Run a tier sequence
This tutorial explains the core functionality of the bonsai_ipcc package.
Let´s assume we want to determine the CO2 emissions from waste incineration based on the tier 1 approach. According to the IPCC guidelines, different waste fractions are available. Here we chose year 2010, Germany and waste type msw_plastics, which stands for municipal plastic waste (you can check all availbe waste types by my_ipcc.waste.incineration.dimension.waste_type) and unspecified incineration technology.
import bonsai_ipcc
my_ipcc = bonsai_ipcc.IPCC()
my_ipcc.waste.incineration.sequence.tier1_co2(year=2010, region="DE",wastetype="msw_plastics", incintype="inc_unspecified", uncertainty="def")
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[1], line 1
----> 1 import bonsai_ipcc
2 my_ipcc = bonsai_ipcc.IPCC()
3 my_ipcc.waste.incineration.sequence.tier1_co2(year=2010, region="DE",wastetype="msw_plastics", incintype="inc_unspecified", uncertainty="def")
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/__init__.py:9
6 from os.path import dirname
7 from pathlib import Path
----> 9 from .core import IPCC, PPF
10 from .log_setup import setup_logger
12 # setup the default logger
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/core.py:8
5 import pandas as pd
6 import yaml
----> 8 from . import agriculture, industry, ppf, waste
9 from ._metadata import MetaData # TODO
10 from .sample import create_sample
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/__init__.py:1
----> 1 from . import biological, incineration, swd, waste_generation, wastewater
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/__init__.py:1
----> 1 from . import elementary, sequence
2 from ._data import concordance, dimension, parameter
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/sequence.py:24
22 from ..._sequence import Sequence
23 from . import elementary as elem
---> 24 from ._data import concordance as conc
25 from ._data import dimension as dim
26 from ._data import parameter as par
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/_data.py:3
1 from ..._data import Concordance, Dimension, Parameter
----> 3 dimension = Dimension(path_in="data/", activitycode="incineration", productcode="waste")
5 parameter = Parameter(["data/waste/incineration/", "data/waste/waste_generation/"])
7 concordance = Concordance("data/")
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py:40, in Dimension.__init__(self, path_in, activitycode, productcode)
38 df = self._filter_dataframe(df, activitycode)
39 elif filename == "dim_product.csv":
---> 40 df = self._filter_dataframe(df, productcode)
41 setattr(self, filename[4:-4], df)
42 except Exception:
43 # print(f"error reading {filename}")
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py:56, in Dimension._filter_dataframe(self, df, code)
54 child_codes.append(index)
55 # Recursively call the function to find child codes of this child code
---> 56 child_codes.extend(self._filter_dataframe(df, index).index.tolist())
58 # Filter the DataFrame to include all child codes found
59 filtered_df = df.loc[child_codes]
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py:56, in Dimension._filter_dataframe(self, df, code)
54 child_codes.append(index)
55 # Recursively call the function to find child codes of this child code
---> 56 child_codes.extend(self._filter_dataframe(df, index).index.tolist())
58 # Filter the DataFrame to include all child codes found
59 filtered_df = df.loc[child_codes]
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py:56, in Dimension._filter_dataframe(self, df, code)
54 child_codes.append(index)
55 # Recursively call the function to find child codes of this child code
---> 56 child_codes.extend(self._filter_dataframe(df, index).index.tolist())
58 # Filter the DataFrame to include all child codes found
59 filtered_df = df.loc[child_codes]
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py:51, in Dimension._filter_dataframe(self, df, code)
48 child_codes = []
50 # Iterate through the index of the DataFrame to find child codes
---> 51 for index, row in df.iterrows():
52 if row["parent_code"] == code:
53 # Add the child code to the list
54 child_codes.append(index)
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/frame.py:1559, in DataFrame.iterrows(self)
1557 using_cow = using_copy_on_write()
1558 for k, v in zip(self.index, self.values):
-> 1559 s = klass(v, index=columns, name=k).__finalize__(self)
1560 if using_cow and self._mgr.is_single_block:
1561 s._mgr.add_references(self._mgr) # type: ignore[arg-type]
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/series.py:584, in Series.__init__(self, data, index, dtype, name, copy, fastpath)
582 data = data.copy()
583 else:
--> 584 data = sanitize_array(data, index, dtype, copy)
586 manager = _get_option("mode.data_manager", silent=True)
587 if manager == "block":
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/construction.py:604, in sanitize_array(data, index, dtype, copy, allow_2d)
602 subarr = data
603 if data.dtype == object:
--> 604 subarr = maybe_infer_to_datetimelike(data)
605 if object_index and using_string_dtype() and is_string_dtype(subarr):
606 # Avoid inference when string option is set
607 subarr = data
File /builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/dtypes/cast.py:1205, in maybe_infer_to_datetimelike(value, convert_to_nullable_dtype)
1193 return value
1195 # error: Incompatible return value type (got "Union[ExtensionArray,
1196 # ndarray[Any, Any]]", expected "Union[ndarray[Any, Any], DatetimeArray,
1197 # TimedeltaArray, PeriodArray, IntervalArray]")
1198 return lib.maybe_convert_objects( # type: ignore[return-value]
1199 value,
1200 # Here we do not convert numeric dtypes, as if we wanted that,
1201 # numpy would have done it for us.
1202 convert_numeric=False,
1203 convert_non_numeric=True,
1204 convert_to_nullable_dtype=convert_to_nullable_dtype,
-> 1205 dtype_if_all_nat=np.dtype("M8[ns]"),
1206 )
KeyboardInterrupt:
Trying to do so, generates a an KeyError mentioning that it could not find the coordinate for year 2010 and Germany in the paramter table urb_population. Let´s have a look into this parameter table.
my_ipcc.waste.incineration.parameter.urb_population
It is empty. The reason is that the IPCC guidelines do not provide default data for all parameters. In these cases we need to add the data to the specific parameter. Let´s do this for the urban population in Germany in 2010.
import pandas as pd
# urban population
d = {
"year": [2010,2010,2010,2010,2010],
"region": ["DE","DE","DE","DE","DE"],
"property": [
"def","min","max","abs_min","abs_max"
],
"value": [
62940432,61996325.52,63884538.48,0.0,"inf",
],
"unit": [
"cap/yr","cap/yr","cap/yr","cap/yr","cap/yr",
],
}
urb_pop = pd.DataFrame(d).set_index(["year", "region", "property"])
my_ipcc.waste.incineration.parameter.urb_population=urb_pop
And run the sequence again.
my_tier = my_ipcc.waste.incineration.sequence.tier1_co2(
year=2010, region="DE",wastetype="msw_plastics", incintype="inc_unspecified", uncertainty="def")
my_tier.to_dict()
Each step involved in the calculation is stored in the dictionary. It starts with the input signature and ends with the final result of CO2 emsissions.