import copy
import importlib
import itertools
import logging
import os
import sys
import numpy as np
import pandas as pd
from pathlib import Path
from tempfile import TemporaryDirectory
try:
from datapackage import Package
HAS_DATAPACKAGE = True
except ImportError:
Package = None
HAS_DATAPACKAGE = False
try:
import ixmp
ixmp.TimeSeries
has_ix = True
except (ImportError, AttributeError):
has_ix = False
from pyam.run_control import run_control
from pyam.utils import (
write_sheet,
read_file,
read_pandas,
format_data,
format_time_col,
merge_meta,
find_depth,
pattern_match,
years_match,
month_match,
hour_match,
day_match,
datetime_match,
isstr,
islistable,
print_list,
s,
DEFAULT_META_INDEX,
META_IDX,
IAMC_IDX,
SORT_IDX,
ILLEGAL_COLS,
)
from pyam.read_ixmp import read_ix
from pyam.plotting import PlotAccessor
from pyam._compare import _compare
from pyam.aggregation import (
_aggregate,
_aggregate_region,
_aggregate_time,
_aggregate_recursive,
_group_and_agg,
)
from pyam._ops import _op_data
from pyam.units import convert_unit
from pyam.index import (
get_index_levels,
get_index_levels_codes,
get_keep_col,
verify_index_integrity,
replace_index_values,
)
from pyam.time import swap_time_for_year, swap_year_for_time
from pyam._debiasing import _compute_bias
from pyam.logging import deprecation_warning, raise_data_error
logger = logging.getLogger(__name__)
[docs]class IamDataFrame(object):
"""Scenario timeseries data and meta indicators
The class provides a number of diagnostic features (including validation of
data, completeness of variables provided), processing tools (e.g.,
unit conversion), as well as visualization and plotting tools.
Parameters
----------
data : :class:`pandas.DataFrame`, :class:`ixmp.Scenario`,\
or file-like object as str or :class:`pathlib.Path`
Scenario timeseries data following the IAMC data format or
a supported variation as pandas object, a path to a file,
or a scenario of an ixmp instance.
meta : :class:`pandas.DataFrame`, optional
A dataframe with suitable 'meta' indicators for the new instance.
The index will be downselected to scenarios present in `data`.
index : list, optional
Columns to use for resulting IamDataFrame index.
kwargs
If `value=<col>`, melt column `<col>` to 'value' and use `<col>` name
as 'variable'; or mapping of required columns (:code:`IAMC_IDX`) to
any of the following:
- one column in `data`
- multiple columns, to be concatenated by :code:`|`
- a string to be used as value for this column
Notes
-----
A :class:`pandas.DataFrame` can have the required dimensions
as columns or index.
R-style integer column headers (i.e., `X2015`) are acceptable.
When initializing an :class:`IamDataFrame` from an xlsx file,
|pyam| will per default parse all sheets starting with 'data'
for timeseries and a sheet 'meta' to populate the respective table.
Sheet names can be specified with kwargs :code:`sheet_name` ('data')
and :code:`meta_sheet_name` ('meta'), where
values can be a string or a list and '*' is interpreted as a wildcard.
Calling the class with :code:`meta_sheet_name=False` will
skip the import of the 'meta' table.
When initializing an :class:`IamDataFrame` from an object that is already
an :class:`IamDataFrame` instance, the new object will be hard-linked to
all attributes of the original object - so any changes on one object
(e.g., with :code:`inplace=True`) may also modify the other object!
This is intended behaviour and consistent with pandas but may be confusing
for those who are not used to the pandas/Python universe.
"""
def __init__(self, data, meta=None, index=DEFAULT_META_INDEX, **kwargs):
"""Initialize an instance of an IamDataFrame"""
if isinstance(data, IamDataFrame):
if kwargs:
msg = "Invalid arguments {} for initializing from IamDataFrame"
raise ValueError(msg.format(list(kwargs)))
if index != data.index.names:
msg = f"Incompatible `index={index}` with {type(data)} "
raise ValueError(msg + f"(index={data.index.names})")
for attr, value in data.__dict__.items():
setattr(self, attr, value)
else:
self._init(data, meta, index=index, **kwargs)
def _init(self, data, meta=None, index=DEFAULT_META_INDEX, **kwargs):
"""Process data and set attributes for new instance"""
# pop kwarg for meta_sheet_name (prior to reading data from file)
meta_sheet = kwargs.pop("meta_sheet_name", "meta")
# if meta is given explicitly, verify that index matches
if meta is not None and not meta.index.names == index:
raise ValueError(
f"Incompatible `index={index}` with `meta` "
f"(index={meta.index.names})!"
)
# cast data from pandas
if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):
_data = format_data(data.copy(), index=index, **kwargs)
# read data from ixmp Platform instance
elif has_ix and isinstance(data, ixmp.TimeSeries):
# TODO read meta indicators from ixmp
_data = read_ix(data, **kwargs)
else:
if islistable(data):
raise ValueError(
"Initializing from list is not supported, "
"use `IamDataFrame.append()` or `pyam.concat()`"
)
# read from file
try:
data = Path(data) # casting str or LocalPath to Path
if data.is_file():
logger.info(f"Reading file {data}")
_data = read_file(data, index=index, **kwargs)
else:
raise FileNotFoundError(f"File {data} does not exist")
except TypeError: # `data` cannot be cast to Path
msg = "IamDataFrame constructor not properly called!"
raise ValueError(msg)
self._data, index, self.time_col, self.extra_cols = _data
# define `meta` dataframe for categorization & quantitative indicators
self.meta = pd.DataFrame(index=_make_index(self._data, cols=index))
self.reset_exclude()
# if given explicitly, merge meta dataframe after downselecting
if meta is not None:
meta = meta.loc[self.meta.index.intersection(meta.index)]
self.meta = merge_meta(meta, self.meta, ignore_conflict=True)
# if initializing from xlsx, try to load `meta` table from file
if meta_sheet and isinstance(data, Path) and data.suffix == ".xlsx":
excel_file = pd.ExcelFile(data, engine="openpyxl")
if meta_sheet in excel_file.sheet_names:
self.load_meta(excel_file, sheet_name=meta_sheet, ignore_conflict=True)
self._set_attributes()
# execute user-defined code
if "exec" in run_control():
self._execute_run_control()
# add the `plot` handler
self.plot = PlotAccessor(self)
def _set_attributes(self):
"""Utility function to set attributes"""
# add time domain as attributes
if self.time_col == "year":
setattr(self, "year", get_index_levels(self._data, "year"))
else:
setattr(self, "time", pd.Index(get_index_levels(self._data, "time")))
# set non-standard index columns as attributes
for c in self.meta.index.names:
if c not in META_IDX:
setattr(self, c, get_index_levels(self.meta, c))
# set extra data columns as attributes
for c in self.extra_cols:
setattr(self, c, get_index_levels(self._data, c))
def __getitem__(self, key):
_key_check = [key] if isstr(key) else key
if key == "value":
return pd.Series(self._data.values, name="value")
elif set(_key_check).issubset(self.meta.columns):
return self.meta.__getitem__(key)
else:
return self.get_data_column(key)
def __len__(self):
return len(self._data)
def __repr__(self):
return self.info()
[docs] def info(self, n=80, meta_rows=5, memory_usage=False):
"""Print a summary of the object index dimensions and meta indicators
Parameters
----------
n : int
The maximum line length
meta_rows : int
The maximum number of meta indicators printed
"""
# concatenate list of index dimensions and levels
info = f"{type(self)}\nIndex:\n"
c1 = max([len(i) for i in self.dimensions]) + 1
c2 = n - c1 - 5
info += "\n".join(
[
f" * {i:{c1}}: {print_list(get_index_levels(self._data, i), c2)}"
for i in self.index.names
]
)
# concatenate list of index of _data (not in index.names)
info += "\nTimeseries data coordinates:\n"
info += "\n".join(
[
f" {i:{c1}}: {print_list(get_index_levels(self._data, i), c2)}"
for i in self.dimensions
if i not in self.index.names
]
)
# concatenate list of (head of) meta indicators and levels/values
def print_meta_row(m, t, lst):
_lst = print_list(lst, n - len(m) - len(t) - 7)
return f" {m} ({t}) {_lst}"
info += "\nMeta indicators:\n"
info += "\n".join(
[
print_meta_row(m, t, self.meta[m].unique())
for m, t in zip(
self.meta.columns[0:meta_rows], self.meta.dtypes[0:meta_rows]
)
]
)
# print `...` if more than `meta_rows` columns
if len(self.meta.columns) > meta_rows:
info += "\n ..."
# add info on size (optional)
if memory_usage:
size = self._data.memory_usage() + sum(self.meta.memory_usage())
info += f"\nMemory usage: {size} bytes"
return info
def _execute_run_control(self):
for module_block in run_control()["exec"]:
fname = module_block["file"]
functions = module_block["functions"]
dirname = os.path.dirname(fname)
if dirname:
sys.path.append(dirname)
module = os.path.basename(fname).split(".")[0]
mod = importlib.import_module(module)
for func in functions:
f = getattr(mod, func)
f(self)
@property
def index(self):
"""Return all model-scenario combinations as :class:`pandas.MultiIndex`
The index allows to loop over the available model-scenario combinations
using:
.. code-block:: python
for model, scenario in df.index:
...
"""
return self.meta.index
@property
def model(self):
"""Return the list of (unique) model names"""
return self._get_meta_index_levels("model")
@property
def scenario(self):
"""Return the list of (unique) scenario names"""
return self._get_meta_index_levels("scenario")
def _get_meta_index_levels(self, name):
"""Return the list of a level from meta"""
if name in self.meta.index.names:
return get_index_levels(self.meta, name)
# in case of non-standard meta.index.names
raise KeyError(f"Index `{name}` does not exist!")
@property
def region(self):
"""Return the list of (unique) regions"""
return get_index_levels(self._data, "region")
@property
def variable(self):
"""Return the list of (unique) variables"""
return get_index_levels(self._data, "variable")
@property
def unit(self):
"""Return the list of (unique) units"""
return get_index_levels(self._data, "unit")
@property
def unit_mapping(self):
"""Return a dictionary of variables to (list of) correspoding units"""
def list_or_str(x):
x = list(x.drop_duplicates())
return x if len(x) > 1 else x[0]
return (
pd.DataFrame(
zip(self.get_data_column("variable"), self.get_data_column("unit")),
columns=["variable", "unit"],
)
.groupby("variable")
.apply(lambda u: list_or_str(u.unit))
.to_dict()
)
@property
def data(self):
"""Return the timeseries data as a long :class:`pandas.DataFrame`"""
if self.empty: # reset_index fails on empty with `datetime` column
return pd.DataFrame([], columns=self.dimensions + ["value"])
return self._data.reset_index()
[docs] def get_data_column(self, column):
"""Return a `column` from the timeseries data in long format
Equivalent to :meth:`IamDataFrame.data[column] <IamDataFrame.data>`.
Parameters
----------
column : str
The column name.
Returns
-------
pd.Series
"""
return pd.Series(self._data.index.get_level_values(column), name=column)
@property
def dimensions(self):
"""Return the list of `data` columns (index names & data coordinates)"""
return list(self._data.index.names)
@property
def _LONG_IDX(self):
"""DEPRECATED - please use `IamDataFrame.dimensions`"""
# TODO: deprecated, remove for release >= 1.2
deprecation_warning("Use the attribute `dimensions` instead.", "This attribute")
return self.dimensions
[docs] def copy(self):
"""Make a deepcopy of this object
See :func:`copy.deepcopy` for details.
"""
return copy.deepcopy(self)
[docs] def head(self, *args, **kwargs):
"""Identical to :meth:`pandas.DataFrame.head()` operating on data"""
return self.data.head(*args, **kwargs)
[docs] def tail(self, *args, **kwargs):
"""Identical to :meth:`pandas.DataFrame.tail()` operating on data"""
return self.data.tail(*args, **kwargs)
@property
def empty(self):
"""Indicator whether this object is empty"""
return self._data.empty
[docs] def equals(self, other):
"""Test if two objects contain the same data and meta indicators
This function allows two IamDataFrame instances to be compared against
each other to see if they have the same timeseries data and meta
indicators. nan's in the same location of the meta table are considered
equal.
Parameters
----------
other : IamDataFrame
the other :class:`IamDataFrame` to be compared with `self`
"""
if not isinstance(other, IamDataFrame):
raise ValueError("`other` is not an `IamDataFrame` instance")
if compare(self, other).empty and self.meta.equals(other.meta):
return True
else:
return False
[docs] def append(
self,
other,
ignore_meta_conflict=False,
inplace=False,
verify_integrity=True,
**kwargs,
):
"""Append any IamDataFrame-like object to this object
Indicators in `other.meta` that are not in `self.meta` are merged.
Missing values are set to `NaN`.
Conflicting `data` rows always raise a `ValueError`.
Parameters
----------
other : IamDataFrame, ixmp.Scenario, pandas.DataFrame or data file
Any object castable as IamDataFrame to be appended
ignore_meta_conflict : bool, optional
If False and `other` is an IamDataFrame, raise an error if
any meta columns present in `self` and `other` are not identical.
inplace : bool, optional
If True, do operation inplace and return None
verify_integrity : bool, optional
If True, verify integrity of index
kwargs
Passed to :class:`IamDataFrame(other, **kwargs) <IamDataFrame>`
if `other` is not already an IamDataFrame
Returns
-------
IamDataFrame
If *inplace* is :obj:`False`.
None
If *inplace* is :obj:`True`.
Raises
------
ValueError
If time domain or other timeseries data index dimension don't match.
"""
if other is None:
return None if inplace else self.copy()
if not isinstance(other, IamDataFrame):
other = IamDataFrame(other, **kwargs)
ignore_meta_conflict = True
if self.time_col != other.time_col:
raise ValueError("Incompatible time format (`year` vs. `time`)")
if self.dimensions != other.dimensions:
raise ValueError("Incompatible timeseries data index dimensions")
if other.empty:
return None if inplace else self.copy()
ret = self.copy() if not inplace else self
# merge `meta` tables
ret.meta = merge_meta(ret.meta, other.meta, ignore_meta_conflict)
# append other.data (verify integrity for no duplicates)
_data = ret._data.append(other._data)
if verify_integrity:
verify_index_integrity(_data)
# merge extra columns in `data`
ret.extra_cols += [i for i in other.extra_cols if i not in ret.extra_cols]
ret._data = _data.sort_index()
ret._set_attributes()
if not inplace:
return ret
[docs] def pivot_table(
self,
index,
columns,
values="value",
aggfunc="count",
fill_value=None,
style=None,
):
"""Returns a pivot table
Parameters
----------
index : str or list of str
rows for Pivot table
columns : str or list of str
columns for Pivot table
values : str, default 'value'
dataframe column to aggregate or count
aggfunc : str or function, default 'count'
function used for aggregation,
accepts 'count', 'mean', and 'sum'
fill_value : scalar, default None
value to replace missing values with
style : str, default None
output style for pivot table formatting
accepts 'highlight_not_max', 'heatmap'
"""
index = [index] if isstr(index) else index
columns = [columns] if isstr(columns) else columns
if values != "value":
raise ValueError("This method only supports `values='value'`!")
df = self._data
# allow 'aggfunc' to be passed as string for easier user interface
if isstr(aggfunc):
if aggfunc == "count":
df = self._data.groupby(index + columns).count()
fill_value = 0
elif aggfunc == "mean":
df = self._data.groupby(index + columns).mean().round(2)
fill_value = 0 if style == "heatmap" else ""
elif aggfunc == "sum":
df = self._data.groupby(index + columns).sum()
fill_value = 0 if style == "heatmap" else ""
df = df.unstack(level=columns, fill_value=fill_value)
return df
[docs] def interpolate(self, time, inplace=False, **kwargs):
"""Interpolate missing values in the timeseries data
This method uses :meth:`pandas.DataFrame.interpolate`,
which applies linear interpolation by default
Parameters
----------
time : int or datetime, or list-like thereof
Year or :class:`datetime.datetime` to be interpolated.
This must match the datetime/year format of `self`.
inplace : bool, optional
if True, do operation inplace and return None
kwargs
passed to :meth:`pandas.DataFrame.interpolate`
"""
ret = self.copy() if not inplace else self
interp_kwargs = dict(method="slinear", axis=1)
interp_kwargs.update(kwargs)
time = list(time) if islistable(time) else [time]
# TODO - have to explicitly cast to numpy datetime to sort later,
# could enforce as we do for year below
if self.time_col == "time":
time = list(map(np.datetime64, time))
elif not all(isinstance(x, int) for x in time):
raise ValueError(f"The `time` argument {time} contains non-integers")
old_cols = list(ret[ret.time_col].unique())
columns = np.sort(np.unique(old_cols + time))
# calculate a separate dataframe with full interpolation
df = ret.timeseries()
newdf = df.reindex(columns=columns).interpolate(**interp_kwargs)
# replace only columns asked for
for col in time:
df[col] = newdf[col]
# replace underlying data object
# TODO naming time_col could be done in timeseries()
df.columns.name = ret.time_col
df = df.stack() # long-data to pd.Series
df.name = "value"
ret._data = df.sort_index()
ret._set_attributes()
if not inplace:
return ret
[docs] def swap_time_for_year(self, subannual=False, inplace=False):
"""Convert the `time` dimension to `year` (as integer).
Parameters
----------
subannual : bool, str or func, optional
Merge non-year components of the "time" domain as new column "subannual".
Apply :meth:`strftime() <datetime.date.strftime>` on the values of the
"time" domain using `subannual` (if a string) or "%m-%d %H:%M%z" (if True).
If it is a function, apply the function on the values of the "time" domain.
inplace : bool, optional
If True, do operation inplace and return None.
Returns
-------
:class:`IamDataFrame` or **None**
Object with altered time domain or None if `inplace=True`.
Raises
------
ValueError
"time" is not a column of `self.data`
See Also
--------
swap_year_for_time
"""
return swap_time_for_year(self, subannual=subannual, inplace=inplace)
[docs] def swap_year_for_time(self, inplace=False):
"""Convert the `year` and `subannual` dimensions to `time` (as datetime).
The method applies :meth:`dateutil.parser.parse` on the combined columns
`year` and `subannual`:
.. code-block:: python
dateutil.parser.parse([f"{y}-{s}" for y, s in zip(year, subannual)])
Parameters
----------
inplace : bool, optional
If True, do operation inplace and return None.
Returns
-------
:class:`IamDataFrame` or **None**
Object with altered time domain or None if `inplace=True`.
Raises
------
ValueError
"year" or "subannual" are not a column of `self.data`
See Also
--------
swap_time_for_year
"""
return swap_year_for_time(self, inplace=inplace)
[docs] def as_pandas(self, meta_cols=True):
"""Return object as a pandas.DataFrame
Parameters
----------
meta_cols : list, default None
join `data` with all `meta` columns if True (default)
or only with columns in list, or return copy of `data` if False
"""
# merge data and (downselected) meta, or return copy of data
if meta_cols:
meta_cols = self.meta.columns if meta_cols is True else meta_cols
return (
self.data.set_index(META_IDX).join(self.meta[meta_cols]).reset_index()
)
else:
return self.data.copy()
[docs] def timeseries(self, iamc_index=False):
"""Returns `data` as :class:`pandas.DataFrame` in wide format
Parameters
----------
iamc_index : bool, optional
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all 'data' columns
Raises
------
ValueError
`IamDataFrame` is empty
ValueError
reducing to IAMC-index yields an index with duplicates
"""
if self.empty:
raise ValueError("This IamDataFrame is empty!")
s = self._data
if iamc_index:
if self.time_col == "time":
raise ValueError(
"Cannot use IAMC-index with continuous-time data format!"
)
s = s.droplevel(self.extra_cols)
df = s.unstack(level=self.time_col).rename_axis(None, axis=1).sort_index(axis=1)
return df
[docs] def reset_exclude(self):
"""Reset exclusion assignment for all scenarios to `exclude: False`"""
self.meta["exclude"] = False
[docs] def categorize(
self, name, value, criteria, color=None, marker=None, linestyle=None
):
"""Assign scenarios to a category according to specific criteria
Parameters
----------
name : str
column name of the 'meta' table
value : str
category identifier
criteria : dict
dictionary with variables mapped to applicable checks
('up' and 'lo' for respective bounds, 'year' for years - optional)
color : str, optional
assign a color to this category for plotting
marker : str, optional
assign a marker to this category for plotting
linestyle : str, optional
assign a linestyle to this category for plotting
"""
# add plotting run control
for kind, arg in [
("color", color),
("marker", marker),
("linestyle", linestyle),
]:
if arg:
run_control().update({kind: {name: {value: arg}}})
# find all data that matches categorization
rows = _apply_criteria(self._data, criteria, in_range=True, return_test="all")
idx = _make_index(rows, cols=self.index.names)
if len(idx) == 0:
logger.info("No scenarios satisfy the criteria")
return # EXIT FUNCTION
# update meta dataframe
self._new_meta_column(name)
self.meta.loc[idx, name] = value
msg = "{} scenario{} categorized as `{}: {}`"
logger.info(msg.format(len(idx), "" if len(idx) == 1 else "s", name, value))
def _new_meta_column(self, name):
"""Add a column to meta if it doesn't exist, set value to nan"""
if name is None:
raise ValueError(f"Cannot add a meta column {name}")
if name not in self.meta:
self.meta[name] = np.nan
[docs] def require_variable(self, variable, unit=None, year=None, exclude_on_fail=False):
"""Check whether all scenarios have a required variable
Parameters
----------
variable : str
required variable
unit : str, default None
name of unit (optional)
year : int or list, default None
check whether the variable exists for ANY of the years (if a list)
exclude_on_fail : bool, default False
flag scenarios missing the required variables as `exclude: True`
"""
criteria = {"variable": variable}
if unit:
criteria.update({"unit": unit})
if year:
criteria.update({"year": year})
keep = self._apply_filters(**criteria)
idx = self.meta.index.difference(_meta_idx(self.data[keep]))
n = len(idx)
if n == 0:
logger.info(
"All scenarios have the required variable `{}`".format(variable)
)
return
msg = (
"{} scenario does not include required variable `{}`"
if n == 1
else "{} scenarios do not include required variable `{}`"
)
if exclude_on_fail:
self.meta.loc[idx, "exclude"] = True
msg += ", marked as `exclude: True` in `meta`"
logger.info(msg.format(n, variable))
return pd.DataFrame(index=idx).reset_index()
[docs] def validate(self, criteria={}, exclude_on_fail=False):
"""Validate scenarios using criteria on timeseries values
Returns all scenarios which do not match the criteria and prints a log
message, or returns None if all scenarios match the criteria.
When called with `exclude_on_fail=True`, scenarios not
satisfying the criteria will be marked as `exclude=True`.
Parameters
----------
criteria : dict
dictionary with variable keys and validation mappings
('up' and 'lo' for respective bounds, 'year' for years)
exclude_on_fail : bool, optional
flag scenarios failing validation as `exclude: True`
Returns
-------
:class:`pandas.DataFrame`
All data points that do not satisfy the criteria.
None
If all scenarios satisfy the criteria.
"""
df = _apply_criteria(self._data, criteria, in_range=False)
if not df.empty:
msg = "{} of {} data points do not satisfy the criteria"
logger.info(msg.format(len(df), len(self.data)))
if exclude_on_fail and len(df) > 0:
self._exclude_on_fail(df)
return df.reset_index()
[docs] def compute_bias(self, name, method, axis):
"""Compute the bias weights and add to 'meta'
Parameters
----------
name : str
Column name in the 'meta' dataframe
method : str
Method to compute the bias weights, see the notes
axis : str
Index dimensions on which to apply the `method`
Notes
-----
The following methods are implemented:
- "count": use the inverse of the number of scenarios grouped by `axis` names.
Using the following method on an IamDataFrame with three scenarios
.. code-block:: python
df.compute_bias(name="bias-weight", method="count", axis="scenario")
results in the following column to be added to *df.meta*:
.. list-table::
:header-rows: 1
* - model
- scenario
- bias-weight
* - model_a
- scen_a
- 0.5
* - model_a
- scen_b
- 1
* - model_b
- scen_a
- 0.5
"""
_compute_bias(self, name, method, axis)
[docs] def rename(
self, mapping=None, inplace=False, append=False, check_duplicates=True, **kwargs
):
"""Rename any index dimension or data coordinate.
When renaming models or scenarios, the uniqueness of the index must be
maintained, and the function will raise an error otherwise.
Renaming is only applied to any data row that matches for all
columns given in `mapping`. Renaming can only be applied to the `model`
and `scenario` columns, or to other data coordinates simultaneously.
Parameters
----------
mapping : dict or kwargs
mapping of column name to rename-dictionary of that column
.. code-block:: python
dict(<column_name>: {<current_name_1>: <target_name_1>,
<current_name_2>: <target_name_2>})
or kwargs as `column_name={<current_name_1>: <target_name_1>, ...}`
inplace : bool, optional
Do operation inplace and return None.
append : bool, optional
Whether to append aggregated timeseries data to this instance
(if `inplace=True`) or to a returned new instance (if `inplace=False`).
check_duplicates : bool, optional
Check whether conflicts exist after renaming of timeseries data coordinates.
If True, raise a ValueError; if False, rename and merge
with :meth:`groupby().sum() <pandas.core.groupby.GroupBy.sum>`.
Returns
-------
:class:`IamDataFrame` or **None**
Aggregated timeseries data as new object or None if `inplace=True`.
"""
# combine `mapping` arg and mapping kwargs, ensure no rename conflicts
mapping = mapping or {}
duplicate = set(mapping).intersection(kwargs)
if duplicate:
raise ValueError(f"Conflicting rename args for columns {duplicate}")
mapping.update(kwargs)
# determine columns that are not in the meta index
meta_idx = self.meta.index.names
data_cols = set(self.dimensions) - set(meta_idx)
# changing index and data columns can cause model-scenario mismatch
if any(i in mapping for i in meta_idx) and any(i in mapping for i in data_cols):
msg = "Renaming index and data columns simultaneously not supported!"
raise ValueError(msg)
# translate rename mapping to `filter()` arguments
filters = {col: _from.keys() for col, _from in mapping.items()}
# if append is True, downselect and append renamed data
if append:
df = self.filter(**filters)
# note that `append(other, inplace=True)` returns None
return self.append(df.rename(mapping), inplace=inplace)
# if append is False, iterate over rename mapping and do groupby
ret = self.copy() if not inplace else self
# renaming is only applied where a filter matches for all given columns
rows = ret._apply_filters(**filters)
idx = ret.meta.index.isin(_make_index(ret._data[rows], cols=meta_idx))
# apply renaming changes (for `data` only on the index)
_data_index = ret._data.index
for col, _mapping in mapping.items():
if col in meta_idx:
_index = pd.DataFrame(index=ret.meta.index).reset_index()
_index.loc[idx, col] = _index.loc[idx, col].replace(_mapping)
if _index.duplicated().any():
raise ValueError(f"Renaming to non-unique {col} index!")
ret.meta.index = _index.set_index(meta_idx).index
elif col not in data_cols:
raise ValueError(f"Renaming by {col} not supported!")
_data_index = replace_index_values(_data_index, col, _mapping, rows)
# check if duplicates exist in the new timeseries data index
duplicate_rows = _data_index.duplicated()
has_duplicates = any(duplicate_rows)
if has_duplicates and check_duplicates:
raise_data_error(
"Conflicting data rows after renaming "
"(use `aggregate()` or `check_duplicates=False` instead)",
_data_index[duplicate_rows].to_frame(index=False),
)
ret._data.index = _data_index
ret._set_attributes()
# merge using `groupby().sum()` only if duplicates exist
if has_duplicates:
ret._data = ret._data.reset_index().groupby(ret.dimensions).sum().value
if not inplace:
return ret
[docs] def convert_unit(
self, current, to, factor=None, registry=None, context=None, inplace=False
):
"""Convert all timeseries data having *current* units to new units.
If *factor* is given, existing values are multiplied by it, and the
*to* units are assigned to the 'unit' column.
Otherwise, the :mod:`pint` package is used to convert from *current* ->
*to* units without an explicit conversion factor. Pint natively handles
conversion between any standard (SI) units that have compatible
dimensionality, such as exajoule to terawatt-hours, :code:`EJ -> TWh`,
or tonne per year to gram per second, :code:`t / yr -> g / sec`.
The default *registry* includes additional unit definitions relevant
for integrated assessment models and energy systems analysis, via the
`iam-units <https://github.com/IAMconsortium/units>`_ package.
This registry can also be accessed directly, using:
.. code-block:: python
from iam_units import registry
When using this registry, *current* and *to* may contain the symbols of
greenhouse gas (GHG) species, such as 'CO2e', 'C', 'CH4', 'N2O',
'HFC236fa', etc., as well as lower-case aliases like 'co2' supported by
:mod:`pyam`. In this case, *context* must be the name of a specific
global warming potential (GWP) metric supported by :mod:`iam_units`,
e.g. 'AR5GWP100' (optionally prefixed by '\gwp_', e.g. 'gwp_AR5GWP100').
Rows with units other than *current* are not altered.
Parameters
----------
current : str
Current units to be converted.
to : str
New unit (to be converted to) or symbol for target GHG species. If
only the GHG species is provided, the units (e.g. :code:`Mt /
year`) will be the same as `current`, and an expression combining
units and species (e.g. 'Mt CO2e / yr') will be placed in the
'unit' column.
factor : value, optional
Explicit factor for conversion without `pint`.
registry : :class:`pint.UnitRegistry`, optional
Specific unit registry to use for conversion. Default: the
`iam-units <https://github.com/IAMconsortium/units>`_ registry.
context : str or :class:`pint.Context`, optional
(Name of) the context to use in conversion.
Required when converting between GHG species using GWP metrics,
unless the species indicated by *current* and *to* are the same.
inplace : bool, optional
Whether to return a new IamDataFrame.
Returns
-------
IamDataFrame
If *inplace* is :obj:`False`.
None
If *inplace* is :obj:`True`.
Raises
------
pint.UndefinedUnitError
if attempting a GWP conversion but *context* is not given.
pint.DimensionalityError
without *factor*, when *current* and *to* are not compatible units.
"""
# check that (only) either factor or registry/context is provided
if factor and any([registry, context]):
raise ValueError("Use either `factor` or `registry`!")
return convert_unit(self, current, to, factor, registry, context, inplace)
[docs] def normalize(self, inplace=False, **kwargs):
"""Normalize data to a specific data point
Note: Currently only supports normalizing to a specific time.
Parameters
----------
inplace : bool, optional
if :obj:`True`, do operation inplace and return None
kwargs
the column and value on which to normalize (e.g., `year=2005`)
"""
if len(kwargs) > 1 or self.time_col not in kwargs:
raise ValueError("Only time(year)-based normalization supported")
ret = self.copy() if not inplace else self
df = ret.data
# change all below if supporting more in the future
cols = self.time_col
value = kwargs[self.time_col]
x = df.set_index(IAMC_IDX)
x["value"] /= x[x[cols] == value]["value"]
x = x.reset_index()
ret._data = format_time_col(x, self.time_col).set_index(self.dimensions).value
if not inplace:
return ret
[docs] def aggregate(
self,
variable,
components=None,
method="sum",
recursive=False,
append=False,
):
"""Aggregate timeseries by components or subcategories within each region
Parameters
----------
variable : str or list of str
Variable(s) for which the aggregate will be computed.
components : list of str, optional
Components to be aggregate, defaults to all subcategories of `variable`.
method : func or str, optional
Aggregation method, e.g. :func:`numpy.mean`, :func:`numpy.sum`, 'min', 'max'
recursive : bool or str, optional
Iterate recursively (bottom-up) over all subcategories of `variable`.
If there are existing intermediate variables, it validates the aggregated
value.
If recursive='skip-validate', it skips the validation.
append : bool, optional
Whether to append aggregated timeseries data to this instance.
Returns
-------
:class:`IamDataFrame` or **None**
Aggregated timeseries data or None if `append=True`.
See Also
--------
add : Add timeseries data items along an `axis`.
aggregate_region : Aggregate timeseries data along the `region` dimension.
Notes
-----
The aggregation function interprets any missing values (:any:`numpy.nan`)
for individual components as 0.
"""
if recursive:
if components is not None:
raise ValueError("Recursive aggregation cannot take `components`!")
if method != "sum":
raise ValueError(
"Recursive aggregation only supported with `method='sum'`!"
)
_df = IamDataFrame(
_aggregate_recursive(self, variable, recursive), meta=self.meta
)
else:
_df = _aggregate(self, variable, components=components, method=method)
# else, append to `self` or return as `IamDataFrame`
if append:
self.append(_df, inplace=True)
else:
if _df is None or _df.empty:
return _empty_iamframe(self.dimensions + ["value"])
return IamDataFrame(_df, meta=self.meta)
[docs] def check_aggregate(
self,
variable,
components=None,
method="sum",
exclude_on_fail=False,
multiplier=1,
**kwargs,
):
"""Check whether a timeseries matches the aggregation of its components
Parameters
----------
variable : str or list of str
variable(s) checked for matching aggregation of sub-categories
components : list of str, default None
list of variables, defaults to all sub-categories of `variable`
method : func or str, optional
method to use for aggregation,
e.g. :func:`numpy.mean`, :func:`numpy.sum`, 'min', 'max'
exclude_on_fail : bool, optional
flag scenarios failing validation as `exclude: True`
multiplier : number, optional
factor when comparing variable and sum of components
kwargs : arguments for comparison of values
passed to :func:`numpy.isclose`
"""
# compute aggregate from components, return None if no components
df_components = _aggregate(self, variable, components, method)
if df_components is None:
return
# filter and groupby data, use `pd.Series.align` for matching index
rows = self._apply_filters(variable=variable)
df_var, df_components = _group_and_agg(self._data[rows], [], method).align(
df_components
)
# use `np.isclose` for checking match
rows = ~np.isclose(df_var, multiplier * df_components, **kwargs)
# if aggregate and components don't match, return inconsistent data
if sum(rows):
msg = "`{}` - {} of {} rows are not aggregates of components"
logger.info(msg.format(variable, sum(rows), len(df_var)))
if exclude_on_fail:
self._exclude_on_fail(_meta_idx(df_var[rows].reset_index()))
return pd.concat(
[df_var[rows], df_components[rows]],
axis=1,
keys=(["variable", "components"]),
)
[docs] def aggregate_region(
self,
variable,
region="World",
subregions=None,
components=False,
method="sum",
weight=None,
append=False,
drop_negative_weights=True,
):
"""Aggregate a timeseries over a number of subregions
This function allows to add variable sub-categories that are only
defined at the `region` level by setting `components=True`
Parameters
----------
variable : str or list of str
variable(s) to be aggregated
region : str, optional
region to which data will be aggregated
subregions : list of str, optional
list of subregions, defaults to all regions other than `region`
components : bool or list of str, optional
variables at the `region` level to be included in the aggregation
(ignored if False); if `True`, use all sub-categories of `variable`
included in `region` but not in any of the `subregions`;
or explicit list of variables
method : func or str, optional
method to use for aggregation,
e.g. :func:`numpy.mean`, :func:`numpy.sum`, 'min', 'max'
weight : str, optional
variable to use as weight for the aggregation
(currently only supported with `method='sum'`)
append : bool, optional
append the aggregate timeseries to `self` and return None,
else return aggregate timeseries as new :class:`IamDataFrame`
drop_negative_weights : bool, optional
removes any aggregated values that are computed using negative weights
Returns
-------
:class:`IamDataFrame` or **None**
Aggregated timeseries data or None if `append=True`.
See Also
--------
add : Add timeseries data items `a` and `b` along an `axis`
aggregate : Aggregate timeseries data along the `variable` hierarchy.
"""
_df = _aggregate_region(
self,
variable,
region=region,
subregions=subregions,
components=components,
method=method,
weight=weight,
drop_negative_weights=drop_negative_weights,
)
# else, append to `self` or return as `IamDataFrame`
if append:
self.append(_df, region=region, inplace=True)
else:
if _df is None or _df.empty:
return _empty_iamframe(self.dimensions + ["value"])
return IamDataFrame(_df, region=region, meta=self.meta)
[docs] def check_aggregate_region(
self,
variable,
region="World",
subregions=None,
components=False,
method="sum",
weight=None,
exclude_on_fail=False,
drop_negative_weights=True,
**kwargs,
):
"""Check whether a timeseries matches the aggregation across subregions
Parameters
----------
variable : str or list of str
variable(s) to be checked for matching aggregation of subregions
region : str, optional
region to be checked for matching aggregation of subregions
subregions : list of str, optional
list of subregions, defaults to all regions other than `region`
components : bool or list of str, optional
variables at the `region` level to be included in the aggregation
(ignored if False); if `True`, use all sub-categories of `variable`
included in `region` but not in any of the `subregions`;
or explicit list of variables
method : func or str, optional
method to use for aggregation,
e.g. :func:`numpy.mean`, :func:`numpy.sum`, 'min', 'max'
weight : str, optional
variable to use as weight for the aggregation
(currently only supported with `method='sum'`)
exclude_on_fail : boolean, optional
flag scenarios failing validation as `exclude: True`
drop_negative_weights : bool, optional
removes any aggregated values that are computed using negative weights
kwargs : arguments for comparison of values
passed to :func:`numpy.isclose`
"""
# compute aggregate from subregions, return None if no subregions
df_subregions = _aggregate_region(
self,
variable,
region,
subregions,
components,
method,
weight,
drop_negative_weights,
)
if df_subregions is None:
return
# filter and groupby data, use `pd.Series.align` for matching index
rows = self._apply_filters(region=region, variable=variable)
if not rows.any():
logger.info(f"Variable '{variable}' does not exist in region '{region}'!")
return
df_region, df_subregions = _group_and_agg(self._data[rows], "region").align(
df_subregions
)
# use `np.isclose` for checking match
rows = ~np.isclose(df_region, df_subregions, **kwargs)
# if region and subregions don't match, return inconsistent data
if sum(rows):
msg = "`{}` - {} of {} rows are not aggregates of subregions"
logger.info(msg.format(variable, sum(rows), len(df_region)))
if exclude_on_fail:
self._exclude_on_fail(_meta_idx(df_region[rows].reset_index()))
_df = pd.concat(
[
pd.concat(
[df_region[rows], df_subregions[rows]],
axis=1,
keys=(["region", "subregions"]),
)
],
keys=[region],
names=["region"],
)
_df.index = _df.index.reorder_levels(self.dimensions)
return _df
[docs] def aggregate_time(
self,
variable,
column="subannual",
value="year",
components=None,
method="sum",
append=False,
):
"""Aggregate a timeseries over a subannual time resolution
Parameters
----------
variable : str or list of str
variable(s) to be aggregated
column : str, optional
the data column to be used as subannual time representation
value : str, optional
the name of the aggregated (subannual) time
components : list of str
subannual timeslices to be aggregated; defaults to all subannual
timeslices other than `value`
method : func or str, optional
method to use for aggregation,
e.g. :func:`numpy.mean`, :func:`numpy.sum`, 'min', 'max'
append : bool, optional
append the aggregate timeseries to `self` and return None,
else return aggregate timeseries as new :class:`IamDataFrame`
"""
_df = _aggregate_time(
self,
variable,
column=column,
value=value,
components=components,
method=method,
)
# return None if there is nothing to aggregate
if _df is None:
return None
# else, append to `self` or return as `IamDataFrame`
if append is True:
self.append(_df, inplace=True)
else:
return IamDataFrame(_df, meta=self.meta)
[docs] def downscale_region(
self,
variable,
region="World",
subregions=None,
proxy=None,
weight=None,
append=False,
):
"""Downscale a timeseries to a number of subregions
Parameters
----------
variable : str or list of str
variable(s) to be downscaled
region : str, optional
region from which data will be downscaled
subregions : list of str, optional
list of subregions, defaults to all regions other than `region`
(if using `proxy`) or `region` index (if using `weight`)
proxy : str, optional
variable (within the :class:`IamDataFrame`) to be used as proxy
for regional downscaling
weight : class:`pandas.DataFrame`, optional
dataframe with time dimension as columns (year or
:class:`datetime.datetime`) and regions[, model, scenario] as index
append : bool, optional
append the downscaled timeseries to `self` and return None,
else return downscaled data as new IamDataFrame
"""
if proxy is not None and weight is not None:
raise ValueError("Using both 'proxy' and 'weight' arguments is not valid!")
elif proxy is not None:
# get default subregions if not specified and select data from self
subregions = subregions or self._all_other_regions(region)
rows = self._apply_filters(variable=proxy, region=subregions)
cols = self._get_cols(["region", self.time_col])
_proxy = self.data[rows].set_index(cols).value
elif weight is not None:
# downselect weight to subregions or remove `region` from index
if subregions is not None:
rows = weight.index.isin(subregions, level="region")
else:
rows = ~weight.index.isin([region], level="region")
_proxy = weight[rows].stack()
else:
raise ValueError("Either a 'proxy' or 'weight' argument is required!")
_value = (
self.data[self._apply_filters(variable=variable, region=region)]
.set_index(self._get_cols(["variable", "unit", self.time_col]))
.value
)
# compute downscaled data
_total = _proxy.groupby(self.time_col).sum()
_data = _value * _proxy / _total
if append is True:
self.append(_data, inplace=True)
else:
return IamDataFrame(_data, meta=self.meta)
def _all_other_regions(self, region, variable=None):
"""Return list of regions other than `region` containing `variable`"""
rows = self._apply_filters(variable=variable)
return self._data[rows].index.get_level_values("region").difference([region])
def _variable_components(self, variable, level=0):
"""Get all components (sub-categories) of a variable for a given level
If `level=0`, for `variable='foo'`, return `['foo|bar']`, but don't
include `'foo|bar|baz'`, which is a sub-sub-category. If `level=None`,
all variables below `variable` in the hierarchy are returned."""
var_list = pd.Series(self.variable)
return var_list[pattern_match(var_list, "{}|*".format(variable), level=level)]
def _get_cols(self, cols):
"""Return a list of columns of `self.data`"""
return META_IDX + cols + self.extra_cols
[docs] def check_internal_consistency(self, components=False, **kwargs):
"""Check whether a scenario ensemble is internally consistent
We check that all variables are equal to the sum of their sectoral
components and that all the regions add up to the World total. If
the check is passed, None is returned, otherwise a DataFrame of
inconsistent variables is returned.
Note: at the moment, this method's regional checking is limited to
checking that all the regions sum to the World region. We cannot
make this more automatic unless we store how the regions relate,
see `this issue <https://github.com/IAMconsortium/pyam/issues/106>`_.
Parameters
----------
kwargs : arguments for comparison of values
passed to :func:`numpy.isclose`
components : bool, optional
passed to :meth:`check_aggregate_region` if `True`, use all
sub-categories of each `variable` included in `World` but not in
any of the subregions; if `False`, only aggregate variables over
subregions
"""
lst = []
for variable in self.variable:
diff_agg = self.check_aggregate(variable, **kwargs)
if diff_agg is not None:
lst.append(diff_agg)
diff_regional = self.check_aggregate_region(
variable, components=components, **kwargs
)
if diff_regional is not None:
lst.append(diff_regional)
if len(lst):
_df = pd.concat(lst, sort=True).sort_index()
return _df[
[
c
for c in ["variable", "components", "region", "subregions"]
if c in _df.columns
]
]
def _exclude_on_fail(self, df):
"""Assign a selection of scenarios as `exclude: True` in meta"""
idx = (
df
if isinstance(df, pd.MultiIndex)
else _make_index(df, cols=self.index.names)
)
self.meta.loc[idx, "exclude"] = True
logger.info(
"{} non-valid scenario{} will be excluded".format(
len(idx), "" if len(idx) == 1 else "s"
)
)
[docs] def filter(self, keep=True, inplace=False, **kwargs):
"""Return a (copy of a) filtered (downselected) IamDataFrame
Parameters
----------
keep : bool, optional
keep all scenarios satisfying the filters (if True) or the inverse
inplace : bool, optional
if True, do operation inplace and return None
filters by kwargs:
The following columns are available for filtering:
- 'meta' columns: filter by string value of that column
- 'model', 'scenario', 'region', 'variable', 'unit':
string or list of strings, where `*` can be used as a wildcard
- 'level': the maximum "depth" of IAM variables (number of '|')
(excluding the strings given in the 'variable' argument)
- 'year': takes an integer (int/np.int64), a list of integers or
a range. Note that the last year of a range is not included,
so `range(2010, 2015)` is interpreted as `[2010, ..., 2014]`
- arguments for filtering by `datetime.datetime` or np.datetime64
('month', 'hour', 'time')
- 'regexp=True' disables pseudo-regexp syntax in `pattern_match()`
"""
if not isinstance(keep, bool):
raise ValueError(f"Cannot filter by `keep={keep}`, must be a boolean!")
_keep = self._apply_filters(**kwargs)
_keep = _keep if keep else ~_keep
ret = self.copy() if not inplace else self
ret._data = ret._data[_keep]
ret._data.index = ret._data.index.remove_unused_levels()
idx = _make_index(ret._data, cols=self.index.names)
if len(idx) == 0:
logger.warning("Filtered IamDataFrame is empty!")
ret.meta = ret.meta.loc[idx]
ret._set_attributes()
if not inplace:
return ret
def _apply_filters(self, level=None, **filters):
"""Determine rows to keep in data for given set of filters
Parameters
----------
filters : dict
dictionary of filters of the format (`{col: values}`);
uses a pseudo-regexp syntax by default,
but accepts `regexp: True` in the dictionary to use regexp directly
"""
regexp = filters.pop("regexp", False)
keep = np.ones(len(self), dtype=bool)
# filter by columns and list of values
for col, values in filters.items():
# treat `_apply_filters(col=None)` as no filter applied
if values is None:
continue
if col in self.meta.columns:
matches = pattern_match(
self.meta[col], values, regexp=regexp, has_nan=True
)
cat_idx = self.meta[matches].index
keep_col = _make_index(
self._data, cols=self.index.names, unique=False
).isin(cat_idx)
elif col == "year":
if self.time_col == "year":
_data = self.get_data_column(col)
else:
_data = self.get_data_column("time").apply(lambda x: x.year)
keep_col = years_match(_data, values)
elif col == "month" and self.time_col == "time":
keep_col = month_match(
self.get_data_column("time").apply(lambda x: x.month), values
)
elif col == "day" and self.time_col == "time":
if isinstance(values, str):
wday = True
elif isinstance(values, list) and isinstance(values[0], str):
wday = True
else:
wday = False
if wday:
days = self.get_data_column("time").apply(lambda x: x.weekday())
else: # ints or list of ints
days = self.get_data_column("time").apply(lambda x: x.day)
keep_col = day_match(days, values)
elif col == "hour" and self.time_col == "time":
keep_col = hour_match(
self.get_data_column("time").apply(lambda x: x.hour), values
)
elif col == "time" and self.time_col == "time":
keep_col = datetime_match(self.get_data_column("time"), values)
elif col in self.dimensions:
lvl_index, lvl_codes = get_index_levels_codes(self._data, col)
codes = pattern_match(
lvl_index,
values,
regexp=regexp,
level=level if col == "variable" else None,
has_nan=True,
return_codes=True,
)
keep_col = get_keep_col(lvl_codes, codes)
else:
_raise_filter_error(col)
keep = np.logical_and(keep, keep_col)
if level is not None and "variable" not in filters:
col = "variable"
lvl_index, lvl_codes = get_index_levels_codes(self._data, col)
matches = find_depth(lvl_index, level=level)
keep_col = get_keep_col(lvl_codes, matches)
keep = np.logical_and(keep, keep_col)
return keep
[docs] def col_apply(self, col, func, *args, **kwargs):
"""Apply a function to a column of data or meta
Parameters
----------
col: str
column in either data or meta dataframes
func: function
function to apply
"""
if col in self.data:
self.data[col] = self.data[col].apply(func, *args, **kwargs)
else:
self.meta[col] = self.meta[col].apply(func, *args, **kwargs)
[docs] def add(
self, a, b, name, axis="variable", fillna=None, ignore_units=False, append=False
):
"""Add timeseries data items `a` and `b` along an `axis`
This function computes `a + b`. If `a` or `b` are lists, the method applies
:meth:`pandas.groupby().sum() <pandas.core.groupby.GroupBy.sum>` on each group.
If either `a` or `b` are not defined for a row and `fillna` is not specified,
no value is computed for that row.
Parameters
----------
a, b : str, list of str or a number
Items to be used for the addition.
name : str
Name of the computed timeseries data on the `axis`.
axis : str, optional
Axis along which to compute.
fillna : dict or scalar, optional
Value to fill holes when rows are not defined for either `a` or `b`.
Can be a scalar or a dictionary of the form :code:`{arg: default}`.
ignore_units : bool or str, optional
Perform operation on values without considering units. Set units of returned
data to `unknown` (if True) or the value of `ignore_units` (if str).
append : bool, optional
Whether to append aggregated timeseries data to this instance.
Returns
-------
:class:`IamDataFrame` or **None**
Computed timeseries data or None if `append=True`.
See Also
--------
subtract, multiply, divide
apply : Apply a custom function on the timeseries data along any axis.
aggregate : Aggregate timeseries data along the `variable` hierarchy.
aggregate_region : Aggregate timeseries data along the `region` dimension.
Notes
-----
This function uses the :mod:`pint` package and the :mod:`iam-units` registry
(`read the docs <https://github.com/IAMconsortium/units>`_) to handle units.
:mod:`pyam` will keep notation consistent with the input format (if possible)
and otherwise uses abbreviated units :code:`'{:~}'.format(u)` (see
`here <https://pint.readthedocs.io/en/stable/tutorial.html#string-formatting>`_
for more information).
As a result, the notation of returned units may differ from the input format.
For example, the unit :code:`EJ/yr` may be reformatted to :code:`EJ / a`.
"""
kwds = dict(axis=axis, fillna=fillna, ignore_units=ignore_units)
_value = _op_data(self, name, "add", **kwds, a=a, b=b)
if append:
self.append(_value, inplace=True)
else:
return IamDataFrame(_value, meta=self.meta)
[docs] def subtract(
self, a, b, name, axis="variable", fillna=None, ignore_units=False, append=False
):
"""Compute the difference of timeseries data items `a` and `b` along an `axis`
This function computes `a - b`. If `a` or `b` are lists, the method applies
:meth:`pandas.groupby().sum() <pandas.core.groupby.GroupBy.sum>` on each group.
If either `a` or `b` are not defined for a row and `fillna` is not specified,
no value is computed for that row.
Parameters
----------
a, b : str, list of str or a number
Items to be used for the subtraction.
name : str
Name of the computed timeseries data on the `axis`.
axis : str, optional
Axis along which to compute.
fillna : dict or scalar, optional
Value to fill holes when rows are not defined for either `a` or `b`.
Can be a scalar or a dictionary of the form :code:`{arg: default}`.
ignore_units : bool or str, optional
Perform operation on values without considering units. Set units of returned
data to `unknown` (if True) or the value of `ignore_units` (if str).
append : bool, optional
Whether to append aggregated timeseries data to this instance.
Returns
-------
:class:`IamDataFrame` or **None**
Computed timeseries data or None if `append=True`.
See Also
--------
add, multiply, divide
apply : Apply a custom function on the timeseries data along any axis.
Notes
-----
This function uses the :mod:`pint` package and the :mod:`iam-units` registry
(`read the docs <https://github.com/IAMconsortium/units>`_) to handle units.
:mod:`pyam` will keep notation consistent with the input format (if possible)
and otherwise uses abbreviated units :code:`'{:~}'.format(u)` (see
`here <https://pint.readthedocs.io/en/stable/tutorial.html#string-formatting>`_
for more information).
As a result, the notation of returned units may differ from the input format.
For example, the unit :code:`EJ/yr` may be reformatted to :code:`EJ / a`.
"""
kwds = dict(axis=axis, fillna=fillna, ignore_units=ignore_units)
_value = _op_data(self, name, "subtract", **kwds, a=a, b=b)
if append:
self.append(_value, inplace=True)
else:
return IamDataFrame(_value, meta=self.meta)
[docs] def multiply(
self, a, b, name, axis="variable", fillna=None, ignore_units=False, append=False
):
"""Multiply timeseries data items `a` and `b` along an `axis`
This function computes `a * b`. If `a` or `b` are lists, the method applies
:meth:`pandas.groupby().sum() <pandas.core.groupby.GroupBy.sum>` on each group.
If either `a` or `b` are not defined for a row and `fillna` is not specified,
no value is computed for that row.
Parameters
----------
a, b : str, list of str or a number
Items to be used for the division.
name : str
Name of the computed timeseries data on the `axis`.
axis : str, optional
Axis along which to compute.
fillna : dict or scalar, optional
Value to fill holes when rows are not defined for either `a` or `b`.
Can be a scalar or a dictionary of the form :code:`{arg: default}`.
ignore_units : bool or str, optional
Perform operation on values without considering units. Set units of returned
data to `unknown` (if True) or the value of `ignore_units` (if str).
append : bool, optional
Whether to append aggregated timeseries data to this instance.
Returns
-------
:class:`IamDataFrame` or **None**
Computed timeseries data or None if `append=True`.
See Also
--------
add, subtract, divide
apply : Apply a custom function on the timeseries data along any axis.
Notes
-----
This function uses the :mod:`pint` package and the :mod:`iam-units` registry
(`read the docs <https://github.com/IAMconsortium/units>`_) to handle units.
:mod:`pyam` will keep notation consistent with the input format (if possible)
and otherwise uses abbreviated units :code:`'{:~}'.format(u)` (see
`here <https://pint.readthedocs.io/en/stable/tutorial.html#string-formatting>`_
for more information).
As a result, the notation of returned units may differ from the input format.
For example, the unit :code:`EJ/yr` may be reformatted to :code:`EJ / a`.
"""
kwds = dict(axis=axis, fillna=fillna, ignore_units=ignore_units)
_value = _op_data(self, name, "multiply", **kwds, a=a, b=b)
if append:
self.append(_value, inplace=True)
else:
return IamDataFrame(_value, meta=self.meta)
[docs] def divide(
self, a, b, name, axis="variable", fillna=None, ignore_units=False, append=False
):
"""Divide the timeseries data items `a` and `b` along an `axis`
This function computes `a / b`. If `a` or `b` are lists, the method applies
:meth:`pandas.groupby().sum() <pandas.core.groupby.GroupBy.sum>` on each group.
If either `a` or `b` are not defined for a row and `fillna` is not specified,
no value is computed for that row.
Parameters
----------
a, b : str, list of str or a number
Items to be used for the division.
name : str
Name of the computed timeseries data on the `axis`.
axis : str, optional
Axis along which to compute.
fillna : dict or scalar, optional
Value to fill holes when rows are not defined for either `a` or `b`.
Can be a scalar or a dictionary of the form :code:`{arg: default}`.
ignore_units : bool or str, optional
Perform operation on values without considering units. Set units of returned
data to `unknown` (if True) or the value of `ignore_units` (if str).
append : bool, optional
Whether to append aggregated timeseries data to this instance.
Returns
-------
:class:`IamDataFrame` or **None**
Computed timeseries data or None if `append=True`.
See Also
--------
add, subtract, multiply
apply : Apply a custom function on the timeseries data along any axis.
Notes
-----
This function uses the :mod:`pint` package and the :mod:`iam-units` registry
(`read the docs <https://github.com/IAMconsortium/units>`_) to handle units.
:mod:`pyam` will keep notation consistent with the input format (if possible)
and otherwise uses abbreviated units :code:`'{:~}'.format(u)` (see
`here <https://pint.readthedocs.io/en/stable/tutorial.html#string-formatting>`_
for more information).
As a result, the notation of returned units may differ from the input format.
For example, the unit :code:`EJ/yr` may be reformatted to :code:`EJ / a`.
"""
kwds = dict(axis=axis, fillna=fillna, ignore_units=ignore_units)
_value = _op_data(self, name, "divide", **kwds, a=a, b=b)
if append:
self.append(_value, inplace=True)
else:
return IamDataFrame(_value, meta=self.meta)
[docs] def apply(
self, func, name, axis="variable", fillna=None, append=False, args=(), **kwds
):
"""Apply a function to components of timeseries data along an `axis`
This function computes a function `func` using timeseries data selected
along an `axis` downselected by keyword arguments.
The length of components needs to match the number of required arguments
of `func`.
Parameters
----------
func : function
Function to apply to `components` along `axis`.
name : str
Name of the computed timeseries data on the `axis`.
axis : str, optional
Axis along which to compute.
fillna : dict or scalar, optional
Value to fill holes when rows are not defined for items in `args` or `kwds`.
Can be a scalar or a dictionary of the form :code:`{kwd: default}`.
append : bool, optional
Whether to append aggregated timeseries data to this instance.
args : tuple or list of str
List of variables to pass as positional arguments to `func`.
**kwds
Additional keyword arguments to pass as keyword arguments to `func`. If the
name of a variable is given, the associated timeseries is passed. Otherwise
the value itself is passed.
Returns
-------
:class:`IamDataFrame` or **None**
Computed timeseries data or None if `append=True`.
Notes
-----
This function uses the :mod:`pint` package and the :mod:`iam-units` registry
(`read the docs <https://github.com/IAMconsortium/units>`_) to handle units.
:mod:`pyam` uses abbreviated units :code:`'{:~}'.format(u)` (see
`here <https://pint.readthedocs.io/en/stable/tutorial.html#string-formatting>`_
for more information).
As a result, the notation of returned units may differ from the input format.
For example, the unit :code:`EJ/yr` may be reformatted to :code:`EJ / a`.
"""
_value = _op_data(self, name, func, axis=axis, fillna=fillna, args=args, **kwds)
if append:
self.append(_value, inplace=True)
else:
return IamDataFrame(_value, meta=self.meta)
def _to_file_format(self, iamc_index):
"""Return a dataframe suitable for writing to a file"""
df = self.timeseries(iamc_index=iamc_index).reset_index()
df = df.rename(columns={c: str(c).title() for c in df.columns})
return df
[docs] def to_csv(self, path, iamc_index=False, **kwargs):
"""Write timeseries data of this object to a csv file
Parameters
----------
path : str or path object
file path or :class:`pathlib.Path`
iamc_index : bool, default False
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all 'data' columns
"""
self._to_file_format(iamc_index).to_csv(path, index=False, **kwargs)
[docs] def to_excel(
self,
excel_writer,
sheet_name="data",
iamc_index=False,
include_meta=True,
**kwargs,
):
"""Write object to an Excel spreadsheet
Parameters
----------
excel_writer : str, path object or ExcelWriter object
any valid string path, :class:`pathlib.Path`
or :class:`pandas.ExcelWriter`
sheet_name : string
name of sheet which will contain :meth:`timeseries` data
iamc_index : bool, default False
if True, use `['model', 'scenario', 'region', 'variable', 'unit']`;
else, use all 'data' columns
include_meta : boolean or string
if True, write 'meta' to an Excel sheet name 'meta' (default);
if this is a string, use it as sheet name
"""
# open a new ExcelWriter instance (if necessary)
close = False
if not isinstance(excel_writer, pd.ExcelWriter):
close = True
excel_writer = pd.ExcelWriter(excel_writer, engine="openpyxl")
# write data table
write_sheet(excel_writer, sheet_name, self._to_file_format(iamc_index))
# write meta table unless `include_meta=False`
if include_meta:
meta_rename = dict([(i, i.capitalize()) for i in META_IDX])
write_sheet(
excel_writer,
"meta" if include_meta is True else include_meta,
self.meta.reset_index().rename(columns=meta_rename),
)
# close the file if `excel_writer` arg was a file name
if close:
excel_writer.close()
[docs] def to_datapackage(self, path):
"""Write object to a frictionless Data Package
More information: https://frictionlessdata.io
Returns the saved :class:`datapackage.Package`
(|datapackage.Package.docs|).
When adding metadata (descriptors), please follow the `template`
defined by https://github.com/OpenEnergyPlatform/metadata
Parameters
----------
path : string or path object
any valid string path or :class:`pathlib.Path`
"""
if not HAS_DATAPACKAGE:
raise ImportError("Required package `datapackage` not found!")
with TemporaryDirectory(dir=".") as tmp:
# save data and meta tables to a temporary folder
self.data.to_csv(Path(tmp) / "data.csv", index=False)
self.meta.to_csv(Path(tmp) / "meta.csv")
# cast tables to datapackage
package = Package()
package.infer("{}/*.csv".format(tmp))
if not package.valid:
logger.warning("The exported datapackage is not valid")
package.save(path)
# return the package (needs to reloaded because `tmp` was deleted)
return Package(path)
[docs] 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 : str
The column used to map new regions to. Common examples include
iso and 5_region.
agg : str, optional
Perform a data aggregation. Options include: sum.
copy_col : str, optional
Copy the existing region data into a new column for later use.
fname : str, 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
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, optional
if True, do operation inplace and return None
"""
fname = fname or run_control()["region_mapping"]["default"]
mapping = read_pandas(Path(fname)).rename(str.lower, axis="columns")
map_col = map_col.lower()
ret = self.copy() if not inplace else self
_df = ret.data
columns_orderd = _df.columns
# merge data
dfs = []
for model in self.model:
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(self.dimensions).sum().reset_index()
df = (
df.reindex(columns=columns_orderd)
.sort_values(SORT_IDX)
.reset_index(drop=True)
)
ret._data = format_time_col(df, self.time_col).set_index(self.dimensions).value
if not inplace:
return ret
def _meta_idx(data):
"""Return the 'META_IDX' from data by index"""
return data[META_IDX].drop_duplicates().set_index(META_IDX).index
def _raise_filter_error(col):
"""Raise an error if not possible to filter by col"""
raise ValueError(f"Filter by `{col}` not supported!")
def _check_rows(rows, check, in_range=True, return_test="any"):
"""Check all rows to be in/out of a certain range and provide testing on
return values based on provided conditions
Parameters
----------
rows : pd.DataFrame
data rows
check : dict
dictionary with possible values of 'up', 'lo', and 'year'
in_range : bool, optional
check if values are inside or outside of provided range
return_test : str, optional
possible values:
- 'any': default, return scenarios where check passes for any entry
- 'all': test if all values match checks, if not, return empty set
"""
valid_checks = set(["up", "lo", "year"])
if not set(check.keys()).issubset(valid_checks):
msg = "Unknown checking type: {}"
raise ValueError(msg.format(check.keys() - valid_checks))
if "year" not in check:
where_idx = set(rows.index)
else:
if "time" in rows.index.names:
_years = rows.index.get_level_values("time").year
else:
_years = rows.index.get_level_values("year")
where_idx = set(rows.index[_years == check["year"]])
rows = rows.loc[list(where_idx)]
up_op = rows.values.__le__ if in_range else rows.values.__gt__
lo_op = rows.values.__ge__ if in_range else rows.values.__lt__
check_idx = []
for (bd, op) in [("up", up_op), ("lo", lo_op)]:
if bd in check:
check_idx.append(set(rows.index[op(check[bd])]))
if return_test == "any":
ret = where_idx & set.union(*check_idx)
elif return_test == "all":
ret = where_idx if where_idx == set.intersection(*check_idx) else set()
else:
raise ValueError("Unknown return test: {}".format(return_test))
return ret
def _apply_criteria(df, criteria, **kwargs):
"""Apply criteria individually to every model/scenario instance"""
idxs = []
for var, check in criteria.items():
_df = df[df.index.get_level_values("variable") == var]
for group in _df.groupby(META_IDX):
grp_idxs = _check_rows(group[-1], check, **kwargs)
idxs.append(grp_idxs)
df = df.loc[itertools.chain(*idxs)]
return df
def _make_index(df, cols=META_IDX, unique=True):
"""Create an index from the columns/index of a dataframe or series"""
def _get_col(c):
try:
return df.index.get_level_values(c)
except KeyError:
return df[c]
index = list(zip(*[_get_col(col) for col in cols]))
if unique:
index = pd.unique(index)
return pd.MultiIndex.from_tuples(index, names=tuple(cols))
def _empty_iamframe(index):
"""Return an empty IamDataFrame with the correct index columns"""
return IamDataFrame(pd.DataFrame([], columns=index))
[docs]def validate(df, criteria={}, exclude_on_fail=False, **kwargs):
"""Validate scenarios using criteria on timeseries values
Returns all scenarios which do not match the criteria and prints a log
message or returns None if all scenarios match the criteria.
When called with `exclude_on_fail=True`, scenarios in `df` not satisfying
the criteria will be marked as `exclude=True` (object modified in place).
Parameters
----------
df : IamDataFrame
args : passed to :meth:`IamDataFrame.validate`
kwargs : used for downselecting IamDataFrame
passed to :meth:`IamDataFrame.filter`
"""
fdf = df.filter(**kwargs)
if len(fdf.data) > 0:
vdf = fdf.validate(criteria=criteria, exclude_on_fail=exclude_on_fail)
df.meta["exclude"] |= fdf.meta["exclude"] # update if any excluded
return vdf
[docs]def require_variable(
df, variable, unit=None, year=None, exclude_on_fail=False, **kwargs
):
"""Check whether all scenarios have a required variable
Parameters
----------
df : IamDataFrame
args : passed to :meth:`IamDataFrame.require_variable`
kwargs : used for downselecting IamDataFrame
passed to :meth:`IamDataFrame.filter`
"""
fdf = df.filter(**kwargs)
if len(fdf.data) > 0:
vdf = fdf.require_variable(
variable=variable, unit=unit, year=year, exclude_on_fail=exclude_on_fail
)
df.meta["exclude"] |= fdf.meta["exclude"] # update if any excluded
return vdf
[docs]def categorize(
df, name, value, criteria, color=None, marker=None, linestyle=None, **kwargs
):
"""Assign scenarios to a category according to specific criteria
or display the category assignment
Parameters
----------
df : IamDataFrame
args : passed to :meth:`IamDataFrame.categorize`
kwargs : used for downselecting IamDataFrame
passed to :meth:`IamDataFrame.filter`
"""
fdf = df.filter(**kwargs)
fdf.categorize(
name=name,
value=value,
criteria=criteria,
color=color,
marker=marker,
linestyle=linestyle,
)
# update meta indicators
if name in df.meta:
df.meta[name].update(fdf.meta[name])
else:
df.meta[name] = fdf.meta[name]
[docs]def check_aggregate(
df, variable, components=None, exclude_on_fail=False, multiplier=1, **kwargs
):
"""Check whether the timeseries values match the aggregation
of sub-categories
Parameters
----------
df : IamDataFrame
args : passed to :meth:`IamDataFrame.check_aggregate`
kwargs : used for downselecting IamDataFrame
passed to :meth:`IamDataFrame.filter`
"""
fdf = df.filter(**kwargs)
if len(fdf.data) > 0:
vdf = fdf.check_aggregate(
variable=variable,
components=components,
exclude_on_fail=exclude_on_fail,
multiplier=multiplier,
)
df.meta["exclude"] |= fdf.meta["exclude"] # update if any excluded
return vdf
[docs]def compare(
left, right, left_label="left", right_label="right", drop_close=True, **kwargs
):
"""Compare the data in two IamDataFrames and return a pandas.DataFrame
Parameters
----------
left, right : IamDataFrames
two :class:`IamDataFrame` instances to be compared
left_label, right_label : str, default `left`, `right`
column names of the returned :class:`pandas.DataFrame`
drop_close : bool, optional
remove all data where `left` and `right` are close
kwargs : arguments for comparison of values
passed to :func:`numpy.isclose`
"""
return _compare(left, right, left_label, right_label, drop_close=True, **kwargs)
[docs]def concat(dfs, ignore_meta_conflict=False, **kwargs):
"""Concatenate a series of IamDataFrame-like objects
Parameters
----------
dfs : iterable of IamDataFrames
A list of objects castable to :class:`IamDataFrame`
ignore_meta_conflict : bool, default False
If False, raise an error if any meta columns present in `dfs` are not identical.
If True, values in earlier elements of `dfs` take precendence.
kwargs
Passed to :class:`IamDataFrame(other, **kwargs) <IamDataFrame>`
for any item of `dfs` which isn't already an IamDataFrame.
Returns
-------
IamDataFrame
Raises
------
TypeError
If `dfs` is not a list.
ValueError
If time domain or other timeseries data index dimension don't match.
"""
if not islistable(dfs) or isinstance(dfs, pd.DataFrame):
raise TypeError(
f"First argument must be an iterable, "
f"you passed an object of type '{dfs.__class__.__name__}'!"
)
dfs = list(dfs)
if len(dfs) < 1:
raise ValueError("No objects to concatenate")
# cast to IamDataFrame if necessary
def as_iamdataframe(df):
return df if isinstance(df, IamDataFrame) else IamDataFrame(df, **kwargs)
df = as_iamdataframe(dfs[0])
ret_data, ret_meta = [df._data], df.meta
index, time_col = df.dimensions, df.time_col
for df in dfs[1:]:
# skip merging meta if element is a pd.DataFrame
_meta_merge = not isinstance(df, pd.DataFrame)
df = as_iamdataframe(df)
if df.time_col != time_col:
raise ValueError("Items have incompatible time format ('year' vs. 'time')!")
if df.dimensions != index:
raise ValueError("Items have incompatible timeseries data dimensions!")
ret_data.append(df._data)
if _meta_merge:
ret_meta = merge_meta(ret_meta, df.meta, ignore_meta_conflict)
# return as new IamDataFrame, this will verify integrity as part of `__init__()`
return IamDataFrame(pd.concat(ret_data, verify_integrity=False), meta=ret_meta)
[docs]def read_datapackage(path, data="data", meta="meta"):
"""Read timeseries data and meta-indicators from frictionless Data Package
Parameters
----------
path : string or path object
any valid string path or :class:`pathlib.Path`, |br|
passed to :class:`datapackage.Package` (|datapackage.Package.docs|)
data : str, optional
resource containing timeseries data in IAMC-compatible format
meta : str, optional
(optional) resource containing a table of categorization and
quantitative indicators
"""
if not HAS_DATAPACKAGE: # pragma: no cover
raise ImportError("Required package `datapackage` not found!")
package = Package(path)
def _get_column_names(x):
return [i["name"] for i in x.descriptor["schema"]["fields"]]
# read `data` table
resource_data = package.get_resource(data)
_data = pd.DataFrame(resource_data.read())
_data.columns = _get_column_names(resource_data)
df = IamDataFrame(_data)
# read `meta` table
if meta in package.resource_names:
resource_meta = package.get_resource(meta)
_meta = pd.DataFrame(resource_meta.read())
_meta.columns = _get_column_names(resource_meta)
df.meta = _meta.set_index(META_IDX)
return df
