Enabling daily mass balance functionality

.github/workflows/test_suite.yml

@@ -73,4 +73,5 @@ jobs:
           python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_01_basics.py
           python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_02_config.py
           python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_03_notebooks.py
-          python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_04_postproc.py
+          python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_04_auxiliary.py
+          python3 -m pytest --cov=pygem -v --durations=20 pygem/tests/test_05_postproc.py

docs/calibration_options.md

@@ -6,11 +6,10 @@ Several calibration options exist, which vary with respect to complexity and com
 
 | Calibration option | Overview | Reference |
 | :--- | :--- | :--- |
-| ['HH2015'](HH2015_target) | Finds single set of parameters.<br>Varies in order: $f_{snow}$, $k_{p}$, $T_{bias}$ | [Huss and Hock (2015)](https://www.frontiersin.org/articles/10.3389/feart.2015.00054/full) |
-| ['HH2015mod'](HH2015mod_target) | Finds single set of parameters.<br>Varies in order: $k_{p}$, $T_{bias}$ | [Rounce et al. 2020](https://www.cambridge.org/core/journals/journal-of-glaciology/article/quantifying-parameter-uncertainty-in-a-largescale-glacier-evolution-model-using-bayesian-inference-application-to-high-mountain-asia/61D8956E9A6C27CC1A5AEBFCDADC0432) |
-| ['emulator'](emulator_target) | Creates emulator for ['MCMC'](MCMC_target).<br>Finds single set of parameters with emulator following ['HH2015mod'](HH2015mod_target) | [Rounce et al. 2023](https://www.science.org/doi/10.1126/science.abo1324) |
-| ['MCMC'](MCMC_target) | Finds multiple sets of parameters using Bayesian inference with [emulator](emulator_target).<br> Varies $f_{snow}$, $k_{p}$, $T_{bias}$ | [Rounce et al. 2023](https://www.science.org/doi/10.1126/science.abo1324) |
-| ['MCMC_fullsim'](MCMC_target) | Finds multiple sets of parameters using Bayesian inference with full model simulations.<br> Varies $f_{snow}$, $k_{p}$, $T_{bias}$ | [Rounce et al. 2020](https://www.cambridge.org/core/journals/journal-of-glaciology/article/quantifying-parameter-uncertainty-in-a-largescale-glacier-evolution-model-using-bayesian-inference-application-to-high-mountain-asia/61D8956E9A6C27CC1A5AEBFCDADC0432) |
+| ['HH2015'](HH2015_target) | Finds single set of parameters.<br>Varies in order: $f_{snow}$, $k_{p}$, $T_{bias}$ | [Huss and Hock, 2015](https://www.frontiersin.org/articles/10.3389/feart.2015.00054/full) |
+| ['HH2015mod'](HH2015mod_target) | Finds single set of parameters.<br>Varies in order: $k_{p}$, $T_{bias}$ | [Rounce et al., 2020](https://www.cambridge.org/core/journals/journal-of-glaciology/article/quantifying-parameter-uncertainty-in-a-largescale-glacier-evolution-model-using-bayesian-inference-application-to-high-mountain-asia/61D8956E9A6C27CC1A5AEBFCDADC0432) |
+| ['emulator'](emulator_target) | Creates emulator for ['MCMC'](MCMC_target).<br>Finds single set of parameters with emulator following ['HH2015mod'](HH2015mod_target) | [Rounce et al., 2023](https://www.science.org/doi/10.1126/science.abo1324) |
+| ['MCMC'](MCMC_target) | Finds many sets of parameters using Bayesian inference. Setting `calib.MCMC_params.option_use_emulator=True` in ~/PyGEM/config.yaml will run Bayesian inference using the mass balance emulator. Setting `calib.MCMC_params.option_use_emulator=False` (or when performing dynamical calibration against elevation change data)$^*$ will run Bayesian inference calibration with full model simulations.<br>Varies $f_{snow}$, $k_{p}$, $T_{bias}$, (optionally $\rho_{ablation}$, $\rho_{accumulation}$)$^*$ |  [Rounce et al., 2020](https://www.cambridge.org/core/journals/journal-of-glaciology/article/quantifying-parameter-uncertainty-in-a-largescale-glacier-evolution-model-using-bayesian-inference-application-to-high-mountain-asia/61D8956E9A6C27CC1A5AEBFCDADC0432); [2023](https://www.science.org/doi/10.1126/science.abo1324) |
 | [Future options](cal_custom_target) | Stay tuned for new options coming in 2023/2024! | | 
 
 The output of each calibration is a .json file that holds a dictionary of the calibration options and the subsequent model parameters.  Thus, the .json file will store several calibration options.  Each calibration option is a key to the dictionary. The model parameters are also stored in a dictionary (i.e., a dictionary within a dictionary) with each model parameter being a key to the dictionary that provides access to a list of values for that specific model parameter. The following shows an example of how to print a list of the precipitation factors ($k_{p}$) for the calibration option specified in the input file: