Merge pull request #23 from IMMM-SFA/feature/issues

Feature/issues
IMMM-SFA · Mar 2, 2022 · 8cf03b5 · 8cf03b5
2 parents bcbeb30 + bbe7d71
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diff --git a/docs/source/2.2_perspectives_on_diagnostic_model_evaluation.rst b/docs/source/2.2_perspectives_on_diagnostic_model_evaluation.rst
@@ -15,4 +15,4 @@ Multiple authors have proposed that the traditional reliance on single measures
 
 As a final point, when a model is used in a projection mode, its results are also subject to additional uncertainty, as there is no guarantee that the model’s functionality and predictive ability will stay the same as the baseline, where the verification and validation tests were conducted. This challenge requires an additional expansion of the scope of model evaluation: a broader set of uncertain conditions needs to be explored, spanning beyond historical observation and exploring a wide range of unprecedented conditions. This perspective on modeling, termed exploratory :cite:`bankes_exploratory_1993`, views models as computational experiments that can be used to explore vast ensembles of potential scenarios to identify those with consequential effects. Exploratory modeling literature explicitly orients experiments toward stakeholder consequences and decision-relevant inferences and shifts the focus from predicting future conditions to *discovering* which conditions lead to undesirable or desirable consequences.
 
-**This evolution in modeling perspectives can be mirrored by the IM3 family of models in a progression from evaluating models relative to observed history to advanced formalized analyses to make inferences on multisector, multiscale vulnerabilities and resilience. Exploratory modeling approaches can help fashion experiments with large numbers of alternative hypotheses on the co-evolutionary dynamics of influences, stressors, as well as path-dependent changes in the form and function of human-natural systems :cite:`weaver_improving_2013`. The aim of this text is to therefore guide the reader through the use of sensitivity analysis (SA) methods across these perspectives on diagnostic and exploratory modeling.**
+This evolution in modeling perspectives can be mirrored by the IM3 family of models in a progression from evaluating models relative to observed history to advanced formalized analyses to make inferences on multisector, multiscale vulnerabilities and resilience. Exploratory modeling approaches can help fashion experiments with large numbers of alternative hypotheses on the co-evolutionary dynamics of influences, stressors, as well as path-dependent changes in the form and function of human-natural systems :cite:`weaver_improving_2013`. The aim of this text is to therefore guide the reader through the use of sensitivity analysis (SA) methods across these perspectives on diagnostic and exploratory modeling.
diff --git a/docs/source/A1.4_Markov_Chain_Monte_Carlo.rst b/docs/source/A1.4_Markov_Chain_Monte_Carlo.rst
@@ -5,7 +5,7 @@ Markov chain Monte Carlo (MCMC) is a “gold standard” approach to full uncert
 
 MCMC algorithms construct a Markov chain of samples from a parameter space (the combination of model and statistical parameters). This Markov chain is constructed so that the stationary distribution is a target distribution, in this case the (Bayesian) posterior distribution. As a result, after the transient period, the resulting samples can be viewed as a set of dependent samples from the posterior (the dependence is due to the autocorrelation between samples resulting from the Markov chain transitions). Expected values can be computed from these samples (for example, using batch-means estimators :cite:p:`flegal_markov_2008`), or the chain can be sub-sampled or thinned and the resulting samples used as independent Monte Carlo samples due to the reduced or eliminated autocorrelation.
 
-A general workflow for MCMC is shown in :numref:`Figure_A1_4`. The first decision is whether to use the full model or a surrogate model (or emulator). Typical surrogates include Gaussian process emulation :cite:p:`currin_bayesian_1991, sacks_design_1989`, polynomial chaos expansions :cite:p:`ghanem_spectral_1991,xiu_wiener--askey_2002`, support vector machines :cite:p`ciccazzo_svm_2016, pruett_creation_2016`, and neural networks :cite:p:`eason_adaptive_2014, gorissen_sequential_2009`. Surrogate modeling can be faster, but requires a sufficient number of model evaluations for the surrogate to accurately represent the model’s response surface, and this typically limits the number of parameters which can be included in the analysis.
+A general workflow for MCMC is shown in :numref:`Figure_A1_4`. The first decision is whether to use the full model or a surrogate model (or emulator). Typical surrogates include Gaussian process emulation :cite:p:`currin_bayesian_1991, sacks_design_1989`, polynomial chaos expansions :cite:p:`ghanem_spectral_1991, xiu_wiener--askey_2002`, support vector machines :cite:p:`ciccazzo_svm_2016, pruett_creation_2016`, and neural networks :cite:p:`eason_adaptive_2014, gorissen_sequential_2009`. Surrogate modeling can be faster, but requires a sufficient number of model evaluations for the surrogate to accurately represent the model’s response surface, and this typically limits the number of parameters which can be included in the analysis.
 
 .. _Figure_A1_4:
 .. figure:: _static/figureA1_4_mcmc_workflow.png

diff --git a/docs/source/_static/custom.js b/docs/source/_static/custom.js
@@ -0,0 +1,4 @@
+$(document).ready(function () {
+    $('a.repository-button').attr('target', '_blank');
+    $('a.issues-button').attr('target', '_blank').attr('href', 'https://github.com/IMMM-SFA/msd_uncertainty_ebook/issues/new?assignees=thurber%2C+crvernon&labels=documentation%2C+triage&template=custom.md&title=Publication+Feedback');
+});
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -90,11 +90,7 @@
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 html_css_files = ['custom.css']
-
-# add in the IM3 logo into the top left sidebar if so desired
-# html_theme_options = {
-#     'logo': 'im3.png'
-# }
+html_js_files = ['custom.js']
 
 # -- Options for Latex
 master_doc = 'index'

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -46,7 +46,7 @@ Addressing Uncertainty in MultiSector Dynamics Research
         .. raw:: html
 
             <p>
-                See something that could be improved? <a target="_blank" href="https://github.com/IMMM-SFA/msd_uncertainty_ebook/issues/new">Open an issue</a> on GitHub!
+                Report a typo or just pass along something you like about the book by <a target="_blank" href="https://github.com/IMMM-SFA/msd_uncertainty_ebook/issues/new?assignees=thurber%2C+crvernon&labels=documentation%2C+triage&template=custom.md&title=Publication+Feedback">opening an issue</a> on GitHub!
                 <br/>
                 Click or tap the <i style="padding: 0 0.25rem;" class="fab fa-github"></i> icon to find this link again.
             </p>

diff --git a/setup.py b/setup.py
@@ -49,7 +49,7 @@ def get_requirements():
             'nbsphinx~=0.8.6',
             'setuptools~=57.0.0',
             'sphinx~=4.0.2',
-            'sphinx-rtd-theme~=0.5.2',
+            'sphinx-book-theme~=0.2.0',
             'sphinxcontrib-bibtex~=2.4.1',
             'twine~=3.4.1'
         ]
Original file line number	Diff line number	Diff line change
Expand Up		@@ -15,4 +15,4 @@ Multiple authors have proposed that the traditional reliance on single measures

		As a final point, when a model is used in a projection mode, its results are also subject to additional uncertainty, as there is no guarantee that the model’s functionality and predictive ability will stay the same as the baseline, where the verification and validation tests were conducted. This challenge requires an additional expansion of the scope of model evaluation: a broader set of uncertain conditions needs to be explored, spanning beyond historical observation and exploring a wide range of unprecedented conditions. This perspective on modeling, termed exploratory :cite:`bankes_exploratory_1993`, views models as computational experiments that can be used to explore vast ensembles of potential scenarios to identify those with consequential effects. Exploratory modeling literature explicitly orients experiments toward stakeholder consequences and decision-relevant inferences and shifts the focus from predicting future conditions to discovering which conditions lead to undesirable or desirable consequences.

		This evolution in modeling perspectives can be mirrored by the IM3 family of models in a progression from evaluating models relative to observed history to advanced formalized analyses to make inferences on multisector, multiscale vulnerabilities and resilience. Exploratory modeling approaches can help fashion experiments with large numbers of alternative hypotheses on the co-evolutionary dynamics of influences, stressors, as well as path-dependent changes in the form and function of human-natural systems :cite:`weaver_improving_2013`. The aim of this text is to therefore guide the reader through the use of sensitivity analysis (SA) methods across these perspectives on diagnostic and exploratory modeling.
		This evolution in modeling perspectives can be mirrored by the IM3 family of models in a progression from evaluating models relative to observed history to advanced formalized analyses to make inferences on multisector, multiscale vulnerabilities and resilience. Exploratory modeling approaches can help fashion experiments with large numbers of alternative hypotheses on the co-evolutionary dynamics of influences, stressors, as well as path-dependent changes in the form and function of human-natural systems :cite:`weaver_improving_2013`. The aim of this text is to therefore guide the reader through the use of sensitivity analysis (SA) methods across these perspectives on diagnostic and exploratory modeling.