert_array_equal(exp[cols_str], obs[cols_str])
npt.assert_array_almost_equal(exp['value'], obs['value'])
scen.check_out(timeseries_only=True)
df = pd.DataFrame.from_dict({
'region': ['Hell'],
'variable': ['Testing'],
'unit': ['???'],
'2000': [21.7],
'2010': [22.7],
'2020': [23.7],
'2030': [24.7],
'2040': [25.7],
'2050': [25.8]
})
scen.preload_timeseries()
scen.add_timeseries(df)
scen.commit('updating timeseries in IAMC format')
exp = pd.DataFrame.from_dict({
'region': ['World'] * 6,
'variable': ['Testing'] * 6,
'unit': ['???'] * 6,
'year': [2000, 2010, 2020, 2030, 2040, 2050],
'value': [21.7, 22.7, 23.7, 24.7, 25.7, 25.8]
})
obs = scen.timeseries()
npt.assert_array_equal(exp[cols_str], obs[cols_str])
npt.assert_array_almost_equal(exp['value'], obs['value'])
test_mp.close_db()
def test_new_timeseries_error(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
df = {'year': [2010, 2020], 'value': [23.5, 23.6]}
df = pd.DataFrame.from_dict(df)
df['region'] = 'World'
df['variable'] = 'Testing'
pytest.raises(ValueError, scen.add_timeseries, df)
def test_export_ts_of_all_runs(mp, tmp_path):
"""Export timeseries of all runs."""
path = tmp_path / "export.csv"
# Add a new version of a run
ts = ixmp.TimeSeries(mp, **models["h2g2"], version="new", annotation="fo")
ts.add_timeseries(DATA[0])
ts.commit("create a new version")
ts.set_as_default()
# Export all default model+scenario runs
mp.export_timeseries_data(path,
unit="???",
region="World",
default=True,
export_all_runs=True)
obs = pd.read_csv(path, index_col=False, header=0)
exp = (DATA[0].assign(**models["h2g2"],
version=2,
subannual="Year",
meta=0).rename(columns=lambda c: c.upper()).reindex(
columns=FIELDS["write_file"]))
assert_frame_equal(exp, obs)
# Export all model+scenario run versions (including non-default)
mp.export_timeseries_data(path,
unit="???",
region="World",
default=False,
export_all_runs=True)
obs = pd.read_csv(path, index_col=False, header=0)
assert 4 == len(obs)
def test_remove_multiple_geodata(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_geodata(GEODATA)
row = GEODATA.loc[[False, True, True]]
scen.remove_geodata(row)
scen.commit('adding geodata (references to map layers)')
assert_geodata(scen.get_geodata(), GEODATA.loc[[True, False, False]])
def test_get_timeseries(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version=2)
obs = scen.timeseries(regions='World', variables='Testing', units='???',
years=2020)
df = {'region': ['World'], 'variable': ['Testing'], 'unit': ['???'],
'year': [2020], 'value': [23.6]}
exp = pd.DataFrame.from_dict(df)
npt.assert_array_equal(exp[cols_str], obs[cols_str])
npt.assert_array_almost_equal(exp['value'], obs['value'])
def test_new_timeseries(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
df = {'year': [2010, 2020], 'value': [23.5, 23.6]}
df = pd.DataFrame.from_dict(df)
df['region'] = 'World'
df['variable'] = 'Testing'
df['unit'] = '???'
scen.add_timeseries(df)
scen.commit('importing a testing timeseries')
def test_get_timeseries_iamc(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args)
obs = scen.timeseries(iamc=True, regions='World', variables='Testing')
exp = TS_DF.pivot_table(index=['region', 'variable', 'unit'],
columns='year')['value'].reset_index()
exp['model'] = 'Douglas Adams'
exp['scenario'] = 'Hitchhiker'
npt.assert_array_equal(exp[iamc_idx_cols], obs[iamc_idx_cols])
npt.assert_array_almost_equal(exp[2010], obs[2010])
def test_resource_limit(resource_limit, test_mp):
"""Exercise :func:`memory_usage` and :func:`resource_limit`."""
# TODO expand to cover other missed lines in those functions
memory_usage("setup")
s = ixmp.TimeSeries(test_mp, **models["h2g2"], version="new")
memory_usage("1 TimeSeries")
del s
def mp(test_mp_f):
"""A test Platform.
The platform contains one time series with the "dantizg" model & scenario name from
:data:`testing.models`.
Unlike the other submodules of :mod:`ixmp.tests.core`, the tests in this file
generally require a clean platform each time, so this is a function-scoped fixture.
"""
# Don't call populate_test_platform(), since this is all that's needed
ts = ixmp.TimeSeries(test_mp_f, **models["dantzig"], version="new")
ts.commit("")
yield test_mp_f
def test_get_timeseries_iamc(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version=2)
obs = scen.timeseries(iamc=True, regions='World', variables='Testing')
df = {'year': [2010, 2020], 'value': [23.5, 23.6]}
df = pd.DataFrame.from_dict(df)
df['model'] = 'Douglas Adams'
df['scenario'] = 'Hitchhiker'
df['region'] = 'World'
df['variable'] = 'Testing'
df['unit'] = '???'
df = df.pivot_table(index=iamc_idx_cols, columns='year')['value']
df.reset_index(inplace=True)
exp = pd.DataFrame.from_dict(df)
npt.assert_array_equal(exp[iamc_idx_cols], obs[iamc_idx_cols])
npt.assert_array_almost_equal(exp[2010], obs[2010])
def test_export_ts_of_all_runs(mp, tmp_path):
"""Export timeseries of all runs."""
path = tmp_path / "export.csv"
# Add a new version of a run
scen = ixmp.TimeSeries(mp,
"Douglas Adams",
"Hitchhiker",
version="new",
annotation="fo")
timeseries = pd.DataFrame.from_dict(
dict(
region="World",
variable="Testing",
unit="???",
year=[2010, 2020],
value=[24.7, 24.8],
))
scen.add_timeseries(timeseries)
scen.commit("create a new version")
scen.set_as_default()
columns = [
"model",
"scenario",
"version",
"variable",
"unit",
"region",
"meta",
"subannual",
"year",
"value",
]
csv_dtype = dict(
model=str,
scenario=str,
version=int,
variable=str,
unit=str,
region=str,
year=int,
subannual=str,
meta=int,
value=float,
)
# Export all default model+scenario runs
mp.export_timeseries_data(path,
unit="???",
region="World",
default=True,
export_all_runs=True)
obs = pd.read_csv(path,
index_col=False,
header=0,
names=columns,
dtype=csv_dtype)
assert len(obs.model) == 2
assert pd.Series.all(obs.value == timeseries.value)
# Export all model+scenario run versions (including non-default)
mp.export_timeseries_data(path,
unit="???",
region="World",
default=False,
export_all_runs=True)
obs = pd.read_csv(path,
index_col=False,
header=0,
names=columns,
dtype=csv_dtype)
assert len(obs.model) == 4
def test_add_geodata(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_geodata(GEODATA)
scen.commit('adding geodata (references to map layers)')
assert_geodata(scen.get_geodata(), GEODATA)
def test_fetch_empty_geodata(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
empty = scen.get_geodata()
assert_geodata(empty, GEODATA.loc[[False, False, False]])
def test_new_timeseries_as_iamc(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_timeseries(TS_DF.pivot_table(values='value', index=cols_str))
scen.commit('importing a testing timeseries')
assert_timeseries(scen)
def test_new_timeseries_as_year_value(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args, version='new', annotation='fo')
scen.add_timeseries(TS_DF)
scen.commit('importing a testing timeseries')
assert_timeseries(scen)
os.path.join(db_dir, 'ixmptest.lobs'),
os.path.join(db_dir, 'ixmptest.properties'),
os.path.join(db_dir, 'ixmptest.script')
]:
if os.path.isfile(fname):
os.remove(fname)
# %% launch the modeling platform instance, creating a new test database file
mp = ixmp.Platform(dbprops=test_db, dbtype='HSQLDB')
# %% initialize a new timeseries instance
ts = ixmp.TimeSeries(mp,
'Douglas Adams',
'Hitchhiker',
version='new',
annotation='testing')
df = {
'region': ['World', 'World'],
'variable': ['Testing', 'Testing'],
'unit': ['???', '???'],
'year': [2010, 2020],
'value': [23.7, 23.8]
}
df = pd.DataFrame.from_dict(df)
ts.add_timeseries(df)
ts.commit('importing a testing timeseries')
ts.set_as_default()
# %% initialize the standard canning problem
def test_get_timeseries(test_mp):
scen = ixmp.TimeSeries(test_mp, *test_args)
assert_timeseries(scen)
