def map_regions(self,
map_col,
agg=None,
copy_col=None,
fname=None,
region_col=None,
inplace=False):
"""Plot regional data for a single model, scenario, variable, and year
see pyam.plotting.region_plot() for all available options
Parameters
----------
map_col: string
The column used to map new regions to. Common examples include
iso and 5_region.
agg: string, optional
Perform a data aggregation. Options include: sum.
copy_col: string, optional
Copy the existing region data into a new column for later use.
fname: string, optional
Use a non-default region mapping file
region_col: string, optional
Use a non-default column name for regions to map from.
inplace : bool, default False
if True, do operation inplace and return None
"""
models = self.meta.index.get_level_values('model').unique()
fname = fname or run_control()['region_mapping']['default']
mapping = read_pandas(fname).rename(str.lower, axis='columns')
map_col = map_col.lower()
ret = copy.deepcopy(self) if not inplace else self
_df = ret.data
columns_orderd = _df.columns
# merge data
dfs = []
for model in models:
df = _df[_df['model'] == model]
_col = region_col or '{}.REGION'.format(model)
_map = mapping.rename(columns={_col.lower(): 'region'})
_map = _map[['region', map_col]].dropna().drop_duplicates()
if copy_col is not None:
df[copy_col] = df['region']
df = (df.merge(_map, on='region').drop(
'region', axis=1).rename(columns={map_col: 'region'}))
dfs.append(df)
df = pd.concat(dfs)
# perform aggregations
if agg == 'sum':
df = df.groupby(LONG_IDX).sum().reset_index()
ret.data = (df.reindex(
columns=columns_orderd).sort_values(SORT_IDX).reset_index(
drop=True))
if not inplace:
return ret
def map_regions(self, map_col, agg=None, copy_col=None, fname=None,
region_col=None, remove_duplicates=False, inplace=False):
"""Plot regional data for a single model, scenario, variable, and year
see pyam.plotting.region_plot() for all available options
Parameters
----------
map_col: string
The column used to map new regions to. Common examples include
iso and 5_region.
agg: string, optional
Perform a data aggregation. Options include: sum.
copy_col: string, optional
Copy the existing region data into a new column for later use.
fname: string, optional
Use a non-default region mapping file
region_col: string, optional
Use a non-default column name for regions to map from.
remove_duplicates: bool, optional, default: False
If there are duplicates in the mapping from one regional level to
another, then remove these duplicates by counting the most common
mapped value.
This option is most useful when mapping from high resolution
(e.g., model regions) to low resolution (e.g., 5_region).
inplace : bool, default False
if True, do operation inplace and return None
"""
models = self.meta.index.get_level_values('model').unique()
fname = fname or run_control()['region_mapping']['default']
mapping = read_pandas(fname).rename(str.lower, axis='columns')
map_col = map_col.lower()
ret = copy.deepcopy(self) if not inplace else self
_df = ret.data
columns_orderd = _df.columns
# merge data
dfs = []
for model in models:
df = _df[_df['model'] == model]
_col = region_col or '{}.REGION'.format(model)
_map = mapping.rename(columns={_col.lower(): 'region'})
_map = _map[['region', map_col]].dropna().drop_duplicates()
_map = _map[_map['region'].isin(_df['region'])]
if remove_duplicates and _map['region'].duplicated().any():
# find duplicates
where_dup = _map['region'].duplicated(keep=False)
dups = _map[where_dup]
logger().warning("""
Duplicate entries found for the following regions.
Mapping will occur only for the most common instance.
{}""".format(dups['region'].unique()))
# get non duplicates
_map = _map[~where_dup]
# order duplicates by the count frequency
dups = (dups
.groupby(['region', map_col])
.size()
.reset_index(name='count')
.sort_values(by='count', ascending=False)
.drop('count', axis=1))
# take top occurance
dups = dups[~dups['region'].duplicated(keep='first')]
# combine them back
_map = pd.concat([_map, dups])
if copy_col is not None:
df[copy_col] = df['region']
df = (df
.merge(_map, on='region')
.drop('region', axis=1)
.rename(columns={map_col: 'region'})
)
dfs.append(df)
df = pd.concat(dfs)
# perform aggregations
if agg == 'sum':
df = df.groupby(LONG_IDX).sum().reset_index()
ret.data = (df
.reindex(columns=columns_orderd)
.sort_values(SORT_IDX)
.reset_index(drop=True)
)
if not inplace:
return ret