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"figure = project.plot_svi(1, geopackages)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.doctrees/nbsphinx/Examples_SVInsight_example_SETx_6_0.png b/.doctrees/nbsphinx/Examples_SVInsight_example_SETx_6_0.png new file mode 100644 index 0000000000000000000000000000000000000000..84b70b983b074dba38d67f4de8841083f36500bc GIT binary patch literal 75741 zcmbTdbyQVt)IAD{3ZkNbgtU}|C@n40CEeZKAstFcmvpJLbeDukcXy{WNJ{?JdB5NN z?!R~3cMONV`<#9D-p{kvTyxH~cA(6Ak%#x6-A6(~dMG9;D2IfEe1e2@>kaB%I1<0u zy$XMEI0&gY$XgpZe9*NsM3T~Vu(7aqurSplbuzTGH?_86W_ZKELQiVq;9z6V`RbMB 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Acknowledgements

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We would like to thank the many collaborators who have helped us developing our methodologies. This work has been incorporated into numerous projects that have been supported by the following grants:

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  • National Science Foundation Graduate Research Fellowship (grant no. DGE-1610403)

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  • Future Investigators in NASA Earth and Space Science and Technology (NASA FINESST, grant no. 21-EARTH21-0264)

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  • Planet Texas 2050, a research grand challenge at the University of Texas at Austin.

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  • U.S. Department of Energy, Office of Science, Biological and Environmental Research Program’s South-East Texas Urban Integrated Field Laboratory under Award Number DE-SC0023216.

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Background

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Motivation

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SVInsight was developed to provide an open-source tool for creating social vulnerability indices or SVIs based on user defined study areas and input variables. SVInsight uses two methodologies to create indices based on existing scholarly research: (1) a factor analysis method and (2) a rank method. This tool’s purpose is to provide researchers with the ability to quickly create exploratory estimates of vulnerability based on existing and known relationships between socio-demographic variables and a community’s sensitivity (a proxy for vulnerability).

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While SVInsight is capable of rapidly producing tailor made estimates of relative vulnerability of a given region for researchers and/or practitioners, it does not replace the in-depth investigation required to definitively define what makes someone more or less vulnerable. Indices created with SVInsight can be used as a starting point for understanding the complex relationship between socio-demographic variables and community characteristics including vulnerability, sensitivity, and adaptive capacity by highlighting prominent geographic disparities. It is further important to note that vulnerability estimates are relative, and calculated scores should be taken in the context of the study area being examined.

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This package was specifically written in conjunction with past and ongoing projects at the University of Texas at Austin [2] [3] [4]. More detailed information regarding funding sources can be found in the Acknowledgements section.

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Methodology

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Data Source

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All data comes from the American Community Survey 5-Year Estimates because they contain data at the Census block group and tract level. More information on data quality can be found on their website.

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Iterative Factor Analysis

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This methodology is heavily influenced by the original SoVI® [1] developed by Dr. Susan Cutter. By utilizing a data reduction methodology, a large subset of American Community Survey 5-Year Estimate variables can be dimensionally reduced and combined into a single index. Both principal component analyses and factor analyses are data reduction techniques that have been utilized to create vulnerability indices. A principal component analysis (PCA) reduces data by creating one or more index variables, or ‘components,’ by using a linear combination (i.e., a weighted average) from a larger set of measured variables. The purpose of a PCA is to determine the optimal number of components, the optimal choice of measured variables for each component, and the optimal weights. A factor analysis (FA) is a model of latent variables. A ‘latent variable’ cannot be directly measured with a single variable. A common example used to describe this is we cannot directly measure something like social anxiety, but we can measure whether someone has a high or low social anxiety based on a set of questions like “I am uncomfortable in large groups” or “ I get nervous talking to strangers.” We utilize a factor analysis approach because it is the data reduction methodology most often associated with creating composite indices, which is our end goal.

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The follow are the steps in the iterative factor analysis approach:

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    Scale the data
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    • We standardize the data to a range of 0-1. Since all of the variable have a different scale, this makes each variable have an equal weight going into the analysis.

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    • Inputs for a factor analysis include the number of factors we are attempting to reduce the data down to, the rotation method, and the fitting method. For this first factor analysis, we set the number of factors to the number of variables under investigation (the maximum number of possible factors) in order to determine if there are any variables at the start we can eliminate for having too low of an influence. The rotation method used is varimax, or orthogonal rotation, which maximizes the sum of the variances of the squared loadings. In simpler terms, ‘loadings’ refer to the correlations between variables and factors. With varimax rotation, the resultant factors are uncorrelated with each other. The fitting method we use is minimum residual (‘minres’).

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    Calculate Eigenvalues

    -With our initial factors, we need to determine which ones are significant and which can be removed. If we stopped here, we would have reduced X variables to X factors, (i.e., we wouldn’t have reduced anything). Our goal is to find the weakest factors to eliminate so that while we are eliminating data, we maintain the highest level of reported variance in our final factors. +-The first step in doing this is calculating the eigenvalues, which is defined as a measure of how much of the common variance of the observed variables a factor explains. A factor with an eigenvalue greater than or equal to 1 explains more variance than a single observed variable and is therefore beneficial. This is known as the Kaiser Criterion. The number of factors whose eigenvalues is greater than 1 now becomes our new number of factors.

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    Recompute Factor Analysis based on Kaiser Criterion
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    • Similar to step 2, we run the factor analysis again with the new optimized number of factors.

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    Calculate Loading Factors
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    • The loading factor is the correlation coefficient for the variable and factor. It shows the variance explained by the variable on that particular factor. This will become the weight of the variable on that factor. Various standards exist on what makes a significant loading factor. For the sake of this research, we identify any loading factor that is greater than 0.5 or less than -0.5 is significant.

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    -The variance statistics for each factor that we are interested in tracking are the SS Loadings, Proportion Variance, Cumulative Variance, and Ratio of Variance.
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    • SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.

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    • Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance.

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    • Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is incredibly important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.

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    • Ratio of Variance: Ratio of proportion of variance to cumulative variance.

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    Determine Significant Variables

    -Any variable whose loading factor is greater than 0.5 or less than -0.5 at any point is significant and therefore needs to be included in the analysis. Any variable that is not significant in any factor can be eliminated. With the new dataset we can again re-run the factor analysis until only the variables that are significantly contributing to at least one factor are included.

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    Begin Iterative Loop
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    • With the newly created list of variables that have a significant contribution to at least a single factor, we can eliminate those that are not contributing and re-run the factor analysis without it. This process is repeated until every variable is significantly contributing to at least one factor.

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    Compose the Final Index

    -We first recalculate the final loadings and then for each factor, multiply the loading by the ratio of variance to scale the data. Therefore, factor 1 is rated higher than factor 2, factor 2 is rated higher than 3, and so on. From this, we can examine each factor to see which is the largest source of ‘vulnerability’ within the composite index (i.e., what is contributing the most). We create the composite index by adding each factor value together to calculate the unscaled composite index. The final composite index is minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.

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The purpose of the factor analysis and composite score index in the scope of social vulnerability is to determine which variables are most distinguishable across the study area. The variables with the highest variability are likely to influence the index the most and therefore they must be taken into context of the study area. For example, in Austin, TX a variable that is highly variant across the city is the percent of population that identifies as Hispanic. If this trait contributes to social vulnerability, then our index is working properly to identify areas of higher vulnerability. However, if we discovered that this trait does not contribute to social vulnerability, we cannot include it in this workflow because we are unintentionally weighting the index incorrectly. That is why it is imperative that indices are developed through collaborations with local experts to identify which variables are likely contributing to vulnerability.

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Rank Method

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Another commonly employed method to estimate social vulnerability is a ranking method, which was popularized by the Center for Disease Control’s Social Vulnerability Index [5]. This method is a more simplified way to produce an index for a given area. Each variable of interest is sorted from high to low and ranked. For each location within the study area, the ranks are summed so that locations with a higher overall rank have a greater vulnerability score. In our method, the final summed ranks are also minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.

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Variables

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SVInsight comes with a standard set of 27 variables which come from the original principal component analysis method, SoVI®. We omit two variables from the original list of 29 due to data availability issues for all of the years and geographic boundaries of interest (the percentage of the population living in nursing facilities, and the number of hospitals per capita). It is important to note that the CDC SVI uses a list of 16 variables that are similar to those from the SoVI® method. Determining which or how many variables to use depends completely on the study area and objectives of the researcher/practitioner and can greatly influence the estimates. Special consideration must be taken to determine the most appropriate set of variables to use.

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The variables, there definitions, and the American Community Survey 5-Year Estimate sources can be found below:

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    QAGDDEP: Percent of population under the age of 5 or over the age of 65
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    • [‘B01001_001E’, ‘B01001_026E’, ‘B01001_003E’, ‘B01001_020E’, ‘B01001_021E’, ‘B01001_022E’, ‘B01001_023E’, ‘B01001_024E’, ‘B01001_025E’, ‘B01001_027E’, ‘B01001_044E’, ‘B01001_045E’, ‘B01001_046E’, ‘B01001_047E’, ‘B01001_048E’, ‘B01001_049E’]

      • +
    +
    +
    +
    • +
  • +
    QFEMALE: Percent of population that is female
      +

    • [‘B01001_001E’, ‘B01001_026E’]

      • +
    +
    +
    +
    • +
  • +
    MEDAGE: Median age
      +

    • [‘B01002_001E’]

      • +
    +
    +
    +
    • +
  • +
    QBLACK: Percent of population that is non-Hispanic Black/African-American
      +

    • [‘B03002_001E’, ‘B03002_004E’]

      • +
    +
    +
    +
    • +
  • +
    QNATIVE: Percent of population that is non-Hispanic Native American
      +

    • [‘B03002_001E’, ‘B03002_005E’]

      • +
    +
    +
    +
    • +
  • +
    QASIAN: Percent of population that is non-Hispanic Asian
      +

    • [‘B03002_001E’, ‘B03002_006E’]

      • +
    +
    +
    +
    • +
  • +
    QHISPC: Percent of population that is Hispanic
      +

    • [‘B03002_001E’, ‘B03002_012E’]

      • +
    +
    +
    +
    • +
  • +
    QFAM: Percent of families where only one spouse is present in the household
      +

    • [‘B11005_003E’, ‘B11005_005E’]

      • +
    +
    +
    +
    • +
  • +
    PPUNIT: People per unit, or average household size
      +

    • [‘B25010_001E’]

      • +
    +
    +
    +
    • +
  • +
    QFHH: Percent of households with Female householder and no spouse present
      +

    • [‘B11001_001E’, ‘B11001_006E’]

      • +
    +
    +
    +
    • +
  • +
    QEDLESHI: Percent of population over the age of 25 with less than a high school diploma (or equivalent)
      +

    • [‘B15003_001E’, ‘B15003_002E’, ‘B15003_003E’, ‘B15003_004E’, ‘B15003_005E’, ‘B15003_006E’, ‘B15003_007E’, ‘B15003_008E’, ‘B15003_009E’, ‘B15003_010E’, ‘B15003_011E’, ‘B15003_012E’, ‘B15003_013E’, ‘B15003_014E’, ‘B15003_015E’, ‘B15003_016E’]

      • +
    +
    +
    +
    • +
  • +
    QCVLUN: Percent of civilian population over the age of 15 that is unemployed
      +

    • [‘B23025_003E’, ‘B23025_005E’]

      • +
    +
    +
    +
    • +
  • +
    QRICH: Percent of households earning over $200,000 annually (inversely related to vulnerability)
      +

    • [‘B19001_001E’, ‘B19001_017E’]

      • +
    +
    +
    +
    • +
  • +
    QSSBEN: Percent of households with social security income
      +

    • [‘B19055_001E’, ‘B19055_002E’]

      • +
    +
    +
    +
    • +
  • +
    PERCAP: Per capita income in the past 12 months (inversely related to vulnerability)
      +

    • [‘B19301_001E’]

      • +
    +
    +
    +
    • +
  • +
    QRENTER: Percent of households that are renters
      +

    • [‘B25003_001E’, ‘B25003_003E’]

      • +
    +
    +
    +
    • +
  • +
    QUNOCCHU: Percent of housing units that are unoccupied
      +

    • [‘B25002_001E’, ‘B25002_003E’]

      • +
    +
    +
    +
    • +
  • +
    QMOHO: Percent of housing units that are mobile homes
      +

    • [‘B25024_001E’, ‘B25024_010E’]

      • +
    +
    +
    +
    • +
  • +
    MDHSEVAL: Median housing value (inversely related to vulnerability)
      +

    • [‘B25077_001E’]

      • +
    +
    +
    +
    • +
  • +
    MDGRENT: Median gross rent
      +

    • [‘B25064_001E’]

      • +
    +
    +
    +
    • +
  • +
    QPOVTY: Percent of population whose income in the past 12 months was below the poverty level
      +

    • [‘B17021_001E’, ‘B17021_002E’]

      • +
    +
    +
    +
    • +
  • +
    QNOAUTO: Percent of households without access to a car
      +

    • [‘B25044_001E’, ‘B25044_003E’, ‘B25044_010E’]

      • +
    +
    +
    +
    • +
  • +
    QNOHLTH: Percent of population without health insurance
      +

    • [‘B27001_001E’, ‘B27001_005E’, ‘B27001_008E’, ‘B27001_011E’, ‘B27001_014E’, ‘B27001_017E’, ‘B27001_020E’, ‘B27001_023E’, ‘B27001_026E’, ‘B27001_029E’, ‘B27001_033E’, ‘B27001_036E’, ‘B27001_039E’, ‘B27001_042E’, ‘B27001_045E’, ‘B27001_048E’, ‘B27001_051E’, ‘B27001_054E’, ‘B27001_057E’]

      • +
    +
    +
    +
    • +
  • +
    QESL: Percent of population who speaks English “not well” or “not at all”
      +

    • [‘B16004_001E’, ‘B16004_007E’, ‘B16004_008E’, ‘B16004_012E’, ‘B16004_013E’, ‘B16004_017E’, ‘B16004_018E’, ‘B16004_022E’, ‘B16004_023E’, ‘B16004_029E’, ‘B16004_030E’, ‘B16004_034E’, ‘B16004_035E’, ‘B16004_039E’, ‘B16004_040E’, ‘B16004_044E’, ‘B16004_045E’, ‘B16004_051E’, ‘B16004_052E’, ‘B16004_056E’, ‘B16004_057E’, ‘B16004_061E’, ‘B16004_062E’, ‘B16004_066E’, ‘B16004_067E’]

      • +
    +
    +
    +
    • +
  • +
    QFEMLBR: Percent of the civilian employed population over the age of 16 that is female
      +

    • [‘C24010_001E’, ‘C24010_038E’]

      • +
    +
    +
    +
    • +
  • +
    QSERV: Percent of the civilian employed population that has a service occupation
      +

    • [‘C24010_001E’, ‘C24010_019E’, ‘C24010_055E’]

      • +
    +
    +
    +
    • +
  • +
    QEXTRCT: Percent of the civilian employed population that has a construction and extraction occupation
      +

    • [‘C24010_001E’, ‘C24010_032E’, ‘C24010_068E’]

      • +
    +
    +
    +
    • +
+
+
+

References

+ +
+
+ +
+
+ +
+ +
+
+ + + + + + \ No newline at end of file diff --git a/Background/paper.html b/Background/paper.html new file mode 100644 index 0000000..76e2434 --- /dev/null +++ b/Background/paper.html @@ -0,0 +1,323 @@ + + + + + + + + + Papers - SVInsight documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
SVInsight documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Papers

+
+

This Package

+

A paper accompanying this software package will be published in the Journal of Open Source Software. Once published, it will be linked here.

+
+
+

Projects Using This Work

+

You can also read the following papers that have explicitly used these methodologies:

+
    +

  • Preisser, M., Passalacqua, P., Bixler, R. P., & Boyles, S. (2023). A network-based analysis of critical resource accessibility during floods. Frontiers in Water, 5, 1278205, 10.3389/frwa.2023.1278205

    • +

  • Preisser, M., Passalacqua, P., Bixler, R. P., & Hofmann, J. (2022). Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index. Hydrology and Earth System Sciences, 26(15), 3941-3964, 10.5194/hess-26-3941-2022

    • +

  • Bixler, R. P., Yang, E., Richter, S. M., & Coudert, M. (2021). Boundary crossing for urban community resilience: A social vulnerability and multi-hazard approach in Austin, Texas, USA. International Journal of Disaster Risk Reduction, 66, 102613, 10.1016/j.ijdrr.2021.102613

    • +
+
+
+

Additional Literature

+

Additionally, there are a number of relevant peer-reviewed articles we recommend to users if they are interested in learning more about social vulnerability topics:

+
    +

  • Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2012). Social vulnerability to environmental hazards. In Hazards vulnerability and environmental justice (pp. 143-160). Routledge.

    • +

  • Fatemi, F., Ardalan, A., Aguirre, B., Mansouri, N., & Mohammadfam, I. (2017). Social vulnerability indicators in disasters: Findings from a systematic review. International journal of disaster risk reduction, 22, 219-227, 10.1016/j.ijdrr.2016.09.006

    • +

  • Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of homeland security and emergency management, 8(1), 0000102202154773551792, 10.2202/1547-7355.1792

    • +

  • Spielman, S. E., Tuccillo, J., Folch, D. C., Schweikert, A., Davies, R., Wood, N., & Tate, E. (2020). Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index. Natural hazards, 100, 417-436, 10.1007/s11069-019-03820-z

    • +
+
+
+ +
+
+ +
+ +
+
+ + + + + + \ No newline at end of file diff --git a/Background/understanding.html b/Background/understanding.html new file mode 100644 index 0000000..e17302d --- /dev/null +++ b/Background/understanding.html @@ -0,0 +1,365 @@ + + + + + + + + + Understanding Iterative Factor Analysis Results - SVInsight documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
SVInsight documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Understanding Iterative Factor Analysis Results

+

One of the outputs of the SVI workflow is an excel file containing documentation describing the characteristics of the index. This file can be found in the Documentation project folder and has the following naming convention:

+
    +

  • folder location: {file_path}/{project_name}/{Documentation}/

    • +

  • file name: {project_name}_{year}_{boundary}_{config_file}.xlsx

    • +
+

Each tab of the excel sheet contains pertinent information regrading the iterative factor analysis SVI. The below subsections discusses what information is stored in each sheet and shows examples from an SVI run using the standard set of 27 variables (see background for more information) for Travis County, Texas in 2017 at the block group level.

+
+

Significant Components

+

This sheet shows what components are significantly contributing to each factor from the final factor analysis. The number of iterations can be determined based on the name of each factor. For example, in this run ‘F2’ means that this went through three rounds of factor analysis (zero-based numbering rules). As can be seen, there are 5 factors in the final index. Information on which variables contribute to which factor is useful in determining “themes” for the factors (e.g., wealth theme, social characteristics theme, etc.). In this example, one could identify the first factor as Social Status, and the second factor as Economic Status. These are for description only and will be unique based on the study area and year being analyzed. They may or may not be easily definable into distinct themes, but do show what variables are correlated within a study area.

+
+Table of significant components for 2017 Travis County, Texas Block Group SVI estimate +
+

Table 1: Significant components for 2017 Travis County, Texas Block Group SVI estimate

+
+
+

Loading Factors

+

Based on the variables within the final index, each has a loading factor associated with it for each factor. This information is critical in determining the weights for each variable for each factor when calculating the final index. For every boundary in the study area and for every factor, the variable is multiplied by the loading factor and summed within each factor. This is how the factor scores are determined.

+
+Table of loading factors for 2017 Travis County, Texas Block Group SVI estimate +
+

Table 2: Loading factors for 2017 Travis County, Texas Block Group SVI estimate

+
+
+

All Refactor Variances

+

This shows the four main variance statistics associated with a factor analysis for each iteration.

+
    +

  • SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.

    • +

  • Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance. When calculating the final index, each factor is multiplied by its proportion of variance to weight the variables.

    • +

  • Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.

    • +

  • Ratio Variance: Ratio of proportion of variance to cumulative variance.

    • +
+

For most studies, especially exploratory analyses, it is acceptable to have a cumulative variance as low as 60%, and sometimes as low as 50% [1] [2] [3] .

+
+
+

Final Variances

+

An abbreviated version of All Refactor Variances, showing only the final table for the final iteration.

+
+Table of Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate +
+

Table 4: Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate

+
+
+

Included and Excluded

+

contains the lists of what variables make the final index and which variables are excluded due to not significantly contributing to a any factors. This can be useful for double checking that the index is including variables you would likely expect to find given your study area.

+
+
+

References

+ +
+
+ +
+
+ +
+ +
+
+ + + + + + \ No newline at end of file diff --git a/Contributions/contributions.html b/Contributions/contributions.html new file mode 100644 index 0000000..660ca95 --- /dev/null +++ b/Contributions/contributions.html @@ -0,0 +1,308 @@ + + + + + + + + + Issues, Questions, and Contributions - SVInsight documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
SVInsight documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Issues, Questions, and Contributions

+

We welcome contributions to the dorado package in the form of pull requests and issues made in the source repository.

+
+

Issues and Questions

+

If you are having any problems using SVInsight we suggest opening an issue.When you open an issue, please provide a clear description of what your scenario is, and what error message you are receiving. If possible, please include a minimal working example of some code that breaks, and the error output you receive. If there is some functionality you would like to see added to SVInsight you can also open an issue up to discuss that. This can be code you plan to write and contribute, or it can be something you would like to have available but are not comfortable coding yourself. Either way we are happy to help!

+
+
+

Pull Requests

+

If you have a feature that you would like to propose be integrated into dorado, then you should open a pull request. To create a pull request, we recommend first forking the repository, and then creating a separate branch to develop your feature (for reference see the GitHub flow guide and this Git branching guide). Then you can commit and develop your feature in your branch. We ask that new features be accompanied by additional unit tests, to ensure that they operate as expected. For unit testing, we use pytest. When you are satisfied with the code you have developed, you can open a pull request to the main branch of the project repository. Please write a concise and descriptive title for the pull request, and provide a clear description of what the feature does and why you are proposing its addition to the project.

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+
+ +
+
+ +
+ +
+
+ + + + + + \ No newline at end of file diff --git a/Examples/SVInsight_example_SETx.html b/Examples/SVInsight_example_SETx.html new file mode 100644 index 0000000..0e19136 --- /dev/null +++ b/Examples/SVInsight_example_SETx.html @@ -0,0 +1,367 @@ + + + + + + + + + SVInsight Example: Southeast Texas - SVInsight documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
SVInsight documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

SVInsight Example: Southeast Texas

+
+
[1]:
+
+
+
# Example notebook to compute SVInsight
+# Script to compute the Social Vulnerability Index according to the SVInsight method of Preisser et al. (2024). This example shows how to compute the index for the counties in Southeast Texas with geoids 4819, 48245, 48361, 48241, 48351.
+# Created on 28 April 2024
+
+
+
+
+
[1]:
+
+
+
# After installing SVInsight (and all dependencies) with pip install, we can import it into this notebook:
+
+from svinsight import SVInsight as svi
+
+
+
+
+
[2]:
+
+
+
# Set the necessary variables and create the SVInsight object. In this example we are using counties in Southeast Texas:
+project_name = 'SETx_SVI'
+file_path = "C:/Users/pp7545/Documents/research/SVInsight"
+api_key = 'your api key here'
+geoids = ['48199', '48245', '48361', '48241', '48351']
+project = svi(project_name, file_path, api_key, geoids)
+
+
+
+
+
[3]:
+
+
+
# In this example we will estimate the SVI for the Southeast Texas counties using data from 2021 at the block group level:
+boundary = 'bg'
+year = 2021
+config_file = 'config_file'
+
+
+
+
+
[4]:
+
+
+
# We now follow the simplest workflow:
+#1. extract boundaries
+#2. extract census data
+#3. configure variables
+#4. calculate social vulnerability index
+
+project.boundaries_data(boundary, year)
+project.census_data(boundary, year)
+project.configure_variables(config_file)
+project.calculate_svi(config_file, boundary, year)
+
+
+
+
+
[5]:
+
+
+
# The output SVI.csv and .gpkg are stored in the following file format:
+# folder location: {file_path}/{project_name}/{SVIs}/
+# file name: {project_name}_{year}_{boundadry}_{config_file}_svi
+
+# We can load the results and plot them here:
+
+import matplotlib.pyplot as plt
+geopackages = [2021, boundary, config_file, 'FA_SVI_Percentile']
+figure = project.plot_svi(1, geopackages)
+plt.show()
+
+
+
+
+
+
+
+../_images/Examples_SVInsight_example_SETx_6_0.png +
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/Examples/SVInsight_example_SETx.ipynb b/Examples/SVInsight_example_SETx.ipynb new file mode 100644 index 0000000..46b0d6c --- /dev/null +++ b/Examples/SVInsight_example_SETx.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9c3de87c", + "metadata": {}, + "source": [ + "# SVInsight Example: Southeast Texas\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22963e8c-1b5e-4965-8159-6296e69248ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Example notebook to compute SVInsight\n", + "# Script to compute the Social Vulnerability Index according to the SVInsight method of Preisser et al. (2024). This example shows how to compute the index for the counties in Southeast Texas with geoids 4819, 48245, 48361, 48241, 48351.\n", + "# Created on 28 April 2024" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "79a6de98-bc3f-4535-a2b4-242154280af2", + "metadata": {}, + "outputs": [], + "source": [ + "# After installing SVInsight (and all dependencies) with pip install, we can import it into this notebook:\n", + "\n", + "from svinsight import SVInsight as svi" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "515c1f8d-b1e1-45d7-8423-6f24ba9e9d4e", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the necessary variables and create the SVInsight object. In this example we are using counties in Southeast Texas:\n", + "project_name = 'SETx_SVI'\n", + "file_path = \"C:/Users/pp7545/Documents/research/SVInsight\"\n", + "api_key = 'your api key here'\n", + "geoids = ['48199', '48245', '48361', '48241', '48351']\n", + "project = svi(project_name, file_path, api_key, geoids)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7e0011fd-3474-4ddf-a578-0f71c57f08b8", + "metadata": {}, + "outputs": [], + "source": [ + "# In this example we will estimate the SVI for the Southeast Texas counties using data from 2021 at the block group level:\n", + "boundary = 'bg'\n", + "year = 2021\n", + "config_file = 'config_file'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "700333cc-8aef-48ff-b650-08aa0043072a", + "metadata": {}, + "outputs": [], + "source": [ + "# We now follow the simplest workflow:\n", + "#1. extract boundaries\n", + "#2. extract census data\n", + "#3. configure variables\n", + "#4. calculate social vulnerability index\n", + "\n", + "project.boundaries_data(boundary, year)\n", + "project.census_data(boundary, year)\n", + "project.configure_variables(config_file)\n", + "project.calculate_svi(config_file, boundary, year)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6ab8e1f9-0f21-433f-afc9-08b61055a11e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The output SVI.csv and .gpkg are stored in the following file format:\n", + "# folder location: {file_path}/{project_name}/{SVIs}/\n", + "# file name: {project_name}_{year}_{boundadry}_{config_file}_svi\n", + "\n", + "# We can load the results and plot them here:\n", + "\n", + "import matplotlib.pyplot as plt\n", + "geopackages = [2021, boundary, config_file, 'FA_SVI_Percentile']\n", + "figure = project.plot_svi(1, geopackages)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Examples/examples.html b/Examples/examples.html new file mode 100644 index 0000000..e35dce4 --- /dev/null +++ b/Examples/examples.html @@ -0,0 +1,309 @@ + + + + + + + + + Examples - SVInsight documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
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Install

+

SVInsight has been tested with Python 3.9 through 3.12. It also requires the following dependencies: census, factor_analyzer, geopandas, numpy, pandas, pytest, PyYAML, scikit_learn, openpyxl, and matplotlib.

+
+

Installation via pip

+

To pip-install this package use the following command:

+
$ pip install SVInsight
+
+
+

SVInsight can then be imported into python:

+
>>> from svinsight import SVInsight as svi
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+

Installation via conda

+

This package will be available from conda-forge soon.

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+ + + + + + \ No newline at end of file diff --git a/Getting_Started/quickstart.html b/Getting_Started/quickstart.html new file mode 100644 index 0000000..14080aa --- /dev/null +++ b/Getting_Started/quickstart.html @@ -0,0 +1,344 @@ + + + + + + + + + Getting Started - SVInsight documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + + + +
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Getting Started

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+

Quickstart

+

This is a quick guide to using SVInsight. In this demonstration, we show how to load and create the SVInsight object, determine an area of interest, set a boundary level (e.g., tract or block groups), download the necessary boundary and raw data, calculate input variables, and compute the social vulnerability estimate.

+

First we load the SVInsight package, set the necessary variables, and create the SVInsight object.

+
>>> from svinsight import SVInsight as svi
+>>> project_name = 'YOUR PROJECT NAME HERE'
+>>> file_path = "YOUR PARENT FOLDER HERE"
+>>> api_key = 'YOUR CENSUS API KEY HERE'
+>>> geoids = ['48453']
+>>> project = svi(project_name, file_path, api_key, geoids)
+
+
+

These variables create the instance of a project. Each have the following purpose:

+
    +

  • project_name: This is the name of your project. In this case, it’s ‘Travis_County_SVI’. The project name is used when saving files throughout the project.

    • +

  • file_path: This is the path to the parent folder where your project files will be stored. Replace “YOUR PARENT FOLDER HERE” with the actual path. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the file_path and project_name.

    • +

  • api_key: This is your Census API key. Replace ‘YOUR CENSUS API KEY HERE’ with your actual API key. You can obtain an API key from the Census Bureau’s developer page.

    • +

  • geoids: This is a list of geographic identifiers for the areas you’re interested in. In this case, it’s [‘48453’], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this FCC page.

    • +
+

Each instance of SVInsight can be seen as a standalone project for a constant study area. Within each project, the user can explore different vulnerability estimates based on different years or boundaries (e.g., block group versus tract).

+

In this quickstart, we will examine a vulnerability estimate for Travis County using 2019 data at the block group level.

+
>>> boundary = 'bg'
+>>> year = 2019
+>>> config_file = 'config_file'
+
+
+

The simplest workflow is as follows:

+
    +

  1. Extract boundaries

    2. +

  2. Extract Census data

    3. +

  3. Configure variables

    4. +

  4. Calculate vulnerability estimate

    5. +
+
>>> project.boundaries_data(boundary, year)
+>>> project.census_data(boundary, year)
+>>> project.configure_variables(config_file)
+>>> project.calculate_svi(config_file, boundary, year)
+
+
+

The output SVI .csv and .gpkg files built will be saved with the following file format:

+
    +

  • folder location: {file_path}/{project_name}/{SVIs}/

    • +

  • file name: {project_name}_{year}_{boundadry}_{config_file}_svi

    • +
+

More detailed examples of the complete functionality of SVInsight can be found under Examples.

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+

User Guide

+
+

Overview

+

The basic workflow of SVInsight works as follows:

+
    +

  1. Initialize the project class (SVInsight) with your project name, folder path, Census API key, and your study area.

    2. +

  2. Define the initial project configuration variables with a boundary (tract or block groups) and year.

    3. +

  3. Extract the necessary boundary geopackage using SVInsight.boundaries_data().

    4. +

  4. Extract the raw census data and fill in holes in the data using SVInsight.census_data().

    5. +

  5. Configure the run and calculate the SVI using SVInsight.configure_variables() and SVInsight.calculate_svi().

    6. +
+

Below you will find the detailed user guide that walks through each of these steps, with additional options for project customization. SVInsight is supposed to be a flexible and exploratory estimator of various social vulnerability indices, and is therefore highly customizable within the framework of Factor Analysis and Rank Method methodologies. The User Guide below walks through the methods available for calculating SVIs. For a larger discussion on the Factor Analysis and Rank Method methodologies, please refer to the Background and Understanding Iterative Factor Analysis Results pages.

+
+
+

1. Initializing the Project

+

The first step to the project is importing SVInsight and setting the project params. Each instance of the class can be seen as a standalone project for the input geoids, where users can configure different runs based on years, boundaries, and variables of interest. In this User Guide we are using Travis_County, Texas as a working example.

+
>>> from svinsight import SVInsight as svi
+>>> project_name = 'Travis_County'
+>>> file_path = "/User/Documents"
+>>> api_key = os.environ.get('API_KEY')
+>>> geoids = ['48453']
+>>> proj = svi(project_name, file_path, api_key, geoids)
+
+
+

These variables create the instance of a project. Each have the following purpose:

+
    +

  • project_name: This is the name of your project. In this case, it’s ‘Travis_County’. The project name is used when saving files throughout the project.

    • +

  • file_path: This is the path to the parent folder where your project files will be stored. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the file_path and project_name.

    • +

  • api_key: This is your Census API key. You can obtain an API key from the Census Bureau’s developer page.

    • +

  • geoids: This is a list of geographic identifiers for the areas you’re interested in. In this case, it’s [‘48453’], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this FCC page.

    • +
+

During the initialization, a series of folder is generated for storing the files created during a project. The folder locations and their intended contents are as follows:

+
    +

  • Boundaries: Will contain geopackages of the boundary areas based on the input geoids.

    • +

  • Data: Where the raw census data csv and geopackages will be stored, in addition to other csv files that contain information about missing variables and how they were rectified.

    • +

  • Documentation: Will contain excel sheets of information pertinent to the factor analysis svi.

    • +

  • SVIs: Where the geopackages of SVIs will be stored.

    • +

  • Variables: Where the configuration files will be stored.

    • +
+
+
+

2. Defining the Initial Project Configuration Variables

+

The initial project configuration variables allows us to pull the appropriate boundary information. The boundary can be either bg or tract for block group or tract. Block groups and tracts are the two highest spatial resolutions that the US Census releases American Community Survey 5-Year estimates for. While block groups have a higher spatial resolution, they come with greater uncertainty in their estimates compared to tracts. More information can be found on the Census website.

+
>>> boundary='bg'
+>>> year=2017
+>>> config_file = 'config'
+
+
+
+Map showing the Census Divisions +
+

Visual representation of the different Census boundaries.

+
+
+
+
+

3. Extracting the Boundary

+

With the boundary and year set, we can extract the necessary raw census data. In the SVInsight.boundaries_data() method, there is a parameter overwrite which can be set to True or False. When True, if an existing geopackage for the given boundary and year is already found in the folder it will be overwritten. this method returns a geopandas dataframe. Furthermore, because Census boundaries include areas that may not have permanent populations (i.e., military installations, airports, national parks, etc.), a filter is automatically applied such that boundaries with less than a population of 75 are excluded from the analysis.

+
>>> areas = proj.boundaries_data(boundary, year, overwrite=False)
+
+
+
+
+

4. Extract Raw Data and Filling Holes

+

The next step is to extract the raw census data using the SVInsight.census_data() method. SVInsight starts has a default set of 27 social and economic variables of interest, which are detailed in the Variables section. This method specifically pulls the raw data, and the variables themselves are calculated later. In this example, we are continuing to us the same boundary and year as the boundaries we just extracted, in order to produce a geopackaged version of raw data. Furthermore we have two additional parameters: interpolate and verbose. It is important to note that the Census API will sometimes time out, and if Census data isn’t being pulled, waiting a few seconds/minutes and re-running usually solves the problem.

+
>>> areas = proj.census_data(boundary, year, interpolate=True, verbose=True, overwrite=False)
+
+
+
    +

  • interpolate (bool): Census data, especially at the block group level, might have missing data points. Missing data can occur if there are not enough sample points in a given time period to accurately calculate the variable. In these instances, the holes must be filled to accurately calculate the SVIs. SVInsight can accomplish this two different ways, either with or without interpolation. When interpolation is True, missing values are determined by recalculating the median based on the detailed tables (e.g., the breakdown table of response ranges for a given variable) from the adjacent boundaries. Because interpolating isn’t exact, any boundary that has less than 40 neighboring data points are not interpolated. For areas that are not interpolated, or if interpolation is set to False, holes are filled by taking the next available boundary value. For example, if a block group is missing a value it is replaced with the tract value, and if a tract value is missing it is filled with the county value.

    • +

  • verbose (bool): When set to True, two additional csv values are saved in the Data folder that highlight which locations had holes that needed to be filled and the methodology in which it is filled (_missing_values.csv). If there happens to be an entire column that is missing information, the next highest available boundary is used to fill the entire variable (_missing_columns.csv).

    • +
+

Similar to the SVInsight.boundaries_data() method, there is an overwrite parameter as well.

+
+
+

5. Configuring and Calculating SVI

+

Finally, the SVInsight.configure_variables() and SVInsight.calculate_svi() methods are used to configure and run the SVI calculation.

+
>>> proj.configure_variables(config_file, exclude=None, include=None, inverse_vars=['PERCAP', 'QRICH', 'MDHSEVAL'])
+>>> proj.calculate_svi(config_file, boundary, year)
+
+
+
+

Configure Variables

+

This method prepares the information that will be included in the SVI calculation. This method is useful if you are interested in creating multiple different scenarios of SVI. For example, in some runs you may be interested in all 27 variables, while in others you may only want to include economic related variables. By creating a configuration file for each type of run, each SVI will be saved with the config_name associated with it for easier access and comparison. There are four main parameters of the SVInsight.configure_variables() method:

+
    +

  1. config_file (str): The name given to the configuration file

    2. +

  2. exclude (list): In order to remove a variable from the original list of 27, this parameter accepts a list of variables the user would like to exclude from the configuration.

    3. +

  3. include (list): the opposite of exclude, in that this parameter will remove all variables except those in this list. An exclude and an include parameter value cannot be given.

    4. +

  4. inverse_vars (list): Certain variables have an inverse relationship with vulnerability. For example, household with a higher per capita income, make over $250,000 annually, and/or have a higher median housing value are less vulnerable. The list of variables passed to inverse_vars are flipped to capture these dynamics. This parameter defaults to this list of three variables, and users can feel free to pass their own list of variables that they believe to be inversely related.

    5. +
+
+
+

Calculate SVI

+

With the configuration file set, the final SVI can be calculated. The output geopackage and CSV files will have each variable that is within the configuration file as well as the following SVI variables:

+
    +

  • FA_SVI_Unscaled: The factor analysis method SVI estimate that is unscaled

    • +

  • FA_SVI_Scaled: The factor analysis method SVI estimate that is scaled 0 to 1

    • +

  • FA_SVI_Percentile: The factor analysis method SVI estimate percentile

    • +

  • FA_SVI_Rank: The factor analysis method SVI estimate rank

    • +

  • RM_SVI_Percentile: The rank method SVI estimate percentile

    • +

  • RM_SVI_Rank: The rank method SVI estimate rank

    • +
+

It is important to remember that SVI estimates are exploratory and relative. That means that SVI values’ accuracy is dependent on the configuration of the run for a given location and only relative to within that study area.

+
+
+
+

6. Examine Variables

+

An additional method to investigate variables is the SVInsight.var_descriptions() method. Given a list of variables, the description can be printed. Similarly, users can access the variables based on the instance of the project to see the long description as well as the raw census variables that make up the numerator and denominator of the variable. Both methods below will print similar information.

+
>>> print(proj.all_vars_eqs['MEDAGE'])
+>>> proj.var_descriptions(['MEDAGE'])
+
+
+
+
+

7. Customizing Variables

+

Users can also define their own variables using the SVInsight.var_descriptions() method. If a user is interested in defining an additional variable this would need to be run before the configuration file is produced, which would automatically include the new variable in the calculated SVI (unless placed on the excluded list).

+
>>> proj.add_variable(boundary, year, name, num, den: list = [1], description: str = None)
+
+
+

The method’s parameters are as follows (boundary and year the same as before):

+
    +

  • name (str): the abbreviated name of the variable

    • +

  • num (list): the list of Census raw data variables that make up the sum of the numerator

    • +

  • den (list): the list of Census raw data variables that make up the sum of the denominator

    • +

  • description (str): An option string description of what the variable is

    • +
+
+
+

8. Plotting

+

There is also a simple plotting method, SVInsight.plot_svi(), to quickly plot a few example plots. There are three options for plotting:

+
+

Plot option 1: Single percentile

+

With plot_option equal to 1, a single SVI will be plotted based on a given year, boundary, configuration file, and variable name. While the variable name can be any existing variable within the geopackage, it will automatically default to plotting as a percentile because of the relative nature of the SVI calculation and it is most appropriate to analyze calculated SVIs in terms of their rank/percentile.

+
>>> import matplotlib.pyplot as plt
+>>> geopackages = [2017, boundary, config_file, 'FA_SVI_Percentile']
+>>> figure = proj.plot_svi(plot_option=1, geopackages)
+>>> plt.show()
+
+
+
+Map showing SVI for 2017 Travis County +
+

2017 Block Group Social Vulnerability Estimate for Travis County using Factor Analysis method

+
+
+
+
+

Plot option 2: Double percentile

+

With plot_option equal to 2, two different SVIs can be plotted next to each other for a visual comparison. These could be from the same geopackage comparing the factor analysis and rank methods, or two different configurations, two different boundaries, etc. The geopackages input should be a nested list in the same format as plot_option 1.

+
>>> geopackages = [[2017, boundary, config_file, 'FA_SVI_Percentile'], [2018, boundary, config_file, 'FA_SVI_Percentile']]
+>>> figure = proj.plot_svi(plot_option=2, geopackages)
+>>> plt.show()
+
+
+
+Map showing SVI for 2017 and 2018 Travis County +
+

2017 and 2018 Block Group Social Vulnerability Estimate for Travis County using Factor Analysis method

+
+
+
+
+

Plot option 3: Full comparison

+

When plot_option is equal to 3, a more detailed comparison is calculated that will produce a difference plot and calculate a linear regression. Because the difference map and linear regression require the same set of input geoids (i.e., the same locations in the geopackage), it is currently required that the variables come from the same geopackage, and its intended purpose is to therefore compare the differences between the Factor Analysis and Rank Method methodologies that have the same configuration. The geopackages input should be formatted as follows: [year, boundary, config]. If a variable option is passed it is ignored. The additional plots show the following information:

+
    +

  • Difference plot: Shows the The FA_SVI_Rank minus the RM_SVI_Rank to highlight areas where the factor analysis method is under (negative) and over (positive) predicting SVI rank when compared to the rank method.

    • +

  • Linear Regression: Shows linear correlation between factor analysis and rank method SVI estimates and automatically computes an r-squared value with p-value, 95% confidence interval, and 95% prediction interval.

    • +
+
>>> geopackages = [2018, boundary, config_file]
+>>> figure = proj.plot_svi(plot_option=3, geopackages)
+>>> plt.show()
+
+
+
+Map showing SVI comparison for 2018 Travis County (factor analysis method versus rank method) +
+

Comparison between factor analysis and rank methods: 2018 Block Group Social Vulnerability Estimate for Travis County

+
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More information on data quality can be found on their `website `_. + +Iterative Factor Analysis +^^^^^^^^^^^^^^^^^^^^^^^^^ + +This methodology is heavily influenced by the original SoVI® [1]_ developed by Dr. Susan Cutter. By utilizing a data reduction methodology, a large subset of American Community Survey 5-Year Estimate variables can be dimensionally reduced and combined into a single index. Both principal component analyses and factor analyses are data reduction techniques that have been utilized to create vulnerability indices. A principal component analysis (PCA) reduces data by creating one or more index variables, or ‘components,’ by using a linear combination (i.e., a weighted average) from a larger set of measured variables. The purpose of a PCA is to determine the optimal number of components, the optimal choice of measured variables for each component, and the optimal weights. A factor analysis (FA) is a model of latent variables. A ‘latent variable’ cannot be directly measured with a single variable. A common example used to describe this is we cannot directly measure something like social anxiety, but we can measure whether someone has a high or low social anxiety based on a set of questions like “I am uncomfortable in large groups” or “ I get nervous talking to strangers.” We utilize a factor analysis approach because it is the data reduction methodology most often associated with creating composite indices, which is our end goal. + +The follow are the steps in the iterative factor analysis approach: + + +1. **Scale the data** + - We standardize the data to a range of 0-1. Since all of the variable have a different scale, this makes each variable have an equal weight going into the analysis. + +2. **Conduct Initial Factor Analysis** + - Inputs for a factor analysis include the number of factors we are attempting to reduce the data down to, the rotation method, and the fitting method. For this first factor analysis, we set the number of factors to the number of variables under investigation (the maximum number of possible factors) in order to determine if there are any variables at the start we can eliminate for having too low of an influence. The rotation method used is varimax, or orthogonal rotation, which maximizes the sum of the variances of the squared loadings. In simpler terms, ‘loadings’ refer to the correlations between variables and factors. With varimax rotation, the resultant factors are uncorrelated with each other. The fitting method we use is minimum residual (‘minres’). + +3. **Calculate Eigenvalues** + -With our initial factors, we need to determine which ones are significant and which can be removed. If we stopped here, we would have reduced X variables to X factors, (i.e., we wouldn’t have reduced anything). Our goal is to find the weakest factors to eliminate so that while we are eliminating data, we maintain the highest level of reported variance in our final factors. + -The first step in doing this is calculating the eigenvalues, which is defined as a measure of how much of the common variance of the observed variables a factor explains. A factor with an eigenvalue greater than or equal to 1 explains more variance than a single observed variable and is therefore beneficial. This is known as the Kaiser Criterion. The number of factors whose eigenvalues is greater than 1 now becomes our new number of factors. + +4. **Recompute Factor Analysis based on Kaiser Criterion** + - Similar to step 2, we run the factor analysis again with the new optimized number of factors. + +5. **Calculate Loading Factors** + - The loading factor is the correlation coefficient for the variable and factor. It shows the variance explained by the variable on that particular factor. This will become the weight of the variable on that factor. Various standards exist on what makes a significant loading factor. For the sake of this research, we identify any loading factor that is greater than 0.5 or less than -0.5 is significant. + +6. **Calculate Variance Statistics** + -The variance statistics for each factor that we are interested in tracking are the SS Loadings, Proportion Variance, Cumulative Variance, and Ratio of Variance. + + *SS Loadings:* The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping. + + *Proportion Variance:* The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance. + + *Cumulative Variance:* The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is incredibly important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more. + + *Ratio of Variance:* Ratio of proportion of variance to cumulative variance. + +7. **Determine Significant Variables** + -Any variable whose loading factor is greater than 0.5 or less than -0.5 at any point is significant and therefore needs to be included in the analysis. Any variable that is not significant in any factor can be eliminated. With the new dataset we can again re-run the factor analysis until only the variables that are significantly contributing to at least one factor are included. + +8. **Begin Iterative Loop** + - With the newly created list of variables that have a significant contribution to at least a single factor, we can eliminate those that are not contributing and re-run the factor analysis without it. This process is repeated until every variable is significantly contributing to at least one factor. + +9. **Compose the Final Index** + -We first recalculate the final loadings and then for each factor, multiply the loading by the ratio of variance to scale the data. Therefore, factor 1 is rated higher than factor 2, factor 2 is rated higher than 3, and so on. From this, we can examine each factor to see which is the largest source of ‘vulnerability’ within the composite index (i.e., what is contributing the most). We create the composite index by adding each factor value together to calculate the unscaled composite index. The final composite index is minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0. + +The purpose of the factor analysis and composite score index in the scope of social vulnerability is to determine which variables are most distinguishable across the study area. The variables with the highest variability are likely to influence the index the most and therefore they must be taken into context of the study area. For example, in Austin, TX a variable that is highly variant across the city is the percent of population that identifies as Hispanic. If this trait contributes to social vulnerability, then our index is working properly to identify areas of higher vulnerability. However, if we discovered that this trait does not contribute to social vulnerability, we cannot include it in this workflow because we are unintentionally weighting the index incorrectly. That is why it is *imperative that indices are developed through collaborations with local experts* to identify which variables are likely contributing to vulnerability. + + + +Rank Method +^^^^^^^^^^^ +Another commonly employed method to estimate social vulnerability is a ranking method, which was popularized by the `Center for Disease Control's Social Vulnerability Index `_ [5]_. This method is a more simplified way to produce an index for a given area. Each variable of interest is sorted from high to low and ranked. For each location within the study area, the ranks are summed so that locations with a higher overall rank have a greater vulnerability score. In our method, the final summed ranks are also minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0. + + +.. _variables: + +Variables +--------- +SVInsight comes with a standard set of 27 variables which come from the original principal component analysis method, SoVI®. We omit two variables from the original list of 29 due to data availability issues for all of the years and geographic boundaries of interest (the percentage of the population living in nursing facilities, and the number of hospitals per capita). It is important to note that the CDC SVI uses a list of 16 variables that are similar to those from the SoVI® method. Determining which or how many variables to use depends completely on the study area and objectives of the researcher/practitioner and can greatly influence the estimates. Special consideration must be taken to determine the most appropriate set of variables to use. + +The variables, there definitions, and the American Community Survey 5-Year Estimate sources can be found below: + + ++ QAGDDEP: Percent of population under the age of 5 or over the age of 65 + - ['B01001_001E', 'B01001_026E', 'B01001_003E', 'B01001_020E', 'B01001_021E', 'B01001_022E', 'B01001_023E', 'B01001_024E', 'B01001_025E', 'B01001_027E', 'B01001_044E', 'B01001_045E', 'B01001_046E', 'B01001_047E', 'B01001_048E', 'B01001_049E'] + ++ QFEMALE: Percent of population that is female + - ['B01001_001E', 'B01001_026E'] + ++ MEDAGE: Median age + - ['B01002_001E'] + ++ QBLACK: Percent of population that is non-Hispanic Black/African-American + - ['B03002_001E', 'B03002_004E'] + ++ QNATIVE: Percent of population that is non-Hispanic Native American + - ['B03002_001E', 'B03002_005E'] + ++ QASIAN: Percent of population that is non-Hispanic Asian + - ['B03002_001E', 'B03002_006E'] + ++ QHISPC: Percent of population that is Hispanic + - ['B03002_001E', 'B03002_012E'] + ++ QFAM: Percent of families where only one spouse is present in the household + - ['B11005_003E', 'B11005_005E'] + ++ PPUNIT: People per unit, or average household size + - ['B25010_001E'] + ++ QFHH: Percent of households with Female householder and no spouse present + - ['B11001_001E', 'B11001_006E'] + ++ QEDLESHI: Percent of population over the age of 25 with less than a high school diploma (or equivalent) + - ['B15003_001E', 'B15003_002E', 'B15003_003E', 'B15003_004E', 'B15003_005E', 'B15003_006E', 'B15003_007E', 'B15003_008E', 'B15003_009E', 'B15003_010E', 'B15003_011E', 'B15003_012E', 'B15003_013E', 'B15003_014E', 'B15003_015E', 'B15003_016E'] + ++ QCVLUN: Percent of civilian population over the age of 15 that is unemployed + - ['B23025_003E', 'B23025_005E'] + ++ QRICH: Percent of households earning over $200,000 annually (inversely related to vulnerability) + - ['B19001_001E', 'B19001_017E'] + ++ QSSBEN: Percent of households with social security income + - ['B19055_001E', 'B19055_002E'] + ++ PERCAP: Per capita income in the past 12 months (inversely related to vulnerability) + - ['B19301_001E'] + ++ QRENTER: Percent of households that are renters + - ['B25003_001E', 'B25003_003E'] + ++ QUNOCCHU: Percent of housing units that are unoccupied + - ['B25002_001E', 'B25002_003E'] + ++ QMOHO: Percent of housing units that are mobile homes + - ['B25024_001E', 'B25024_010E'] + ++ MDHSEVAL: Median housing value (inversely related to vulnerability) + - ['B25077_001E'] + ++ MDGRENT: Median gross rent + - ['B25064_001E'] + ++ QPOVTY: Percent of population whose income in the past 12 months was below the poverty level + - ['B17021_001E', 'B17021_002E'] + ++ QNOAUTO: Percent of households without access to a car + - ['B25044_001E', 'B25044_003E', 'B25044_010E'] + ++ QNOHLTH: Percent of population without health insurance + - ['B27001_001E', 'B27001_005E', 'B27001_008E', 'B27001_011E', 'B27001_014E', 'B27001_017E', 'B27001_020E', 'B27001_023E', 'B27001_026E', 'B27001_029E', 'B27001_033E', 'B27001_036E', 'B27001_039E', 'B27001_042E', 'B27001_045E', 'B27001_048E', 'B27001_051E', 'B27001_054E', 'B27001_057E'] + ++ QESL: Percent of population who speaks English "not well" or "not at all" + - ['B16004_001E', 'B16004_007E', 'B16004_008E', 'B16004_012E', 'B16004_013E', 'B16004_017E', 'B16004_018E', 'B16004_022E', 'B16004_023E', 'B16004_029E', 'B16004_030E', 'B16004_034E', 'B16004_035E', 'B16004_039E', 'B16004_040E', 'B16004_044E', 'B16004_045E', 'B16004_051E', 'B16004_052E', 'B16004_056E', 'B16004_057E', 'B16004_061E', 'B16004_062E', 'B16004_066E', 'B16004_067E'] + ++ QFEMLBR: Percent of the civilian employed population over the age of 16 that is female + - ['C24010_001E', 'C24010_038E'] + ++ QSERV: Percent of the civilian employed population that has a service occupation + - ['C24010_001E', 'C24010_019E', 'C24010_055E'] + ++ QEXTRCT: Percent of the civilian employed population that has a construction and extraction occupation + - ['C24010_001E', 'C24010_032E', 'C24010_068E'] + + + + + + +References +---------- + +.. [1] Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2012). Social vulnerability to environmental hazards. In Hazards vulnerability and environmental justice (pp. 143-160). Routledge. + +.. [2] Bixler, R. P., Yang, E., Richter, S. M., & Coudert, M. (2021). Boundary crossing for urban community resilience: A social vulnerability and multi-hazard approach in Austin, Texas, USA. International Journal of Disaster Risk Reduction, 66, 102613. + +.. [3] Preisser, M., Passalacqua, P., Bixler, R. P., & Hofmann, J. (2022). Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index. Hydrology and Earth System Sciences, 26(15), 3941-3964. + +.. [4] Preisser, M., Passalacqua, P., Bixler, R. P., & Boyles, S. (2023). A network-based analysis of critical resource accessibility during floods. Frontiers in Water, 5, 1278205. + +.. [5] Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of homeland security and emergency management, 8(1), 0000102202154773551792. \ No newline at end of file diff --git a/_sources/Background/paper.rst.txt b/_sources/Background/paper.rst.txt new file mode 100644 index 0000000..124cbfd --- /dev/null +++ b/_sources/Background/paper.rst.txt @@ -0,0 +1,35 @@ +Papers +====== + +This Package +^^^^^^^^^^^^ + +A paper accompanying this software package will be published in the Journal of Open Source Software. Once published, it will be linked here. + + +Projects Using This Work +^^^^^^^^^^^^^^^^^^^^^^^^^ +You can also read the following papers that have explicitly used these methodologies: + +- Preisser, M., Passalacqua, P., Bixler, R. P., & Boyles, S. (2023). A network-based analysis of critical resource accessibility during floods. Frontiers in Water, 5, 1278205, `10.3389/frwa.2023.1278205 `_ + +- Preisser, M., Passalacqua, P., Bixler, R. P., & Hofmann, J. (2022). Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index. Hydrology and Earth System Sciences, 26(15), 3941-3964, `10.5194/hess-26-3941-2022 `_ + +- Bixler, R. P., Yang, E., Richter, S. M., & Coudert, M. (2021). Boundary crossing for urban community resilience: A social vulnerability and multi-hazard approach in Austin, Texas, USA. International Journal of Disaster Risk Reduction, 66, 102613, `10.1016/j.ijdrr.2021.102613 `_ + + +Additional Literature +^^^^^^^^^^^^^^^^^^^^^ + +Additionally, there are a number of relevant peer-reviewed articles we recommend to users if they are interested in learning more about social vulnerability topics: + +- Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2012). Social vulnerability to environmental hazards. In Hazards vulnerability and environmental justice (pp. 143-160). Routledge. + +- Fatemi, F., Ardalan, A., Aguirre, B., Mansouri, N., & Mohammadfam, I. (2017). Social vulnerability indicators in disasters: Findings from a systematic review. International journal of disaster risk reduction, 22, 219-227, `10.1016/j.ijdrr.2016.09.006 `_ + +- Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of homeland security and emergency management, 8(1), 0000102202154773551792, `10.2202/1547-7355.1792 `_ + +- Spielman, S. E., Tuccillo, J., Folch, D. C., Schweikert, A., Davies, R., Wood, N., & Tate, E. (2020). Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index. Natural hazards, 100, 417-436, `10.1007/s11069-019-03820-z `_ + + + diff --git a/_sources/Background/understanding.rst.txt b/_sources/Background/understanding.rst.txt new file mode 100644 index 0000000..5e32cba --- /dev/null +++ b/_sources/Background/understanding.rst.txt @@ -0,0 +1,71 @@ +Understanding Iterative Factor Analysis Results +=============================================== + +One of the outputs of the SVI workflow is an excel file containing documentation describing the characteristics of the index. This file can be found in the Documentation project folder and has the following naming convention: + +- folder location: ``{file_path}/{project_name}/{Documentation}/`` +- file name: ``{project_name}_{year}_{boundary}_{config_file}.xlsx`` + +Each tab of the excel sheet contains pertinent information regrading the iterative factor analysis SVI. The below subsections discusses what information is stored in each sheet and shows examples from an SVI run using the standard set of 27 variables (see :doc:`background <../Background/background>` for more information) for Travis County, Texas in 2017 at the block group level. + + +Significant Components +---------------------- +This sheet shows what components are significantly contributing to each factor from the final factor analysis. The number of iterations can be determined based on the name of each factor. For example, in this run *'F2'* means that this went through three rounds of factor analysis (zero-based numbering rules). As can be seen, there are 5 factors in the final index. Information on which variables contribute to which factor is useful in determining "themes" for the factors (e.g., wealth theme, social characteristics theme, etc.). In this example, one could identify the first factor as Social Status, and the second factor as Economic Status. These are for description only and will be unique based on the study area and year being analyzed. They may or may not be easily definable into distinct themes, but do show what variables are correlated within a study area. + +.. figure:: ../Background/sig_comp_.png + :alt: Table of significant components for 2017 Travis County, Texas Block Group SVI estimate + +*Table 1: Significant components for 2017 Travis County, Texas Block Group SVI estimate* + + + +Loading Factors +--------------- +Based on the variables within the final index, each has a loading factor associated with it for each factor. This information is critical in determining the weights for each variable for each factor when calculating the final index. For every boundary in the study area and for every factor, the variable is multiplied by the loading factor and summed within each factor. This is how the factor scores are determined. + +.. figure:: ../Background/loading_fac_.png + :alt: Table of loading factors for 2017 Travis County, Texas Block Group SVI estimate + +*Table 2: Loading factors for 2017 Travis County, Texas Block Group SVI estimate* + + + +All Refactor Variances +---------------------- +This shows the four main variance statistics associated with a factor analysis for each iteration. + +- SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping. +- Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance. **When calculating the final index, each factor is multiplied by its proportion of variance to weight the variables.** +- Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. **This is important because it shows us how much of the original data’s variance we are losing.** If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more. +- Ratio Variance: Ratio of proportion of variance to cumulative variance. + +For most studies, especially exploratory analyses, it is acceptable to have a cumulative variance as low as 60%, and sometimes as low as 50% [1]_ [2]_ [3]_ . + + +Final Variances +--------------- +An abbreviated version of All Refactor Variances, showing only the final table for the final iteration. + +.. figure:: ../Background/final_fac_.png + :alt: Table of Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate + +*Table 4: Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate* + + + +Included and Excluded +--------------------- +contains the lists of what variables make the final index and which variables are excluded due to not significantly contributing to a any factors. This can be useful for double checking that the index is including variables you would likely expect to find given your study area. + + + +References +---------- + +.. [1] Hair, J. S., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006). Multivariate Data Analysis. New Jersey: Prentice-Hall. + +.. [2] Peterson, R. A. (2000). A Meta-Analysis of Variance Accounted for and Factor Loadings in Exploratory Factor Analysis. Marketing Letters, 11(3), 261–275. http://www.jstor.org/stable/40239882 + +.. [3] Advice for Exploratory Factor Analyses: `Link `_ + diff --git a/_sources/Contributions/contributions.rst.txt b/_sources/Contributions/contributions.rst.txt new file mode 100644 index 0000000..95a7451 --- /dev/null +++ b/_sources/Contributions/contributions.rst.txt @@ -0,0 +1,15 @@ +Issues, Questions, and Contributions +===================================== + +We welcome contributions to the dorado package in the form of pull requests and issues made in the source `repository `_. + +Issues and Questions +-------------------- + +If you are having any problems using SVInsight we suggest opening an `issue `_.When you open an issue, please provide a clear description of what your scenario is, and what error message you are receiving. If possible, please include a minimal working example of some code that breaks, and the error output you receive. If there is some functionality you would like to see added to SVInsight you can also open an issue up to discuss that. This can be code you plan to write and contribute, or it can be something you would like to have available but are not comfortable coding yourself. Either way we are happy to help! + + +Pull Requests +------------- + +If you have a feature that you would like to propose be integrated into dorado, then you should open a `pull request `_. To create a pull request, we recommend first `forking `_ the repository, and then creating a separate branch to develop your feature (for reference see the `GitHub flow guide `_ and this `Git branching `_ guide). Then you can commit and develop your feature in your branch. We ask that new features be accompanied by additional unit tests, to ensure that they operate as expected. For unit testing, we use `pytest `_. When you are satisfied with the code you have developed, you can open a pull request to the main branch of the project repository. Please write a concise and descriptive title for the pull request, and provide a clear description of what the feature does and why you are proposing its addition to the project. \ No newline at end of file diff --git a/_sources/Examples/SVInsight_example_SETx.ipynb.txt b/_sources/Examples/SVInsight_example_SETx.ipynb.txt new file mode 100644 index 0000000..46b0d6c --- /dev/null +++ b/_sources/Examples/SVInsight_example_SETx.ipynb.txt @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9c3de87c", + "metadata": {}, + "source": [ + "# SVInsight Example: Southeast Texas\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22963e8c-1b5e-4965-8159-6296e69248ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Example notebook to compute SVInsight\n", + "# Script to compute the Social Vulnerability Index according to the SVInsight method of Preisser et al. (2024). This example shows how to compute the index for the counties in Southeast Texas with geoids 4819, 48245, 48361, 48241, 48351.\n", + "# Created on 28 April 2024" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "79a6de98-bc3f-4535-a2b4-242154280af2", + "metadata": {}, + "outputs": [], + "source": [ + "# After installing SVInsight (and all dependencies) with pip install, we can import it into this notebook:\n", + "\n", + "from svinsight import SVInsight as svi" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "515c1f8d-b1e1-45d7-8423-6f24ba9e9d4e", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the necessary variables and create the SVInsight object. In this example we are using counties in Southeast Texas:\n", + "project_name = 'SETx_SVI'\n", + "file_path = \"C:/Users/pp7545/Documents/research/SVInsight\"\n", + "api_key = 'your api key here'\n", + "geoids = ['48199', '48245', '48361', '48241', '48351']\n", + "project = svi(project_name, file_path, api_key, geoids)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7e0011fd-3474-4ddf-a578-0f71c57f08b8", + "metadata": {}, + "outputs": [], + "source": [ + "# In this example we will estimate the SVI for the Southeast Texas counties using data from 2021 at the block group level:\n", + "boundary = 'bg'\n", + "year = 2021\n", + "config_file = 'config_file'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "700333cc-8aef-48ff-b650-08aa0043072a", + "metadata": {}, + "outputs": [], + "source": [ + "# We now follow the simplest workflow:\n", + "#1. extract boundaries\n", + "#2. extract census data\n", + "#3. configure variables\n", + "#4. calculate social vulnerability index\n", + "\n", + "project.boundaries_data(boundary, year)\n", + "project.census_data(boundary, year)\n", + "project.configure_variables(config_file)\n", + "project.calculate_svi(config_file, boundary, year)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6ab8e1f9-0f21-433f-afc9-08b61055a11e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The output SVI.csv and .gpkg are stored in the following file format:\n", + "# folder location: {file_path}/{project_name}/{SVIs}/\n", + "# file name: {project_name}_{year}_{boundadry}_{config_file}_svi\n", + "\n", + "# We can load the results and plot them here:\n", + "\n", + "import matplotlib.pyplot as plt\n", + "geopackages = [2021, boundary, config_file, 'FA_SVI_Percentile']\n", + "figure = project.plot_svi(1, geopackages)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/Examples/examples.rst.txt b/_sources/Examples/examples.rst.txt new file mode 100644 index 0000000..9147e9d --- /dev/null +++ b/_sources/Examples/examples.rst.txt @@ -0,0 +1,13 @@ +Examples +======== + +Bellow you will find a Jupyter Notebook that computes an SVI for the Southeast Texas region. More example scripts can be found on the `GitHub page in the Examples folder `_. + + + +Example Notebooks +################# +.. toctree:: + :maxdepth: 2 + + SVInsight_example_SETx.ipynb \ No newline at end of file diff --git a/_sources/Getting_Started/install.rst.txt b/_sources/Getting_Started/install.rst.txt new file mode 100644 index 0000000..bc183b8 --- /dev/null +++ b/_sources/Getting_Started/install.rst.txt @@ -0,0 +1,28 @@ +Install +======= +`SVInsight` has been tested with Python 3.9 through 3.12. It also requires the following dependencies: `census `_, `factor_analyzer `_, `geopandas `_, `numpy `_, `pandas `_, `pytest `_, `PyYAML `_, `scikit_learn `_, `openpyxl `_, and `matplotlib `_. + +Installation via *pip* +---------------------- + + +To *pip*-install this package use the following command: + +.. code-block:: console + + $ pip install SVInsight + + +`SVInsight` can then be imported into python: + +.. doctest:: + + >>> from svinsight import SVInsight as svi + + +Installation via *conda* +------------------------ + +This package will be available from conda-forge soon. + + diff --git a/_sources/Getting_Started/quickstart.rst.txt b/_sources/Getting_Started/quickstart.rst.txt new file mode 100644 index 0000000..96b2b09 --- /dev/null +++ b/_sources/Getting_Started/quickstart.rst.txt @@ -0,0 +1,57 @@ +Getting Started +=============== + +Quickstart +---------- + +This is a quick guide to using SVInsight. In this demonstration, we show how to load and create the SVInsight object, determine an area of interest, set a boundary level (e.g., tract or block groups), download the necessary boundary and raw data, calculate input variables, and compute the social vulnerability estimate. + +First we load the SVInsight package, set the necessary variables, and create the SVInsight object. + +.. doctest:: + + >>> from svinsight import SVInsight as svi + >>> project_name = 'YOUR PROJECT NAME HERE' + >>> file_path = "YOUR PARENT FOLDER HERE" + >>> api_key = 'YOUR CENSUS API KEY HERE' + >>> geoids = ['48453'] + >>> project = svi(project_name, file_path, api_key, geoids) + +These variables create the instance of a project. Each have the following purpose: + +- ``project_name``: This is the name of your project. In this case, it's 'Travis_County_SVI'. The project name is used when saving files throughout the project. +- ``file_path``: This is the path to the parent folder where your project files will be stored. Replace "YOUR PARENT FOLDER HERE" with the actual path. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the ``file_path`` and ``project_name``. +- ``api_key``: This is your Census API key. Replace 'YOUR CENSUS API KEY HERE' with your actual API key. You can obtain an API key from the `Census Bureau's developer page `_. +- ``geoids``: This is a list of geographic identifiers for the areas you're interested in. In this case, it's ['48453'], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this `FCC page `_. + + +Each instance of SVInsight can be seen as a standalone project for a constant study area. Within each project, the user can explore different vulnerability estimates based on different years or boundaries (e.g., block group versus tract). + +In this quickstart, we will examine a vulnerability estimate for Travis County using 2019 data at the block group level. + +.. doctest:: + + >>> boundary = 'bg' + >>> year = 2019 + >>> config_file = 'config_file' + +The simplest workflow is as follows: + +(1) Extract boundaries +(2) Extract Census data +(3) Configure variables +(4) Calculate vulnerability estimate + +.. doctest:: + + >>> project.boundaries_data(boundary, year) + >>> project.census_data(boundary, year) + >>> project.configure_variables(config_file) + >>> project.calculate_svi(config_file, boundary, year) + +The output SVI .csv and .gpkg files built will be saved with the following file format: + +- folder location: ``{file_path}/{project_name}/{SVIs}/`` +- file name: ``{project_name}_{year}_{boundadry}_{config_file}_svi`` + +More detailed examples of the complete functionality of SVInsight can be found under :doc:`Examples <../Examples/examples>`. \ No newline at end of file diff --git a/_sources/User_Guide/userguide.rst.txt b/_sources/User_Guide/userguide.rst.txt new file mode 100644 index 0000000..0e40048 --- /dev/null +++ b/_sources/User_Guide/userguide.rst.txt @@ -0,0 +1,235 @@ +.. currentmodule:: SVInsight.svi + +User Guide +========== + +Overview +-------- + +The basic workflow of `SVInsight` works as follows: + +1. Initialize the project class (:obj:`SVInsight`) with your project name, folder path, Census API key, and your study area. + +2. Define the initial project configuration variables with a boundary (tract or block groups) and year. + +3. Extract the necessary boundary geopackage using :meth:`SVInsight.boundaries_data`. + +4. Extract the raw census data and fill in holes in the data using :meth:`SVInsight.census_data`. + +5. Configure the run and calculate the SVI using :meth:`SVInsight.configure_variables` and :meth:`SVInsight.calculate_svi`. + + +Below you will find the detailed user guide that walks through each of these steps, with additional options for project customization. `SVInsight` is supposed to be a flexible and exploratory estimator of various social vulnerability indices, and is therefore highly customizable within the framework of Factor Analysis and Rank Method methodologies. The User Guide below walks through the methods available for calculating SVIs. For a larger discussion on the Factor Analysis and Rank Method methodologies, please refer to the :doc:`Background ` and :doc:`Understanding Iterative Factor Analysis Results ` pages. + + +1. Initializing the Project +--------------------------- + +The first step to the project is importing :obj:`SVInsight` and setting the project `params.` Each instance of the class can be seen as a standalone project for the input geoids, where users can configure different runs based on years, boundaries, and variables of interest. In this User Guide we are using Travis_County, Texas as a working example. + +.. doctest:: + + >>> from svinsight import SVInsight as svi + >>> project_name = 'Travis_County' + >>> file_path = "/User/Documents" + >>> api_key = os.environ.get('API_KEY') + >>> geoids = ['48453'] + >>> proj = svi(project_name, file_path, api_key, geoids) + +These variables create the instance of a project. Each have the following purpose: + +- ``project_name``: This is the name of your project. In this case, it's 'Travis_County'. The project name is used when saving files throughout the project. +- ``file_path``: This is the path to the parent folder where your project files will be stored. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the ``file_path`` and ``project_name``. +- ``api_key``: This is your Census API key. You can obtain an API key from the `Census Bureau's developer page `_. +- ``geoids``: This is a list of geographic identifiers for the areas you're interested in. In this case, it's ['48453'], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this `FCC page `_. + +During the initialization, a series of folder is generated for storing the files created during a project. The folder locations and their intended contents are as follows: + +- **Boundaries:** Will contain geopackages of the boundary areas based on the input geoids. +- **Data:** Where the raw census data csv and geopackages will be stored, in addition to other csv files that contain information about missing variables and how they were rectified. +- **Documentation:** Will contain excel sheets of information pertinent to the factor analysis svi. +- **SVIs:** Where the geopackages of SVIs will be stored. +- **Variables:** Where the configuration files will be stored. + + +2. Defining the Initial Project Configuration Variables +------------------------------------------------------- + +The initial project configuration variables allows us to pull the appropriate boundary information. The boundary can be either `bg` or `tract` for block group or tract. Block groups and tracts are the two highest spatial resolutions that the US Census releases American Community Survey 5-Year estimates for. While block groups have a higher spatial resolution, they come with greater uncertainty in their estimates compared to tracts. More information can be found on the `Census website `_. + +.. doctest:: + + >>> boundary='bg' + >>> year=2017 + >>> config_file = 'config' + + +.. figure:: census_divisions.jpeg + :scale: 100 % + :alt: Map showing the Census Divisions + + Visual representation of the different Census boundaries. + + +3. Extracting the Boundary +-------------------------- + +With the boundary and year set, we can extract the necessary raw census data. In the :meth:`SVInsight.boundaries_data` method, there is a parameter ``overwrite`` which can be set to True or False. When True, if an existing geopackage for the given boundary and year is already found in the folder it will be overwritten. this method returns a geopandas dataframe. Furthermore, because Census boundaries include areas that may not have permanent populations (i.e., military installations, airports, national parks, etc.), a filter is automatically applied such that boundaries with less than a population of 75 are excluded from the analysis. + +.. doctest:: + + >>> areas = proj.boundaries_data(boundary, year, overwrite=False) + + +4. Extract Raw Data and Filling Holes +------------------------------------- + +The next step is to extract the raw census data using the :meth:`SVInsight.census_data` method. :obj:`SVInsight` starts has a default set of 27 social and economic variables of interest, which are detailed in the :ref:`variables` section. This method specifically pulls the raw data, and the variables themselves are calculated later. In this example, we are continuing to us the same boundary and year as the boundaries we just extracted, in order to produce a geopackaged version of raw data. Furthermore we have two additional parameters: ``interpolate`` and ``verbose``. **It is important to note** that the Census API will sometimes time out, and if Census data isn't being pulled, waiting a few seconds/minutes and re-running usually solves the problem. + +.. doctest:: + + >>> areas = proj.census_data(boundary, year, interpolate=True, verbose=True, overwrite=False) + + +- ``interpolate`` (bool): Census data, especially at the block group level, might have missing data points. Missing data can occur if there are not enough sample points in a given time period to accurately calculate the variable. In these instances, the holes must be filled to accurately calculate the SVIs. :obj:`SVInsight` can accomplish this two different ways, either with or without **interpolation**. When interpolation is True, missing values are determined by recalculating the median based on the detailed tables (e.g., the breakdown table of response ranges for a given variable) from the adjacent boundaries. Because interpolating isn't exact, any boundary that has less than 40 neighboring data points are not interpolated. For areas that are not interpolated, or if interpolation is set to False, holes are filled by taking the next available boundary value. For example, if a block group is missing a value it is replaced with the tract value, and if a tract value is missing it is filled with the county value. + +- ``verbose`` (bool): When set to True, two additional csv values are saved in the Data folder that highlight which locations had holes that needed to be filled and the methodology in which it is filled (_missing_values.csv). If there happens to be an entire column that is missing information, the next highest available boundary is used to fill the entire variable (_missing_columns.csv). + +Similar to the :meth:`SVInsight.boundaries_data` method, there is an overwrite parameter as well. + + + +5. Configuring and Calculating SVI +---------------------------------- + +Finally, the :meth:`SVInsight.configure_variables` and :meth:`SVInsight.calculate_svi` methods are used to configure and run the SVI calculation. + +.. doctest:: + + >>> proj.configure_variables(config_file, exclude=None, include=None, inverse_vars=['PERCAP', 'QRICH', 'MDHSEVAL']) + >>> proj.calculate_svi(config_file, boundary, year) + + +Configure Variables +^^^^^^^^^^^^^^^^^^^ + +This method prepares the information that will be included in the SVI calculation. This method is useful if you are interested in creating multiple different scenarios of SVI. For example, in some runs you may be interested in all 27 variables, while in others you may only want to include economic related variables. By creating a configuration file for each type of run, each SVI will be saved with the config_name associated with it for easier access and comparison. There are four main parameters of the :meth:`SVInsight.configure_variables` method: + +1. ``config_file`` (str): The name given to the configuration file + +2. ``exclude`` (list): In order to remove a variable from the original list of 27, this parameter accepts a list of variables the user would like to exclude from the configuration. + +3. ``include`` (list): the opposite of exclude, in that this parameter will remove all variables except those in this list. An exclude and an include parameter value cannot be given. + +4. ``inverse_vars`` (list): Certain variables have an inverse relationship with vulnerability. For example, household with a higher per capita income, make over $250,000 annually, and/or have a higher median housing value are less vulnerable. The list of variables passed to inverse_vars are flipped to capture these dynamics. This parameter defaults to this list of three variables, and users can feel free to pass their own list of variables that they believe to be inversely related. + + +Calculate SVI +^^^^^^^^^^^^^^ + +With the configuration file set, the final SVI can be calculated. The output geopackage and CSV files will have each variable that is within the configuration file as well as the following SVI variables: + +- FA_SVI_Unscaled: The factor analysis method SVI estimate that is unscaled +- FA_SVI_Scaled: The factor analysis method SVI estimate that is scaled 0 to 1 +- FA_SVI_Percentile: The factor analysis method SVI estimate percentile +- FA_SVI_Rank: The factor analysis method SVI estimate rank +- RM_SVI_Percentile: The rank method SVI estimate percentile +- RM_SVI_Rank: The rank method SVI estimate rank + +It is important to remember that **SVI estimates are exploratory and relative.** That means that SVI values' accuracy is dependent on the configuration of the run for a given location and only relative to within that study area. + +6. Examine Variables +-------------------- + +An additional method to investigate variables is the :meth:`SVInsight.var_descriptions` method. Given a list of variables, the description can be printed. Similarly, users can access the variables based on the instance of the project to see the long description as well as the raw census variables that make up the numerator and denominator of the variable. Both methods below will print similar information. + +.. doctest:: + + >>> print(proj.all_vars_eqs['MEDAGE']) + >>> proj.var_descriptions(['MEDAGE']) + + + + +7. Customizing Variables +------------------------ + +Users can also define their own variables using the :meth:`SVInsight.var_descriptions` method. If a user is interested in defining an additional variable this would need to be run before the configuration file is produced, which would automatically include the new variable in the calculated SVI (unless placed on the excluded list). + +.. doctest:: + + >>> proj.add_variable(boundary, year, name, num, den: list = [1], description: str = None) + + +The method's parameters are as follows (boundary and year the same as before): + +- ``name`` (str): the abbreviated name of the variable +- ``num`` (list): the list of Census raw data variables that make up the sum of the numerator +- ``den`` (list): the list of Census raw data variables that make up the sum of the denominator +- ``description`` (str): An option string description of what the variable is + + +8. Plotting +----------- + +There is also a simple plotting method, :meth:`SVInsight.plot_svi`, to quickly plot a few example plots. There are three options for plotting: + +Plot option 1: Single percentile +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +With plot_option equal to 1, a single SVI will be plotted based on a given year, boundary, configuration file, and variable name. While the variable name can be any existing variable within the geopackage, it will automatically default to plotting as a percentile because of the relative nature of the SVI calculation and it is **most appropriate to analyze calculated SVIs in terms of their rank/percentile.** + +.. doctest:: + + >>> import matplotlib.pyplot as plt + >>> geopackages = [2017, boundary, config_file, 'FA_SVI_Percentile'] + >>> figure = proj.plot_svi(plot_option=1, geopackages) + >>> plt.show() + +.. figure:: single_plot.png + :scale: 75 % + :alt: Map showing SVI for 2017 Travis County + + 2017 Block Group Social Vulnerability Estimate for Travis County using Factor Analysis method + + + +Plot option 2: Double percentile +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +With plot_option equal to 2, two different SVIs can be plotted next to each other for a visual comparison. These could be from the same geopackage comparing the factor analysis and rank methods, or two different configurations, two different boundaries, etc. The geopackages input should be a nested list in the same format as plot_option 1. + +.. doctest:: + + >>> geopackages = [[2017, boundary, config_file, 'FA_SVI_Percentile'], [2018, boundary, config_file, 'FA_SVI_Percentile']] + >>> figure = proj.plot_svi(plot_option=2, geopackages) + >>> plt.show() + +.. figure:: double_plot.png + :scale: 75 % + :alt: Map showing SVI for 2017 and 2018 Travis County + + 2017 and 2018 Block Group Social Vulnerability Estimate for Travis County using Factor Analysis method + + + +Plot option 3: Full comparison +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +When plot_option is equal to 3, a more detailed comparison is calculated that will produce a difference plot and calculate a linear regression. Because the difference map and linear regression require the same set of input geoids (i.e., the same locations in the geopackage), it is currently required that the variables come from the same geopackage, and its intended purpose is to therefore compare the differences between the Factor Analysis and Rank Method methodologies that have the same configuration. The geopackages input should be formatted as follows: [year, boundary, config]. If a variable option is passed it is ignored. The additional plots show the following information: + +- Difference plot: Shows the The FA_SVI_Rank minus the RM_SVI_Rank to highlight areas where the factor analysis method is under (negative) and over (positive) predicting SVI rank when compared to the rank method. +- Linear Regression: Shows linear correlation between factor analysis and rank method SVI estimates and automatically computes an r-squared value with p-value, 95% confidence interval, and 95% prediction interval. + +.. doctest:: + + >>> geopackages = [2018, boundary, config_file] + >>> figure = proj.plot_svi(plot_option=3, geopackages) + >>> plt.show() + + +.. figure:: complete_plot.png + :scale: 75 % + :alt: Map showing SVI comparison for 2018 Travis County (factor analysis method versus rank method) + + Comparison between factor analysis and rank methods: 2018 Block Group Social Vulnerability Estimate for Travis County + + + diff --git a/_sources/apiref/apiref.rst.txt b/_sources/apiref/apiref.rst.txt new file mode 100644 index 0000000..582c3d5 --- /dev/null +++ b/_sources/apiref/apiref.rst.txt @@ -0,0 +1,7 @@ +SVInsight +========= +A Census API key is needed to run this package. You can obtain an API key from the `Census Bureau's developer page `_. + +.. automodule:: svinsight.svi + :members: + :special-members: \ No newline at end of file diff --git a/_sources/apiref/license.rst.txt b/_sources/apiref/license.rst.txt new file mode 100644 index 0000000..a1c9299 --- /dev/null +++ b/_sources/apiref/license.rst.txt @@ -0,0 +1,6 @@ +License +======= + +This project is provided under the `MIT License `_. + + diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..5c14eee --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,87 @@ +.. SVInsight documentation master file, created by + sphinx-quickstart on Wed Apr 10 16:47:20 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +SVInsight +========= + +**SVInsight** is a python package for calculating an exploratory social vulnerability index. This package calculates SVI using two methods: (1) an iterative factor analysis method and (2) a rank method, both of which have been heavily utilized in scholarly research. This package automates the creation and comparisons of indices using U.S. American Community Survey 5-Year Data (ACS5) at the block group or tract level. Users can customize which social, demographic, and economic variables are included in their own custom indices. + +This package is a tool to efficiently calculate an exploratory estimate of social vulnerability for a given region. Social vulnerability is an incredibly complex and constantly evolving concept, and researchers, practitioners, and users of this software should always consult relevant peer-reviewed literature and local experts to validate findings. + + +.. figure:: all_time_steps.gif + :align: center + :alt: Gif of time-series for SVI estimates in Travis County, Texas + + Travis County SVI estimates from 2013 to 2021 + + +Getting Started +############### +.. toctree:: + :maxdepth: 2 + + Getting_Started/install + Getting_Started/quickstart + + +Background +########## +.. toctree:: + :maxdepth: 1 + + Background/background + Background/understanding + Background/paper + + +User Guide +########## +.. toctree:: + :maxdepth: 2 + + User_Guide/userguide + +API Reference +############# +.. toctree:: + :maxdepth: 1 + + apiref/apiref + apiref/license + + +Examples +######## +.. toctree:: + :maxdepth: 2 + + Examples/examples + + + +Issues, Questions, and Contributions +#################################### +.. toctree:: + :maxdepth: 2 + + Contributions/contributions + + +Acknowledgements +################ +.. toctree:: + :maxdepth: 2 + + Acknowledgements/acknowledgements + + + +.. Indices and tables + ================== + + * :ref:`genindex` + * :ref:`modindex` + * :ref:`search` diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..f316efc --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/debug.css b/_static/debug.css new file mode 100644 index 0000000..74d4aec --- /dev/null +++ b/_static/debug.css @@ -0,0 +1,69 @@ +/* + This CSS file should be overridden by the theme authors. It's + meant for debugging and developing the skeleton that this theme provides. +*/ +body { + font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, + "Apple Color Emoji", "Segoe UI Emoji"; + background: lavender; +} +.sb-announcement { + background: rgb(131, 131, 131); +} +.sb-announcement__inner { + background: black; + color: white; +} +.sb-header { + background: lightskyblue; +} +.sb-header__inner { + background: royalblue; + color: white; +} +.sb-header-secondary { + background: lightcyan; +} +.sb-header-secondary__inner { + background: cornflowerblue; + color: white; +} +.sb-sidebar-primary { + background: lightgreen; +} +.sb-main { + background: blanchedalmond; +} +.sb-main__inner { + background: antiquewhite; +} +.sb-header-article { + background: lightsteelblue; +} +.sb-article-container { + background: snow; +} +.sb-article-main { + background: white; +} +.sb-footer-article { + background: lightpink; +} +.sb-sidebar-secondary { + background: lightgoldenrodyellow; +} +.sb-footer-content { + background: plum; +} +.sb-footer-content__inner { + background: palevioletred; +} +.sb-footer { + background: pink; +} +.sb-footer__inner { + background: salmon; +} +.sb-article { + background: white; +} diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..4d67807 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..7e4c114 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 0000000..367b8ed --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n 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content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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\ No newline at end of file diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 0000000..92da3f8 --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,619 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/skeleton.css b/_static/skeleton.css new file mode 100644 index 0000000..467c878 --- /dev/null +++ b/_static/skeleton.css @@ -0,0 +1,296 @@ +/* Some sane resets. */ +html { + height: 100%; +} + +body { + margin: 0; + min-height: 100%; +} + +/* All the flexbox magic! */ +body, +.sb-announcement, +.sb-content, +.sb-main, +.sb-container, +.sb-container__inner, +.sb-article-container, +.sb-footer-content, +.sb-header, +.sb-header-secondary, +.sb-footer { + display: flex; +} + +/* These order things vertically */ +body, +.sb-main, +.sb-article-container { + flex-direction: column; +} + +/* Put elements in the center */ +.sb-header, +.sb-header-secondary, +.sb-container, +.sb-content, +.sb-footer, +.sb-footer-content { + justify-content: center; +} +/* Put elements at the ends */ +.sb-article-container { + justify-content: space-between; +} + +/* These elements grow. */ +.sb-main, +.sb-content, +.sb-container, +article { + flex-grow: 1; +} + +/* Because padding making this wider is not fun */ +article { + box-sizing: border-box; +} + +/* The announcements element should never be wider than the page. */ +.sb-announcement { + max-width: 100%; +} + +.sb-sidebar-primary, +.sb-sidebar-secondary { + flex-shrink: 0; + width: 17rem; +} + +.sb-announcement__inner { + justify-content: center; + + box-sizing: border-box; + height: 3rem; + + overflow-x: auto; + white-space: nowrap; +} + +/* Sidebars, with checkbox-based toggle */ +.sb-sidebar-primary, +.sb-sidebar-secondary { + position: fixed; + height: 100%; + top: 0; +} + +.sb-sidebar-primary { + left: -17rem; + transition: left 250ms ease-in-out; +} +.sb-sidebar-secondary { + right: -17rem; + transition: right 250ms ease-in-out; +} + +.sb-sidebar-toggle { + display: none; +} +.sb-sidebar-overlay { + position: fixed; + top: 0; + width: 0; + height: 0; + + transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; + + opacity: 0; + background-color: rgba(0, 0, 0, 0.54); +} + +#sb-sidebar-toggle--primary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], +#sb-sidebar-toggle--secondary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { + width: 100%; + height: 100%; + opacity: 1; + transition: width 0ms ease, height 0ms ease, opacity 250ms ease; +} + +#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { + left: 0; +} +#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { + right: 0; +} + +/* Full-width mode */ +.drop-secondary-sidebar-for-full-width-content + .hide-when-secondary-sidebar-shown { + display: none !important; +} +.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { + display: none !important; +} + +/* Mobile views */ +.sb-page-width { + width: 100%; +} + +.sb-article-container, +.sb-footer-content__inner, +.drop-secondary-sidebar-for-full-width-content .sb-article, +.drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 100vw; +} + +.sb-article, +.match-content-width { + padding: 0 1rem; + box-sizing: border-box; +} + +@media (min-width: 32rem) { + .sb-article, + .match-content-width { + padding: 0 2rem; + } +} + +/* Tablet views */ +@media (min-width: 42rem) { + .sb-article-container { + width: auto; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 42rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 46rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 46rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 50rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 50rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Tablet views */ +@media (min-width: 59rem) { + .sb-sidebar-secondary { + position: static; + } + .hide-when-secondary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 63rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 67rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Desktop views */ +@media (min-width: 76rem) { + .sb-sidebar-primary { + position: static; + } + .hide-when-primary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} + +/* Full desktop views */ +@media (min-width: 80rem) { + .sb-article, + .match-content-width { + width: 46rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } +} + +@media (min-width: 84rem) { + .sb-article, + .match-content-width { + width: 50rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } +} + +@media (min-width: 88rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-page-width { + width: 88rem; + } +} diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/_static/styles/furo-extensions.css b/_static/styles/furo-extensions.css new file mode 100644 index 0000000..bc447f2 --- /dev/null +++ b/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the