ENH: Add framework for property model parameter generation

[bocklund]

This is a significant feature improvement that introduces the concept of a ModelFittingDescription made up of FittingStep objects to tell ESPEI how to generate model parameters (with a capability to support generating parameters for custom models). In addition, these changes include:

• Significant refactoring and simplification of espei.paramselect, the main module for parameter selection:
  • Most of the code that was duplicated for fitting endmembers, binary interactions, and ternary interactions was unified.
  • The fit_ternary_interactions function was completely removed and binary and ternary interactions now run the exact same code path.
  • The most significant changes are in fit_parameters, which now relies on dependency injection of a fitting description to know what fitting steps to take (we try to not make any assumptions about what parameters or data types are being fit) and this function is more streamlined to 1) select relevant data from datasets, 2) get the RHS (b in Ax=b) of the linear problem from the fitting step, 3) build candidate models (build a collection feature matrices, A), 4) select the best model from the candidates, and 5) insert the parameter and coefficients for the best model into the database
• Refactoring to remove espei.parameter_selection.utils. Some of the functionality got moved to be in fitting steps, and get_sample_condition_dicts was moved to espei.error_functions.non_equilibrium_thermochemical_error, with get_prop_samples that it is used in conjunction with. Trying to have fewer "utils" modules.
• Tests to verify that molar volume and custom property models (elastic) work against various edge cases
• New docs page with a tutorial for using a custom model and fitting description for fitting elastic constants with a repository containing the code
• Small tweak to follow links in dataset recursive glob search. This is useful for organizing datasets for different runs while having one single source of truth for the data

This PR relies on https://github.com/pycalphad/pycalphad/pull/501 that includes fixes for molar volume models. Tests will probably fail until that gets into the development branch.

Closes #5

[bocklund]

@nury12n if you could review :)

[bocklund]

Added some comments regarding the fitting steps, mainly thoughts I had when working with the FittingStep and AbstractLinearFittingStep classes, but I think it'd probably be better to leave as is.

I gave some thought about the diffusing species and I think it'll make more sense to consider them after there's some support in kawin for mobility fitting.

Thanks for reviewing! Since PyCalphad supports diffusion species, it's something that could be in scope for ESPEI, but my guess is that there are assumptions elsewhere in the parameter generation code that would need to be broken to be able to handle that extra dimension that parameters can also be a function of diffusing species. At least, I think having these FittingStep and FittingDescription abstractions are compatible with what would be needed to fit parameters with diffusion species and it's mostly a book-keeping and dataset selection operations in espei.paramselect.phase_fit and espei.paramselect.fit_parameters

[bocklund]

Tests are passing on pycalphad development versions and not release versions (as expected from https://github.com/pycalphad/pycalphad/pull/501). Merging