bonsai_ipcc

Enables users to calculate national greenhouse gas (GHG) inventories based on the guidelines provided by the International Panel on Climate Change.
https://gitlab.com/bonsamurais/bonsai/util/ipcc

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Repository metadata

• Host: GitLab.com
• URL: https://gitlab.com/bonsamurais/bonsai/util/ipcc
• Owner: bonsamurais
• License: mit
• Created: 2022-06-24T09:33:07.000Z (almost 2 years ago)
• Default Branch: main
• Last Synced: 2024-05-09T08:31:04.715Z (2 days ago)
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• Open Issues: 1

README

        
# bonsai_ipcc

The `bonsai_ipcc` python package enables users to calculate national greenhouse gas (GHG) inventories based on the guidelines provided by the International Panel on Climate Change.

By using `volumes` and `chapters`, the python package follows the structure of the IPCC guidelines and allows users to access individual equations and sequences of equations as `bonsai_ipcc...elementary.` or `bonsai_ipcc...sequence.`. The package allows users to access the data as Pandas dataframes in `bonsai_ipcc...dimension.` or `bonsai_ipcc...parameter.`: dimensions list valid coordinates to access parameters; parameters are values to be used in equations. When using the `bonsai_ipcc` python package, it may helpful to also use the [pdf documents](https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html) for additional information.

The package also allows uncertainty information to be taken into account. Within a sequence, the user can choose between analytical error propagation and Monte Carlo simulation. Thereby, the values of an equation are transformed into [ufloat](https://uncertainties-python-package.readthedocs.io/en/latest/) or [numpy.array](https://numpy.org/doc/stable/reference/generated/numpy.array.html), respectively.

A comrehensive documentation is available [here](https://bonsamurais.gitlab.io/bonsai/util/ipcc).

## Installation for users

You can install the package from PyPi sing `pip`:

```bash

pip install bonsai_ipcc

```

You can also download the package from gitlab.com

Replace the keyword `tag` by the specific version, e.g., `v0.3.0`.

```bash

pip install git+ssh://[email protected]/bonsamurais/bonsai/util/ipcc.git@tag

```

Change `pip` to `pip3` in Linux. Note that the path may change in future versions.

## Installation for developers

### Create a python environment

The `bonsai_ipcc` package requires `python>=3.9`. If you use conda as your python package manager, you could do something like:

```

conda create --name py311 python=3.11

conda activate py311

```

### Install the package in editable mode

```bash

git clone [email protected]:bonsamurais/bonsai/util/ipcc.git

cd bonsai_ipcc

pip install -e .

```

## Basic use

Inside a Python console or notebook, create an instance of the `IPCC` class like this:

```python

import bonsai_ipcc

my_ipcc = bonsai_ipcc.IPCC()

```

With `my_ipcc..` the `bonsai_ipcc` package follows the structure of the IPCC guidelines.

To show the elementary equations and sequences of a certain chapter in a specific volume:

```python

dir(my_ipcc.waste.swd.elementary)

# dir(my_ipcc.waste.swd.sequence)

```

To find information about a elementary equation:

```python

help(my_ipcc.waste.swd.elementary.ddoc_from_wd_data)

```

The following will print the docstring with information on the parameters and the reqiured units:

```

Help on function ddoc_from_wd_data in module bonsai_ipcc.waste.swd.elementary:

ddoc_from_wd_data_tier1(waste, doc, doc_f, mcf)

    Equation 3.2 (tier 1)

    Calculates the decomposable doc (ddocm) from waste disposal data.

    Argument

    ---------

    waste (tonnes) : float

        Amount of waste

        (either wet or dry-matter, but attention to doc!)

    doc (kg/kg) : float

        Fraction of degradable organic carbon in waste.

    doc_F (kg/kg) : float

        Fraction of doc that can decompose.

    mcf (kg/kg) : float

        CH4 correction factor for aerobic decomposition in the year of decompostion.

    Returns

    -------

    VALUE: float

        Decomposable doc (tonnes/year)

```

To show the dimensions of a certain parameter:

```python

my_ipcc.waste.swd.parameter.mcf.index.names

```

```

FrozenList(['swds_type', 'property'])

```

To find the possible values of a dimension:

```python

my_ipcc.waste.swd.dimension.swds_type.index

```

```

Index(['managed', 'managed_well_s-a', 'managed_poorly_s-a', 'managed_well_a-a',

       'managed_poorly_a-a', 'unmanaged_deep', 'unmanaged_shallow',

       'uncharacterised'],

      dtype='object', name='code')

```

To retrieve the value and the unit of a certain parameter.

```python

my_ipcc.waste.swd.parameter.mcf.loc[("managed","def")]

```

```

value      1.0

unit     kg/kg

Name: (managed, def), dtype: object

```

### Run a tier sequence

Despite the fact that various default data for parameter tables is provided within the `bonsai_ipcc` package, in most cases, the user still needs to collect data to calculate the greenhouse gas inventories.

For the `tier1_co2` sequence in the `incineration` chapter of volume `waste`, data for urban population is required.

The data can be added as a pandas DataFrame.

```python

import bonsai_ipcc

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=bonsai_ipcc.IPCC()

my_ipcc.waste.incineration.parameter.urb_population=urb_pop

```

> **_NOTE:_** When adding own data, the user is encouraged to also specify uncertainty information. Property "def" is always required and specifies the mean value. For uncertainty analysis "min", "max", "abs_min" and "abs_max" are required ("min": 2.5 percentile, "max": 97.5 percentile, "abs_min": absolute minimum, "abs_max": absolute maximum).

To get a list of all parameters involved in the sequence, you can do:

```python

my_ipcc.inspect(my_ipcc.waste.incineration.sequence.tier1_co2)

```

To calculate the GHG inventory based on a tier method, specifiy the keywords of the sequence. The keywords are in most cases `year`, `region`, `product`, `activity` and `uncertainty`. Only in view cases more than these are required due to the complexity of the sequence.

```python

my_tier = my_ipcc.waste.incineration.sequence.tier1_co2(

          year=2010, region="DE", product="msw_plastics", activity="inc_unspecified", uncertainty="def")

# show the list of steps of the sequence

my_tier.to_dict()

```

For uncertainty calculation based on Monte Carlo use `uncertainty="monte_carlo"`, for analytical error propagation use `uncertainty="analytical"`.

To retrieve the result's value of a sequence's step, type:

```python

my_tier.co2_emissions.value

```

> **_NOTE:_** The type of `value` depends on the uncertainty assessment. For `uncertainty = "def"`: `type = float`, for `uncertainty = "analytical"`: `type = ufloat` and for `uncertainty = "monte-carlo"`: `type = numpy.array`. Furthermore, some tier sequences provide time series instead of one single value. If so, `value` is of type `numpy.array`, including the values for different years. The type of each years' value also depend on the uncertainty assessment.

      

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