def simple_df(request):
_df = FULL_FEATURE_DF.copy()
if request.param == "datetime":
_df.rename(DTS_MAPPING, axis="columns", inplace=True)
df = IamDataFrame(model="model_a", scenario="scen_a", data=_df)
df.set_meta("foo", "string")
yield df
def simple_df(request):
_df = FULL_FEATURE_DF.copy()
if request.param == 'datetime':
_df.rename(DTS_MAPPING, axis="columns", inplace=True)
df = IamDataFrame(model='model_a', scenario='scen_a', data=_df)
df.set_meta('foo', 'string')
yield df
def test_df(request):
tdf = TEST_DF.rename({2005: request.param[0], 2010: request.param[1]},
axis="columns")
df = IamDataFrame(data=tdf)
for i in META_COLS:
df.set_meta(META_DF[i])
yield df
def _validate_meta(df: pyam.IamDataFrame,
allowed_meta: dict) -> pyam.IamDataFrame:
"""Validation function for meta indicators"""
# remove unexpected meta columns
expected_meta = list(allowed_meta) + ["exclude"]
unexpected_meta = [c for c in df.meta.columns if c not in expected_meta]
if unexpected_meta:
logger.warning(
f"Removing unexpected meta indicators: {unexpected_meta}")
df.meta.drop(unexpected_meta, axis=1, inplace=True)
# validate meta columns for accepted values (if provided) or assign default
for key, value in allowed_meta.items():
# if the meta column exists, check that values are allowed
if key in df.meta.columns:
unknown = [v for v in df.meta[key].unique() if v not in value]
if unknown:
logger.warning(f"Unknown values {unknown} for `{key}`, "
f"setting to default `{value[0]}`")
df.meta[key] = [
v if v in value else value[0] for v in df.meta[key]
]
# if meta indicated was not provided, set to default
else:
logger.info(f"Setting `{key}` to default `{value[0]}`")
df.set_meta(name=key, meta=value[0])
return df
def test_df_time():
df = IamDataFrame(data=TEST_DF.rename(
{
2005: TEST_DTS[0],
2010: TEST_DTS[1]
}, axis="columns"))
for i in META_COLS:
df.set_meta(META_DF[i])
yield df
def test_query_with_meta_arg(conn, test_pd_df, kwargs):
# test reading timeseries data (including subannual data)
exp = IamDataFrame(test_pd_df, subannual='Year')\
.append(MODEL_B_DF, model='model_b', scenario='scen_a', region='World')
for i in ['version', 'string']:
exp.set_meta(META_DF.iloc[[0, 1, 3]][i])
# test method via Connection
df = conn.query(meta=['string'], **kwargs)
assert_iamframe_equal(df, exp.filter(**kwargs))
# test top-level method
df = read_iiasa(TEST_API, meta=['string'], **kwargs)
assert_iamframe_equal(df, exp.filter(**kwargs))
def subannual_df():
_df = FULL_FEATURE_DF.iloc[0:6].copy()
def add_subannual(_data, name, value):
_data["subannual"] = name
_data[TEST_YEARS] = _data[TEST_YEARS] * value
return _data
# primary energy is a direct sum across sub-annual timeslices
mapping = [("year", 1), ("winter", 0.7), ("summer", 0.3)]
lst = [add_subannual(_df.copy(), name, value) for name, value in mapping]
df = IamDataFrame(model="model_a", scenario="scen_a", data=pd.concat(lst))
df.set_meta("foo", "string")
yield df
def subannual_df():
_df = FULL_FEATURE_DF.iloc[0:6].copy()
def add_subannual(_data, name, value):
_data['subannual'] = name
_data[TEST_YEARS] = _data[TEST_YEARS] * value
return _data
# primary energy is a direct sum across sub-annual timeslices
mapping = [('year', 1), ('winter', 0.7), ('summer', 0.3)]
lst = [add_subannual(_df.copy(), name, value) for name, value in mapping]
df = IamDataFrame(model='model_a', scenario='scen_a', data=pd.concat(lst))
df.set_meta('foo', 'string')
yield df
def test_query_with_meta_arg(conn, test_pd_df, meta, kwargs):
# test reading timeseries data (including subannual data)
exp = IamDataFrame(test_pd_df, subannual="Year").append(
MODEL_B_DF, model="model_b", scenario="scen_a", region="World"
)
for i in ["version", "string"]:
exp.set_meta(META_DF.iloc[[0, 1, 3]][i])
# test method via Connection
df = conn.query(meta=meta, **kwargs)
assert_iamframe_equal(df, exp.filter(**kwargs))
# test top-level method
df = read_iiasa(TEST_API, meta=meta, **kwargs)
assert_iamframe_equal(df, exp.filter(**kwargs))
def test_df_year():
df = IamDataFrame(data=TEST_DF)
for i in META_COLS:
df.set_meta(META_DF[i])
yield df
def test_df(request):
df = IamDataFrame(data=TEST_DF.rename(request.param, axis="columns"))
for i in META_COLS:
df.set_meta(META_DF[i])
yield df
def test_df_mixed():
mapping = dict([(i, j) for i, j in zip(TEST_YEARS, TEST_TIME_MIXED)])
df = IamDataFrame(data=TEST_DF.rename(mapping, axis="columns"))
for i in META_COLS:
df.set_meta(META_DF[i])
yield df
