General concept
This tutorial demonstrates basic concepts of the package.
import bonsai_ipcc
my_ipcc = bonsai_ipcc.IPCC()
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KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_4991/1308313284.py in ?()
----> 1 import bonsai_ipcc
2 my_ipcc = bonsai_ipcc.IPCC()
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/__init__.py in ?()
5 from os import listdir
6 from os.path import dirname
7 from pathlib import Path
8
----> 9 from .core import IPCC, PPF
10 from .log_setup import setup_logger
11
12 # setup the default logger
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/core.py in ?()
4
5 import pandas as pd
6 import yaml
7
----> 8 from . import agriculture, industry, ppf, waste
9 from ._metadata import MetaData # TODO
10 from .sample import create_sample
11
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/__init__.py in ?()
----> 1 from . import biological, incineration, swd, waste_generation, wastewater
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/__init__.py in ?()
----> 1 from . import elementary, sequence
2 from ._data import concordance, dimension, parameter
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/sequence.py in ?()
20 import logging
21
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
27
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/waste/incineration/_data.py in ?()
1 from ..._data import Concordance, Dimension, Parameter
2
----> 3 dimension = Dimension(path_in="data/", activitycode="incineration", productcode="waste")
4
5 parameter = Parameter(["data/waste/incineration/", "data/waste/waste_generation/"])
6
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py in ?(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}")
44 raise Exception
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py in ?(self, df, code)
52 if row["parent_code"] == code:
53 # Add the child code to the list
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())
57
58 # Filter the DataFrame to include all child codes found
59 filtered_df = df.loc[child_codes]
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/bonsai_ipcc/_data.py in ?(self, df, code)
47 # Initialize list to store child codes
48 child_codes = []
49
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)
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/frame.py in ?(self)
1555 columns = self.columns
1556 klass = self._constructor_sliced
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]
1562 yield k, s
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/generic.py in ?(self, other, method, **kwargs)
6277 # One could make the deepcopy unconditionally, but a deepcopy
6278 # of an empty dict is 50x more expensive than the empty check.
6279 self.attrs = deepcopy(other.attrs)
6280
-> 6281 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6282 # For subclasses using _metadata.
6283 for name in set(self._metadata) & set(other._metadata):
6284 assert isinstance(name, str)
/builds/bonsamurais/bonsai/util/ipcc/.tox/docs/lib/python3.12/site-packages/pandas/core/generic.py in ?(self)
--> 405 @final
406 @property
407 def flags(self) -> Flags:
408 """
KeyboardInterrupt:
Structure
The structure of the package is as followed:
ipcc.
<volume>.<chapter>.sequence.<tier_name>ipcc.
<volume>.<chapter>.parameter.<parameter_name>ipcc.
<volume>.<chapter>.dimension.<dimension_name>ipcc.
<volume>.<chapter>.concordance.<concordance_name>ipcc.
<volume>.<chapter>.elementary.<equation_name>
Get information about a tier sequence
help(my_ipcc.waste.incineration.sequence.tier1_co2)
Help on function tier1_co2 in module ipcc.waste.incineration.sequence:
tier1_co2(year=2010, region='BG', wastetype='msw_food', incintype='open_burn', uncertainty='def')
Tier 1 method CO2 Emissions.
Default data to quantify waste generation, composition and management practice
(requirement: incineration or open burning is not a key category)
Argument
---------
year : int
year under study
region : str
region under study
wastetype : str
Fraction of solid waste.
incintype : str
Type of incineration.
uncertainty : str
'analytical', 'monte_carlo' or a property dimension, e.g. 'def'
Returns
-------
VALUE: DataClass
Inlcudes the results of each step of the sequence.
Inspect the involved parameters
my_ipcc.inspect(my_ipcc.waste.incineration.sequence.tier1_co2)
['cf',
'SW',
'isw_gen_rate',
'msw_frac_to_incin',
'of',
'fcf',
'msw_type_frac',
'gdp',
'b_frac',
'isw_frac_to_incin',
'urb_population',
'msw_gen_rate',
'dm',
'total_population',
'incintype_frac',
'p_frac']
Show the table of a parameter
my_ipcc.waste.incineration.parameter.dm.head()
| value | unit | ||||
|---|---|---|---|---|---|
| year | region | waste_type | property | ||
| 2006 | World | msw_food | def | 0.40 | kg/kg |
| msw_garden | def | 0.40 | kg/kg | ||
| msw_paper | def | 0.90 | kg/kg | ||
| msw_wood | def | 0.84 | kg/kg | ||
| msw_textiles | def | 0.80 | kg/kg |
help(my_ipcc.waste.incineration.elementary.msw_to_incin)
Help on function msw_to_incin in module ipcc.waste.waste_generation.elementary:
msw_to_incin(urb_population, msw_gen_rate, msw_frac_to_incin, msw_type_frac)
Equation 2.y (not explicit in guidelines, tier 1)
Calculates the amount of municipal solid waste (MSW) disposed to incineration sites,
by using default data from chapter 2 (Waste Generation, Composition amd Management).
Argument
--------
urb_population (cap/year) : float
Urban population of a region in a given year.
msw_gen_rate (t/cap) : float
rate of municipal solid waste per capita.
msw_frac_to_incin (kg/kg) : float
Fraction of waste disposed to incineration in municipal solid waste.
Returns
-------
VALUE: float
amount of certain MSW type which is incinerated (tonnes/year)
Check dimension tables of the parameter msw_frac_to_swds
my_ipcc.waste.incineration.dimension.region
| description | region_type | |
|---|---|---|
| code | ||
| AF | Afghanistan | country |
| AX | Åland Islands | country |
| AL | Albania | country |
| DZ | Algeria | country |
| AS | American Samoa | country |
| ... | ... | ... |
| ANT | Antarctica | continent |
| AFR | Africa | continent |
| EUR | Europe | continent |
| ASI | Asia | continent |
| AUS | Australia and New Zealand | continent |
285 rows × 2 columns
my_ipcc.waste.incineration.dimension.year.head()
| description | |
|---|---|
| code | |
| 0 | place holder for emtpy tables |
| 1900 | year in which activity occurs |
| 1901 | year in which activity occurs |
| 1902 | year in which activity occurs |
| 1903 | year in which activity occurs |
my_ipcc.waste.incineration.dimension.property
| description | remarks | |
|---|---|---|
| code | ||
| def | default | mean |
| min | minimum | 2.5th percentile |
| max | maximum | 97.5th percentile |
| abs_max | absolute maximun | theoretical upper bound |
| abs_min | absolute minimum | theoretical lower bound |
| dummy | place holder for emtpy tables | NaN |
Concordance tables
Concordance tables are used to automated reading parameter values within a tier sequence.
When reading the parameter value for a certain dimension, in general, this value is specified in the parameter table.
However, the requirement to define all parameter values for each dimension explicitally in the parameter table would lead to a lot of repetition. To avoid this, the concept of concordance tables is used. Each attribute of a dimension can be hierarchically mapped to others. When using the concordance table for a dimension, the algorithm checks automatically if values for other dimension attributes are available in the parameter table
Let´s have a look to the concordance table for the dimension ‘region’.
my_ipcc.waste.incineration.concordance.region.head()
| unregion | geographicregion | continent | world | |
|---|---|---|---|---|
| country | ||||
| AF | Southern Asia | NaN | ASI | World |
| AX | Northern Europe | NaN | EUR | World |
| AL | Southern Europe | NaN | EUR | World |
| DZ | Northern Africa | NaN | AFR | World |
| AS | Polynesia | NaN | AUS | World |
When reading a parameter within a sequence for the region Albania (‘AL’), the algorithm would start trying to find the value for ‘AL’. If there is no value for ‘AL’ in the parameter table, it would try to find the value for ‘Southern Europe’, stepwise until to the last item of the column. In this case this is ‘World’, which indicates the validity of the parameter value for all regions.
(‘NaN’ is skipped during the approach)
my_ipcc.waste.incineration.concordance.year.head()
| cat3 | cat2 | cat1 | cat0 | |
|---|---|---|---|---|
| cat4 | ||||
| 2006 | 2019 | NaN | NaN | NaN |
| 2007 | 2006 | 2019.0 | NaN | NaN |
| 2008 | 2006 | 2019.0 | NaN | NaN |
| 2009 | 2006 | 2019.0 | NaN | NaN |
| 2010 | 2006 | 2019.0 | NaN | NaN |
Look at the additional tutorials how to run a tier sequence.