Add sum model #364
Add sum model #364
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…operty to TPL models
…re derived from correlation not cor
…into add_sum_model
MuellerSeb
added
enhancement
…into add_sum_model
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@LSchueler this is ready for review. |
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Awesome, I'll need some time to thoroughly go throw your changes. |
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First come, first serve :-D |
| We'll combine a Spherical and a Gaussian covariance model to construct | ||
| a sum model, visualize its variogram, and generate spatial random fields. | ||
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| Let's start with importing GSTools setting up the domain size. |
| # First, we create two individual covariance models: a :any:`Spherical` model and a | ||
| # :any:`Gaussian` model. The Spherical model will emphasize small-scale variability, | ||
| # while the Gaussian model captures larger-scale patterns. | ||
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This formulation might give the impression that the small-scale and large-scale patterns are due to the covariance models. You should clarify that this is due to the chosen length scales.
| # As shown, the Spherical model controls the behavior at shorter distances, | ||
| # while the Gaussian model dominates at longer distances. |
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I think you should also briefly mention how the variance of the covariance models influences the resulting fields.
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| ############################################################################### | ||
| # We fit the sum model to the empirical variogram using GSTools' built-in | ||
| # fitting capabilities. As seen in the representation, the variances and length |
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I don't really understand the "As seen in the representation" part.
| ############################################################################### | ||
| # We fit the sum model to the empirical variogram using GSTools' built-in | ||
| # fitting capabilities. As seen in the representation, the variances and length | ||
| # scales of the individual models can be accessed by the attributes |
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I would also generalise this from "the variances and length scales" to "the properties of the individual models, e.g. variance and length scales" or something similar.
| raise ValueError(msg) | ||
| ids = range(len(summod)) | ||
| if fail := set(skip) - set(ids): | ||
| msg = f"SumModel.set_var_weights: skip ids not valid: {fail}" |
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"skip invalid ids"
fix this further down too, please.
| var_sum = sum(summod.models[i].var for i in skip) | ||
| var_diff = var - var_sum | ||
| if var_diff < 0: | ||
| msg = "SumModel.set_var_weights: skipped variances to big." |
| len_sum = sum(summod[i].len_scale * summod.ratios[i] for i in skip) | ||
| len_diff = len_scale - len_sum | ||
| if len_diff < 0: | ||
| msg = "SumModel.set_len_weights: skipped length scales to big." |
| return para, sill, anis, sum_cfg | ||
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| def _check_sill(model, para_select, sill, bnd, sum_cfg): |
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Please add a short description of what exactly is checked.
| if is_sum: | ||
| if sum_cfg["var_size"] > 0: | ||
| var_vals = popt[para_skip : para_skip + sum_cfg["var_size"]] | ||
| para_skip += sum_cfg["var_size"] | ||
| if sum_cfg["var_fix"]: | ||
| model.set_var_weights( | ||
| stick_breaking_uniform(var_vals), | ||
| sum_cfg["var_skip"], | ||
| sum_cfg["fix"]["var"], | ||
| ) | ||
| else: | ||
| for i, val in zip(sum_cfg["var_fit"], var_vals): | ||
| setattr(model, f"var_{i}", val) | ||
| if sum_cfg["len_size"] > 0: | ||
| len_vals = popt[para_skip : para_skip + sum_cfg["len_size"]] | ||
| para_skip += sum_cfg["len_size"] | ||
| if sum_cfg["len_fix"]: | ||
| model.set_len_weights( | ||
| stick_breaking_uniform(len_vals), | ||
| sum_cfg["len_skip"], | ||
| sum_cfg["fix"]["len_scale"], | ||
| ) | ||
| else: | ||
| for i, val in zip(sum_cfg["len_fit"], len_vals): | ||
| setattr(model, f"len_scale_{i}", val) | ||
| for i in range(model.size): | ||
| fit_para[f"var_{i}"] = model.vars[i] | ||
| fit_para[f"len_scale_{i}"] = model.len_scales[i] | ||
| # handle sill | ||
| if sill is not None and para["var"]: | ||
| nugget = sill - model.var | ||
| fit_para["nugget"] = nugget | ||
| model.nugget = nugget |
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There are quite a few pretty large if is_sum branches all over the place here. Maybe you can find a better structure for the code in this module?
This PR adds a
SumModelclass to represent sums of Covariance models.Sum-Model
SumModelclass+operator:model = m1 + m2m1 = model[0]model[0].nu == model.nu_0len_scaleis fixed, none of thelen_scale_<i>can be fixed since len_scale is calculated from variance ratioszero_varandmodelattributes to Generator ABC to shortcut field generation for pure nugget modelsOther changes
var_rawattribute from CovModel (was rarely used and only relevant for the truncated power law models)intensityattribute which calculates whatvar_rawwas beforevar_rawwas a bad idea in the first place)