casegenmc 0.1.1

Case generator, optimizer, and summarizer for models.

CASEGEN MC

Probe a model to see the possibilities. Takes model and input dictionary and evaluates cases to explore the model space using grids, random sampling, and optimization techniques.

The input dictionary is defined by the user with mean value, uncertainty, uncertainty distribution, range, and bounds. Sparse definition okay and assumes 0 unc and range by default. Numerical and categorical parameters are supported. For categorical parameters, the range is defined as a list of options (subset of options), and the uncertainty distribution is defined as "choice" with unc defining the probability of each option. If no unc is defined, it is assumed to be uniform.

Includes matplotlib utility functions for standard plotting.

Analysis Type	Description
estimate	Runs the model with the mean values of the input parameters.
estimate_with_unc	Runs the model with sampled input parameters based on their uncertainty distributions.
estimate_with_unc_combos	Runs the model with combinations of extreme values of the input parameters.
sensitivity_analysis_unc	Performs sensitivity analysis by varying each parameter individually based on its uncertainty distribution.
sensitivity_analysis_range	Performs sensitivity analysis by varying each parameter individually over its entire range.
sensitivity_analysis_2D	Performs 2D sensitivity analysis by varying two parameters simultaneously over a grid.
regular_grid	Runs the model over a regular grid of input parameter values.
random_uniform_grid	Runs the model over a grid of randomly sampled input

Install

pip install casegenmc

Use

import casegenmc as cgm 

# Define model
def model(x):
    out = {}
    out["y0"] = x["x0"]**2 + np.exp(x["x1"]) + x['x3']
    out["y1"] = x["x0"] + x["x1"] + x["x2"] + x["x3"]
    return out

# Create input stack. some parameters fixed - don't have uncertainty or range of options.
# mean, unc, range, bounds (minimum and maximum value), unc_type
input_stack = {
    "x0": {"mean": 1., "unc": .2, 'range': [0, 5], 'bounds': [0, 100], 'unc_type': 'normal'},
    "x1": {"mean": 1., "unc": .2, 'range': [0, 3], 'unc_type': 'lognormal'},
    "x2": 3., 
    "x3": 4, 
    "x4": {"mean": "a",  'range': ["a", "b"], "options": ["a", "b", "c"], "unc_type": "choice", },
    "x5": {"mean": "a", 'unc':[.2,.8], 'range': ["a", "b"], "options": ["a", "b", "c"], "unc_type": "choice", },
    }

# Pre-process input stack
input_stack = cgm.process_input_stack(input_stack)
print(input_stack)

# Evaluate the model with the input_stack.
cgm.run_analysis(model=model, input_stack=input_stack, analyses=["estimate"])

# Estimate with uncertainty.
cgm.run_analysis(model, input_stack, n_samples=1000, analyses=["estimate_with_unc"], par_output="y0")  

# Estimate with uncertainty combinations.
cgm.run_analysis(model, input_stack, n_samples=1000, analyses=["estimate_with_unc_combos"], par_output="y0")  

# 2d sensitivity analysis and analysis w.r.t 1 output parameter.
cgm.run_analysis(model, input_stack, n_samples=1000, analyses=["sensitivity_analysis_2D"],  par_grid_xy=["x0", "x1"], par_output="y0")

# sample a regular grid defined by range.
cgm.run_analysis(model, input_stack, n_samples=1000, analyses=["regular_grid"],  par_output="y0")