bonsai_ipcc package
Subpackages
- bonsai_ipcc.agriculture package
- bonsai_ipcc.industry package
- bonsai_ipcc.waste package
Submodules
bonsai_ipcc.core module
- class bonsai_ipcc.core.IPCC[source]
Bases:
object- static inspect(func)[source]
Get the required parameters of a tier method.
Argument
- funcfunction
tier sequence of a volume, chapter
- returns:
VALUE – parameter names
- rtype:
list of str
- static metadata(volume, chapter, parameter)[source]
Get the metadata of a parameter.
Argument
- volumestring
volume name
- chapterstring
chapter name
- paramaterstring
parameter name
- returns:
VALUE – metadata pointing to the source in the IPCC pdf documents (year, volume, chapter, page, equation)
- rtype:
dict
bonsai_ipcc.log_setup module
bonsai_ipcc.uncertainties module
Chapter 3 (Uncertainties) of Volume 1 (General Guidance and Reporting)
Min and max values provided by the IPCC guidelines are considerred to be 2.5th and 97.5th percentile, respectively (p 3.9).
Two ways of uncertainty propagation are implemented: analytical error propagation and Monte Carlo simulation. If analytical error propagation is choosen, asymmetric parameter ranges are transferred by a simple rule of thumb into symmetric ranges. If Monte Carlo, propability distributions can be determined automatically in regard to the properties of the parameter (by “check”).
- bonsai_ipcc.uncertainties.analytical(min95, max95)[source]
Creates ufloat value to be used in error propagation. Simple asumption of normal distribution.
Argument
- minfloat
2.5th percentile
- maxfloat
97.5th percentile
- returns:
VALUE
- rtype:
ufloat
- bonsai_ipcc.uncertainties.monte_carlo(min95, max95, default=None, abs_min=None, abs_max=None, size=1000, distribution='check')[source]
Creates numpy array of random numbers to be used in monte carlo simulation. Choices of different distributions possible.
Argument
- minfloat
2.5th percentile
- maxfloat
97.5th percentile
- defaultfloat
default value (mean)
- abs_minfloat
theoretic lower bound of parameter
- abs_maxfloat
theoretic upper bound of parameter
- distributionstr
Specifies type of density distribution. ‘normal’, ‘lognormal’, ‘truncnormal’, ‘uniform’, ‘check’ for automatized choice
- sizeint
Number of random numbers to be generated.
- returns:
VALUE
- rtype:
numpy.array
- bonsai_ipcc.uncertainties.one_side_trunc(mobs, sobs)[source]
Helper function for adjusting mean and sd for one-sided truncated normal distribution.
Given observed mean m > 0.0 and std-dev, 0.0 < s < m, return parameter mean and std-dev of one-sided truncated Gaussian distribution X, with X > 0.0
Rodrigues 2015, Maximum-Entropy Prior Uncertainty and Correlation of Statistical Economic Data: Supplementary Informatiom.
Argument
- mobsfloat
observed mean
- sobsfloat
observed standard deviation
- returns:
VALUES – adjusted mean, adjusted standard deviation
- rtype:
float, float
- bonsai_ipcc.uncertainties.two_side_trunc(mobs, sobs, vmin, vmax)[source]
Helper function for adjusting mean and sd for two-sided truncated normal distribution.
Given observed mean m, std-dev s, lower and upper bounds, vmin and vmax, (all floats) such that: vmin < mobs < vmax, and 0.0 < sobs < min(mobs - vmin, vmax - mobs)
Argument
- mobsfloat
observed mean
- sobsfloat
observed standard deviation
- vminfloat
absolut minimum
- vmaxfloat
absolut maximum
- returns:
VALUES – adjusted mean, adjusted standard deviation
- rtype:
float, float