From 9c505133b9b4a1edbc338ddad203c3287ff865cd Mon Sep 17 00:00:00 2001 From: Arno Timmer Date: Thu, 27 Aug 2026 13:50:26 +0200 Subject: [PATCH 1/3] setup quarto --- .gitignore | 3 + _quarto.yml | 7 + _site/index.html | 1437 +++++++++++ _site/search.json | 72 + ...p-3fd26f1720109480f9692f3993e9ff04.min.css | 12 + _site/site_libs/bootstrap/bootstrap-icons.css | 2106 +++++++++++++++++ .../site_libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 180288 bytes _site/site_libs/bootstrap/bootstrap.min.js | 7 + _site/site_libs/clipboard/clipboard.min.js | 7 + _site/site_libs/quarto-html/anchor.min.js | 9 + _site/site_libs/quarto-html/popper.min.js | 6 + ...hting-f1a5c2bb43452cd3d80060817c0f5863.css | 219 ++ _site/site_libs/quarto-html/quarto.js | 845 +++++++ .../site_libs/quarto-html/tabsets/tabsets.js | 95 + _site/site_libs/quarto-html/tippy.css | 1 + _site/site_libs/quarto-html/tippy.umd.min.js | 2 + _site/site_libs/quarto-nav/quarto-nav.js | 325 +++ .../quarto-search/autocomplete.umd.js | 3 + _site/site_libs/quarto-search/fuse.min.js | 9 + .../site_libs/quarto-search/quarto-search.js | 1457 ++++++++++++ _site/styles.css | 37 + index.Rmd | 730 ------ index.html | 2043 ---------------- styles.css | 37 + 24 files changed, 6696 insertions(+), 2773 deletions(-) create mode 100644 _quarto.yml create mode 100644 _site/index.html create mode 100644 _site/search.json create mode 100644 _site/site_libs/bootstrap/bootstrap-3fd26f1720109480f9692f3993e9ff04.min.css create mode 100644 _site/site_libs/bootstrap/bootstrap-icons.css create mode 100644 _site/site_libs/bootstrap/bootstrap-icons.woff create mode 100644 _site/site_libs/bootstrap/bootstrap.min.js create mode 100644 _site/site_libs/clipboard/clipboard.min.js create mode 100644 _site/site_libs/quarto-html/anchor.min.js create mode 100644 _site/site_libs/quarto-html/popper.min.js create mode 100644 _site/site_libs/quarto-html/quarto-syntax-highlighting-f1a5c2bb43452cd3d80060817c0f5863.css create mode 100644 _site/site_libs/quarto-html/quarto.js create mode 100644 _site/site_libs/quarto-html/tabsets/tabsets.js create mode 100644 _site/site_libs/quarto-html/tippy.css create mode 100644 _site/site_libs/quarto-html/tippy.umd.min.js create mode 100644 _site/site_libs/quarto-nav/quarto-nav.js create mode 100644 _site/site_libs/quarto-search/autocomplete.umd.js create mode 100644 _site/site_libs/quarto-search/fuse.min.js create mode 100644 _site/site_libs/quarto-search/quarto-search.js create mode 100644 _site/styles.css delete mode 100644 index.Rmd delete mode 100644 index.html create mode 100644 styles.css diff --git a/.gitignore b/.gitignore index e75435c..d3223fe 100644 --- a/.gitignore +++ b/.gitignore @@ -47,3 +47,6 @@ po/*~ # RStudio Connect folder rsconnect/ + +/.quarto/ +**/*.quarto_ipynb diff --git a/_quarto.yml b/_quarto.yml new file mode 100644 index 0000000..de29693 --- /dev/null +++ b/_quarto.yml @@ -0,0 +1,7 @@ +# _quarto.yml +project: + type: website + output-dir: _site + +website: + title: "R & Python Basics" \ No newline at end of file diff --git a/_site/index.html b/_site/index.html new file mode 100644 index 0000000..5b5451d --- /dev/null +++ b/_site/index.html @@ -0,0 +1,1437 @@ + + + + + + + + + + + +R and Python basics – R & Python Basics + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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R & Python basics

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Author
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Dainius Masiliūnas, Arno Timmer

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Published
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August 27, 2026

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WUR Geoscripting

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R and Python basics

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Throughout the course we will be learning R and Python. Both are programming languages, and both can be used for handling geospatial data. So what is the difference between them? Why would you choose one over another? And how does equivalent code look like in each language? We will go over these questions in this tutorial.

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Learning objectives of the day

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In this tutorial, you will learn to:

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  • describe the differences between R and Python;
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  • apply R and Python for basic handling of data types in each language;
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  • create new object classes and use inheritance.
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What are R and Python?

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If you ask an average person what Python is, they will tell you that it’s a type of snake. If you ask an average person what R is, they will tell you that it’s a letter of the alphabet. Today you will learn beyond that: the details of the programming languages of R and Python!

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Both R and Python programming languages were created at around the same time: Python was created in 1991 in the Netherlands, and R in 1993. However, R actually has a much longer history, because it is an extension of the S programming language that was created all the way back in 1976 in Bell Labs. R was created specifically as a statistical package, similar to SPSS or Stata. However, it has since evolved into a general-purpose programming language. Due to its focus on statistics, R is mainly used in academia (schools and universities).

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Python was created as a general-purpose programming language. It was designed with readability in mind, and it has become the most popular programming language in the world. Python is therefore widely used in the industry, such as startups and large corporations. Python is especially strong in the field of deep learning, since packages such as Google’s TensorFlow are implemented primarily in Python. Additionally, because of it’s popularity, it is very easy to find online examples and it is used often to communicate with other software.

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In terms of spatial data handling, both R and Python can perform the same tasks. Both are relatively easy to integrate with other software as well, which allows extending the capabilities of each language. For example, the open source GIS software QGIS, including all its plugins, are written in Python. R scripts can be run directly from the QGIS interface as processing tools. R has a package reticulate that can run Python scripts, and Python has a package rpy2 that can run R scripts.

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Both R and Python have extremely active communities maintaining a wealth of packages. The package ecosystems differ significantly between the two languages, however. Python packages are hosted on PyPI, the Python Package Index, which is a very easily accessible place where anyone can upload the Python packages they create. There is minimal oversight over them and the package authors are free to do with their packages what they please. The R package repository is called CRAN, the Comprehensive R Archive Network. CRAN is curated: any package submitted to CRAN goes through a review process, and has to pass a suite of tests before it gets accepted. This ensures that, among other things, every argument in every function in every package is documented, and that each package is compatible with all the other packages in CRAN. The result is that it is much more difficult to publish R packages, but the quality of R packages is generally much higher than Python packages. It also means that package management in R is very easy, as no conflicts can happen between packages. Packages are also more inclined to depend on other packages, as it is more certain that their dependencies will stay maintained. In contrast, Python has a lot more packages, but they are often poorly documented and often do not interoperate with other packages as well. Package management is a big issue in Python, as many packages work only with certain versions of other packages.

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In practice, this means that while all geospatial data handling tasks can be done in R or Python, R is better integrated for this. In this course you will learn about the packages terra for raster handling and sf for vector handling, which are both integrated with each other and offer a full suite of processing tools, and all of the other packages that use vectors or rasters in R also make use of sf and terra package objects. In Python, raster reading is done using the rasterio package, but processing needs to be done using other packages, many of which do not support rasterio objects. Vector handling is done in for example geopandas, which has spatial processing tools, but once again they are not always integrated with raster object support. So rasters often need to be converted into number matrices for processing, then converted back into raster objects, by the user.

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Both R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in plot() function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, ggplot2 can be used. In Python, the standard plotting package is matplotlib. Spatial packages often implement their own plotting functions for their own objects, therefore putting multiple objects into one plot can often be more challenging than in R.

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Ultimately, the choice of language to use is often decided by interoperability needs (e.g. if you work in a company that uses Python, you will be expected to also write in Python, so that your script can be used in a pipeline) and personal preference.

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Running Python and R from Bash

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Both the R and the python interpreters can be run from Bash. Here is an example of code that is correct in both Python and R. You can use this example to execute a script written in either language:

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# Create a script file "script" (no extension)
+echo 'print("Hello world!")' > script
+# Run the script with R
+Rscript ./script
+# Run the script with Python
+python3 ./script
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+

This allows you to run script files even without having any graphical user interface installed or running, which is the fastest way to run any script from top to bottom. This way, you can even combine R and Python code from a Bash script!

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Now, let’s explore the similarities and differences between the two languages. For that, you need to have Python set up. Let’s install the needed packages through the Bash terminal:

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sudo apt install python3-pandas
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Now you can run R in one terminal, and run Python in another, to try out the code snippets below. To run R in interactive mode:

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R
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To run Python in interactive mode:

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python3
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Data types

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Both Python and R provide a set of primitive data types. The ones they have in common are integers, floating-point numbers, logical boolean values, character strings, associative arrays (dictionaries) and lists. To find out the type of a variable, use type() in Python and class() in R.

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A whole number is called an integer, and a number with decimals (real number) is called a float (floating point number). In Python, if you enter a number without decimal points, it will be an integer, otherwise it will be a float.

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type(10)
+type(10.1)
+type(10.)
+type(int(10.))
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In R, any number will be a float (called “numeric”) by default, and integers are obtained by explicitly casting to an integer using as.integer():

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class(10)
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[1] "numeric"
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class(10.1)
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[1] "numeric"
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class(as.integer(10))
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[1] "integer"
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+Question 1: What is the type/class of the sum 3+5 and of 3.0+5 in R and in Python? Write a sum of two numbers that returns an integer in R and in Python. +

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Boolean, or logical, types can only have two values: true or false. When cast to an integer, true is represented by 1 and false is represented by 0. Both R and Python are case-sensitive, and use different cases to represent booleans (True in Python and TRUE in R).

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type(True)
+type(True + 2)
+True + 2
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class(TRUE)
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[1] "logical"
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class(TRUE + 2)
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[1] "numeric"
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TRUE + 2
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[1] 3
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Character strings are letters and words. They can also include numbers, but the numbers do not have a mathematical meaning, and therefore you cannot do arithmetics on strings. Strings are marked with quotes, either single or double:

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"Hello world #1!"
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'Hello world #1!'
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5 + '6'
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TypeError: unsupported operand type(s) for +: 'int' and 'str'
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type('6')
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<class 'str'>
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"Hello world #1!"
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[1] "Hello world #1!"
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5 + '6'
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Error in 5 + "6": non-numeric argument to binary operator
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class('6')
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[1] "character"
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You can combine strings to produce longer strings. This is done with the function paste() in R and using the + operator in Python (as long as all parts are strings).

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WorldNr = 2
+"Hello world #" + str(WorldNr) + "!"
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WorldNr = 2
+paste("Hello world #", WorldNr, "!", sep="") # The 'sep' argument avoids adding spaces in between
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[1] "Hello world #2!"
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+Note that Python uses = to define a new variable or to give it a new value, as was done above for the variable WorldNr. On the other hand, R typically uses <- to assign a value to a variable. However R also accepts =. In this tutorial, we use = for R to keep it simple, but you will find later tutorials using <-. Keep in mind that either option is valid as long as it is kept consistent throughout your code! +

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Additionally, in Python, string formatting can be used, in for example a print function. To do this, we start a string with an f in front of the quote.

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WorldNr = 2
+print(f"Hello world # {WorldNr} !")
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A variable can also hold multiple values of a particular data type, or even mix data types. Python supports associative arrays, called dictionaries, that are created using curly braces:

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WUR = {"name": "Wageningen University", "x": 5.7, "y": 52}
+WUR
+type(WUR)
+WUR["x"]
+type(WUR["x"])
+
+

The dictionary type allows naming its elements and accessing them by name. The elements can be of any type.

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In R, a similar functionality is called a vector (not to be confused with the geographical data type “vector”!). All variables we have created so far have been vectors of length 1. They didn’t have names, but you can give names to elements of a vector. However, unlike Python dictionaries, vectors can only contain elements of the same type, and any items of a different type will get automatically converted to fit the rest. We create vectors longer than the size of one by using the concatenation function c():

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WUR = c(name = "Wageningen University", x = 5.7, y=52)
+WUR
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                   name                       x                       y 
+"Wageningen University"                   "5.7"                    "52"
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class(WUR)
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[1] "character"
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WUR["x"]
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    x 
+"5.7"
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class(WUR["x"])
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[1] "character"
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As you can see, the numbers have been implicitly converted (“coerced” in programming terms) into characters. The disadvantage of named vectors over dictionaries is that we can only use the same type in a single vector. The advantage is that it allows us to use vectorised functions, i.e. functions that perform some work on each element of a vector. Because we know that the vector is going to be entirely composed of characters, we can do something like:

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paste(WUR, "addition!")
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[1] "Wageningen University addition!" "5.7 addition!"                  
+[3] "52 addition!"
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+
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Both R and Python support lists, which allow combining any data types into one variable. In Python they are created using square brackets, and in R using list():

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WUR = ["Wageningen University", 5.7, 52]
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WUR = list("Wageningen University", 5.7, 52)
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Elements of a list are sequential and can be accessed by slicing the list using a number (index) in square brackets. An important difference between the two languages is that R starts counting from 1, but Python starts counting from 0! This is similar to how in different countries, floors of buildings are counted starting from 1 or from 0. Netherlands is a “Python” style country where the ground floor is number 0, whereas Canada is an “R” style country where the ground floor is number 1. The R style has the advantage of the index matching the element number, i.e. [2] will give you the second element, where in Python the second element is [1]:

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WUR[1]
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WUR[2]
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[[1]]
+[1] 5.7
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There is also a difference in what happens if you use a negative index. Python uses negative indices to wrap around, so [-1] means “last element”, whereas in R it stands for exclusion, so [-1] means “all elements except for the first one”:

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WUR[-1]
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WUR[-1]
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[[1]]
+[1] 5.7
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+[[2]]
+[1] 52
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+
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What is the exact equivalent of a dictionary in R? It’s a named list! When creating a list, you can specify a name of each element, and then slice the list using names. Unlike the colon : used in Python for dictionaries, R simply uses the equal sign =:

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WUR = list("name"="Wageningen University", "x"=5.7, "y"=52)
+WUR
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$name
+[1] "Wageningen University"
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+$x
+[1] 5.7
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+$y
+[1] 52
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class(WUR)
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[1] "list"
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WUR["x"]
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$x
+[1] 5.7
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class(WUR["x"])
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[1] "list"
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+
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Here we can see another difference in how R and Python deal with lists. In R, a list always consists of lists, recursively. Each element of a list is always a list itself. To obtain the value, we need to access it using the double square bracket operator:

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WUR[["x"]]
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[1] 5.7
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class(WUR[["x"]])
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[1] "numeric"
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+

There are several data types that are specific to R, though there are Python packages that implement equivalent functionality as well. The most basic type is a vector, which can hold multiple values of the same type. An extension of a vector is a matrix, that has two dimensions. Adding further dimensions, we get an array. A matrix is a special case of an array (two-dimensional), as is a vector (one-dimensional array).

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As we have covered above, in R, almost everything appears as a vector. That is why in the R print output above you can see [1] next to most output, indicating that the value is just the first in a 1-length vector. To make longer vectors, the function c() is used. While you can give names to elements, it’s optional, we can just omit them:

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WURcoords = c(5.7, 52)
+WURcoords
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[1]  5.7 52.0
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class(WURcoords)
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[1] "numeric"
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+
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Matrices are made using the function matrix by combining multiple vectors:

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# `nrow` specifies how many rows the matrix will have
+LocMat = matrix(c(WURcoords, WURcoords + 1), nrow = 2)
+LocMat
+
+
     [,1] [,2]
+[1,]  5.7  6.7
+[2,] 52.0 53.0
+
+
class(LocMat)
+
+
[1] "matrix" "array"
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+
+
+
+
+

+Question 2: Using the c() and matrix() functions, build a tic-tac-toe board with X’s and O’s in R. +

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+
+
+

And arrays of higher order are likewise created using array():

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# Here "dim" defines the shape, i.e. number of rows, columns, layers, etc.
+LocArray = array(c(LocMat, LocMat+1), dim=c(2,2,2))
+LocArray
+
+
, , 1
+
+     [,1] [,2]
+[1,]  5.7  6.7
+[2,] 52.0 53.0
+
+, , 2
+
+     [,1] [,2]
+[1,]  6.7  7.7
+[2,] 53.0 54.0
+
+
class(LocArray)
+
+
[1] "array"
+
+
+

As you can see, we have a cube with two elements in each dimension. Cubes are difficult to visualise, as they are 3D, and our screen is 2D. Therefore, when trying to print one, R presents the cube in “slices” of matrices.

+

Base Python can only achieve a similar effect using nested lists:

+
+
WURcoords = [5.7, 52]
+WURcoords
+LocMat = [WURcoords, WURcoords]
+LocMat
+LocArray = [LocMat, LocMat]
+LocArray
+
+

The structure is also a cube, but it is less structured, and therefore printing it does not make it obvious that it is a cube.

+

Here we can also notice that R can perform vectorised arithmetics: WURcoords + 1 added 1 to each value of the vector WURcoords, and LocMat + 1 added 1 to each value of the matrix LocMat. Core Python does not allow doing so without writing a loop. However, since arrays and vectorised arithmetics are very useful, it has all been implemented in the package NumPy, which is now considered to be an essential package in Python:

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+
import numpy
+
+npWURcoords = numpy.array(WURcoords)
+npWURcoords
+type(npWURcoords)
+
+npLocMat = numpy.array([npWURcoords, npWURcoords + 1])
+npLocMat
+type(npLocMat)
+
+npLocArray = numpy.array([npLocMat, npLocMat + 1])
+npLocArray
+type(npLocArray)
+
+

As we can see here, NumPy arrays are more structured, and when printing a cube, it also shown sliced, just like in the R example above. The NumPy output also includes square brackets, which you will notice are in the same order and quanity as the plain Python list example we had above, implying that it is ultimately still a list-of-lists.

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+
+
+

+Question 3: Make the same tic-tac-toe board as in question 2 in Python. Hint: You may wish to use numpy. +

+
+
+
+

Another useful data type in R is Data Frames. A Data Frame is a table, similar to a matrix, but with one key difference: while matrices require all values to be of the same type, a data.frame only requires each column to have a consistent type. The Data Frame concept comes from R’s statistical background, where it is useful to have multiple variables that are being studied, as columns, and multiple records or observations of those values, as rows. For example:

+
+
WURbuildings = data.frame(name = c("Gaia", "Aurora"), x = c(5.665, 5.657), y = c(51.987, 51.982))
+WURbuildings
+
+
    name     x      y
+1   Gaia 5.665 51.987
+2 Aurora 5.657 51.982
+
+
class(WURbuildings)
+
+
[1] "data.frame"
+
+
+

To know the type of each column, we can use the function str (structure):

+
+
str(WURbuildings)
+
+
'data.frame':   2 obs. of  3 variables:
+ $ name: chr  "Gaia" "Aurora"
+ $ x   : num  5.66 5.66
+ $ y   : num  52 52
+
+
+

We see here that the name column is made of character strings, whereas x and y columns are floating point numbers. The Data Frame gives more structure than a plain list does, ensuring that the data has rows and columns and that the column types are consistent.

+

In Python, Data Frames are implemented in the package pandas:

+
+
import pandas
+WURbuildings = pandas.DataFrame({"name": ["Gaia", "Aurora"],  "x": [5.665, 5.657], "y": [51.987, 51.982]})
+WURbuildings
+type(WURbuildings)
+WURbuildings.dtypes
+
+

The .dtypes accessor is equivalent to str() in R, though you can see one difference: the strings are reported as “objects”.

+
+
+

Slicing and accessors

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When dealing with variables that hold multiple values, we often need to select some smaller subset of those values. This is called slicing an array. It is done using operators that are called accessors. We used three of them in the examples above: [] (both languages), [[]] (R) and . (Python). These are the main accessors, though there are a few others that the languages support.

+

The [] accessor accepts indices of values to select. As we saw earlier, the indices can also be negative, or a combination of positive and negative. Both R and Python have a way to quickly create ranges for slicing arrays, using the : operator, but there are some differences in how the ranges behave:

+
+
Buildings = c("Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas")
+Buildings[2:4] # We get the second, third and fourth elements
+
+
[1] "Lumen" "Forum" "Orion"
+
+
+
+
Buildings = ["Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas"]
+Buildings[2:4] # We get the third and fourth elements
+
+

As we can see, slicing in R is inclusive, i.e. any number you mention will be included in the resulting slice. In Python, it is one-sided: the fist number of the range will be included, but the last number will be excluded. And, of course, Python counts from 0 whereas R counts from 1.

+

Let’s try to slice the first half and the second half of the string array in both languages:

+
+
Buildings[1:3]
+
+
[1] "Gaia"  "Lumen" "Forum"
+
+
Buildings[4:6]
+
+
[1] "Orion"  "Aurora" "Atlas"
+
+
+
+
Buildings[:3] # The fourth element is not included
+Buildings[3:] # It is included here
+
+

As you see, Python interprets the missing number as the first or the last element of the array, depending on it’s missing before or after the :. R does not, and requires you to enter 1 for the first element and length(YourArray) (e.g. length(Buildings)) for the last one.

+
+
+
+

+Question 4: Create a subset of Buildings with only Gaia and Aurora, using index slicing in R and Python. Hint: You may wish to use the functions c() for R. To vectorize a list in Python, you can use numpy.array(), but creating a plain list doesn’t require it :) +

+
+
+
+

When slicing two-dimensional arrays (matrices or data frames), two indices are used, separated by a comma, in the order [row, column]:

+
+
LocMat
+
+
     [,1] [,2]
+[1,]  5.7  6.7
+[2,] 52.0 53.0
+
+
LocMat[2, 1] # Get y coordinate of WUR campus
+
+
[1] 52
+
+
+
+
npLocMat
+npLocMat[0, 1] # Get y coordinate of WUR campus
+
+

If you want to select the whole row/column, without manually specifying the size of your array, in Python you can use :, and in R you can omit the index:

+
+
LocMat[, 1] # Get both coordinates of WUR campus
+
+
[1]  5.7 52.0
+
+
+
+
npLocMat[0, :] # Get both coordinates of WUR campus
+
+

Likewise, if we have an array with more dimensions, we can specify more indices (e.g. a three-dimensional array will accept three indices, x, y, z).

+

Previously we saw another accessor in R, namely, the [[]] accessor. It is a list accessor, and generally is used to directly access a value, treating the variable as a list. The accessor accepts numbers and character strings as input. Another accessor popular in R is the $ accessor, which allows accessing values by name. The $ accessor is generally equivalent to the [[]] accessor with a string input. Here’s an example of how these work with data frames:

+
+
WURbuildings
+
+
    name     x      y
+1   Gaia 5.665 51.987
+2 Aurora 5.657 51.982
+
+
WURbuildings[1] # When accessed using a single number, it selects a column
+
+
    name
+1   Gaia
+2 Aurora
+
+
class(WURbuildings[1]) # Single-column data.frame
+
+
[1] "data.frame"
+
+
WURbuildings[[1]] # Also selects a column
+
+
[1] "Gaia"   "Aurora"
+
+
class(WURbuildings[[1]]) # But now the result is a vector!
+
+
[1] "character"
+
+
WURbuildings[["name"]] # Column by name
+
+
[1] "Gaia"   "Aurora"
+
+
WURbuildings$name # Same
+
+
[1] "Gaia"   "Aurora"
+
+
+

Generally the $ accessor is not recommended, because it does not allow the use of variables, it only works with literal strings.

+

Note that in Python, the operator [[]] has a very different meaning: the outer [] is the accessor, and the inner [] is a list constructor, in other words, it’s equivalent to the R [c()]:

+
+
npLocArray = numpy.array([npLocMat, npLocMat + 1, npLocMat + 2, npLocMat + 3])
+npLocArray
+npLocArray[0,1] # Meaning "first layer, second row, all columns"
+npLocArray[[0,2]] # Meaning "first and third layers, all rows and all columns"
+
+

Similar to the R $ accessor, Python has the accessor . to select by literal string:

+
+
WURbuildings
+WURbuildings["name"]
+WURbuildings.name
+
+

Instead of supplying numbers or strings, we can also slice by using a boolean array of the same dimensions as what we are trying to slice. This is very handy, as we can make use of this to select by a rule:

+
+
LocMat
+
+
     [,1] [,2]
+[1,]  5.7  6.7
+[2,] 52.0 53.0
+
+
LocMat[LocMat > 50] # Get only the values above 50
+
+
[1] 52 53
+
+
+

What happens here is that the inner part of the accessor generates a boolean array, which is subsequently used for slicing, as if it was a mask:

+
+
LocMat > 50
+
+
      [,1]  [,2]
+[1,] FALSE FALSE
+[2,]  TRUE  TRUE
+
+
+

The same applies in NumPy:

+
+
npLocMat
+npLocMat[npLocMat > 50]
+
+
+
+
+

+Question 5: In R and Python, slice LocMat to select all values below 6 and above 52. Hint: You can combine conditions by using & (AND) or | (OR). Note: In Python, always use parentheses around each condition when combining them — otherwise, operator precedence may cause unexpected behavior. +

+
+
+
+
+
+

Conditionals and control flow

+

We just saw that we can slice arrays based on conditions. Note that the boolean operators & and | (for and and or respectively) are valid in both R and Python, but they mean different things!

+

In R, & is a vectorised form of &&, in other words, it can compare boolean vectors against each other:

+
+
c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE)
+
+
[1]  TRUE FALSE FALSE FALSE
+
+
+

This is good in some cases, such as slicing arrays using boolean arrays (like in Question 5, where && doesn’t really work), and not so good in other cases, like if statements that control program flow. This is because if requires a single TRUE or FALSE, not an array.

+

The conditionals if and else are used to structure our code, so that part of the code only runs when certain conditions are met. In this case, if (c(TRUE, FALSE)) is impossible to determine and thus will result in an error. The form && only compares one boolean with another boolean, which makes sure you do not accidentally use arrays where a single boolean is needed, and thus is suitable.

+

Here’s an example in R:

+
+
MyLatitude = 52
+GaiaLatitude = WURbuildings[WURbuildings[["name"]] == "Gaia", "y"]
+AuroraLatitude = WURbuildings[WURbuildings[["name"]] == "Aurora", "y"]
+
+if (MyLatitude > GaiaLatitude && MyLatitude > AuroraLatitude) {
+  print("I am to the north of both Gaia and Aurora")
+} else if (MyLatitude < GaiaLatitude && MyLatitude < AuroraLatitude) {
+  print("I am to the south of both Gaia and Aurora")
+} else {
+  print("I am in between Gaia and Aurora")
+}
+
+
[1] "I am to the north of both Gaia and Aurora"
+
+
+

The & operator can still work in if statements when both sides are single boolean values. However, it is less strict and may lead to unexpected behavior if vectors are accidentally passed.

+

In Python, & is a bitwise comparison, that is, it does not work on booleans, but numbers. It just so happens that Python implicitly converts booleans to numbers (True to 1 and False to 0), so it often works with booleans as well, but it is not intended for that. This behavior makes them useful in contexts like Question 5, where we compare an array of values against numeric thresholds and want element-wise boolean logic. Instead, the regular boolean comparison is done using and, the logical comparison. It expects the entire variable to represent a single boolean value (True or False). That’s why and or or cannot be used for element-wise slicing in Question 5. So just like in R, when you need a single boolean result to control program flow in if statement, and is more appropriate than &.

+

Equivalent example code in Python (note the syntax differs a bit between R and Python, e.g. the lack of parentheses, the words for and and or, and the special elif keyword):

+
+
MyLatitude = 52
+# Note that boolean conditionals only work with regular booleans, not with Series/DataFrames, hence the .values[0]
+GaiaLatitude = WURbuildings.loc[WURbuildings["name"] == "Gaia", "y"].values[0]
+AuroraLatitude = WURbuildings.loc[WURbuildings["name"] == "Aurora", "y"].values[0]
+
+if MyLatitude > GaiaLatitude and MyLatitude > AuroraLatitude:
+  print("I am to the north of both Gaia and Aurora")
+elif MyLatitude < GaiaLatitude and MyLatitude < AuroraLatitude:
+  print("I am to the south of both Gaia and Aurora")
+else:
+  print("I am in between Gaia and Aurora")
+
+
+
+
+

+Question 6: Why don’t we need parentheses here in this Python code? Hint: it’s something to do with operator precedence. +

+
+
+
+
+
+

Objects and inheritance

+

Both R and Python are object-oriented languages, and in this tutorial we have already worked with many objects. For example, data frames, lists and matrices are objects, and the class() or type() function prints what type of object it is, in other words, what is the class of the object. In R, str() allows investigating the structure of an object. Python does not have a unified function for this and different packages implement this functionality differently, if at all.

+
+
WUR = list("name"="Wageningen University", "x"=5.7, "y"=52)
+str(WUR) # Show the structure of a list
+
+
List of 3
+ $ name: chr "Wageningen University"
+ $ x   : num 5.7
+ $ y   : num 52
+
+
WURbuildings
+
+
    name     x      y
+1   Gaia 5.665 51.987
+2 Aurora 5.657 51.982
+
+
str(WURbuildings) # Show the structure of a data frame (which is also a type of list)
+
+
'data.frame':   2 obs. of  3 variables:
+ $ name: chr  "Gaia" "Aurora"
+ $ x   : num  5.66 5.66
+ $ y   : num  52 52
+
+
+
+
WURbuildings # pandas data frame
+WURbuildings.dtypes # Shows data types
+vars(WURbuildings) # More generic, but does not work with lists and dicts
+dir(WURbuildings) # All of the methods contained in the object
+
+

In the last example we can see that objects can contain other objects and/or functions. A class is a definition of an object, and a function contained inside an object is traditionally called a method.

+

R and Python both support classes and objects, but their philosophy regarding them differs significantly. R is very lax and allows the users to freely (re)define object classes as they see fit. Python is a lot more formal and requires a formal class definition to define an object class. Python prefers self-contained objects that contain all the methods that can interact with the object embedded inside the object. In contrast, the R philosophy is to define global functions whose behaviour is different depending on the class the function is run on.

+

As an example, we might want to define a class building which, when instantiated as an object, will contain a name, and a vector coordinates. We want to also have a function that prints these attributes in a nice to read way. This is how it would be done in R:

+
+
Gaia = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987))
+class(Gaia) # It is a list
+
+
[1] "list"
+
+
class(Gaia) = "building" # And now it's a `building`!
+class(Gaia)
+
+
[1] "building"
+
+
Aurora = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982))
+class(Aurora) = "building"
+class(Aurora)
+
+
[1] "building"
+
+
# Define a function to print buildings
+# We use the "paste" function to format strings. 
+print.building = function(x)
+{
+  print(paste(x[["name"]], "is a building that is located at x:",
+    x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"]))
+}
+# Now we simply use print() and get our custom output:
+print(Gaia)
+
+
[1] "Gaia is a building that is located at x: 5.665 y: 51.987"
+
+
print(Aurora)
+
+
[1] "Aurora is a building that is located at x: 5.657 y: 51.982"
+
+
+

The reason why this works is because R uses the concept of function signatures. When running print(), R will first check what is the class of the object you are calling the function on, and checks if there is a function defined that is called print.class (in our case print.building). On a match, it will run that function instead of the print.default function.

+

In Python, we need to formally define a class and then instantiate it:

+
+
class building:
+  def __init__(self, name, coordinates):
+    self.name = name
+    self.coordinates = coordinates
+
+  def print(self):
+    # Note that we use "string formatting". If a "f" is put before a string quote, 
+    # all text inside curly brackets will be executed as plain Python 
+    print(f'{self.name} is a building that is located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}')
+
+# Instantiate the class by calling it as if it was its __init__ function
+Gaia = building(name="Gaia", coordinates={"x": 5.665, "y": 51.987})
+Aurora = building(name="Aurora", coordinates={"x": 5.657, "y": 51.982})
+type(Gaia)
+type(Aurora)
+Gaia.print()
+Aurora.print()
+
+

Note that we called the method print() that is inside our object, not the global function print(), which would give a different output:

+
+
print(Gaia)
+print(Aurora)
+
+

A key concept in object-oriented programming is inheritance: a class can inherit properties and methods of its parent class. This allows us to make extensions of existing classes without having to duplicate a lot of work. Let’s say we want to extend our building class with an attribute purpose, calling the new child class purposeBuilding. In R, inheritance works by making an object part of multiple classes:

+
+
GaiaPurpose = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987), purpose="office")
+class(GaiaPurpose) = c("purposeBuilding", "building")
+print(GaiaPurpose) # We reuse the parent class `print()`
+
+
[1] "Gaia is a building that is located at x: 5.665 y: 51.987"
+
+
AuroraPurpose = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982), purpose="education")
+class(AuroraPurpose) = c("purposeBuilding", "building")
+print(AuroraPurpose)
+
+
[1] "Aurora is a building that is located at x: 5.657 y: 51.982"
+
+
# Make a custom print function for purposeBuilding
+print.purposeBuilding = function(x)
+{
+  print(paste(x[["name"]], "is an", x[["purpose"]], "building that is located at x:",
+    x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"]))
+}
+
+print(GaiaPurpose)
+
+
[1] "Gaia is an office building that is located at x: 5.665 y: 51.987"
+
+
print(AuroraPurpose)
+
+
[1] "Aurora is an education building that is located at x: 5.657 y: 51.982"
+
+
+

In Python, inheritance is also formally declared in the definition of the new class:

+
+
class purposeBuilding(building):
+  def __init__(self, name, coordinates, purpose):
+    super().__init__(name, coordinates) # Let the parent class handle these
+    self.purpose = purpose
+
+Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office")
+Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education")
+Gaia.print()
+Aurora.print()
+
+

We can override methods by redeclaring them, but any instantiated objects will have to be reinstantiated for the changes to apply:

+
+
# Let's also override the print now:
+class purposeBuilding(building):
+  def __init__(self, name, coordinates, purpose):
+    super().__init__(name, coordinates) # Let the parent class handle these
+    self.purpose = purpose
+
+  def print(self):
+    print(f'{self.name} is a building used for {self.purpose} purposes, located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}')
+  
+
+# If we don't reinstantiate, the old definition applies:
+Gaia.print()
+Aurora.print()
+
+Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office")
+Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education")
+Gaia.print()
+Aurora.print()
+
+

Note that R also has a more formalised type of classes, called S4 classes, that behave a bit more similar to the Python classes, but S4 classes are generally not recommended to use as they are further away from the R philosophy.

+
+
+

Scope and side effects

+

Another difference between R and Python you may also have noticed in the examples above: R uses curly braces {} to denote scope, whereas Python uses a colon : and enforces indentation. As an example:

+
+
def scope():
+  print("I am inside the scope of the function scope()!")
+  print("I am also inside the scope of the function scope()!")
+print("I am outside the scope of the function scope()!")
+
+scope()
+
+
+
scope = function() {
+  print("I am inside the scope of the function scope()!")
+  print("I am also inside the scope of the function scope()!")
+}
+  print("I am outside the scope of the function scope()!")
+
+
[1] "I am outside the scope of the function scope()!"
+
+
scope()
+
+
[1] "I am inside the scope of the function scope()!"
+[1] "I am also inside the scope of the function scope()!"
+
+
+

In addition, in R, typically anything that happens inside of a function scope stays in the function, i.e. it does not alter the global state.

+
+
location = "Gaia"
+move = function(where)
+{
+  location = where
+  print(paste("Moved to", location))
+}
+
+move("Aurora")
+
+
[1] "Moved to Aurora"
+
+
print(location)
+
+
[1] "Gaia"
+
+
+

In Python it is also often true, but the list of exceptions is much longer.

+
+
location = "Gaia"
+def move(where):
+  location = where
+  print("Moved to " + location)
+
+move("Aurora")
+print(location)
+
+

For instance, lists are mutable even outside of the function scope:

+
+
locations = ["Gaia"]
+def addLocation(where):
+  locations.append(where)
+  print(locations)
+
+addLocation("Aurora")
+print(locations)
+
+

Such mutability that goes outside of the function scope is called a side effect. They are best avoided as much as possible, as it brings confusion to the users (and may even destroy their work)! Normally, you expect that if you as a user run a function, it will process your arguments and return a new, processed object, but will not change your global environment, or the object that you passed to the function itself.

+

To avoid such side effects, in Python we need to explicitly copy mutable objects:

+
+
locations = ["Gaia"]
+def addLocation(where):
+  result = locations.copy()
+  result.append(where)
+  print(result)
+
+addLocation("Aurora")
+print(locations)
+
+
+
+

Summary

+

We have looked at some similarities and differences between R and Python. As you can see, the same or similar functionality is available in both languages, but the philosophies of the languages are sometimes different, so it is important to be aware of them. We will make extensive use of the basics in the upcoming tutorials, and will build further upon them to specifically handle spatial data. We will first go through the specifics of R, then the specifics of Python, but it’s good to keep in mind throughout the course that both languages can do what we want, just in a slightly different way. You can use this tutorial as a reference for the basics if you get stuck in future tutorials. Calling R and Python from Bash can also be a very useful technique to combine them during the project at the end of the course.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_site/search.json b/_site/search.json new file mode 100644 index 0000000..0d34416 --- /dev/null +++ b/_site/search.json @@ -0,0 +1,72 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "R & Python basics", + "section": "", + "text": "WUR Geoscripting" + }, + { + "objectID": "index.html#learning-objectives-of-the-day", + "href": "index.html#learning-objectives-of-the-day", + "title": "R & Python basics", + "section": "Learning objectives of the day", + "text": "Learning objectives of the day\nIn this tutorial, you will learn to:\n\ndescribe the differences between R and Python;\napply R and Python for basic handling of data types in each language;\ncreate new object classes and use inheritance." + }, + { + "objectID": "index.html#what-are-r-and-python", + "href": "index.html#what-are-r-and-python", + "title": "R & Python basics", + "section": "What are R and Python?", + "text": "What are R and Python?\nIf you ask an average person what Python is, they will tell you that it’s a type of snake. If you ask an average person what R is, they will tell you that it’s a letter of the alphabet. Today you will learn beyond that: the details of the programming languages of R and Python!\nBoth R and Python programming languages were created at around the same time: Python was created in 1991 in the Netherlands, and R in 1993. However, R actually has a much longer history, because it is an extension of the S programming language that was created all the way back in 1976 in Bell Labs. R was created specifically as a statistical package, similar to SPSS or Stata. However, it has since evolved into a general-purpose programming language. Due to its focus on statistics, R is mainly used in academia (schools and universities).\nPython was created as a general-purpose programming language. It was designed with readability in mind, and it has become the most popular programming language in the world. Python is therefore widely used in the industry, such as startups and large corporations. Python is especially strong in the field of deep learning, since packages such as Google’s TensorFlow are implemented primarily in Python. Additionally, because of it’s popularity, it is very easy to find online examples and it is used often to communicate with other software.\nIn terms of spatial data handling, both R and Python can perform the same tasks. Both are relatively easy to integrate with other software as well, which allows extending the capabilities of each language. For example, the open source GIS software QGIS, including all its plugins, are written in Python. R scripts can be run directly from the QGIS interface as processing tools. R has a package reticulate that can run Python scripts, and Python has a package rpy2 that can run R scripts.\nBoth R and Python have extremely active communities maintaining a wealth of packages. The package ecosystems differ significantly between the two languages, however. Python packages are hosted on PyPI, the Python Package Index, which is a very easily accessible place where anyone can upload the Python packages they create. There is minimal oversight over them and the package authors are free to do with their packages what they please. The R package repository is called CRAN, the Comprehensive R Archive Network. CRAN is curated: any package submitted to CRAN goes through a review process, and has to pass a suite of tests before it gets accepted. This ensures that, among other things, every argument in every function in every package is documented, and that each package is compatible with all the other packages in CRAN. The result is that it is much more difficult to publish R packages, but the quality of R packages is generally much higher than Python packages. It also means that package management in R is very easy, as no conflicts can happen between packages. Packages are also more inclined to depend on other packages, as it is more certain that their dependencies will stay maintained. In contrast, Python has a lot more packages, but they are often poorly documented and often do not interoperate with other packages as well. Package management is a big issue in Python, as many packages work only with certain versions of other packages.\nIn practice, this means that while all geospatial data handling tasks can be done in R or Python, R is better integrated for this. In this course you will learn about the packages terra for raster handling and sf for vector handling, which are both integrated with each other and offer a full suite of processing tools, and all of the other packages that use vectors or rasters in R also make use of sf and terra package objects. In Python, raster reading is done using the rasterio package, but processing needs to be done using other packages, many of which do not support rasterio objects. Vector handling is done in for example geopandas, which has spatial processing tools, but once again they are not always integrated with raster object support. So rasters often need to be converted into number matrices for processing, then converted back into raster objects, by the user.\nBoth R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in plot() function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, ggplot2 can be used. In Python, the standard plotting package is matplotlib. Spatial packages often implement their own plotting functions for their own objects, therefore putting multiple objects into one plot can often be more challenging than in R.\nUltimately, the choice of language to use is often decided by interoperability needs (e.g. if you work in a company that uses Python, you will be expected to also write in Python, so that your script can be used in a pipeline) and personal preference." + }, + { + "objectID": "index.html#running-python-and-r-from-bash", + "href": "index.html#running-python-and-r-from-bash", + "title": "R & Python basics", + "section": "Running Python and R from Bash", + "text": "Running Python and R from Bash\nBoth the R and the python interpreters can be run from Bash. Here is an example of code that is correct in both Python and R. You can use this example to execute a script written in either language:\n\n# Create a script file \"script\" (no extension)\necho 'print(\"Hello world!\")' > script\n# Run the script with R\nRscript ./script\n# Run the script with Python\npython3 ./script\n\nThis allows you to run script files even without having any graphical user interface installed or running, which is the fastest way to run any script from top to bottom. This way, you can even combine R and Python code from a Bash script!\nNow, let’s explore the similarities and differences between the two languages. For that, you need to have Python set up. Let’s install the needed packages through the Bash terminal:\n\nsudo apt install python3-pandas\n\nNow you can run R in one terminal, and run Python in another, to try out the code snippets below. To run R in interactive mode:\n\nR\n\nTo run Python in interactive mode:\n\npython3" + }, + { + "objectID": "index.html#data-types", + "href": "index.html#data-types", + "title": "R & Python basics", + "section": "Data types", + "text": "Data types\nBoth Python and R provide a set of primitive data types. The ones they have in common are integers, floating-point numbers, logical boolean values, character strings, associative arrays (dictionaries) and lists. To find out the type of a variable, use type() in Python and class() in R.\nA whole number is called an integer, and a number with decimals (real number) is called a float (floating point number). In Python, if you enter a number without decimal points, it will be an integer, otherwise it will be a float.\n\ntype(10)\ntype(10.1)\ntype(10.)\ntype(int(10.))\n\nIn R, any number will be a float (called “numeric”) by default, and integers are obtained by explicitly casting to an integer using as.integer():\n\nclass(10)\n\n[1] \"numeric\"\n\nclass(10.1)\n\n[1] \"numeric\"\n\nclass(as.integer(10))\n\n[1] \"integer\"\n\n\n\n\n\n\nQuestion 1: What is the type/class of the sum 3+5 and of 3.0+5 in R and in Python? Write a sum of two numbers that returns an integer in R and in Python.\n\n\n\n\nBoolean, or logical, types can only have two values: true or false. When cast to an integer, true is represented by 1 and false is represented by 0. Both R and Python are case-sensitive, and use different cases to represent booleans (True in Python and TRUE in R).\n\ntype(True)\ntype(True + 2)\nTrue + 2\n\n\nclass(TRUE)\n\n[1] \"logical\"\n\nclass(TRUE + 2)\n\n[1] \"numeric\"\n\nTRUE + 2\n\n[1] 3\n\n\nCharacter strings are letters and words. They can also include numbers, but the numbers do not have a mathematical meaning, and therefore you cannot do arithmetics on strings. Strings are marked with quotes, either single or double:\n\n\"Hello world #1!\"\n\n'Hello world #1!'\n\n5 + '6'\n\nTypeError: unsupported operand type(s) for +: 'int' and 'str'\n\ntype('6')\n\n<class 'str'>\n\n\n\n\"Hello world #1!\"\n\n[1] \"Hello world #1!\"\n\n5 + '6'\n\nError in 5 + \"6\": non-numeric argument to binary operator\n\nclass('6')\n\n[1] \"character\"\n\n\nYou can combine strings to produce longer strings. This is done with the function paste() in R and using the + operator in Python (as long as all parts are strings).\n\nWorldNr = 2\n\"Hello world #\" + str(WorldNr) + \"!\"\n\n\nWorldNr = 2\npaste(\"Hello world #\", WorldNr, \"!\", sep=\"\") # The 'sep' argument avoids adding spaces in between\n\n[1] \"Hello world #2!\"\n\n\n\n\n\nNote that Python uses = to define a new variable or to give it a new value, as was done above for the variable WorldNr. On the other hand, R typically uses <- to assign a value to a variable. However R also accepts =. In this tutorial, we use = for R to keep it simple, but you will find later tutorials using <-. Keep in mind that either option is valid as long as it is kept consistent throughout your code!\n\n\n\nAdditionally, in Python, string formatting can be used, in for example a print function. To do this, we start a string with an f in front of the quote.\n\nWorldNr = 2\nprint(f\"Hello world # {WorldNr} !\")\n\nA variable can also hold multiple values of a particular data type, or even mix data types. Python supports associative arrays, called dictionaries, that are created using curly braces:\n\nWUR = {\"name\": \"Wageningen University\", \"x\": 5.7, \"y\": 52}\nWUR\ntype(WUR)\nWUR[\"x\"]\ntype(WUR[\"x\"])\n\nThe dictionary type allows naming its elements and accessing them by name. The elements can be of any type.\nIn R, a similar functionality is called a vector (not to be confused with the geographical data type “vector”!). All variables we have created so far have been vectors of length 1. They didn’t have names, but you can give names to elements of a vector. However, unlike Python dictionaries, vectors can only contain elements of the same type, and any items of a different type will get automatically converted to fit the rest. We create vectors longer than the size of one by using the concatenation function c():\n\nWUR = c(name = \"Wageningen University\", x = 5.7, y=52)\nWUR\n\n name x y \n\"Wageningen University\" \"5.7\" \"52\" \n\nclass(WUR)\n\n[1] \"character\"\n\nWUR[\"x\"]\n\n x \n\"5.7\" \n\nclass(WUR[\"x\"])\n\n[1] \"character\"\n\n\nAs you can see, the numbers have been implicitly converted (“coerced” in programming terms) into characters. The disadvantage of named vectors over dictionaries is that we can only use the same type in a single vector. The advantage is that it allows us to use vectorised functions, i.e. functions that perform some work on each element of a vector. Because we know that the vector is going to be entirely composed of characters, we can do something like:\n\npaste(WUR, \"addition!\")\n\n[1] \"Wageningen University addition!\" \"5.7 addition!\" \n[3] \"52 addition!\" \n\n\nBoth R and Python support lists, which allow combining any data types into one variable. In Python they are created using square brackets, and in R using list():\n\nWUR = [\"Wageningen University\", 5.7, 52]\n\n\nWUR = list(\"Wageningen University\", 5.7, 52)\n\nElements of a list are sequential and can be accessed by slicing the list using a number (index) in square brackets. An important difference between the two languages is that R starts counting from 1, but Python starts counting from 0! This is similar to how in different countries, floors of buildings are counted starting from 1 or from 0. Netherlands is a “Python” style country where the ground floor is number 0, whereas Canada is an “R” style country where the ground floor is number 1. The R style has the advantage of the index matching the element number, i.e. [2] will give you the second element, where in Python the second element is [1]:\n\nWUR[1]\n\n\nWUR[2]\n\n[[1]]\n[1] 5.7\n\n\nThere is also a difference in what happens if you use a negative index. Python uses negative indices to wrap around, so [-1] means “last element”, whereas in R it stands for exclusion, so [-1] means “all elements except for the first one”:\n\nWUR[-1]\n\n\nWUR[-1]\n\n[[1]]\n[1] 5.7\n\n[[2]]\n[1] 52\n\n\nWhat is the exact equivalent of a dictionary in R? It’s a named list! When creating a list, you can specify a name of each element, and then slice the list using names. Unlike the colon : used in Python for dictionaries, R simply uses the equal sign =:\n\nWUR = list(\"name\"=\"Wageningen University\", \"x\"=5.7, \"y\"=52)\nWUR\n\n$name\n[1] \"Wageningen University\"\n\n$x\n[1] 5.7\n\n$y\n[1] 52\n\nclass(WUR)\n\n[1] \"list\"\n\nWUR[\"x\"]\n\n$x\n[1] 5.7\n\nclass(WUR[\"x\"])\n\n[1] \"list\"\n\n\nHere we can see another difference in how R and Python deal with lists. In R, a list always consists of lists, recursively. Each element of a list is always a list itself. To obtain the value, we need to access it using the double square bracket operator:\n\nWUR[[\"x\"]]\n\n[1] 5.7\n\nclass(WUR[[\"x\"]])\n\n[1] \"numeric\"\n\n\nThere are several data types that are specific to R, though there are Python packages that implement equivalent functionality as well. The most basic type is a vector, which can hold multiple values of the same type. An extension of a vector is a matrix, that has two dimensions. Adding further dimensions, we get an array. A matrix is a special case of an array (two-dimensional), as is a vector (one-dimensional array).\nAs we have covered above, in R, almost everything appears as a vector. That is why in the R print output above you can see [1] next to most output, indicating that the value is just the first in a 1-length vector. To make longer vectors, the function c() is used. While you can give names to elements, it’s optional, we can just omit them:\n\nWURcoords = c(5.7, 52)\nWURcoords\n\n[1] 5.7 52.0\n\nclass(WURcoords)\n\n[1] \"numeric\"\n\n\nMatrices are made using the function matrix by combining multiple vectors:\n\n# `nrow` specifies how many rows the matrix will have\nLocMat = matrix(c(WURcoords, WURcoords + 1), nrow = 2)\nLocMat\n\n [,1] [,2]\n[1,] 5.7 6.7\n[2,] 52.0 53.0\n\nclass(LocMat)\n\n[1] \"matrix\" \"array\" \n\n\n\n\n\n\nQuestion 2: Using the c() and matrix() functions, build a tic-tac-toe board with X’s and O’s in R.\n\n\n\n\nAnd arrays of higher order are likewise created using array():\n\n# Here \"dim\" defines the shape, i.e. number of rows, columns, layers, etc.\nLocArray = array(c(LocMat, LocMat+1), dim=c(2,2,2))\nLocArray\n\n, , 1\n\n [,1] [,2]\n[1,] 5.7 6.7\n[2,] 52.0 53.0\n\n, , 2\n\n [,1] [,2]\n[1,] 6.7 7.7\n[2,] 53.0 54.0\n\nclass(LocArray)\n\n[1] \"array\"\n\n\nAs you can see, we have a cube with two elements in each dimension. Cubes are difficult to visualise, as they are 3D, and our screen is 2D. Therefore, when trying to print one, R presents the cube in “slices” of matrices.\nBase Python can only achieve a similar effect using nested lists:\n\nWURcoords = [5.7, 52]\nWURcoords\nLocMat = [WURcoords, WURcoords]\nLocMat\nLocArray = [LocMat, LocMat]\nLocArray\n\nThe structure is also a cube, but it is less structured, and therefore printing it does not make it obvious that it is a cube.\nHere we can also notice that R can perform vectorised arithmetics: WURcoords + 1 added 1 to each value of the vector WURcoords, and LocMat + 1 added 1 to each value of the matrix LocMat. Core Python does not allow doing so without writing a loop. However, since arrays and vectorised arithmetics are very useful, it has all been implemented in the package NumPy, which is now considered to be an essential package in Python:\n\nimport numpy\n\nnpWURcoords = numpy.array(WURcoords)\nnpWURcoords\ntype(npWURcoords)\n\nnpLocMat = numpy.array([npWURcoords, npWURcoords + 1])\nnpLocMat\ntype(npLocMat)\n\nnpLocArray = numpy.array([npLocMat, npLocMat + 1])\nnpLocArray\ntype(npLocArray)\n\nAs we can see here, NumPy arrays are more structured, and when printing a cube, it also shown sliced, just like in the R example above. The NumPy output also includes square brackets, which you will notice are in the same order and quanity as the plain Python list example we had above, implying that it is ultimately still a list-of-lists.\n\n\n\n\nQuestion 3: Make the same tic-tac-toe board as in question 2 in Python. Hint: You may wish to use numpy.\n\n\n\n\nAnother useful data type in R is Data Frames. A Data Frame is a table, similar to a matrix, but with one key difference: while matrices require all values to be of the same type, a data.frame only requires each column to have a consistent type. The Data Frame concept comes from R’s statistical background, where it is useful to have multiple variables that are being studied, as columns, and multiple records or observations of those values, as rows. For example:\n\nWURbuildings = data.frame(name = c(\"Gaia\", \"Aurora\"), x = c(5.665, 5.657), y = c(51.987, 51.982))\nWURbuildings\n\n name x y\n1 Gaia 5.665 51.987\n2 Aurora 5.657 51.982\n\nclass(WURbuildings)\n\n[1] \"data.frame\"\n\n\nTo know the type of each column, we can use the function str (structure):\n\nstr(WURbuildings)\n\n'data.frame': 2 obs. of 3 variables:\n $ name: chr \"Gaia\" \"Aurora\"\n $ x : num 5.66 5.66\n $ y : num 52 52\n\n\nWe see here that the name column is made of character strings, whereas x and y columns are floating point numbers. The Data Frame gives more structure than a plain list does, ensuring that the data has rows and columns and that the column types are consistent.\nIn Python, Data Frames are implemented in the package pandas:\n\nimport pandas\nWURbuildings = pandas.DataFrame({\"name\": [\"Gaia\", \"Aurora\"], \"x\": [5.665, 5.657], \"y\": [51.987, 51.982]})\nWURbuildings\ntype(WURbuildings)\nWURbuildings.dtypes\n\nThe .dtypes accessor is equivalent to str() in R, though you can see one difference: the strings are reported as “objects”." + }, + { + "objectID": "index.html#slicing-and-accessors", + "href": "index.html#slicing-and-accessors", + "title": "R & Python basics", + "section": "Slicing and accessors", + "text": "Slicing and accessors\nWhen dealing with variables that hold multiple values, we often need to select some smaller subset of those values. This is called slicing an array. It is done using operators that are called accessors. We used three of them in the examples above: [] (both languages), [[]] (R) and . (Python). These are the main accessors, though there are a few others that the languages support.\nThe [] accessor accepts indices of values to select. As we saw earlier, the indices can also be negative, or a combination of positive and negative. Both R and Python have a way to quickly create ranges for slicing arrays, using the : operator, but there are some differences in how the ranges behave:\n\nBuildings = c(\"Gaia\", \"Lumen\", \"Forum\", \"Orion\", \"Aurora\", \"Atlas\")\nBuildings[2:4] # We get the second, third and fourth elements\n\n[1] \"Lumen\" \"Forum\" \"Orion\"\n\n\n\nBuildings = [\"Gaia\", \"Lumen\", \"Forum\", \"Orion\", \"Aurora\", \"Atlas\"]\nBuildings[2:4] # We get the third and fourth elements\n\nAs we can see, slicing in R is inclusive, i.e. any number you mention will be included in the resulting slice. In Python, it is one-sided: the fist number of the range will be included, but the last number will be excluded. And, of course, Python counts from 0 whereas R counts from 1.\nLet’s try to slice the first half and the second half of the string array in both languages:\n\nBuildings[1:3]\n\n[1] \"Gaia\" \"Lumen\" \"Forum\"\n\nBuildings[4:6]\n\n[1] \"Orion\" \"Aurora\" \"Atlas\" \n\n\n\nBuildings[:3] # The fourth element is not included\nBuildings[3:] # It is included here\n\nAs you see, Python interprets the missing number as the first or the last element of the array, depending on it’s missing before or after the :. R does not, and requires you to enter 1 for the first element and length(YourArray) (e.g. length(Buildings)) for the last one.\n\n\n\n\nQuestion 4: Create a subset of Buildings with only Gaia and Aurora, using index slicing in R and Python. Hint: You may wish to use the functions c() for R. To vectorize a list in Python, you can use numpy.array(), but creating a plain list doesn’t require it :)\n\n\n\n\nWhen slicing two-dimensional arrays (matrices or data frames), two indices are used, separated by a comma, in the order [row, column]:\n\nLocMat\n\n [,1] [,2]\n[1,] 5.7 6.7\n[2,] 52.0 53.0\n\nLocMat[2, 1] # Get y coordinate of WUR campus\n\n[1] 52\n\n\n\nnpLocMat\nnpLocMat[0, 1] # Get y coordinate of WUR campus\n\nIf you want to select the whole row/column, without manually specifying the size of your array, in Python you can use :, and in R you can omit the index:\n\nLocMat[, 1] # Get both coordinates of WUR campus\n\n[1] 5.7 52.0\n\n\n\nnpLocMat[0, :] # Get both coordinates of WUR campus\n\nLikewise, if we have an array with more dimensions, we can specify more indices (e.g. a three-dimensional array will accept three indices, x, y, z).\nPreviously we saw another accessor in R, namely, the [[]] accessor. It is a list accessor, and generally is used to directly access a value, treating the variable as a list. The accessor accepts numbers and character strings as input. Another accessor popular in R is the $ accessor, which allows accessing values by name. The $ accessor is generally equivalent to the [[]] accessor with a string input. Here’s an example of how these work with data frames:\n\nWURbuildings\n\n name x y\n1 Gaia 5.665 51.987\n2 Aurora 5.657 51.982\n\nWURbuildings[1] # When accessed using a single number, it selects a column\n\n name\n1 Gaia\n2 Aurora\n\nclass(WURbuildings[1]) # Single-column data.frame\n\n[1] \"data.frame\"\n\nWURbuildings[[1]] # Also selects a column\n\n[1] \"Gaia\" \"Aurora\"\n\nclass(WURbuildings[[1]]) # But now the result is a vector!\n\n[1] \"character\"\n\nWURbuildings[[\"name\"]] # Column by name\n\n[1] \"Gaia\" \"Aurora\"\n\nWURbuildings$name # Same\n\n[1] \"Gaia\" \"Aurora\"\n\n\nGenerally the $ accessor is not recommended, because it does not allow the use of variables, it only works with literal strings.\nNote that in Python, the operator [[]] has a very different meaning: the outer [] is the accessor, and the inner [] is a list constructor, in other words, it’s equivalent to the R [c()]:\n\nnpLocArray = numpy.array([npLocMat, npLocMat + 1, npLocMat + 2, npLocMat + 3])\nnpLocArray\nnpLocArray[0,1] # Meaning \"first layer, second row, all columns\"\nnpLocArray[[0,2]] # Meaning \"first and third layers, all rows and all columns\"\n\nSimilar to the R $ accessor, Python has the accessor . to select by literal string:\n\nWURbuildings\nWURbuildings[\"name\"]\nWURbuildings.name\n\nInstead of supplying numbers or strings, we can also slice by using a boolean array of the same dimensions as what we are trying to slice. This is very handy, as we can make use of this to select by a rule:\n\nLocMat\n\n [,1] [,2]\n[1,] 5.7 6.7\n[2,] 52.0 53.0\n\nLocMat[LocMat > 50] # Get only the values above 50\n\n[1] 52 53\n\n\nWhat happens here is that the inner part of the accessor generates a boolean array, which is subsequently used for slicing, as if it was a mask:\n\nLocMat > 50\n\n [,1] [,2]\n[1,] FALSE FALSE\n[2,] TRUE TRUE\n\n\nThe same applies in NumPy:\n\nnpLocMat\nnpLocMat[npLocMat > 50]\n\n\n\n\n\nQuestion 5: In R and Python, slice LocMat to select all values below 6 and above 52. Hint: You can combine conditions by using & (AND) or | (OR). Note: In Python, always use parentheses around each condition when combining them — otherwise, operator precedence may cause unexpected behavior." + }, + { + "objectID": "index.html#conditionals-and-control-flow", + "href": "index.html#conditionals-and-control-flow", + "title": "R & Python basics", + "section": "Conditionals and control flow", + "text": "Conditionals and control flow\nWe just saw that we can slice arrays based on conditions. Note that the boolean operators & and | (for and and or respectively) are valid in both R and Python, but they mean different things!\nIn R, & is a vectorised form of &&, in other words, it can compare boolean vectors against each other:\n\nc(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE)\n\n[1] TRUE FALSE FALSE FALSE\n\n\nThis is good in some cases, such as slicing arrays using boolean arrays (like in Question 5, where && doesn’t really work), and not so good in other cases, like if statements that control program flow. This is because if requires a single TRUE or FALSE, not an array.\nThe conditionals if and else are used to structure our code, so that part of the code only runs when certain conditions are met. In this case, if (c(TRUE, FALSE)) is impossible to determine and thus will result in an error. The form && only compares one boolean with another boolean, which makes sure you do not accidentally use arrays where a single boolean is needed, and thus is suitable.\nHere’s an example in R:\n\nMyLatitude = 52\nGaiaLatitude = WURbuildings[WURbuildings[[\"name\"]] == \"Gaia\", \"y\"]\nAuroraLatitude = WURbuildings[WURbuildings[[\"name\"]] == \"Aurora\", \"y\"]\n\nif (MyLatitude > GaiaLatitude && MyLatitude > AuroraLatitude) {\n print(\"I am to the north of both Gaia and Aurora\")\n} else if (MyLatitude < GaiaLatitude && MyLatitude < AuroraLatitude) {\n print(\"I am to the south of both Gaia and Aurora\")\n} else {\n print(\"I am in between Gaia and Aurora\")\n}\n\n[1] \"I am to the north of both Gaia and Aurora\"\n\n\nThe & operator can still work in if statements when both sides are single boolean values. However, it is less strict and may lead to unexpected behavior if vectors are accidentally passed.\nIn Python, & is a bitwise comparison, that is, it does not work on booleans, but numbers. It just so happens that Python implicitly converts booleans to numbers (True to 1 and False to 0), so it often works with booleans as well, but it is not intended for that. This behavior makes them useful in contexts like Question 5, where we compare an array of values against numeric thresholds and want element-wise boolean logic. Instead, the regular boolean comparison is done using and, the logical comparison. It expects the entire variable to represent a single boolean value (True or False). That’s why and or or cannot be used for element-wise slicing in Question 5. So just like in R, when you need a single boolean result to control program flow in if statement, and is more appropriate than &.\nEquivalent example code in Python (note the syntax differs a bit between R and Python, e.g. the lack of parentheses, the words for and and or, and the special elif keyword):\n\nMyLatitude = 52\n# Note that boolean conditionals only work with regular booleans, not with Series/DataFrames, hence the .values[0]\nGaiaLatitude = WURbuildings.loc[WURbuildings[\"name\"] == \"Gaia\", \"y\"].values[0]\nAuroraLatitude = WURbuildings.loc[WURbuildings[\"name\"] == \"Aurora\", \"y\"].values[0]\n\nif MyLatitude > GaiaLatitude and MyLatitude > AuroraLatitude:\n print(\"I am to the north of both Gaia and Aurora\")\nelif MyLatitude < GaiaLatitude and MyLatitude < AuroraLatitude:\n print(\"I am to the south of both Gaia and Aurora\")\nelse:\n print(\"I am in between Gaia and Aurora\")\n\n\n\n\n\nQuestion 6: Why don’t we need parentheses here in this Python code? Hint: it’s something to do with operator precedence." + }, + { + "objectID": "index.html#objects-and-inheritance", + "href": "index.html#objects-and-inheritance", + "title": "R & Python basics", + "section": "Objects and inheritance", + "text": "Objects and inheritance\nBoth R and Python are object-oriented languages, and in this tutorial we have already worked with many objects. For example, data frames, lists and matrices are objects, and the class() or type() function prints what type of object it is, in other words, what is the class of the object. In R, str() allows investigating the structure of an object. Python does not have a unified function for this and different packages implement this functionality differently, if at all.\n\nWUR = list(\"name\"=\"Wageningen University\", \"x\"=5.7, \"y\"=52)\nstr(WUR) # Show the structure of a list\n\nList of 3\n $ name: chr \"Wageningen University\"\n $ x : num 5.7\n $ y : num 52\n\nWURbuildings\n\n name x y\n1 Gaia 5.665 51.987\n2 Aurora 5.657 51.982\n\nstr(WURbuildings) # Show the structure of a data frame (which is also a type of list)\n\n'data.frame': 2 obs. of 3 variables:\n $ name: chr \"Gaia\" \"Aurora\"\n $ x : num 5.66 5.66\n $ y : num 52 52\n\n\n\nWURbuildings # pandas data frame\nWURbuildings.dtypes # Shows data types\nvars(WURbuildings) # More generic, but does not work with lists and dicts\ndir(WURbuildings) # All of the methods contained in the object\n\nIn the last example we can see that objects can contain other objects and/or functions. A class is a definition of an object, and a function contained inside an object is traditionally called a method.\nR and Python both support classes and objects, but their philosophy regarding them differs significantly. R is very lax and allows the users to freely (re)define object classes as they see fit. Python is a lot more formal and requires a formal class definition to define an object class. Python prefers self-contained objects that contain all the methods that can interact with the object embedded inside the object. In contrast, the R philosophy is to define global functions whose behaviour is different depending on the class the function is run on.\nAs an example, we might want to define a class building which, when instantiated as an object, will contain a name, and a vector coordinates. We want to also have a function that prints these attributes in a nice to read way. This is how it would be done in R:\n\nGaia = list(\"name\"=\"Gaia\", \"coordinates\"=c(\"x\"=5.665, \"y\"=51.987))\nclass(Gaia) # It is a list\n\n[1] \"list\"\n\nclass(Gaia) = \"building\" # And now it's a `building`!\nclass(Gaia)\n\n[1] \"building\"\n\nAurora = list(\"name\"=\"Aurora\", \"coordinates\"=c(\"x\"=5.657, \"y\"=51.982))\nclass(Aurora) = \"building\"\nclass(Aurora)\n\n[1] \"building\"\n\n# Define a function to print buildings\n# We use the \"paste\" function to format strings. \nprint.building = function(x)\n{\n print(paste(x[[\"name\"]], \"is a building that is located at x:\",\n x[[\"coordinates\"]][\"x\"], \"y:\", x[[\"coordinates\"]][\"y\"]))\n}\n# Now we simply use print() and get our custom output:\nprint(Gaia)\n\n[1] \"Gaia is a building that is located at x: 5.665 y: 51.987\"\n\nprint(Aurora)\n\n[1] \"Aurora is a building that is located at x: 5.657 y: 51.982\"\n\n\nThe reason why this works is because R uses the concept of function signatures. When running print(), R will first check what is the class of the object you are calling the function on, and checks if there is a function defined that is called print.class (in our case print.building). On a match, it will run that function instead of the print.default function.\nIn Python, we need to formally define a class and then instantiate it:\n\nclass building:\n def __init__(self, name, coordinates):\n self.name = name\n self.coordinates = coordinates\n\n def print(self):\n # Note that we use \"string formatting\". If a \"f\" is put before a string quote, \n # all text inside curly brackets will be executed as plain Python \n print(f'{self.name} is a building that is located at x: {self.coordinates[\"x\"]}, y: {self.coordinates[\"y\"]}')\n\n# Instantiate the class by calling it as if it was its __init__ function\nGaia = building(name=\"Gaia\", coordinates={\"x\": 5.665, \"y\": 51.987})\nAurora = building(name=\"Aurora\", coordinates={\"x\": 5.657, \"y\": 51.982})\ntype(Gaia)\ntype(Aurora)\nGaia.print()\nAurora.print()\n\nNote that we called the method print() that is inside our object, not the global function print(), which would give a different output:\n\nprint(Gaia)\nprint(Aurora)\n\nA key concept in object-oriented programming is inheritance: a class can inherit properties and methods of its parent class. This allows us to make extensions of existing classes without having to duplicate a lot of work. Let’s say we want to extend our building class with an attribute purpose, calling the new child class purposeBuilding. In R, inheritance works by making an object part of multiple classes:\n\nGaiaPurpose = list(\"name\"=\"Gaia\", \"coordinates\"=c(\"x\"=5.665, \"y\"=51.987), purpose=\"office\")\nclass(GaiaPurpose) = c(\"purposeBuilding\", \"building\")\nprint(GaiaPurpose) # We reuse the parent class `print()`\n\n[1] \"Gaia is a building that is located at x: 5.665 y: 51.987\"\n\nAuroraPurpose = list(\"name\"=\"Aurora\", \"coordinates\"=c(\"x\"=5.657, \"y\"=51.982), purpose=\"education\")\nclass(AuroraPurpose) = c(\"purposeBuilding\", \"building\")\nprint(AuroraPurpose)\n\n[1] \"Aurora is a building that is located at x: 5.657 y: 51.982\"\n\n# Make a custom print function for purposeBuilding\nprint.purposeBuilding = function(x)\n{\n print(paste(x[[\"name\"]], \"is an\", x[[\"purpose\"]], \"building that is located at x:\",\n x[[\"coordinates\"]][\"x\"], \"y:\", x[[\"coordinates\"]][\"y\"]))\n}\n\nprint(GaiaPurpose)\n\n[1] \"Gaia is an office building that is located at x: 5.665 y: 51.987\"\n\nprint(AuroraPurpose)\n\n[1] \"Aurora is an education building that is located at x: 5.657 y: 51.982\"\n\n\nIn Python, inheritance is also formally declared in the definition of the new class:\n\nclass purposeBuilding(building):\n def __init__(self, name, coordinates, purpose):\n super().__init__(name, coordinates) # Let the parent class handle these\n self.purpose = purpose\n\nGaia = purposeBuilding(name=\"Gaia\", coordinates={\"x\": 5.665, \"y\": 51.987}, purpose=\"office\")\nAurora = purposeBuilding(name=\"Aurora\", coordinates={\"x\": 5.657, \"y\": 51.982}, purpose=\"education\")\nGaia.print()\nAurora.print()\n\nWe can override methods by redeclaring them, but any instantiated objects will have to be reinstantiated for the changes to apply:\n\n# Let's also override the print now:\nclass purposeBuilding(building):\n def __init__(self, name, coordinates, purpose):\n super().__init__(name, coordinates) # Let the parent class handle these\n self.purpose = purpose\n\n def print(self):\n print(f'{self.name} is a building used for {self.purpose} purposes, located at x: {self.coordinates[\"x\"]}, y: {self.coordinates[\"y\"]}')\n \n\n# If we don't reinstantiate, the old definition applies:\nGaia.print()\nAurora.print()\n\nGaia = purposeBuilding(name=\"Gaia\", coordinates={\"x\": 5.665, \"y\": 51.987}, purpose=\"office\")\nAurora = purposeBuilding(name=\"Aurora\", coordinates={\"x\": 5.657, \"y\": 51.982}, purpose=\"education\")\nGaia.print()\nAurora.print()\n\nNote that R also has a more formalised type of classes, called S4 classes, that behave a bit more similar to the Python classes, but S4 classes are generally not recommended to use as they are further away from the R philosophy." + }, + { + "objectID": "index.html#scope-and-side-effects", + "href": "index.html#scope-and-side-effects", + "title": "R & Python basics", + "section": "Scope and side effects", + "text": "Scope and side effects\nAnother difference between R and Python you may also have noticed in the examples above: R uses curly braces {} to denote scope, whereas Python uses a colon : and enforces indentation. As an example:\n\ndef scope():\n print(\"I am inside the scope of the function scope()!\")\n print(\"I am also inside the scope of the function scope()!\")\nprint(\"I am outside the scope of the function scope()!\")\n\nscope()\n\n\nscope = function() {\n print(\"I am inside the scope of the function scope()!\")\n print(\"I am also inside the scope of the function scope()!\")\n}\n print(\"I am outside the scope of the function scope()!\")\n\n[1] \"I am outside the scope of the function scope()!\"\n\nscope()\n\n[1] \"I am inside the scope of the function scope()!\"\n[1] \"I am also inside the scope of the function scope()!\"\n\n\nIn addition, in R, typically anything that happens inside of a function scope stays in the function, i.e. it does not alter the global state.\n\nlocation = \"Gaia\"\nmove = function(where)\n{\n location = where\n print(paste(\"Moved to\", location))\n}\n\nmove(\"Aurora\")\n\n[1] \"Moved to Aurora\"\n\nprint(location)\n\n[1] \"Gaia\"\n\n\nIn Python it is also often true, but the list of exceptions is much longer.\n\nlocation = \"Gaia\"\ndef move(where):\n location = where\n print(\"Moved to \" + location)\n\nmove(\"Aurora\")\nprint(location)\n\nFor instance, lists are mutable even outside of the function scope:\n\nlocations = [\"Gaia\"]\ndef addLocation(where):\n locations.append(where)\n print(locations)\n\naddLocation(\"Aurora\")\nprint(locations)\n\nSuch mutability that goes outside of the function scope is called a side effect. They are best avoided as much as possible, as it brings confusion to the users (and may even destroy their work)! Normally, you expect that if you as a user run a function, it will process your arguments and return a new, processed object, but will not change your global environment, or the object that you passed to the function itself.\nTo avoid such side effects, in Python we need to explicitly copy mutable objects:\n\nlocations = [\"Gaia\"]\ndef addLocation(where):\n result = locations.copy()\n result.append(where)\n print(result)\n\naddLocation(\"Aurora\")\nprint(locations)" + }, + { + "objectID": "index.html#summary", + "href": "index.html#summary", + "title": "R & Python basics", + "section": "Summary", + "text": "Summary\nWe have looked at some similarities and differences between R and Python. As you can see, the same or similar functionality is available in both languages, but the philosophies of the languages are sometimes different, so it is important to be aware of them. We will make extensive use of the basics in the upcoming tutorials, and will build further upon them to specifically handle spatial data. We will first go through the specifics of R, then the specifics of Python, but it’s good to keep in mind throughout the course that both languages can do what we want, just in a slightly different way. You can use this tutorial as a reference for the basics if you get stuck in future tutorials. Calling R and Python from Bash can also be a very useful technique to combine them during the project at the end of the course." + } +] \ No newline at end of file diff --git a/_site/site_libs/bootstrap/bootstrap-3fd26f1720109480f9692f3993e9ff04.min.css b/_site/site_libs/bootstrap/bootstrap-3fd26f1720109480f9692f3993e9ff04.min.css new file mode 100644 index 0000000..dc07331 --- /dev/null +++ b/_site/site_libs/bootstrap/bootstrap-3fd26f1720109480f9692f3993e9ff04.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #007bff;--bs-indigo: #6610f2;--bs-purple: #9b479f;--bs-pink: #e83e8c;--bs-red: #d9230f;--bs-orange: #d9831f;--bs-yellow: #ffc107;--bs-green: #469408;--bs-teal: #20c997;--bs-cyan: #029acf;--bs-black: 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.dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group.show .dropdown-toggle{box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.show 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280, 0, 0, 0.6%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280, 0, 0, 0.6%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgb(238.425, 238.425, 238.425);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: rgb(252.45, 252.45, 252.45);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #212529;--bs-accordion-btn-bg: rgb(252.45, 252.45, 252.45);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2886.8, 14, 6%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 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reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28232.2, 123, 111%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28232.2, 123, 111%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #d9230f;--bs-pagination-bg: rgb(252.45, 252.45, 252.45);--bs-pagination-border-width: 1px;--bs-pagination-border-color: rgb(238.425, 238.425, 238.425);--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #d9230f;--bs-pagination-hover-border-color: #d9230f;--bs-pagination-focus-color: rgb(173.6, 28, 12);--bs-pagination-focus-bg: #ddd;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(217, 35, 15, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #d9230f;--bs-pagination-active-border-color: #d9230f;--bs-pagination-disabled-color: #bbb;--bs-pagination-disabled-bg: #ddd;--bs-pagination-disabled-border-color: rgb(238.425, 238.425, 238.425);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ddd;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #d9230f;--bs-progress-bar-bg: #d9230f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);box-shadow:var(--bs-progress-box-shadow)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: rgb(252.45, 252.45, 252.45);--bs-list-group-border-color: rgb(238.425, 238.425, 238.425);--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ddd;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: rgb(238.425, 238.425, 238.425);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #d9230f;--bs-list-group-active-border-color: #d9230f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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#000}.text-success{--bslib-color-fg: #469408}.bg-success{--bslib-color-bg: #469408;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #029acf}.bg-info{--bslib-color-bg: #029acf;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #d9831f}.bg-warning{--bslib-color-bg: #d9831f;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #9b479f}.bg-danger{--bslib-color-bg: #9b479f;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #fff}.bg-light{--bslib-color-bg: #fff;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #343a40}.bg-dark{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(40.8, 80.2, 249.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(40.8,80.2,249.8);color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(62, 102.2, 216.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #9b479f var(--bg-gradient-end, 180%)) rgb(62,102.2,216.6);color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(92.8, 98.6, 209);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(92.8,98.6,209);color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(86.8, 87.8, 159);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #d9230f var(--bg-gradient-end, 180%)) rgb(86.8,87.8,159);color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(86.8, 126.2, 165.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #d9831f var(--bg-gradient-end, 180%)) rgb(86.8,126.2,165.4);color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(102, 151, 155.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) rgb(102,151,155.8);color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(28, 133, 156.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #469408 var(--bg-gradient-end, 180%)) rgb(28,133,156.2);color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(12.8, 154.2, 213.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(12.8,154.2,213.4);color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(0.8, 135.4, 235.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #007bff var(--bg-gradient-start, 36%), #029acf var(--bg-gradient-end, 180%)) rgb(.8,135.4,235.8);color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(61.2, 58.8, 247.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #007bff var(--bg-gradient-end, 180%)) rgb(61.2,58.8,247.2);color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(123.2, 38, 208.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #9b479f var(--bg-gradient-end, 180%)) rgb(123.2,38,208.8);color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(154, 34.4, 201.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(154,34.4,201.2);color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(148, 23.6, 151.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #d9230f var(--bg-gradient-end, 180%)) rgb(148,23.6,151.2);color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(148, 62, 157.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #d9831f var(--bg-gradient-end, 180%)) rgb(148,62,157.6);color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(163.2, 86.8, 148);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) rgb(163.2,86.8,148);color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(89.2, 68.8, 148.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #469408 var(--bg-gradient-end, 180%)) rgb(89.2,68.8,148.4);color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(74, 90, 205.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(74,90,205.6);color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(62, 71.2, 228);background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #029acf var(--bg-gradient-end, 180%)) rgb(62,71.2,228);color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(93, 91.8, 197.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #007bff var(--bg-gradient-end, 180%)) rgb(93,91.8,197.4);color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(133.8, 49, 192.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(133.8,49,192.2);color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(185.8, 67.4, 151.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(185.8,67.4,151.4);color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(179.8, 56.6, 101.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #d9230f var(--bg-gradient-end, 180%)) rgb(179.8,56.6,101.4);color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(179.8, 95, 107.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #d9831f var(--bg-gradient-end, 180%)) rgb(179.8,95,107.8);color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(195, 119.8, 98.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) rgb(195,119.8,98.2);color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(121, 101.8, 98.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #469408 var(--bg-gradient-end, 180%)) rgb(121,101.8,98.6);color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(105.8, 123, 155.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(105.8,123,155.8);color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(93.8, 104.2, 178.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #9b479f var(--bg-gradient-start, 36%), #029acf var(--bg-gradient-end, 180%)) rgb(93.8,104.2,178.2);color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(139.2, 86.4, 186);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #007bff var(--bg-gradient-end, 180%)) rgb(139.2,86.4,186);color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(180, 43.6, 180.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(180,43.6,180.8);color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(201.2, 65.6, 147.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #9b479f var(--bg-gradient-end, 180%)) rgb(201.2,65.6,147.6);color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(226, 51.2, 90);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #d9230f var(--bg-gradient-end, 180%)) rgb(226,51.2,90);color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(226, 89.6, 96.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #d9831f var(--bg-gradient-end, 180%)) rgb(226,89.6,96.4);color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(241.2, 114.4, 86.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) rgb(241.2,114.4,86.8);color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(167.2, 96.4, 87.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #469408 var(--bg-gradient-end, 180%)) rgb(167.2,96.4,87.2);color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(152, 117.6, 144.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(152,117.6,144.4);color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(140, 98.8, 166.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #029acf var(--bg-gradient-end, 180%)) rgb(140,98.8,166.8);color:#fff}.bg-gradient-red-blue{--bslib-color-fg: 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var(--bg-gradient-start, 36%), #9b479f var(--bg-gradient-end, 180%)) rgb(192.2,107,82.2);color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(223, 103.4, 74.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #d9831f var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(223,103.4,74.6);color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(217, 92.6, 24.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #d9831f var(--bg-gradient-start, 36%), #d9230f var(--bg-gradient-end, 180%)) rgb(217,92.6,24.6);color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #000;--bslib-color-bg: rgb(232.2, 155.8, 21.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #d9831f var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) rgb(232.2,155.8,21.4);color:#000}.bg-gradient-orange-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(158.2, 137.8, 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3.2);--quarto-scss-export-green-900: rgb(14, 29.6, 1.6);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(204.4, 234.8, 245.4);--quarto-scss-export-cyan-200: rgb(153.8, 214.6, 235.8);--quarto-scss-export-cyan-300: rgb(103.2, 194.4, 226.2);--quarto-scss-export-cyan-400: rgb(52.6, 174.2, 216.6);--quarto-scss-export-cyan-500: #029acf;--quarto-scss-export-cyan-600: rgb(1.6, 123.2, 165.6);--quarto-scss-export-cyan-700: rgb(1.2, 92.4, 124.2);--quarto-scss-export-cyan-800: rgb(0.8, 61.6, 82.8);--quarto-scss-export-cyan-900: rgb(0.4, 30.8, 41.4);--quarto-scss-export-default: #fff;--quarto-scss-export-primary-text-emphasis: rgb(86.8, 14, 6);--quarto-scss-export-secondary-text-emphasis: #666666;--quarto-scss-export-success-text-emphasis: rgb(28, 59.2, 3.2);--quarto-scss-export-info-text-emphasis: rgb(0.8, 61.6, 82.8);--quarto-scss-export-warning-text-emphasis: rgb(86.8, 52.4, 12.4);--quarto-scss-export-danger-text-emphasis: rgb(62, 28.4, 63.6);--quarto-scss-export-light-text-emphasis: #444;--quarto-scss-export-dark-text-emphasis: #444;--quarto-scss-export-primary-bg-subtle: rgb(247.4, 211, 207);--quarto-scss-export-secondary-bg-subtle: white;--quarto-scss-export-success-bg-subtle: rgb(218, 233.6, 205.6);--quarto-scss-export-info-bg-subtle: rgb(204.4, 234.8, 245.4);--quarto-scss-export-warning-bg-subtle: rgb(247.4, 230.2, 210.2);--quarto-scss-export-danger-bg-subtle: rgb(235, 218.2, 235.8);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #bbb;--quarto-scss-export-primary-border-subtle: rgb(239.8, 167, 159);--quarto-scss-export-secondary-border-subtle: white;--quarto-scss-export-success-border-subtle: rgb(181, 212.2, 156.2);--quarto-scss-export-info-border-subtle: rgb(153.8, 214.6, 235.8);--quarto-scss-export-warning-border-subtle: rgb(239.8, 205.4, 165.4);--quarto-scss-export-danger-border-subtle: rgb(215, 181.4, 216.6);--quarto-scss-export-light-border-subtle: #ddd;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-color: #212529;--quarto-scss-export-body-secondary-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-body-secondary-bg: #ddd;--quarto-scss-export-body-tertiary-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: rgb(173.6, 28, 12);--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #d9230f;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(217, 35, 15, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #ddd;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 242.6, 205.4);--quarto-scss-export-table-color: #212529;--quarto-scss-export-table-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #212529;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #212529;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #212529;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(217, 35, 15, 0.25);--quarto-scss-export-btn-color: #212529;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #d9230f;--quarto-scss-export-btn-link-hover-color: rgb(173.6, 28, 12);--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #ddd;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #212529;--quarto-scss-export-input-focus-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-input-focus-border-color: #ec9187;--quarto-scss-export-input-focus-color: #212529;--quarto-scss-export-input-placeholder-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-plaintext-color: #212529;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-form-check-input-focus-border: #ec9187;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #d9230f;--quarto-scss-export-form-check-input-checked-border-color: #d9230f;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #d9230f;--quarto-scss-export-form-check-input-indeterminate-border-color: #d9230f;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #ec9187;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #212529;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #212529;--quarto-scss-export-form-select-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #ddd;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #ec9187;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #d9230f;--quarto-scss-export-form-range-thumb-active-bg: rgb(243.6, 189, 183);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-file-button-color: #212529;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #ddd;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #469408;--quarto-scss-export-form-feedback-invalid-color: #9b479f;--quarto-scss-export-form-feedback-icon-valid-color: #469408;--quarto-scss-export-form-feedback-icon-invalid-color: #9b479f;--quarto-scss-export-form-valid-color: #469408;--quarto-scss-export-form-valid-border-color: #469408;--quarto-scss-export-form-invalid-color: #9b479f;--quarto-scss-export-form-invalid-border-color: #9b479f;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #d9230f;--quarto-scss-export-nav-link-hover-color: rgb(173.6, 28, 12);--quarto-scss-export-nav-tabs-link-hover-border-color: #ddd #ddd rgb(238.425, 238.425, 238.425);--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-nav-pills-link-active-bg: #d9230f;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #212529;--quarto-scss-export-dropdown-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #212529;--quarto-scss-export-dropdown-link-active-bg: #d9230f;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #d9230f;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #d9230f;--quarto-scss-export-pagination-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-pagination-focus-color: rgb(173.6, 28, 12);--quarto-scss-export-pagination-focus-bg: #ddd;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #d9230f;--quarto-scss-export-pagination-active-border-color: #d9230f;--quarto-scss-export-pagination-disabled-bg: #ddd;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-accordion-color: #212529;--quarto-scss-export-accordion-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #212529;--quarto-scss-export-accordion-button-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-accordion-button-active-bg: rgb(247.4, 211, 207);--quarto-scss-export-accordion-button-active-color: rgb(86.8, 14, 6);--quarto-scss-export-accordion-button-focus-border-color: #ec9187;--quarto-scss-export-accordion-icon-color: #212529;--quarto-scss-export-accordion-icon-active-color: rgb(86.8, 14, 6);--quarto-scss-export-tooltip-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-header-bg: #ddd;--quarto-scss-export-popover-body-color: #212529;--quarto-scss-export-popover-arrow-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(252.45, 252.45, 252.45, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-toast-header-background-color: rgba(252.45, 252.45, 252.45, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: rgb(238.425, 238.425, 238.425);--quarto-scss-export-progress-bg: #ddd;--quarto-scss-export-progress-bar-bg: #d9230f;--quarto-scss-export-list-group-color: #212529;--quarto-scss-export-list-group-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #d9230f;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #d9230f;--quarto-scss-export-list-group-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-action-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #212529;--quarto-scss-export-list-group-action-active-bg: #ddd;--quarto-scss-export-thumbnail-bg: rgb(252.45, 252.45, 252.45);--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgb(238.425, 238.425, 238.425);--quarto-scss-export-offcanvas-bg-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-offcanvas-color: #212529;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-kbd-bg: #212529;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #fff;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: rgb(85.833, 85.833, 85.833);--quarto-scss-export-sidebar-hover-color: rgba(171.43, 27.65, 11.85, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(85.833, 85.833, 85.833, 0.75);--quarto-scss-export-valuebox-bg-primary: #db766b;--quarto-scss-export-valuebox-bg-secondary: #fff;--quarto-scss-export-valuebox-bg-success: #59a343;--quarto-scss-export-valuebox-bg-info: #359ed0;--quarto-scss-export-valuebox-bg-warning: #a59212;--quarto-scss-export-valuebox-bg-danger: #c48282;--quarto-scss-export-valuebox-bg-light: #fff;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-mermaid-edge-color: #fff;--quarto-scss-export-mermaid-node-fg-color: #212529;--quarto-scss-export-mermaid-fg-color: #212529;--quarto-scss-export-mermaid-fg-color--lighter: rgb(55.7432432432, 62.5, 69.2567567568);--quarto-scss-export-mermaid-fg-color--lightest: rgb(78.4864864865, 88, 97.5135135135);--quarto-scss-export-mermaid-label-bg-color: rgb(252.45, 252.45, 252.45);--quarto-scss-export-mermaid-label-fg-color: #d9230f;--quarto-scss-export-mermaid-node-bg-color: rgba(217, 35, 15, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #007bff;--quarto-scss-export-callout-color-tip: #469408;--quarto-scss-export-callout-color-important: #d9230f;--quarto-scss-export-callout-color-caution: #d9831f;--quarto-scss-export-callout-color-warning: #ffc107} \ No newline at end of file diff --git a/_site/site_libs/bootstrap/bootstrap-icons.css b/_site/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..82b40f5 --- /dev/null +++ b/_site/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2106 @@ +/*! + * Bootstrap Icons v1.13.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2024 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return 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b/_site/site_libs/quarto-html/quarto-syntax-highlighting-f1a5c2bb43452cd3d80060817c0f5863.css new file mode 100644 index 0000000..b0e1f80 --- /dev/null +++ b/_site/site_libs/quarto-html/quarto-syntax-highlighting-f1a5c2bb43452cd3d80060817c0f5863.css @@ -0,0 +1,219 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #ffcfaf; + --quarto-hl-an-color: #7f9f7f; + --quarto-hl-at-color: inherit; + --quarto-hl-bn-color: #dca3a3; + --quarto-hl-bu-color: inherit; + --quarto-hl-ch-color: #dca3a3; + --quarto-hl-co-color: #7f9f7f; + --quarto-hl-cv-color: #7f9f7f; + --quarto-hl-cn-color: #dca3a3; + --quarto-hl-cf-color: #f0dfaf; + --quarto-hl-dt-color: #dfdfbf; + --quarto-hl-dv-color: #dcdccc; + --quarto-hl-do-color: #7f9f7f; + --quarto-hl-er-color: #c3bf9f; + --quarto-hl-ex-color: inherit; + --quarto-hl-fl-color: #c0bed1; + --quarto-hl-fu-color: #efef8f; + --quarto-hl-im-color: inherit; + --quarto-hl-in-color: #7f9f7f; + --quarto-hl-kw-color: #f0dfaf; + --quarto-hl-op-color: #f0efd0; + --quarto-hl-ot-color: #efef8f; + --quarto-hl-pp-color: #ffcfaf; + --quarto-hl-sc-color: #dca3a3; + --quarto-hl-ss-color: #cc9393; + --quarto-hl-st-color: #cc9393; + --quarto-hl-va-color: inherit; + --quarto-hl-vs-color: #cc9393; + --quarto-hl-wa-color: #7f9f7f; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +/* syntax highlight based on Pandoc's rules */ +/* Alert */ +code span.al { + color: #ffcfaf; + font-style: inherit; +} + +/* Annotation */ +code span.an { + color: #7f9f7f; + font-weight: bold; + font-style: inherit; +} + +/* Attribute */ +code span.at { + font-style: inherit; +} + +/* BaseN */ +code span.bn { + color: #dca3a3; + font-style: inherit; +} + +/* BuiltIn */ +code span.bu { + font-style: inherit; +} + +/* ControlFlow */ +code span.cf { + color: #f0dfaf; + font-style: inherit; +} + +/* Char */ +code span.ch { + color: #dca3a3; + font-style: inherit; +} + +/* Constant */ +code span.cn { + color: #dca3a3; + font-weight: bold; + font-style: inherit; +} + +/* Comment */ +code span.co { + color: #7f9f7f; + font-style: inherit; +} + +/* CommentVar */ +code span.cv { + color: #7f9f7f; + font-weight: bold; + font-style: inherit; +} + +/* Documentation */ +code span.do { + color: #7f9f7f; + font-style: inherit; +} + +/* DataType */ +code span.dt { + color: #dfdfbf; + font-style: inherit; +} + +/* DecVal */ +code span.dv { + color: #dcdccc; + font-style: inherit; +} + +/* Error */ +code span.er { + color: #c3bf9f; + font-style: inherit; +} + +/* Extension */ +code span.ex { + font-style: inherit; +} + +/* Float */ +code span.fl { + color: #c0bed1; + font-style: inherit; +} + +/* Function */ +code span.fu { + color: #efef8f; + font-style: inherit; +} + +/* Import */ +code span.im { + font-style: inherit; +} + +/* Information */ +code span.in { + color: #7f9f7f; + font-weight: bold; + font-style: inherit; +} + +/* Keyword */ +code span.kw { + color: #f0dfaf; + font-style: inherit; +} + +/* Operator */ +code span.op { + color: #f0efd0; + font-style: inherit; +} + +/* Other */ +code span.ot { + color: #efef8f; + font-style: inherit; +} + +/* Preprocessor */ +code span.pp { + color: #ffcfaf; + font-weight: bold; + font-style: inherit; +} + +/* SpecialChar */ +code span.sc { + color: #dca3a3; + font-style: inherit; +} + +/* SpecialString */ +code span.ss { + color: #cc9393; + font-style: inherit; +} + +/* String */ +code span.st { + color: #cc9393; + font-style: inherit; +} + +/* Variable */ +code span.va { + font-style: inherit; +} + +/* VerbatimString */ +code span.vs { + color: #cc9393; + font-style: inherit; +} + +/* Warning */ +code span.wa { + color: #7f9f7f; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // dispatch for htmlwidgets + // they use slideenter event to trigger resize + function fireSlideEnter() { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // dispatch for shiny + // they use BS shown and hidden events to trigger rendering + function distpatchShinyEvents(previous, current) { + if (window.jQuery) { + if (previous) { + window.jQuery(previous).trigger("hidden"); + } + if (current) { + window.jQuery(current).trigger("shown"); + } + } + } + + // tabby.js listener: Trigger event for htmlwidget and shiny + document.addEventListener( + "tabby", + function (event) { + fireSlideEnter(); + distpatchShinyEvents(event.detail.previousTab, event.detail.tab); + }, + false + ); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + const encodedItem = encodeURI(item); + if ( + encodedItem === currentPagePath || + encodedItem === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (const child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +tabsets.init(); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/_site/site_libs/quarto-html/tabsets/tabsets.js b/_site/site_libs/quarto-html/tabsets/tabsets.js new file mode 100644 index 0000000..51345d0 --- /dev/null +++ b/_site/site_libs/quarto-html/tabsets/tabsets.js @@ -0,0 +1,95 @@ +// grouped tabsets + +export function init() { + window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } + }); +} diff --git a/_site/site_libs/quarto-html/tippy.css b/_site/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/_site/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1} \ No newline at end of file diff --git a/_site/site_libs/quarto-html/tippy.umd.min.js b/_site/site_libs/quarto-html/tippy.umd.min.js new file mode 100644 index 0000000..ca292be --- /dev/null +++ b/_site/site_libs/quarto-html/tippy.umd.min.js @@ -0,0 +1,2 @@ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t(require("@popperjs/core")):"function"==typeof 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b/_site/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 0000000..38cc430 --- /dev/null +++ b/_site/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git 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a/_site/site_libs/quarto-search/quarto-search.js b/_site/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..437e38f --- /dev/null +++ b/_site/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1457 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +// Capture search params and clean ?q= from URL at module load time, before +// any DOMContentLoaded handlers run. quarto-nav.js resolves all hrefs +// against window.location during DOMContentLoaded — if ?q= is still present, +// every link on the page gets the query param baked into its href. +const currentUrl = new URL(window.location); +const kQuery = currentUrl.searchParams.get(kQueryArg); +if (kQuery) { + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); +} + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const query = kQuery; + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + highlight(query, mainEl); + + // Activate tabs on pageshow — after tabsets.js restores localStorage state. + // tabsets.js registers its pageshow handler during module execution (before + // DOMContentLoaded). By registering ours during DOMContentLoaded, listener + // ordering guarantees we run after tabsets.js — so search activation wins. + window.addEventListener("pageshow", function (event) { + if (!event.persisted) { + for (const mark of mainEl.querySelectorAll("mark")) { + openAllTabsetsContainingEl(mark); + } + // Only scroll to first match when there's no hash fragment. + // With a hash, the browser already scrolled to the target section. + if (!currentUrl.hash) { + requestAnimationFrame(() => scrollToFirstVisibleMatch(mainEl)); + } + } + }, { once: true }); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + if (item.text && item.href && !item.href.includes('?q=')) { + const [main, hash] = item.href.split('#') + const hashAppend = hash ? '#' + hash : '' + item.href = main + '?q=' + encodeURIComponent(state.query) + hashAppend + } + + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + // Check if cookie consent is enabled from search options + const cookieConsentEnabled = algoliaOptions["cookie-consent-enabled"] || false; + + // Generate random session token only when cookies are disabled + const userToken = cookieConsentEnabled ? undefined : Array.from(Array(20), () => + Math.floor(Math.random() * 36).toString(36) + ).join(""); + + window.aa("init", { + appId, + apiKey, + useCookie: cookieConsentEnabled, + userToken: userToken, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +/** Get all html nodes under the given `root` that don't have children. */ +function getLeafNodes(root) { + let leaves = []; + + function traverse(node) { + if (node.childNodes.length === 0) { + leaves.push(node); + } else { + node.childNodes.forEach(traverse); + } + } + + traverse(root); + return leaves; +} +/** create and return `${txt}` */ +const markEl = txt => { + const el = document.createElement("mark"); + el.appendChild(document.createTextNode(txt)); + return el +} +/** get all ancestors of an element matching the given css selector */ +const matchAncestors = (el, selector) => { + let ancestors = []; + while (el) { + if (el.matches?.(selector)) ancestors.push(el); + el = el.parentNode; + } + return ancestors; +}; + +const isWhitespace = s => s.trim().length === 0 +// ================= +// MATCHING CODE +// ================= +const initMatch = () => ({ + i: 0, + lohisByNode: new Map() +}) +/** + * keeps track of the start (lo) and end (hi) index of the match per node (leaf) + * note: mutates the contents of `matchContext` + */ +const advanceMatch = (leaf, leafi, matchContext) => { + matchContext.i++ + + const curLoHi = matchContext.lohisByNode.get(leaf) + + matchContext.lohisByNode.set(leaf, { lo: curLoHi?.lo ?? leafi, hi: leafi }) +} +/** + * Finds all non-overlapping matches for a search string in the document. + * The search string may be split between multiple consecutive leaf nodes. + * + * Whitespace in the search string must be present in the document to match, but + * there may be addititional whitespace in the document that is ignored. + * + * e.g. searching for `dogs rock` would match `dogs \n rock`, + * and would contribute the match + * `{ i:9, els: new Map([[textNode, {lo:0, hi:8}],[spanNode,{lo:0,hi:5}]]) }` + * + * @returns {Map[]} + */ +function searchMatches(inSearch, el) { + // searchText has all sequences of whitespace replaced by a single space + const searchText = inSearch.toLowerCase().replace(/\s+/g, ' ') + const leafNodes = getLeafNodes(el) + + /** @type {Map[]} */ + const matches = [] + /** @type {{i:number; els:Map}[]} */ + let curMatchContext = initMatch() + + for (const leaf of leafNodes) { + const leafStr = leaf.textContent.toLowerCase() + // for each character in this leaf's text: + for (let leafi = 0; leafi < leafStr.length; leafi++) { + + if (isWhitespace(leafStr[leafi])) { + // if there is at least one whitespace in the document + // we advance over a search text whitespace. + if (isWhitespace(searchText[curMatchContext.i])) advanceMatch(leaf, leafi, curMatchContext) + // all sequences of whitespace are otherwise ignored. + } else { + if (searchText[curMatchContext.i] === leafStr[leafi]) { + advanceMatch(leaf, leafi, curMatchContext) + } else { + curMatchContext = initMatch() + // if current character in the document did not match at i in the search text, + // reset the search and see if that character matches at 0 in the search text. + if (searchText[curMatchContext.i] === leafStr[leafi]) advanceMatch(leaf, leafi, curMatchContext) + } + } + + const isMatchComplete = curMatchContext.i === searchText.length + if (isMatchComplete) { + matches.push(curMatchContext.lohisByNode) + curMatchContext = initMatch() + } + } + } + + return matches +} + +/** + * e.g. `markMatches(myTextNode, [[0,5],[12,15]])` would wrap the + * character sequences in myTextNode from 0-5 and 12-15 in marks. + * Its important to mark all sequences in a text node at once + * because this function replaces the entire text node; so any + * other references to that text node will no longer be in the DOM. + */ +function markMatches(node, lohis) { + const text = node.nodeValue + + const markFragment = document.createDocumentFragment(); + + let prevHi = 0 + for (const [lo, hi] of lohis) { + markFragment.append( + document.createTextNode(text.slice(prevHi, lo)), + markEl(text.slice(lo, hi + 1)) + ) + prevHi = hi + 1 + } + markFragment.append( + document.createTextNode(text.slice(prevHi, text.length)) + ) + + const parent = node.parentElement + parent?.replaceChild(markFragment, node) + return parent +} + +// Activate ancestor tabs so a search match inside an inactive pane becomes visible. +// When multiple panes in the same tabset contain matches, avoid switching away from +// the currently active pane — the user already sees a match there. +function openAllTabsetsContainingEl(el) { + for (const pane of matchAncestors(el, '.tab-pane')) { + const tabContent = pane.closest('.tab-content'); + if (!tabContent) continue; + const activePane = tabContent.querySelector(':scope > .tab-pane.active'); + if (activePane?.querySelector('mark')) continue; + const tabButton = document.querySelector(`[data-bs-target="#${pane.id}"]`); + if (tabButton) new bootstrap.Tab(tabButton).show(); + } +} + +function scrollToFirstVisibleMatch(mainEl) { + for (const mark of mainEl.querySelectorAll("mark")) { + const isMarkVisible = matchAncestors(mark, '.tab-pane').every(markTabPane => + markTabPane.classList.contains("active") + ) + if (isMarkVisible) { + mark.scrollIntoView({ behavior: "smooth", block: "center" }); + return; + } + } +} + +/** + * e.g. + * ```js + * const m = new Map() + * + * arrayMapPush(m, 'dog', 'Max') + * console.log(m) // Map { dog->['Max'] } + * + * arrayMapPush(m, 'dog', 'Samba') + * arrayMapPush(m, 'cat', 'Scruffle') + * console.log(m) // Map { dog->['Max', 'Samba'], cat->['Scruffle'] } + * ``` + */ +const arrayMapPush = (map, key, item) => { + if (!map.has(key)) map.set(key, []) + map.set(key, [...map.get(key), item]) +} + +// copy&paste any string from a quarto page and +// this should find that string in the page and highlight it. +// exception: text that starts outside/inside a tabset and ends +// inside/outside that tabset. +function highlight(searchStr, el) { + const matches = searchMatches(searchStr, el); + + const matchesGroupedByNode = new Map() + for (const match of matches) { + for (const [mel, { lo, hi }] of match) { + arrayMapPush(matchesGroupedByNode, mel, [lo, hi]) + } + } + + for (const [node, lohis] of matchesGroupedByNode) { + markMatches(node, lohis) + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/_site/styles.css b/_site/styles.css new file mode 100644 index 0000000..cf9b81e --- /dev/null +++ b/_site/styles.css @@ -0,0 +1,37 @@ +@import url("https://netdna.bootstrapcdn.com/bootswatch/3.0.0/simplex/bootstrap.min.css"); + + + +.main-container { max-width: none; } +pre { color: inherit; background-color: inherit; } + +code[class^="sourceCode"]::before { + content: attr(class); + display: block; + text-align: right; + font-size: 70%; +} + +code[class^="sourceCode r"]::before { content: "R Source"; } +code[class^="sourceCode python"]::before { content: "Python Source"; } +code[class^="sourceCode bash"]::before { content: "Bash Source"; } + +/* Only target inline code, not code inside pre blocks */ +p code:not(.sourceCode), +li code:not(.sourceCode), +td code:not(.sourceCode) { + background-color: #f5f5f5; + color: #c7254e; + padding: 2px 4px; + border-radius: 3px; +} + +.page-header-title { + font-size: 2rem; /* roughly equivalent to */ +} + +.page-header-logo { + height: 50px; + vertical-align: middle; +} + diff --git a/index.Rmd b/index.Rmd deleted file mode 100644 index 83e7799..0000000 --- a/index.Rmd +++ /dev/null @@ -1,730 +0,0 @@ ---- -pagetitle: "R and Python basics" -author: "Dainius Masiliūnas" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmdformats::html_clean: - title: "R and Python basics" - highlight: zenburn ---- - - -```{css, echo=FALSE} -@import url("https://netdna.bootstrapcdn.com/bootswatch/3.0.0/simplex/bootstrap.min.css"); -.main-container {max-width: none;} -pre {color: inherit; background-color: inherit;} -code[class^="sourceCode"]::before { - content: attr(class); - display: block; - text-align: right; - font-size: 70%; -} - -pre[class^="sourceCode r"] {background-color: #333366;} -pre[class^="sourceCode python"] {background-color: #555533;} -code[class^="sourceCode r"]::before { content: "R Source";} -code[class^="sourceCode python"]::before { content: "Python Source"; } -code[class^="sourceCode bash"]::before { content: "Bash Source"; } -``` -```{r setup, include=FALSE} -library(reticulate) -use_python("/usr/bin/python3", required = TRUE) -``` -[WUR Geoscripting](https://geoscripting-wur.github.io/) WUR logo - -# R and Python basics - -Throughout the course we will be learning R and Python. Both are programming languages, and both can be used for handling geospatial data. So what is the difference between them? Why would you choose one over another? And how does equivalent code look like in each language? We will go over these questions in this tutorial. - -## Learning objectives of the day - -In this tutorial, you will learn to: - -* describe the differences between R and Python; -* apply R and Python for basic handling of data types in each language; -* create new object classes and use inheritance. - -## What are R and Python? - -If you ask an average person what Python is, they will tell you that it's a type of snake. If you ask an average person what R is, they will tell you that it's a letter of the alphabet. Today you will learn beyond that: the details of the programming languages of R and Python! - -Both R and Python programming languages were created at around the same time: Python was created in 1991 in the Netherlands, and R in 1993. However, R actually has a much longer history, because it is an extension of the S programming language that was created all the way back in 1976 in Bell Labs. R was created specifically as a statistical package, similar to SPSS or Stata. However, it has since evolved into a general-purpose programming language. Due to its focus on statistics, R is mainly used in academia (schools and universities). - -Python was created as a general-purpose programming language. It was designed with readability in mind, and it has become the most popular programming language in the world. Python is therefore widely used in the industry, such as startups and large corporations. Python is especially strong in the field of deep learning, since packages such as Google's TensorFlow are implemented primarily in Python. Additionally, because of it's popularity, it is very easy to find online examples and it is used often to communicate with other software. - -In terms of spatial data handling, both R and Python can perform the same tasks. Both are relatively easy to integrate with other software as well, which allows extending the capabilities of each language. For example, the open source GIS software QGIS, including all its plugins, are written in Python. R scripts can be run directly from the QGIS interface as processing tools. R has a package `reticulate` that can run Python scripts, and Python has a package `rpy2` that can run R scripts. - -Both R and Python have extremely active communities maintaining a wealth of packages. The package ecosystems differ significantly between the two languages, however. Python packages are hosted on PyPI, the Python Package Index, which is a very easily accessible place where anyone can upload the Python packages they create. There is minimal oversight over them and the package authors are free to do with their packages what they please. The R package repository is called CRAN, the Comprehensive R Archive Network. CRAN is curated: any package submitted to CRAN goes through a review process, and has to pass a suite of tests before it gets accepted. This ensures that, among other things, every argument in every function in every package is documented, and that each package is compatible with all the other packages in CRAN. The result is that it is much more difficult to publish R packages, but the quality of R packages is generally much higher than Python packages. It also means that package management in R is very easy, as no conflicts can happen between packages. Packages are also more inclined to depend on other packages, as it is more certain that their dependencies will stay maintained. In contrast, Python has a lot more packages, but they are often poorly documented and often do not interoperate with other packages as well. Package management is a big issue in Python, as many packages work only with certain versions of other packages. - -In practice, this means that while all geospatial data handling tasks can be done in R or Python, R is better integrated for this. In this course you will learn about the packages `terra` for raster handling and `sf` for vector handling, which are both integrated with each other and offer a full suite of processing tools, and all of the other packages that use vectors or rasters in R also make use of `sf` and `terra` package objects. In Python, raster reading is done using the `rasterio` package, but processing needs to be done using other packages, many of which do not support `rasterio` objects. Vector handling is done in for example `geopandas`, which has spatial processing tools, but once again they are not always integrated with raster object support. So rasters often need to be converted into number matrices for processing, then converted back into raster objects, by the user. - -Both R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in `plot()` function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, `ggplot2` can be used. In Python, the standard plotting package is `matplotlib`. Spatial packages often implement their own plotting functions for their own objects, therefore putting multiple objects into one plot can often be more challenging than in R. - -Ultimately, the choice of language to use is often decided by interoperability needs (e.g. if you work in a company that uses Python, you will be expected to also write in Python, so that your script can be used in a pipeline) and personal preference. - -## Running Python and R from Bash - -Both the `R` and the `python` interpreters can be run from Bash. Here is an example of code that is correct in both Python and R. You can use this example to execute a script written in either language: -```{bash} -# Create a script file "script" (no extension) -echo 'print("Hello world!")' > script -# Run the script with R -Rscript ./script -# Run the script with Python -python3 ./script -``` - -This allows you to run script files even without having any graphical user interface installed or running, which is the fastest way to run any script from top to bottom. This way, you can even combine R and Python code from a Bash script! - -Now, let's explore the similarities and differences between the two languages. For that, you need to have Python set up. Let's install the needed packages through the Bash terminal: - -```{bash, eval=FALSE} -sudo apt install python3-pandas -``` - -Now you can run R in one terminal, and run Python in another, to try out the code snippets below. To run R in interactive mode: - -```{bash, eval=FALSE} -R -``` - -To run Python in interactive mode: - -```{bash, eval=FALSE} -python3 -``` - -## Data types - -Both Python and R provide a set of primitive data types. The ones they have in common are integers, floating-point numbers, logical boolean values, character strings, associative arrays (dictionaries) and lists. To find out the type of a variable, use `type()` in Python and `class()` in R. - -A whole number is called an integer, and a number with decimals (real number) is called a float (floating point number). In Python, if you enter a number without decimal points, it will be an integer, otherwise it will be a float. - -```{python} -type(10) -type(10.1) -type(10.) -type(int(10.)) -``` - -In R, any number will be a float (called "numeric") by default, and integers are obtained by explicitly casting to an integer using `as.integer()`: - -```{r} -class(10) -class(10.1) -class(as.integer(10)) -``` - -```{block, type="alert alert-success"} -> **Question 1**: What is the type/class of the sum 3+5 and of 3.0+5 in R and in Python? Write a sum of two numbers that returns an integer in R and in Python. -``` - -Boolean, or logical, types can only have two values: true or false. When cast to an integer, true is represented by 1 and false is represented by 0. Both R and Python are case-sensitive, and use different cases to represent booleans (`True` in Python and `TRUE` in R). - -```{python} -type(True) -type(True + 2) -True + 2 -``` - -```{r} -class(TRUE) -class(TRUE + 2) -TRUE + 2 -``` - -Character strings are letters and words. They can also include numbers, but the numbers do not have a mathematical meaning, and therefore you cannot do arithmetics on strings. Strings are marked with quotes, either single or double: - -```{python error=TRUE} -"Hello world #1!" -5 + '6' -type('6') -``` - -```{r error=TRUE} -"Hello world #1!" -5 + '6' -class('6') -``` - -You can combine strings to produce longer strings. This is done with the function `paste()` in R and using the `+` operator in Python (as long as all parts are strings). - -```{python} -WorldNr = 2 -"Hello world #" + str(WorldNr) + "!" -``` - -```{r} -WorldNr = 2 -paste("Hello world #", WorldNr, "!", sep="") # The 'sep' argument avoids adding spaces in between -``` - -```{block type="alert alert-info"} -**Note** that Python uses `=` to define a new variable or to give it a new value, as was done above for the variable `WorldNr`. On the other hand, R typically uses `<-` to assign a value to a variable. However R also accepts `=`. In this tutorial, we use `=` for R to keep it simple, but you will find later tutorials using `<-`. Keep in mind that either option is valid as long as it is kept consistent throughout your code! -``` - -Additionally, in Python, string formatting can be used, in for example a print function. To do this, we start a string with an `f` in front of the quote. -```{python} -WorldNr = 2 -print(f"Hello world # {WorldNr} !") -``` - -A variable can also hold multiple values of a particular data type, or even mix data types. Python supports associative arrays, called dictionaries, that are created using curly braces: - -```{python} -WUR = {"name": "Wageningen University", "x": 5.7, "y": 52} -WUR -type(WUR) -WUR["x"] -type(WUR["x"]) -``` - -The dictionary type allows naming its elements and accessing them by name. The elements can be of any type. - -In R, a similar functionality is called a vector (not to be confused with the geographical data type "vector"!). All variables we have created so far have been vectors of length 1. They didn't have names, but you can give names to elements of a vector. However, unlike Python dictionaries, vectors can only contain elements of the same type, and any items of a different type will get automatically converted to fit the rest. We create vectors longer than the size of one by using the **c**oncatenation function `c()`: - -```{r} -WUR = c(name = "Wageningen University", x = 5.7, y=52) -WUR -class(WUR) -WUR["x"] -class(WUR["x"]) -``` - -As you can see, the numbers have been implicitly converted ("coerced" in programming terms) into characters. The disadvantage of named vectors over dictionaries is that we can only use the same type in a single vector. The advantage is that it allows us to use vectorised functions, i.e. functions that perform some work on each element of a vector. Because we know that the vector is going to be entirely composed of characters, we can do something like: - -```{r} -paste(WUR, "addition!") -``` - -Both R and Python support lists, which allow combining any data types into one variable. In Python they are created using square brackets, and in R using `list()`: - -```{python} -WUR = ["Wageningen University", 5.7, 52] -``` - -```{r} -WUR = list("Wageningen University", 5.7, 52) -``` - -Elements of a list are sequential and can be accessed by slicing the list using a number (index) in square brackets. An important difference between the two languages is that R starts counting from 1, but Python starts counting from 0! This is similar to how in different countries, floors of buildings are counted starting from 1 or from 0. Netherlands is a "Python" style country where the ground floor is number 0, whereas Canada is an "R" style country where the ground floor is number 1. The R style has the advantage of the index matching the element number, i.e. `[2]` will give you the second element, where in Python the second element is `[1]`: - -```{python} -WUR[1] -``` - -```{r} -WUR[2] -``` - -There is also a difference in what happens if you use a negative index. Python uses negative indices to wrap around, so `[-1]` means "last element", whereas in R it stands for exclusion, so `[-1]` means "all elements except for the first one": - -```{python} -WUR[-1] -``` - -```{r} -WUR[-1] -``` - -What is the exact equivalent of a dictionary in R? It's a named list! When creating a list, you can specify a name of each element, and then slice the list using names. Unlike the colon `:` used in Python for dictionaries, R simply uses the equal sign `=`: - -```{r} -WUR = list("name"="Wageningen University", "x"=5.7, "y"=52) -WUR -class(WUR) -WUR["x"] -class(WUR["x"]) -``` - -Here we can see another difference in how R and Python deal with lists. In R, a list always consists of lists, recursively. Each element of a list is always a list itself. To obtain the value, we need to access it using the double square bracket operator: - -```{r} -WUR[["x"]] -class(WUR[["x"]]) -``` - -There are several data types that are specific to R, though there are Python packages that implement equivalent functionality as well. The most basic type is a vector, which can hold multiple values of the same type. An extension of a vector is a matrix, that has two dimensions. Adding further dimensions, we get an array. A matrix is a special case of an array (two-dimensional), as is a vector (one-dimensional array). - -As we have covered above, in R, almost everything appears as a vector. That is why in the R print output above you can see `[1]` next to most output, indicating that the value is just the first in a 1-length vector. To make longer vectors, the function `c()` is used. While you can give names to elements, it's optional, we can just omit them: - -```{r} -WURcoords = c(5.7, 52) -WURcoords -class(WURcoords) -``` - -Matrices are made using the function `matrix` by combining multiple vectors: - -```{r} -# `nrow` specifies how many rows the matrix will have -LocMat = matrix(c(WURcoords, WURcoords + 1), nrow = 2) -LocMat -class(LocMat) -``` - -```{block, type="alert alert-success"} -> **Question 2**: Using the `c()` and `matrix()` functions, build a tic-tac-toe board with X's and O's in R. -``` - -And arrays of higher order are likewise created using `array()`: - -```{r} -# Here "dim" defines the shape, i.e. number of rows, columns, layers, etc. -LocArray = array(c(LocMat, LocMat+1), dim=c(2,2,2)) -LocArray -class(LocArray) -``` - -As you can see, we have a cube with two elements in each dimension. Cubes are difficult to visualise, as they are 3D, and our screen is 2D. Therefore, when trying to print one, R presents the cube in "slices" of matrices. - -Base Python can only achieve a similar effect using nested lists: -```{python} -WURcoords = [5.7, 52] -WURcoords -LocMat = [WURcoords, WURcoords] -LocMat -LocArray = [LocMat, LocMat] -LocArray -``` - -The structure is also a cube, but it is less structured, and therefore printing it does not make it obvious that it is a cube. - -Here we can also notice that R can perform vectorised arithmetics: `WURcoords + 1` added 1 to each value of the vector `WURcoords`, and `LocMat + 1` added 1 to each value of the matrix `LocMat`. Core Python does not allow doing so without writing a loop. However, since arrays and vectorised arithmetics are very useful, it has all been implemented in the package `NumPy`, which is now considered to be an essential package in Python: - -```{python} -import numpy - -npWURcoords = numpy.array(WURcoords) -npWURcoords -type(npWURcoords) - -npLocMat = numpy.array([npWURcoords, npWURcoords + 1]) -npLocMat -type(npLocMat) - -npLocArray = numpy.array([npLocMat, npLocMat + 1]) -npLocArray -type(npLocArray) -``` - -As we can see here, NumPy arrays are more structured, and when printing a cube, it also shown sliced, just like in the R example above. The NumPy output also includes square brackets, which you will notice are in the same order and quanity as the plain Python list example we had above, implying that it is ultimately still a list-of-lists. - -```{block, type="alert alert-success"} -> **Question 3**: Make the same tic-tac-toe board as in question 2 in Python. Hint: You may wish to use numpy. -``` - -Another useful data type in R is Data Frames. A Data Frame is a table, similar to a matrix, but with one key difference: while matrices require all values to be of the same type, a data.frame only requires each column to have a consistent type. The Data Frame concept comes from R's statistical background, where it is useful to have multiple variables that are being studied, as columns, and multiple records or observations of those values, as rows. For example: - -```{r} -WURbuildings = data.frame(name = c("Gaia", "Aurora"), x = c(5.665, 5.657), y = c(51.987, 51.982)) -WURbuildings -class(WURbuildings) -``` - -To know the type of each column, we can use the function `str` (structure): -```{r} -str(WURbuildings) -``` - -We see here that the `name` column is made of character strings, whereas `x` and `y` columns are floating point numbers. The Data Frame gives more structure than a plain list does, ensuring that the data has rows and columns and that the column types are consistent. - -In Python, Data Frames are implemented in the package `pandas`: - -```{python} -import pandas -WURbuildings = pandas.DataFrame({"name": ["Gaia", "Aurora"], "x": [5.665, 5.657], "y": [51.987, 51.982]}) -WURbuildings -type(WURbuildings) -WURbuildings.dtypes -``` - -The `.dtypes` accessor is equivalent to `str()` in R, though you can see one difference: the strings are reported as "objects". - -## Slicing and accessors - -When dealing with variables that hold multiple values, we often need to select some smaller subset of those values. This is called *slicing* an array. It is done using operators that are called *accessors*. We used three of them in the examples above: `[]` (both languages), `[[]]` (R) and `.` (Python). These are the main accessors, though there are a few others that the languages support. - -The `[]` accessor accepts indices of values to select. As we saw earlier, the indices can also be negative, or a combination of positive and negative. Both R and Python have a way to quickly create ranges for slicing arrays, using the `:` operator, but there are some differences in how the ranges behave: - -```{r} -Buildings = c("Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas") -Buildings[2:4] # We get the second, third and fourth elements -``` - -```{python} -Buildings = ["Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas"] -Buildings[2:4] # We get the third and fourth elements -``` - -As we can see, slicing in R is inclusive, i.e. any number you mention will be included in the resulting slice. In Python, it is one-sided: the fist number of the range will be included, but the last number will be excluded. And, of course, Python counts from 0 whereas R counts from 1. - -Let's try to slice the first half and the second half of the string array in both languages: -```{r} -Buildings[1:3] -Buildings[4:6] -``` - -```{python} -Buildings[:3] # The fourth element is not included -Buildings[3:] # It is included here -``` - -As you see, Python interprets the missing number as the first or the last element of the array, depending on it's missing before or after the `:`. R does not, and requires you to enter `1` for the first element and `length(YourArray)` (e.g. `length(Buildings)`) for the last one. - -```{block, type="alert alert-success"} -> **Question 4**: Create a subset of `Buildings` with only `Gaia` and `Aurora`, using index slicing in R and Python. Hint: You may wish to use the functions `c()` for R. To vectorize a list in Python, you can use `numpy.array()`, but creating a plain list doesn’t require it :) -``` - -When slicing two-dimensional arrays (matrices or data frames), two indices are used, separated by a comma, in the order `[row, column]`: - -```{r} -LocMat -LocMat[2, 1] # Get y coordinate of WUR campus -``` - -```{python} -npLocMat -npLocMat[0, 1] # Get y coordinate of WUR campus -``` - -If you want to select the whole row/column, without manually specifying the size of your array, in Python you can use `:`, and in R you can omit the index: - -```{r} -LocMat[, 1] # Get both coordinates of WUR campus -``` - -```{python} -npLocMat[0, :] # Get both coordinates of WUR campus -``` - -Likewise, if we have an array with more dimensions, we can specify more indices (e.g. a three-dimensional array will accept three indices, `x`, `y`, `z`). - -Previously we saw another accessor in R, namely, the `[[]]` accessor. It is a list accessor, and generally is used to directly access a value, treating the variable as a list. The accessor accepts numbers and character strings as input. Another accessor popular in R is the `$` accessor, which allows accessing values by name. The `$` accessor is generally equivalent to the `[[]]` accessor with a string input. Here's an example of how these work with data frames: - -```{r} -WURbuildings -WURbuildings[1] # When accessed using a single number, it selects a column -class(WURbuildings[1]) # Single-column data.frame -WURbuildings[[1]] # Also selects a column -class(WURbuildings[[1]]) # But now the result is a vector! -WURbuildings[["name"]] # Column by name -WURbuildings$name # Same -``` - -Generally the `$` accessor is not recommended, because it does not allow the use of variables, it only works with literal strings. - -Note that in Python, the operator `[[]]` has a very different meaning: the outer `[]` is the accessor, and the inner `[]` is a list constructor, in other words, it's equivalent to the R `[c()]`: - -```{python} -npLocArray = numpy.array([npLocMat, npLocMat + 1, npLocMat + 2, npLocMat + 3]) -npLocArray -npLocArray[0,1] # Meaning "first layer, second row, all columns" -npLocArray[[0,2]] # Meaning "first and third layers, all rows and all columns" -``` - -Similar to the R `$` accessor, Python has the accessor `.` to select by literal string: - -```{python} -WURbuildings -WURbuildings["name"] -WURbuildings.name -``` - -Instead of supplying numbers or strings, we can also slice by using a boolean array of the same dimensions as what we are trying to slice. This is very handy, as we can make use of this to select by a rule: - -```{r} -LocMat -LocMat[LocMat > 50] # Get only the values above 50 -``` - -What happens here is that the inner part of the accessor generates a boolean array, which is subsequently used for slicing, as if it was a mask: - -```{r} -LocMat > 50 -``` - -The same applies in NumPy: - -```{python} -npLocMat -npLocMat[npLocMat > 50] -``` - -```{block, type="alert alert-success"} -> **Question 5**: In R and Python, slice `LocMat` to select all values below `6` and above `52`. Hint: You can combine conditions by using `&` (AND) or `|` (OR). Note: In Python, always use parentheses around each condition when combining them — otherwise, operator precedence may cause unexpected behavior. -``` - -## Conditionals and control flow - -We just saw that we can slice arrays based on conditions. Note that the boolean operators `&` and `|` (for *and* and *or* respectively) are valid in both R and Python, but they mean different things! - -In R, `&` is a *vectorised* form of `&&`, in other words, it can compare boolean vectors against each other: - -```{r} -c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE) -``` - -This is good in some cases, such as slicing arrays using boolean arrays (like in Question 5, where `&&` doesn't really work), and not so good in other cases, like `if` statements that control program flow. This is because `if` requires a single `TRUE` or `FALSE`, not an array. - -The conditionals `if` and `else` are used to structure our code, so that part of the code only runs when certain conditions are met. In this case, `if (c(TRUE, FALSE))` is impossible to determine and thus will result in an error. The form `&&` only compares one boolean with another boolean, which makes sure you do not accidentally use arrays where a single boolean is needed, and thus is suitable. - -Here's an example in R: - -```{r} -MyLatitude = 52 -GaiaLatitude = WURbuildings[WURbuildings[["name"]] == "Gaia", "y"] -AuroraLatitude = WURbuildings[WURbuildings[["name"]] == "Aurora", "y"] - -if (MyLatitude > GaiaLatitude && MyLatitude > AuroraLatitude) { - print("I am to the north of both Gaia and Aurora") -} else if (MyLatitude < GaiaLatitude && MyLatitude < AuroraLatitude) { - print("I am to the south of both Gaia and Aurora") -} else { - print("I am in between Gaia and Aurora") -} -``` - -The `&` operator can still work in if statements when both sides are single boolean values. However, it is less strict and may lead to unexpected behavior if vectors are accidentally passed. - -In Python, `&` is a *bitwise* comparison, that is, it does not work on booleans, but numbers. -It just so happens that Python implicitly converts booleans to numbers (`True` to 1 and `False` to 0), so it often works with booleans as well, but it is not intended for that. -This behavior makes them useful in contexts like Question 5, where we compare an array of values against numeric thresholds and want element-wise boolean logic. -Instead, the regular boolean comparison is done using `and`, the logical comparison. It expects the entire variable to represent a single boolean value (True or False). That’s why `and` or `or` cannot be used for element-wise slicing in Question 5. -So just like in R, when you need a single boolean result to control program flow in `if` statement, `and` is more appropriate than `&`. - -Equivalent example code in Python (note the syntax differs a bit between R and Python, e.g. the lack of parentheses, the words for `and` and `or`, and the special `elif` keyword): - -```{python} -MyLatitude = 52 -# Note that boolean conditionals only work with regular booleans, not with Series/DataFrames, hence the .values[0] -GaiaLatitude = WURbuildings.loc[WURbuildings["name"] == "Gaia", "y"].values[0] -AuroraLatitude = WURbuildings.loc[WURbuildings["name"] == "Aurora", "y"].values[0] - -if MyLatitude > GaiaLatitude and MyLatitude > AuroraLatitude: - print("I am to the north of both Gaia and Aurora") -elif MyLatitude < GaiaLatitude and MyLatitude < AuroraLatitude: - print("I am to the south of both Gaia and Aurora") -else: - print("I am in between Gaia and Aurora") -``` - -```{block, type="alert alert-success"} -> **Question 6**: Why don't we need parentheses here in this Python code? Hint: it's something to do with operator precedence. -``` - -## Objects and inheritance - -Both R and Python are object-oriented languages, and in this tutorial we have already worked with many objects. For example, data frames, lists and matrices are objects, and the `class()` or `type()` function prints what type of object it is, in other words, what is the class of the object. In R, `str()` allows investigating the structure of an object. Python does not have a unified function for this and different packages implement this functionality differently, if at all. - -```{r} -WUR = list("name"="Wageningen University", "x"=5.7, "y"=52) -str(WUR) # Show the structure of a list -WURbuildings -str(WURbuildings) # Show the structure of a data frame (which is also a type of list) -``` - -```{python} -WURbuildings # pandas data frame -WURbuildings.dtypes # Shows data types -vars(WURbuildings) # More generic, but does not work with lists and dicts -dir(WURbuildings) # All of the methods contained in the object -``` - -In the last example we can see that objects can contain other objects and/or functions. A class is a definition of an object, and a function contained inside an object is traditionally called a method. - -R and Python both support classes and objects, but their philosophy regarding them differs significantly. R is very lax and allows the users to freely (re)define object classes as they see fit. Python is a lot more formal and requires a formal class definition to define an object class. Python prefers self-contained objects that contain all the methods that can interact with the object embedded inside the object. In contrast, the R philosophy is to define global functions whose behaviour is different depending on the class the function is run on. - -As an example, we might want to define a class `building` which, when instantiated as an object, will contain a `name`, and a vector `coordinates`. We want to also have a function that prints these attributes in a nice to read way. This is how it would be done in R: - -```{r} -Gaia = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987)) -class(Gaia) # It is a list -class(Gaia) = "building" # And now it's a `building`! -class(Gaia) -Aurora = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982)) -class(Aurora) = "building" -class(Aurora) - -# Define a function to print buildings -# We use the "paste" function to format strings. -print.building = function(x) -{ - print(paste(x[["name"]], "is a building that is located at x:", - x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"])) -} -# Now we simply use print() and get our custom output: -print(Gaia) -print(Aurora) -``` - -The reason why this works is because R uses the concept of function signatures. When running `print()`, R will first check what is the class of the object you are calling the function on, and checks if there is a function defined that is called `print.class` (in our case `print.building`). On a match, it will run that function instead of the `print.default` function. - -In Python, we need to formally define a class and then instantiate it: -```{python} -class building: - def __init__(self, name, coordinates): - self.name = name - self.coordinates = coordinates - - def print(self): - # Note that we use "string formatting". If a "f" is put before a string quote, - # all text inside curly brackets will be executed as plain Python - print(f'{self.name} is a building that is located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}') - -# Instantiate the class by calling it as if it was its __init__ function -Gaia = building(name="Gaia", coordinates={"x": 5.665, "y": 51.987}) -Aurora = building(name="Aurora", coordinates={"x": 5.657, "y": 51.982}) -type(Gaia) -type(Aurora) -Gaia.print() -Aurora.print() -``` - -Note that we called the method `print()` that is inside our object, not the global function `print()`, which would give a different output: - -```{python} -print(Gaia) -print(Aurora) -``` - -A key concept in object-oriented programming is inheritance: a class can inherit properties and methods of its parent class. This allows us to make extensions of existing classes without having to duplicate a lot of work. Let's say we want to extend our `building` class with an attribute `purpose`, calling the new child class `purposeBuilding`. In R, inheritance works by making an object part of multiple classes: - -```{r} -GaiaPurpose = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987), purpose="office") -class(GaiaPurpose) = c("purposeBuilding", "building") -print(GaiaPurpose) # We reuse the parent class `print()` - -AuroraPurpose = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982), purpose="education") -class(AuroraPurpose) = c("purposeBuilding", "building") -print(AuroraPurpose) - -# Make a custom print function for purposeBuilding -print.purposeBuilding = function(x) -{ - print(paste(x[["name"]], "is an", x[["purpose"]], "building that is located at x:", - x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"])) -} - -print(GaiaPurpose) -print(AuroraPurpose) -``` - -In Python, inheritance is also formally declared in the definition of the new class: -```{python} -class purposeBuilding(building): - def __init__(self, name, coordinates, purpose): - super().__init__(name, coordinates) # Let the parent class handle these - self.purpose = purpose - -Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office") -Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education") -Gaia.print() -Aurora.print() -``` - -We can override methods by redeclaring them, but any instantiated objects will have to be reinstantiated for the changes to apply: - -```{python} -# Let's also override the print now: -class purposeBuilding(building): - def __init__(self, name, coordinates, purpose): - super().__init__(name, coordinates) # Let the parent class handle these - self.purpose = purpose - - def print(self): - print(f'{self.name} is a building used for {self.purpose} purposes, located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}') - - -# If we don't reinstantiate, the old definition applies: -Gaia.print() -Aurora.print() - -Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office") -Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education") -Gaia.print() -Aurora.print() -``` - -Note that R also has a more formalised type of classes, called S4 classes, that behave a bit more similar to the Python classes, but S4 classes are generally not recommended to use as they are further away from the R philosophy. - -## Scope and side effects - -Another difference between R and Python you may also have noticed in the examples above: R uses curly braces `{}` to denote scope, whereas Python uses a colon `:` and enforces indentation. As an example: - -```{python} -def scope(): - print("I am inside the scope of the function scope()!") - print("I am also inside the scope of the function scope()!") -print("I am outside the scope of the function scope()!") - -scope() -``` - -```{r} -scope = function() { - print("I am inside the scope of the function scope()!") - print("I am also inside the scope of the function scope()!") -} - print("I am outside the scope of the function scope()!") - -scope() -``` - -In addition, in R, typically anything that happens inside of a function scope stays in the function, i.e. it does not alter the global state. - -```{r} -location = "Gaia" -move = function(where) -{ - location = where - print(paste("Moved to", location)) -} - -move("Aurora") -print(location) -``` - -In Python it is also often true, but the list of exceptions is much longer. - -```{python} -location = "Gaia" -def move(where): - location = where - print("Moved to " + location) - -move("Aurora") -print(location) -``` - -For instance, lists are mutable even outside of the function scope: - -```{python} -locations = ["Gaia"] -def addLocation(where): - locations.append(where) - print(locations) - -addLocation("Aurora") -print(locations) -``` - -Such mutability that goes outside of the function scope is called a side effect. They are best avoided as much as possible, as it brings confusion to the users (and may even destroy their work)! Normally, you expect that if you as a user run a function, it will process your arguments and return a new, processed object, but will not change your global environment, or the object that you passed to the function itself. - -To avoid such side effects, in Python we need to explicitly copy mutable objects: - -```{python} -locations = ["Gaia"] -def addLocation(where): - result = locations.copy() - result.append(where) - print(result) - -addLocation("Aurora") -print(locations) -``` - -## Summary - -We have looked at some similarities and differences between R and Python. As you can see, the same or similar functionality is available in both languages, but the philosophies of the languages are sometimes different, so it is important to be aware of them. We will make extensive use of the basics in the upcoming tutorials, and will build further upon them to specifically handle spatial data. We will first go through the specifics of R, then the specifics of Python, but it's good to keep in mind throughout the course that both languages can do what we want, just in a slightly different way. You can use this tutorial as a reference for the basics if you get stuck in future tutorials. Calling R and Python from Bash can also be a very useful technique to combine them during the project at the end of the course. diff --git a/index.html b/index.html deleted file mode 100644 index cdc69d6..0000000 --- a/index.html +++ /dev/null @@ -1,2043 +0,0 @@ - - - - - - - - - - - R and Python basics - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Dainius Masiliūnas

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08 August, 2025

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WUR -Geoscripting -WUR logo

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R and Python basics

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Throughout the course we will be learning R and Python. Both are -programming languages, and both can be used for handling geospatial -data. So what is the difference between them? Why would you choose one -over another? And how does equivalent code look like in each language? -We will go over these questions in this tutorial.

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Learning objectives of the day

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In this tutorial, you will learn to:

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  • describe the differences between R and Python;
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  • apply R and Python for basic handling of data types in each -language;
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  • create new object classes and use inheritance.
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What are R and Python?

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If you ask an average person what Python is, they will tell you that -it’s a type of snake. If you ask an average person what R is, they will -tell you that it’s a letter of the alphabet. Today you will learn beyond -that: the details of the programming languages of R and Python!

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Both R and Python programming languages were created at around the -same time: Python was created in 1991 in the Netherlands, and R in 1993. -However, R actually has a much longer history, because it is an -extension of the S programming language that was created all the way -back in 1976 in Bell Labs. R was created specifically as a statistical -package, similar to SPSS or Stata. However, it has since evolved into a -general-purpose programming language. Due to its focus on statistics, R -is mainly used in academia (schools and universities).

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Python was created as a general-purpose programming language. It was -designed with readability in mind, and it has become the most popular -programming language in the world. Python is therefore widely used in -the industry, such as startups and large corporations. Python is -especially strong in the field of deep learning, since packages such as -Google’s TensorFlow are implemented primarily in Python. Additionally, -because of it’s popularity, it is very easy to find online examples and -it is used often to communicate with other software.

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In terms of spatial data handling, both R and Python can perform the -same tasks. Both are relatively easy to integrate with other software as -well, which allows extending the capabilities of each language. For -example, the open source GIS software QGIS, including all its plugins, -are written in Python. R scripts can be run directly from the QGIS -interface as processing tools. R has a package reticulate -that can run Python scripts, and Python has a package rpy2 -that can run R scripts.

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Both R and Python have extremely active communities maintaining a -wealth of packages. The package ecosystems differ significantly between -the two languages, however. Python packages are hosted on PyPI, the -Python Package Index, which is a very easily accessible place where -anyone can upload the Python packages they create. There is minimal -oversight over them and the package authors are free to do with their -packages what they please. The R package repository is called CRAN, the -Comprehensive R Archive Network. CRAN is curated: any package submitted -to CRAN goes through a review process, and has to pass a suite of tests -before it gets accepted. This ensures that, among other things, every -argument in every function in every package is documented, and that each -package is compatible with all the other packages in CRAN. The result is -that it is much more difficult to publish R packages, but the quality of -R packages is generally much higher than Python packages. It also means -that package management in R is very easy, as no conflicts can happen -between packages. Packages are also more inclined to depend on other -packages, as it is more certain that their dependencies will stay -maintained. In contrast, Python has a lot more packages, but they are -often poorly documented and often do not interoperate with other -packages as well. Package management is a big issue in Python, as many -packages work only with certain versions of other packages.

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In practice, this means that while all geospatial data handling tasks -can be done in R or Python, R is better integrated for this. In this -course you will learn about the packages terra for raster -handling and sf for vector handling, which are both -integrated with each other and offer a full suite of processing tools, -and all of the other packages that use vectors or rasters in R also make -use of sf and terra package objects. In -Python, raster reading is done using the rasterio package, -but processing needs to be done using other packages, many of which do -not support rasterio objects. Vector handling is done in -for example geopandas, which has spatial processing tools, -but once again they are not always integrated with raster object -support. So rasters often need to be converted into number matrices for -processing, then converted back into raster objects, by the user.

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Both R and Python can plot geospatial data and have several -frameworks and packages for that. R has a built-in plot() -function that can visualise any type of data quickly, and all packages -make use of it. For more advanced custom visualisation, -ggplot2 can be used. In Python, the standard plotting -package is matplotlib. Spatial packages often implement -their own plotting functions for their own objects, therefore putting -multiple objects into one plot can often be more challenging than in -R.

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Ultimately, the choice of language to use is often decided by -interoperability needs (e.g. if you work in a company that uses Python, -you will be expected to also write in Python, so that your script can be -used in a pipeline) and personal preference.

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Running Python and R from Bash

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Both the R and the python interpreters can -be run from Bash. Here is an example of code that is correct in both -Python and R. You can use this example to execute a script written in -either language:

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# Create a script file "script" (no extension)
-echo 'print("Hello world!")' > script
-# Run the script with R
-Rscript ./script
-# Run the script with Python
-python3 ./script
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## [1] "Hello world!"
-## Hello world!
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This allows you to run script files even without having any graphical -user interface installed or running, which is the fastest way to run any -script from top to bottom. This way, you can even combine R and Python -code from a Bash script!

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Now, let’s explore the similarities and differences between the two -languages. For that, you need to have Python set up. Let’s install the -needed packages through the Bash terminal:

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sudo apt install python3-pandas
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Now you can run R in one terminal, and run Python in another, to try -out the code snippets below. To run R in interactive mode:

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R
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To run Python in interactive mode:

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python3
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Data types

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Both Python and R provide a set of primitive data types. The ones -they have in common are integers, floating-point numbers, logical -boolean values, character strings, associative arrays (dictionaries) and -lists. To find out the type of a variable, use type() in -Python and class() in R.

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A whole number is called an integer, and a number with decimals (real -number) is called a float (floating point number). In Python, if you -enter a number without decimal points, it will be an integer, otherwise -it will be a float.

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type(10)
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## <class 'int'>
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type(10.1)
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## <class 'float'>
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type(10.)
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## <class 'float'>
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type(int(10.))
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## <class 'int'>
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In R, any number will be a float (called “numeric”) by default, and -integers are obtained by explicitly casting to an integer using -as.integer():

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class(10)
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## [1] "numeric"
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class(10.1)
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## [1] "numeric"
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class(as.integer(10))
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## [1] "integer"
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-Question 1: What is the type/class of the sum 3+5 and -of 3.0+5 in R and in Python? Write a sum of two numbers that returns an -integer in R and in Python. -

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Boolean, or logical, types can only have two values: true or false. -When cast to an integer, true is represented by 1 and false is -represented by 0. Both R and Python are case-sensitive, and use -different cases to represent booleans (True in Python and -TRUE in R).

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type(True)
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## <class 'bool'>
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type(True + 2)
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## <class 'int'>
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True + 2
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## 3
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class(TRUE)
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## [1] "logical"
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class(TRUE + 2)
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## [1] "numeric"
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TRUE + 2
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## [1] 3
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Character strings are letters and words. They can also include -numbers, but the numbers do not have a mathematical meaning, and -therefore you cannot do arithmetics on strings. Strings are marked with -quotes, either single or double:

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"Hello world #1!"
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## 'Hello world #1!'
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5 + '6'
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## TypeError: unsupported operand type(s) for +: 'int' and 'str'
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type('6')
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## <class 'str'>
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"Hello world #1!"
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## [1] "Hello world #1!"
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5 + '6'
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## Error in 5 + "6": non-numeric argument to binary operator
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class('6')
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## [1] "character"
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You can combine strings to produce longer strings. This is done with -the function paste() in R and using the + -operator in Python (as long as all parts are strings).

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WorldNr = 2
-"Hello world #" + str(WorldNr) + "!"
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## 'Hello world #2!'
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WorldNr = 2
-paste("Hello world #", WorldNr, "!", sep="") # The 'sep' argument avoids adding spaces in between
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## [1] "Hello world #2!"
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-Note that Python uses = to define a new -variable or to give it a new value, as was done above for the variable -WorldNr. On the other hand, R typically uses -<- to assign a value to a variable. However R also -accepts =. In this tutorial, we use = for R to -keep it simple, but you will find later tutorials using -<-. Keep in mind that either option is valid as long as -it is kept consistent throughout your code! -

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Additionally, in Python, string formatting can be used, in for -example a print function. To do this, we start a string with an -f in front of the quote.

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WorldNr = 2
-print(f"Hello world # {WorldNr} !")
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## Hello world # 2 !
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A variable can also hold multiple values of a particular data type, -or even mix data types. Python supports associative arrays, called -dictionaries, that are created using curly braces:

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WUR = {"name": "Wageningen University", "x": 5.7, "y": 52}
-WUR
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## {'name': 'Wageningen University', 'x': 5.7, 'y': 52}
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type(WUR)
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## <class 'dict'>
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WUR["x"]
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## 5.7
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type(WUR["x"])
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## <class 'float'>
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The dictionary type allows naming its elements and accessing them by -name. The elements can be of any type.

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In R, a similar functionality is called a vector (not to be confused -with the geographical data type “vector”!). All variables we have -created so far have been vectors of length 1. They didn’t have names, -but you can give names to elements of a vector. However, unlike Python -dictionaries, vectors can only contain elements of the same type, and -any items of a different type will get automatically converted to fit -the rest. We create vectors longer than the size of one by using the -concatenation function c():

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WUR = c(name = "Wageningen University", x = 5.7, y=52)
-WUR
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##                    name                       x                       y 
-## "Wageningen University"                   "5.7"                    "52"
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class(WUR)
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## [1] "character"
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WUR["x"]
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##     x 
-## "5.7"
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class(WUR["x"])
-
## [1] "character"
-

As you can see, the numbers have been implicitly converted (“coerced” -in programming terms) into characters. The disadvantage of named vectors -over dictionaries is that we can only use the same type in a single -vector. The advantage is that it allows us to use vectorised functions, -i.e. functions that perform some work on each element of a vector. -Because we know that the vector is going to be entirely composed of -characters, we can do something like:

-
paste(WUR, "addition!")
-
## [1] "Wageningen University addition!" "5.7 addition!"                  
-## [3] "52 addition!"
-

Both R and Python support lists, which allow combining any data types -into one variable. In Python they are created using square brackets, and -in R using list():

-
WUR = ["Wageningen University", 5.7, 52]
-
WUR = list("Wageningen University", 5.7, 52)
-

Elements of a list are sequential and can be accessed by slicing the -list using a number (index) in square brackets. An important difference -between the two languages is that R starts counting from 1, but Python -starts counting from 0! This is similar to how in different countries, -floors of buildings are counted starting from 1 or from 0. Netherlands -is a “Python” style country where the ground floor is number 0, whereas -Canada is an “R” style country where the ground floor is number 1. The R -style has the advantage of the index matching the element number, -i.e. [2] will give you the second element, where in Python -the second element is [1]:

-
WUR[1]
-
## 5.7
-
WUR[2]
-
## [[1]]
-## [1] 5.7
-

There is also a difference in what happens if you use a negative -index. Python uses negative indices to wrap around, so [-1] -means “last element”, whereas in R it stands for exclusion, so -[-1] means “all elements except for the first one”:

-
WUR[-1]
-
## 52
-
WUR[-1]
-
## [[1]]
-## [1] 5.7
-## 
-## [[2]]
-## [1] 52
-

What is the exact equivalent of a dictionary in R? It’s a named list! -When creating a list, you can specify a name of each element, and then -slice the list using names. Unlike the colon : used in -Python for dictionaries, R simply uses the equal sign -=:

-
WUR = list("name"="Wageningen University", "x"=5.7, "y"=52)
-WUR
-
## $name
-## [1] "Wageningen University"
-## 
-## $x
-## [1] 5.7
-## 
-## $y
-## [1] 52
-
class(WUR)
-
## [1] "list"
-
WUR["x"]
-
## $x
-## [1] 5.7
-
class(WUR["x"])
-
## [1] "list"
-

Here we can see another difference in how R and Python deal with -lists. In R, a list always consists of lists, recursively. Each element -of a list is always a list itself. To obtain the value, we need to -access it using the double square bracket operator:

-
WUR[["x"]]
-
## [1] 5.7
-
class(WUR[["x"]])
-
## [1] "numeric"
-

There are several data types that are specific to R, though there are -Python packages that implement equivalent functionality as well. The -most basic type is a vector, which can hold multiple values of the same -type. An extension of a vector is a matrix, that has two dimensions. -Adding further dimensions, we get an array. A matrix is a special case -of an array (two-dimensional), as is a vector (one-dimensional -array).

-

As we have covered above, in R, almost everything appears as a -vector. That is why in the R print output above you can see -[1] next to most output, indicating that the value is just -the first in a 1-length vector. To make longer vectors, the function -c() is used. While you can give names to elements, it’s -optional, we can just omit them:

-
WURcoords = c(5.7, 52)
-WURcoords
-
## [1]  5.7 52.0
-
class(WURcoords)
-
## [1] "numeric"
-

Matrices are made using the function matrix by combining -multiple vectors:

-
# `nrow` specifies how many rows the matrix will have
-LocMat = matrix(c(WURcoords, WURcoords + 1), nrow = 2)
-LocMat
-
##      [,1] [,2]
-## [1,]  5.7  6.7
-## [2,] 52.0 53.0
-
class(LocMat)
-
## [1] "matrix" "array"
-
-
-

-Question 2: Using the c() and -matrix() functions, build a tic-tac-toe board with X’s and -O’s in R. -

-
-
-

And arrays of higher order are likewise created using -array():

-
# Here "dim" defines the shape, i.e. number of rows, columns, layers, etc.
-LocArray = array(c(LocMat, LocMat+1), dim=c(2,2,2))
-LocArray
-
## , , 1
-## 
-##      [,1] [,2]
-## [1,]  5.7  6.7
-## [2,] 52.0 53.0
-## 
-## , , 2
-## 
-##      [,1] [,2]
-## [1,]  6.7  7.7
-## [2,] 53.0 54.0
-
class(LocArray)
-
## [1] "array"
-

As you can see, we have a cube with two elements in each dimension. -Cubes are difficult to visualise, as they are 3D, and our screen is 2D. -Therefore, when trying to print one, R presents the cube in “slices” of -matrices.

-

Base Python can only achieve a similar effect using nested lists:

-
WURcoords = [5.7, 52]
-WURcoords
-
## [5.7, 52]
-
LocMat = [WURcoords, WURcoords]
-LocMat
-
## [[5.7, 52], [5.7, 52]]
-
LocArray = [LocMat, LocMat]
-LocArray
-
## [[[5.7, 52], [5.7, 52]], [[5.7, 52], [5.7, 52]]]
-

The structure is also a cube, but it is less structured, and -therefore printing it does not make it obvious that it is a cube.

-

Here we can also notice that R can perform vectorised arithmetics: -WURcoords + 1 added 1 to each value of the vector -WURcoords, and LocMat + 1 added 1 to each -value of the matrix LocMat. Core Python does not allow -doing so without writing a loop. However, since arrays and vectorised -arithmetics are very useful, it has all been implemented in the package -NumPy, which is now considered to be an essential package -in Python:

-
import numpy
-
-npWURcoords = numpy.array(WURcoords)
-npWURcoords
-
## array([ 5.7, 52. ])
-
type(npWURcoords)
-
## <class 'numpy.ndarray'>
-
npLocMat = numpy.array([npWURcoords, npWURcoords + 1])
-npLocMat
-
## array([[ 5.7, 52. ],
-##        [ 6.7, 53. ]])
-
type(npLocMat)
-
## <class 'numpy.ndarray'>
-
npLocArray = numpy.array([npLocMat, npLocMat + 1])
-npLocArray
-
## array([[[ 5.7, 52. ],
-##         [ 6.7, 53. ]],
-## 
-##        [[ 6.7, 53. ],
-##         [ 7.7, 54. ]]])
-
type(npLocArray)
-
## <class 'numpy.ndarray'>
-

As we can see here, NumPy arrays are more structured, and when -printing a cube, it also shown sliced, just like in the R example above. -The NumPy output also includes square brackets, which you will notice -are in the same order and quanity as the plain Python list example we -had above, implying that it is ultimately still a list-of-lists.

-
-
-

-Question 3: Make the same tic-tac-toe board as in -question 2 in Python. Hint: You may wish to use numpy. -

-
-
-

Another useful data type in R is Data Frames. A Data Frame is a -table, similar to a matrix, but with one key difference: while matrices -require all values to be of the same type, a data.frame only requires -each column to have a consistent type. The Data Frame concept comes from -R’s statistical background, where it is useful to have multiple -variables that are being studied, as columns, and multiple records or -observations of those values, as rows. For example:

-
WURbuildings = data.frame(name = c("Gaia", "Aurora"), x = c(5.665, 5.657), y = c(51.987, 51.982))
-WURbuildings
-
##     name     x      y
-## 1   Gaia 5.665 51.987
-## 2 Aurora 5.657 51.982
-
class(WURbuildings)
-
## [1] "data.frame"
-

To know the type of each column, we can use the function -str (structure):

-
str(WURbuildings)
-
## 'data.frame':    2 obs. of  3 variables:
-##  $ name: chr  "Gaia" "Aurora"
-##  $ x   : num  5.67 5.66
-##  $ y   : num  52 52
-

We see here that the name column is made of character -strings, whereas x and y columns are floating -point numbers. The Data Frame gives more structure than a plain list -does, ensuring that the data has rows and columns and that the column -types are consistent.

-

In Python, Data Frames are implemented in the package -pandas:

-
import pandas
-WURbuildings = pandas.DataFrame({"name": ["Gaia", "Aurora"],  "x": [5.665, 5.657], "y": [51.987, 51.982]})
-WURbuildings
-
##      name      x       y
-## 0    Gaia  5.665  51.987
-## 1  Aurora  5.657  51.982
-
type(WURbuildings)
-
## <class 'pandas.core.frame.DataFrame'>
-
WURbuildings.dtypes
-
## name     object
-## x       float64
-## y       float64
-## dtype: object
-

The .dtypes accessor is equivalent to str() -in R, though you can see one difference: the strings are reported as -“objects”.

-
-
-

Slicing and accessors

-

When dealing with variables that hold multiple values, we often need -to select some smaller subset of those values. This is called -slicing an array. It is done using operators that are called -accessors. We used three of them in the examples above: -[] (both languages), [[]] (R) and -. (Python). These are the main accessors, though there are -a few others that the languages support.

-

The [] accessor accepts indices of values to select. As -we saw earlier, the indices can also be negative, or a combination of -positive and negative. Both R and Python have a way to quickly create -ranges for slicing arrays, using the : operator, but there -are some differences in how the ranges behave:

-
Buildings = c("Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas")
-Buildings[2:4] # We get the second, third and fourth elements
-
## [1] "Lumen" "Forum" "Orion"
-
Buildings = ["Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas"]
-Buildings[2:4] # We get the third and fourth elements
-
## ['Forum', 'Orion']
-

As we can see, slicing in R is inclusive, i.e. any number you mention -will be included in the resulting slice. In Python, it is one-sided: the -fist number of the range will be included, but the last number will be -excluded. And, of course, Python counts from 0 whereas R counts from -1.

-

Let’s try to slice the first half and the second half of the string -array in both languages:

-
Buildings[1:3]
-
## [1] "Gaia"  "Lumen" "Forum"
-
Buildings[4:6]
-
## [1] "Orion"  "Aurora" "Atlas"
-
Buildings[:3] # The fourth element is not included
-
## ['Gaia', 'Lumen', 'Forum']
-
Buildings[3:] # It is included here
-
## ['Orion', 'Aurora', 'Atlas']
-

As you see, Python interprets the missing number as the first or the -last element of the array, depending on it’s missing before or after the -:. R does not, and requires you to enter 1 for -the first element and length(YourArray) -(e.g. length(Buildings)) for the last one.

-
-
-

-Question 4: Create a subset of Buildings -with only Gaia and Aurora, using index slicing -in R and Python. Hint: You may wish to use the functions -c() for R. To vectorize a list in Python, you can use -numpy.array(), but creating a plain list doesn’t require it -:) -

-
-
-

When slicing two-dimensional arrays (matrices or data frames), two -indices are used, separated by a comma, in the order -[row, column]:

-
LocMat
-
##      [,1] [,2]
-## [1,]  5.7  6.7
-## [2,] 52.0 53.0
-
LocMat[2, 1] # Get y coordinate of WUR campus
-
## [1] 52
-
npLocMat
-
## array([[ 5.7, 52. ],
-##        [ 6.7, 53. ]])
-
npLocMat[0, 1] # Get y coordinate of WUR campus
-
## 52.0
-

If you want to select the whole row/column, without manually -specifying the size of your array, in Python you can use :, -and in R you can omit the index:

-
LocMat[, 1] # Get both coordinates of WUR campus
-
## [1]  5.7 52.0
-
npLocMat[0, :] # Get both coordinates of WUR campus
-
## array([ 5.7, 52. ])
-

Likewise, if we have an array with more dimensions, we can specify -more indices (e.g. a three-dimensional array will accept three indices, -x, y, z).

-

Previously we saw another accessor in R, namely, the -[[]] accessor. It is a list accessor, and generally is used -to directly access a value, treating the variable as a list. The -accessor accepts numbers and character strings as input. Another -accessor popular in R is the $ accessor, which allows -accessing values by name. The $ accessor is generally -equivalent to the [[]] accessor with a string input. Here’s -an example of how these work with data frames:

-
WURbuildings
-
##     name     x      y
-## 1   Gaia 5.665 51.987
-## 2 Aurora 5.657 51.982
-
WURbuildings[1] # When accessed using a single number, it selects a column
-
##     name
-## 1   Gaia
-## 2 Aurora
-
class(WURbuildings[1]) # Single-column data.frame
-
## [1] "data.frame"
-
WURbuildings[[1]] # Also selects a column
-
## [1] "Gaia"   "Aurora"
-
class(WURbuildings[[1]]) # But now the result is a vector!
-
## [1] "character"
-
WURbuildings[["name"]] # Column by name
-
## [1] "Gaia"   "Aurora"
-
WURbuildings$name # Same
-
## [1] "Gaia"   "Aurora"
-

Generally the $ accessor is not recommended, because it -does not allow the use of variables, it only works with literal -strings.

-

Note that in Python, the operator [[]] has a very -different meaning: the outer [] is the accessor, and the -inner [] is a list constructor, in other words, it’s -equivalent to the R [c()]:

-
npLocArray = numpy.array([npLocMat, npLocMat + 1, npLocMat + 2, npLocMat + 3])
-npLocArray
-
## array([[[ 5.7, 52. ],
-##         [ 6.7, 53. ]],
-## 
-##        [[ 6.7, 53. ],
-##         [ 7.7, 54. ]],
-## 
-##        [[ 7.7, 54. ],
-##         [ 8.7, 55. ]],
-## 
-##        [[ 8.7, 55. ],
-##         [ 9.7, 56. ]]])
-
npLocArray[0,1] # Meaning "first layer, second row, all columns"
-
## array([ 6.7, 53. ])
-
npLocArray[[0,2]] # Meaning "first and third layers, all rows and all columns"
-
## array([[[ 5.7, 52. ],
-##         [ 6.7, 53. ]],
-## 
-##        [[ 7.7, 54. ],
-##         [ 8.7, 55. ]]])
-

Similar to the R $ accessor, Python has the accessor -. to select by literal string:

-
WURbuildings
-
##      name      x       y
-## 0    Gaia  5.665  51.987
-## 1  Aurora  5.657  51.982
-
WURbuildings["name"]
-
## 0      Gaia
-## 1    Aurora
-## Name: name, dtype: object
-
WURbuildings.name
-
## 0      Gaia
-## 1    Aurora
-## Name: name, dtype: object
-

Instead of supplying numbers or strings, we can also slice by using a -boolean array of the same dimensions as what we are trying to slice. -This is very handy, as we can make use of this to select by a rule:

-
LocMat
-
##      [,1] [,2]
-## [1,]  5.7  6.7
-## [2,] 52.0 53.0
-
LocMat[LocMat > 50] # Get only the values above 50
-
## [1] 52 53
-

What happens here is that the inner part of the accessor generates a -boolean array, which is subsequently used for slicing, as if it was a -mask:

-
LocMat > 50
-
##       [,1]  [,2]
-## [1,] FALSE FALSE
-## [2,]  TRUE  TRUE
-

The same applies in NumPy:

-
npLocMat
-
## array([[ 5.7, 52. ],
-##        [ 6.7, 53. ]])
-
npLocMat[npLocMat > 50]
-
## array([52., 53.])
-
-
-

-Question 5: In R and Python, slice LocMat -to select all values below 6 and above 52. -Hint: You can combine conditions by using & (AND) or -| (OR). Note: In Python, always use parentheses around each -condition when combining them — otherwise, operator precedence may cause -unexpected behavior. -

-
-
-
-
-

Conditionals and control flow

-

We just saw that we can slice arrays based on conditions. Note that -the boolean operators & and | (for -and and or respectively) are valid in both R and -Python, but they mean different things!

-

In R, & is a vectorised form of -&&, in other words, it can compare boolean vectors -against each other:

-
c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE)
-
## [1]  TRUE FALSE FALSE FALSE
-

This is good in some cases, such as slicing arrays using boolean -arrays (like in Question 5, where && doesn’t really -work), and not so good in other cases, like if statements -that control program flow. This is because if requires a -single TRUE or FALSE, not an array.

-

The conditionals if and else are used to -structure our code, so that part of the code only runs when certain -conditions are met. In this case, if (c(TRUE, FALSE)) is -impossible to determine and thus will result in an error. The form -&& only compares one boolean with another boolean, -which makes sure you do not accidentally use arrays where a single -boolean is needed, and thus is suitable.

-

Here’s an example in R:

-
MyLatitude = 52
-GaiaLatitude = WURbuildings[WURbuildings[["name"]] == "Gaia", "y"]
-AuroraLatitude = WURbuildings[WURbuildings[["name"]] == "Aurora", "y"]
-
-if (MyLatitude > GaiaLatitude && MyLatitude > AuroraLatitude) {
-  print("I am to the north of both Gaia and Aurora")
-} else if (MyLatitude < GaiaLatitude && MyLatitude < AuroraLatitude) {
-  print("I am to the south of both Gaia and Aurora")
-} else {
-  print("I am in between Gaia and Aurora")
-}
-
## [1] "I am to the north of both Gaia and Aurora"
-

The & operator can still work in if statements when -both sides are single boolean values. However, it is less strict and may -lead to unexpected behavior if vectors are accidentally passed.

-

In Python, & is a bitwise comparison, that -is, it does not work on booleans, but numbers. It just so happens that -Python implicitly converts booleans to numbers (True to 1 -and False to 0), so it often works with booleans as well, -but it is not intended for that. This behavior makes them useful in -contexts like Question 5, where we compare an array of values against -numeric thresholds and want element-wise boolean logic. Instead, the -regular boolean comparison is done using and, the logical -comparison. It expects the entire variable to represent a single boolean -value (True or False). That’s why and or or -cannot be used for element-wise slicing in Question 5. So just like in -R, when you need a single boolean result to control program flow in -if statement, and is more appropriate than -&.

-

Equivalent example code in Python (note the syntax differs a bit -between R and Python, e.g. the lack of parentheses, the words for -and and or, and the special elif -keyword):

-
MyLatitude = 52
-# Note that boolean conditionals only work with regular booleans, not with Series/DataFrames, hence the .values[0]
-GaiaLatitude = WURbuildings.loc[WURbuildings["name"] == "Gaia", "y"].values[0]
-AuroraLatitude = WURbuildings.loc[WURbuildings["name"] == "Aurora", "y"].values[0]
-
-if MyLatitude > GaiaLatitude and MyLatitude > AuroraLatitude:
-  print("I am to the north of both Gaia and Aurora")
-elif MyLatitude < GaiaLatitude and MyLatitude < AuroraLatitude:
-  print("I am to the south of both Gaia and Aurora")
-else:
-  print("I am in between Gaia and Aurora")
-
## I am to the north of both Gaia and Aurora
-
-
-

-Question 6: Why don’t we need parentheses here in this -Python code? Hint: it’s something to do with operator precedence. -

-
-
-
-
-

Objects and inheritance

-

Both R and Python are object-oriented languages, and in this tutorial -we have already worked with many objects. For example, data frames, -lists and matrices are objects, and the class() or -type() function prints what type of object it is, in other -words, what is the class of the object. In R, str() allows -investigating the structure of an object. Python does not have a unified -function for this and different packages implement this functionality -differently, if at all.

-
WUR = list("name"="Wageningen University", "x"=5.7, "y"=52)
-str(WUR) # Show the structure of a list
-
## List of 3
-##  $ name: chr "Wageningen University"
-##  $ x   : num 5.7
-##  $ y   : num 52
-
WURbuildings
-
##     name     x      y
-## 1   Gaia 5.665 51.987
-## 2 Aurora 5.657 51.982
-
str(WURbuildings) # Show the structure of a data frame (which is also a type of list)
-
## 'data.frame':    2 obs. of  3 variables:
-##  $ name: chr  "Gaia" "Aurora"
-##  $ x   : num  5.67 5.66
-##  $ y   : num  52 52
-
WURbuildings # pandas data frame
-
##      name      x       y
-## 0    Gaia  5.665  51.987
-## 1  Aurora  5.657  51.982
-
WURbuildings.dtypes # Shows data types
-
## name     object
-## x       float64
-## y       float64
-## dtype: object
-
vars(WURbuildings) # More generic, but does not work with lists and dicts
-
## {'_is_copy': None, '_mgr': BlockManager
-## Items: Index(['name', 'x', 'y'], dtype='object')
-## Axis 1: RangeIndex(start=0, stop=2, step=1)
-## NumpyBlock: slice(0, 1, 1), 1 x 2, dtype: object
-## NumpyBlock: slice(1, 3, 1), 2 x 2, dtype: float64, '_item_cache': {'name': 0      Gaia
-## 1    Aurora
-## Name: name, dtype: object, 'y': 0    51.987
-## 1    51.982
-## Name: y, dtype: float64}, '_attrs': {}, '_flags': <Flags(allows_duplicate_labels=True)>}
-
dir(WURbuildings) # All of the methods contained in the object
-
## ['T', '_AXIS_LEN', '_AXIS_ORDERS', '_AXIS_TO_AXIS_NUMBER', '_HANDLED_TYPES', '__abs__', '__add__', '__and__', '__annotations__', '__array__', '__array_priority__', '__array_ufunc__', '__bool__', '__class__', '__contains__', '__copy__', '__dataframe__', '__dataframe_consortium_standard__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__doc__', '__eq__', '__finalize__', '__floordiv__', '__format__', '__ge__', '__getattr__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__imod__', '__imul__', '__init__', '__init_subclass__', '__invert__', '__ior__', '__ipow__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__nonzero__', '__or__', '__pandas_priority__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__weakref__', '__xor__', '_accessors', '_accum_func', '_agg_examples_doc', '_agg_see_also_doc', '_align_for_op', '_align_frame', '_align_series', '_append', '_arith_method', '_arith_method_with_reindex', '_as_manager', '_attrs', '_box_col_values', '_can_fast_transpose', '_check_inplace_and_allows_duplicate_labels', '_check_is_chained_assignment_possible', '_check_label_or_level_ambiguity', '_check_setitem_copy', '_clear_item_cache', '_clip_with_one_bound', '_clip_with_scalar', '_cmp_method', '_combine_frame', '_consolidate', '_consolidate_inplace', '_construct_axes_dict', '_construct_result', '_constructor', '_constructor_from_mgr', '_constructor_sliced', '_constructor_sliced_from_mgr', '_create_data_for_split_and_tight_to_dict', '_data', '_deprecate_downcast', '_dir_additions', '_dir_deletions', '_dispatch_frame_op', '_drop_axis', '_drop_labels_or_levels', '_ensure_valid_index', '_find_valid_index', '_flags', '_flex_arith_method', '_flex_cmp_method', '_from_arrays', '_from_mgr', '_get_agg_axis', '_get_axis', '_get_axis_name', '_get_axis_number', '_get_axis_resolvers', '_get_block_manager_axis', '_get_bool_data', '_get_cleaned_column_resolvers', '_get_column_array', '_get_index_resolvers', '_get_item_cache', '_get_label_or_level_values', '_get_numeric_data', '_get_value', '_getitem_bool_array', '_getitem_multilevel', '_getitem_nocopy', '_getitem_slice', '_gotitem', '_hidden_attrs', '_indexed_same', '_info_axis', '_info_axis_name', '_info_axis_number', '_info_repr', '_init_mgr', '_inplace_method', '_internal_names', '_internal_names_set', '_is_copy', '_is_homogeneous_type', '_is_label_or_level_reference', '_is_label_reference', '_is_level_reference', '_is_mixed_type', '_is_view', '_iset_item', '_iset_item_mgr', '_iset_not_inplace', '_item_cache', '_iter_column_arrays', '_ixs', '_logical_func', '_logical_method', '_maybe_align_series_as_frame', '_maybe_cache_changed', '_maybe_update_cacher', '_metadata', '_mgr', '_min_count_stat_function', '_needs_reindex_multi', '_pad_or_backfill', '_protect_consolidate', '_reduce', '_reduce_axis1', '_reindex_axes', '_reindex_multi', '_reindex_with_indexers', '_rename', '_replace_columnwise', '_repr_data_resource_', '_repr_fits_horizontal_', '_repr_fits_vertical_', '_repr_html_', '_repr_latex_', '_reset_cache', '_reset_cacher', '_sanitize_column', '_series', '_set_axis', '_set_axis_name', '_set_axis_nocheck', '_set_is_copy', '_set_item', '_set_item_frame_value', '_set_item_mgr', '_set_value', '_setitem_array', '_setitem_frame', '_setitem_slice', '_shift_with_freq', '_should_reindex_frame_op', '_slice', '_sliced_from_mgr', '_stat_function', '_stat_function_ddof', '_take_with_is_copy', '_to_dict_of_blocks', '_to_latex_via_styler', '_typ', '_update_inplace', '_validate_dtype', '_values', '_where', 'abs', 'add', 'add_prefix', 'add_suffix', 'agg', 'aggregate', 'align', 'all', 'any', 'apply', 'applymap', 'asfreq', 'asof', 'assign', 'astype', 'at', 'at_time', 'attrs', 'axes', 'backfill', 'between_time', 'bfill', 'bool', 'boxplot', 'clip', 'columns', 'combine', 'combine_first', 'compare', 'convert_dtypes', 'copy', 'corr', 'corrwith', 'count', 'cov', 'cummax', 'cummin', 'cumprod', 'cumsum', 'describe', 'diff', 'div', 'divide', 'dot', 'drop', 'drop_duplicates', 'droplevel', 'dropna', 'dtypes', 'duplicated', 'empty', 'eq', 'equals', 'eval', 'ewm', 'expanding', 'explode', 'ffill', 'fillna', 'filter', 'first', 'first_valid_index', 'flags', 'floordiv', 'from_dict', 'from_records', 'ge', 'get', 'groupby', 'gt', 'head', 'hist', 'iat', 'idxmax', 'idxmin', 'iloc', 'index', 'infer_objects', 'info', 'insert', 'interpolate', 'isetitem', 'isin', 'isna', 'isnull', 'items', 'iterrows', 'itertuples', 'join', 'keys', 'kurt', 'kurtosis', 'last', 'last_valid_index', 'le', 'loc', 'lt', 'map', 'mask', 'max', 'mean', 'median', 'melt', 'memory_usage', 'merge', 'min', 'mod', 'mode', 'mul', 'multiply', 'name', 'ndim', 'ne', 'nlargest', 'notna', 'notnull', 'nsmallest', 'nunique', 'pad', 'pct_change', 'pipe', 'pivot', 'pivot_table', 'plot', 'pop', 'pow', 'prod', 'product', 'quantile', 'query', 'radd', 'rank', 'rdiv', 'reindex', 'reindex_like', 'rename', 'rename_axis', 'reorder_levels', 'replace', 'resample', 'reset_index', 'rfloordiv', 'rmod', 'rmul', 'rolling', 'round', 'rpow', 'rsub', 'rtruediv', 'sample', 'select_dtypes', 'sem', 'set_axis', 'set_flags', 'set_index', 'shape', 'shift', 'size', 'skew', 'sort_index', 'sort_values', 'squeeze', 'stack', 'std', 'style', 'sub', 'subtract', 'sum', 'swapaxes', 'swaplevel', 'tail', 'take', 'to_clipboard', 'to_csv', 'to_dict', 'to_excel', 'to_feather', 'to_gbq', 'to_hdf', 'to_html', 'to_json', 'to_latex', 'to_markdown', 'to_numpy', 'to_orc', 'to_parquet', 'to_period', 'to_pickle', 'to_records', 'to_sql', 'to_stata', 'to_string', 'to_timestamp', 'to_xarray', 'to_xml', 'transform', 'transpose', 'truediv', 'truncate', 'tz_convert', 'tz_localize', 'unstack', 'update', 'value_counts', 'values', 'var', 'where', 'x', 'xs', 'y']
-

In the last example we can see that objects can contain other objects -and/or functions. A class is a definition of an object, and a function -contained inside an object is traditionally called a method.

-

R and Python both support classes and objects, but their philosophy -regarding them differs significantly. R is very lax and allows the users -to freely (re)define object classes as they see fit. Python is a lot -more formal and requires a formal class definition to define an object -class. Python prefers self-contained objects that contain all the -methods that can interact with the object embedded inside the object. In -contrast, the R philosophy is to define global functions whose behaviour -is different depending on the class the function is run on.

-

As an example, we might want to define a class building -which, when instantiated as an object, will contain a name, -and a vector coordinates. We want to also have a function -that prints these attributes in a nice to read way. This is how it would -be done in R:

-
Gaia = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987))
-class(Gaia) # It is a list
-
## [1] "list"
-
class(Gaia) = "building" # And now it's a `building`!
-class(Gaia)
-
## [1] "building"
-
Aurora = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982))
-class(Aurora) = "building"
-class(Aurora)
-
## [1] "building"
-
# Define a function to print buildings
-# We use the "paste" function to format strings. 
-print.building = function(x)
-{
-  print(paste(x[["name"]], "is a building that is located at x:",
-    x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"]))
-}
-# Now we simply use print() and get our custom output:
-print(Gaia)
-
## [1] "Gaia is a building that is located at x: 5.665 y: 51.987"
-
print(Aurora)
-
## [1] "Aurora is a building that is located at x: 5.657 y: 51.982"
-

The reason why this works is because R uses the concept of function -signatures. When running print(), R will first check what -is the class of the object you are calling the function on, and checks -if there is a function defined that is called print.class -(in our case print.building). On a match, it will run that -function instead of the print.default function.

-

In Python, we need to formally define a class and then instantiate -it:

-
class building:
-  def __init__(self, name, coordinates):
-    self.name = name
-    self.coordinates = coordinates
-
-  def print(self):
-    # Note that we use "string formatting". If a "f" is put before a string quote, 
-    # all text inside curly brackets will be executed as plain Python 
-    print(f'{self.name} is a building that is located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}')
-
-# Instantiate the class by calling it as if it was its __init__ function
-Gaia = building(name="Gaia", coordinates={"x": 5.665, "y": 51.987})
-Aurora = building(name="Aurora", coordinates={"x": 5.657, "y": 51.982})
-type(Gaia)
-
## <class '__main__.building'>
-
type(Aurora)
-
## <class '__main__.building'>
-
Gaia.print()
-
## Gaia is a building that is located at x: 5.665, y: 51.987
-
Aurora.print()
-
## Aurora is a building that is located at x: 5.657, y: 51.982
-

Note that we called the method print() that is inside -our object, not the global function print(), which would -give a different output:

-
print(Gaia)
-
## <__main__.building object at 0x7a4f5b8f9ca0>
-
print(Aurora)
-
## <__main__.building object at 0x7a4f5b7d0890>
-

A key concept in object-oriented programming is inheritance: a class -can inherit properties and methods of its parent class. This allows us -to make extensions of existing classes without having to duplicate a lot -of work. Let’s say we want to extend our building class -with an attribute purpose, calling the new child class -purposeBuilding. In R, inheritance works by making an -object part of multiple classes:

-
GaiaPurpose = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987), purpose="office")
-class(GaiaPurpose) = c("purposeBuilding", "building")
-print(GaiaPurpose) # We reuse the parent class `print()`
-
## [1] "Gaia is a building that is located at x: 5.665 y: 51.987"
-
AuroraPurpose = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982), purpose="education")
-class(AuroraPurpose) = c("purposeBuilding", "building")
-print(AuroraPurpose)
-
## [1] "Aurora is a building that is located at x: 5.657 y: 51.982"
-
# Make a custom print function for purposeBuilding
-print.purposeBuilding = function(x)
-{
-  print(paste(x[["name"]], "is an", x[["purpose"]], "building that is located at x:",
-    x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"]))
-}
-
-print(GaiaPurpose)
-
## [1] "Gaia is an office building that is located at x: 5.665 y: 51.987"
-
print(AuroraPurpose)
-
## [1] "Aurora is an education building that is located at x: 5.657 y: 51.982"
-

In Python, inheritance is also formally declared in the definition of -the new class:

-
class purposeBuilding(building):
-  def __init__(self, name, coordinates, purpose):
-    super().__init__(name, coordinates) # Let the parent class handle these
-    self.purpose = purpose
-
-Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office")
-Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education")
-Gaia.print()
-
## Gaia is a building that is located at x: 5.665, y: 51.987
-
Aurora.print()
-
## Aurora is a building that is located at x: 5.657, y: 51.982
-

We can override methods by redeclaring them, but any instantiated -objects will have to be reinstantiated for the changes to apply:

-
# Let's also override the print now:
-class purposeBuilding(building):
-  def __init__(self, name, coordinates, purpose):
-    super().__init__(name, coordinates) # Let the parent class handle these
-    self.purpose = purpose
-
-  def print(self):
-    print(f'{self.name} is a building used for {self.purpose} purposes, located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}')
-  
-
-# If we don't reinstantiate, the old definition applies:
-Gaia.print()
-
## Gaia is a building that is located at x: 5.665, y: 51.987
-
Aurora.print()
-
## Aurora is a building that is located at x: 5.657, y: 51.982
-
Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office")
-Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education")
-Gaia.print()
-
## Gaia is a building used for office purposes, located at x: 5.665, y: 51.987
-
Aurora.print()
-
## Aurora is a building used for education purposes, located at x: 5.657, y: 51.982
-

Note that R also has a more formalised type of classes, called S4 -classes, that behave a bit more similar to the Python classes, but S4 -classes are generally not recommended to use as they are further away -from the R philosophy.

-
-
-

Scope and side effects

-

Another difference between R and Python you may also have noticed in -the examples above: R uses curly braces {} to denote scope, -whereas Python uses a colon : and enforces indentation. As -an example:

-
def scope():
-  print("I am inside the scope of the function scope()!")
-  print("I am also inside the scope of the function scope()!")
-print("I am outside the scope of the function scope()!")
-
## I am outside the scope of the function scope()!
-
scope()
-
## I am inside the scope of the function scope()!
-## I am also inside the scope of the function scope()!
-
scope = function() {
-  print("I am inside the scope of the function scope()!")
-  print("I am also inside the scope of the function scope()!")
-}
-  print("I am outside the scope of the function scope()!")
-
## [1] "I am outside the scope of the function scope()!"
-
scope()
-
## [1] "I am inside the scope of the function scope()!"
-## [1] "I am also inside the scope of the function scope()!"
-

In addition, in R, typically anything that happens inside of a -function scope stays in the function, i.e. it does not alter the global -state.

-
location = "Gaia"
-move = function(where)
-{
-  location = where
-  print(paste("Moved to", location))
-}
-
-move("Aurora")
-
## [1] "Moved to Aurora"
-
print(location)
-
## [1] "Gaia"
-

In Python it is also often true, but the list of exceptions is much -longer.

-
location = "Gaia"
-def move(where):
-  location = where
-  print("Moved to " + location)
-
-move("Aurora")
-
## Moved to Aurora
-
print(location)
-
## Gaia
-

For instance, lists are mutable even outside of the function -scope:

-
locations = ["Gaia"]
-def addLocation(where):
-  locations.append(where)
-  print(locations)
-
-addLocation("Aurora")
-
## ['Gaia', 'Aurora']
-
print(locations)
-
## ['Gaia', 'Aurora']
-

Such mutability that goes outside of the function scope is called a -side effect. They are best avoided as much as possible, as it brings -confusion to the users (and may even destroy their work)! Normally, you -expect that if you as a user run a function, it will process your -arguments and return a new, processed object, but will not change your -global environment, or the object that you passed to the function -itself.

-

To avoid such side effects, in Python we need to explicitly copy -mutable objects:

-
locations = ["Gaia"]
-def addLocation(where):
-  result = locations.copy()
-  result.append(where)
-  print(result)
-
-addLocation("Aurora")
-
## ['Gaia', 'Aurora']
-
print(locations)
-
## ['Gaia']
-
-
-

Summary

-

We have looked at some similarities and differences between R and -Python. As you can see, the same or similar functionality is available -in both languages, but the philosophies of the languages are sometimes -different, so it is important to be aware of them. We will make -extensive use of the basics in the upcoming tutorials, and will build -further upon them to specifically handle spatial data. We will first go -through the specifics of R, then the specifics of Python, but it’s good -to keep in mind throughout the course that both languages can do what we -want, just in a slightly different way. You can use this tutorial as a -reference for the basics if you get stuck in future tutorials. Calling R -and Python from Bash can also be a very useful technique to combine them -during the project at the end of the course.

-
-
-
- - - -
-
- -
-
-
- - - - - - - - - - - - diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..cf9b81e --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +@import url("https://netdna.bootstrapcdn.com/bootswatch/3.0.0/simplex/bootstrap.min.css"); + + + +.main-container { max-width: none; } +pre { color: inherit; background-color: inherit; } + +code[class^="sourceCode"]::before { + content: attr(class); + display: block; + text-align: right; + font-size: 70%; +} + +code[class^="sourceCode r"]::before { content: "R Source"; } +code[class^="sourceCode python"]::before { content: "Python Source"; } +code[class^="sourceCode bash"]::before { content: "Bash Source"; } + +/* Only target inline code, not code inside pre blocks */ +p code:not(.sourceCode), +li code:not(.sourceCode), +td code:not(.sourceCode) { + background-color: #f5f5f5; + color: #c7254e; + padding: 2px 4px; + border-radius: 3px; +} + +.page-header-title { + font-size: 2rem; /* roughly equivalent to */ +} + +.page-header-logo { + height: 50px; + vertical-align: middle; +} + From 8a08b10ed79854d9d5c36e8a20b0e7192ee36029 Mon Sep 17 00:00:00 2001 From: Arno Timmer Date: Thu, 27 Aug 2026 13:50:45 +0200 Subject: [PATCH 2/3] quarto --- index.qmd | 713 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 713 insertions(+) create mode 100644 index.qmd diff --git a/index.qmd b/index.qmd new file mode 100644 index 0000000..57f5605 --- /dev/null +++ b/index.qmd @@ -0,0 +1,713 @@ +--- +pagetitle: "R and Python basics" +author: "Dainius Masiliūnas, Arno Timmer" +date: today +format: + html: + title: "R & Python basics" + theme: simplex + highlight-style: zenburn + toc: true + toc-location: left + css: styles.css +--- + + +[[WUR Geoscripting](https://geoscripting-wur.github.io/)]{.page-header-title} + + +# R and Python basics + +Throughout the course we will be learning R and Python. Both are programming languages, and both can be used for handling geospatial data. So what is the difference between them? Why would you choose one over another? And how does equivalent code look like in each language? We will go over these questions in this tutorial. + +## Learning objectives of the day + +In this tutorial, you will learn to: + +* describe the differences between R and Python; +* apply R and Python for basic handling of data types in each language; +* create new object classes and use inheritance. + +## What are R and Python? + +If you ask an average person what Python is, they will tell you that it's a type of snake. If you ask an average person what R is, they will tell you that it's a letter of the alphabet. Today you will learn beyond that: the details of the programming languages of R and Python! + +Both R and Python programming languages were created at around the same time: Python was created in 1991 in the Netherlands, and R in 1993. However, R actually has a much longer history, because it is an extension of the S programming language that was created all the way back in 1976 in Bell Labs. R was created specifically as a statistical package, similar to SPSS or Stata. However, it has since evolved into a general-purpose programming language. Due to its focus on statistics, R is mainly used in academia (schools and universities). + +Python was created as a general-purpose programming language. It was designed with readability in mind, and it has become the most popular programming language in the world. Python is therefore widely used in the industry, such as startups and large corporations. Python is especially strong in the field of deep learning, since packages such as Google's TensorFlow are implemented primarily in Python. Additionally, because of it's popularity, it is very easy to find online examples and it is relatively easy to communicate with and combine different pieces software. + +In terms of spatial data handling, both R and Python can perform the same tasks. Both are relatively easy to integrate with other software as well, which allows extending the capabilities of each language. For example, the open source GIS software QGIS, including all its plugins, are written in Python. R scripts can be run directly from the QGIS interface as processing tools. R has a package `reticulate` that can run Python scripts, and Python has a package `rpy2` that can run R scripts. + +Both R and Python have extremely active communities maintaining a wealth of packages. The package ecosystems differ significantly between the two languages, however. Python packages are hosted on PyPI, the Python Package Index, which is a very easily accessible place where anyone can upload the Python packages they create. There is minimal oversight over them and the package authors are free to do with their packages what they please. The R package repository is called CRAN, the Comprehensive R Archive Network. CRAN is curated: any package submitted to CRAN goes through a review process, and has to pass a suite of tests before it gets accepted. This ensures that, among other things, every argument in every function in every package is documented, and that each package is compatible with all the other packages in CRAN. The result is that it is much more difficult to publish R packages, but the quality of R packages is generally much higher than Python packages. It also means that package management in R is very easy, as no conflicts can happen between packages. Packages are also more inclined to depend on other packages, as it is more certain that their dependencies will stay maintained. In contrast, Python has a lot more packages, but they are often poorly documented and often do not interoperate with other packages as well. Package management is a big issue in Python, as many packages work only with certain versions of other packages. + +In practice, this means that while all geospatial data handling tasks can be done in R or Python, R is better integrated for this. In this course you will learn about the packages `terra` for raster handling and `sf` for vector handling, which are both integrated with each other and offer a full suite of processing tools, and all of the other packages that use vectors or rasters in R also make use of `sf` and `terra` package objects. In Python, raster reading is done using the `rasterio` package, but processing needs to be done using other packages, many of which do not support `rasterio` objects. Vector handling is done in for example `geopandas`, which has spatial processing tools, but once again they are not always integrated with raster object support. So rasters often need to be converted into number matrices for processing, then converted back into raster objects, by the user. + +Both R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in `plot()` function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, `ggplot2` can be used. In Python, the standard plotting package is `matplotlib`. Spatial packages often implement their own plotting functions for their own objects, therefore putting multiple objects into one plot can often be more challenging than in R. + +Ultimately, the choice of language to use is often decided by interoperability needs (e.g. if you work in a company that uses Python, you will be expected to also write in Python, so that your script can be used in a pipeline) and personal preference. + +## Running Python and R from Bash + +Both the `R` and the `python` interpreters can be run from Bash. Here is an example of code that is correct in both Python and R. You can use this example to execute a script written in either language: +```{bash, eval=FALSE} +# Create a script file "script" (no extension) +echo 'print("Hello world!")' > script +# Run the script with R +Rscript ./script +# Run the script with Python +python3 ./script +``` + +This allows you to run script files even without having any graphical user interface installed or running, which is the fastest way to run any script from top to bottom. This way, you can even combine R and Python code from a Bash script! + +Now, let's explore the similarities and differences between the two languages. For that, you need to have Python set up. Let's install the needed packages through the Bash terminal: + +```{bash, eval=FALSE} +sudo apt install python3-pandas +``` + +Now you can run R in one terminal, and run Python in another, to try out the code snippets below. To run R in interactive mode: + +```{bash, eval=FALSE} +R +``` + +To run Python in interactive mode: + +```{bash, eval=FALSE} +python3 +``` + +## Data types + +Both Python and R provide a set of primitive data types. The ones they have in common are integers, floating-point numbers, logical boolean values, character strings, associative arrays (dictionaries) and lists. To find out the type of a variable, use `type()` in Python and `class()` in R. + +A whole number is called an integer, and a number with decimals (real number) is called a float (floating point number). In Python, if you enter a number without decimal points, it will be an integer, otherwise it will be a float. + +```{python, eval=FALSE} +type(10) +type(10.1) +type(10.) +type(int(10.)) +``` + +In R, any number will be a float (called "numeric") by default, and integers are obtained by explicitly casting to an integer using `as.integer()`: + +```{r} +class(10) +class(10.1) +class(as.integer(10)) +``` + +```{block, type="alert alert-success"} +> **Question 1**: What is the type/class of the sum 3+5 and of 3.0+5 in R and in Python? Write a sum of two numbers that returns an integer in R and in Python. +``` + +Boolean, or logical, types can only have two values: true or false. When cast to an integer, true is represented by 1 and false is represented by 0. Both R and Python are case-sensitive, and use different cases to represent booleans (`True` in Python and `TRUE` in R). + +```{python, eval=FALSE} +type(True) +type(True + 2) +True + 2 +``` + +```{r} +class(TRUE) +class(TRUE + 2) +TRUE + 2 +``` + +Character strings are letters and words. They can also include numbers, but the numbers do not have a mathematical meaning, and therefore you cannot do arithmetics on strings. Strings are marked with quotes, either single or double: + +```{python error=TRUE} +"Hello world #1!" +5 + '6' +type('6') +``` + +```{r error=TRUE} +"Hello world #1!" +5 + '6' +class('6') +``` + +You can combine strings to produce longer strings. This is done with the function `paste()` in R and using the `+` operator in Python (as long as all parts are strings). + +```{python, eval=FALSE} +WorldNr = 2 +"Hello world #" + str(WorldNr) + "!" +``` + +```{r} +WorldNr = 2 +paste("Hello world #", WorldNr, "!", sep="") # The 'sep' argument avoids adding spaces in between +``` + +```{block type="alert alert-info"} +**Note** that Python uses `=` to define a new variable or to give it a new value, as was done above for the variable `WorldNr`. On the other hand, R typically uses `<-` to assign a value to a variable. However R also accepts `=`. In this tutorial, we use `=` for R to keep it simple, but you will find later tutorials using `<-`. Keep in mind that either option is valid as long as it is kept consistent throughout your code! +``` + +Additionally, in Python, string formatting can be used, in for example a print function. To do this, we start a string with an `f` in front of the quote. +```{python, eval=FALSE} +WorldNr = 2 +print(f"Hello world # {WorldNr} !") +``` + +A variable can also hold multiple values of a particular data type, or even mix data types. Python supports associative arrays, called dictionaries, that are created using curly braces: + +```{python, eval=FALSE} +WUR = {"name": "Wageningen University", "x": 5.7, "y": 52} +WUR +type(WUR) +WUR["x"] +type(WUR["x"]) +``` + +The dictionary type allows naming its elements and accessing them by name. The elements can be of any type. + +In R, a similar functionality is called a vector (not to be confused with the geographical data type "vector"!). All variables we have created so far have been vectors of length 1. They didn't have names, but you can give names to elements of a vector. However, unlike Python dictionaries, vectors can only contain elements of the same type, and any items of a different type will get automatically converted to fit the rest. We create vectors longer than the size of one by using the **c**oncatenation function `c()`: + +```{r} +WUR = c(name = "Wageningen University", x = 5.7, y=52) +WUR +class(WUR) +WUR["x"] +class(WUR["x"]) +``` + +As you can see, the numbers have been implicitly converted ("coerced" in programming terms) into characters. The disadvantage of named vectors over dictionaries is that we can only use the same type in a single vector. The advantage is that it allows us to use vectorised functions, i.e. functions that perform some work on each element of a vector. Because we know that the vector is going to be entirely composed of characters, we can do something like: + +```{r} +paste(WUR, "addition!") +``` + +Both R and Python support lists, which allow combining any data types into one variable. In Python they are created using square brackets, and in R using `list()`: + +```{python, eval=FALSE} +WUR = ["Wageningen University", 5.7, 52] +``` + +```{r} +WUR = list("Wageningen University", 5.7, 52) +``` + +Elements of a list are sequential and can be accessed by slicing the list using a number (index) in square brackets. An important difference between the two languages is that R starts counting from 1, but Python starts counting from 0! This is similar to how in different countries, floors of buildings are counted starting from 1 or from 0. Netherlands is a "Python" style country where the ground floor is number 0, whereas Canada is an "R" style country where the ground floor is number 1. The R style has the advantage of the index matching the element number, i.e. `[2]` will give you the second element, where in Python the second element is `[1]`: + +```{python, eval=FALSE} +WUR[1] +``` + +```{r} +WUR[2] +``` + +There is also a difference in what happens if you use a negative index. Python uses negative indices to wrap around, so `[-1]` means "last element", whereas in R it stands for exclusion, so `[-1]` means "all elements except for the first one": + +```{python, eval=FALSE} +WUR[-1] +``` + +```{r} +WUR[-1] +``` + +What is the exact equivalent of a dictionary in R? It's a named list! When creating a list, you can specify a name of each element, and then slice the list using names. Unlike the colon `:` used in Python for dictionaries, R simply uses the equal sign `=`: + +```{r} +WUR = list("name"="Wageningen University", "x"=5.7, "y"=52) +WUR +class(WUR) +WUR["x"] +class(WUR["x"]) +``` + +Here we can see another difference in how R and Python deal with lists. In R, a list always consists of lists, recursively. Each element of a list is always a list itself. To obtain the value, we need to access it using the double square bracket operator: + +```{r} +WUR[["x"]] +class(WUR[["x"]]) +``` + +There are several data types that are specific to R, though there are Python packages that implement equivalent functionality as well. The most basic type is a vector, which can hold multiple values of the same type. An extension of a vector is a matrix, that has two dimensions. Adding further dimensions, we get an array. A matrix is a special case of an array (two-dimensional), as is a vector (one-dimensional array). + +As we have covered above, in R, almost everything appears as a vector. That is why in the R print output above you can see `[1]` next to most output, indicating that the value is just the first in a 1-length vector. To make longer vectors, the function `c()` is used. While you can give names to elements, it's optional, we can just omit them: + +```{r} +WURcoords = c(5.7, 52) +WURcoords +class(WURcoords) +``` + +Matrices are made using the function `matrix` by combining multiple vectors: + +```{r} +# `nrow` specifies how many rows the matrix will have +LocMat = matrix(c(WURcoords, WURcoords + 1), nrow = 2) +LocMat +class(LocMat) +``` + +```{block, type="alert alert-success"} +> **Question 2**: Using the `c()` and `matrix()` functions, build a tic-tac-toe board with X's and O's in R. +``` + +And arrays of higher order are likewise created using `array()`: + +```{r} +# Here "dim" defines the shape, i.e. number of rows, columns, layers, etc. +LocArray = array(c(LocMat, LocMat+1), dim=c(2,2,2)) +LocArray +class(LocArray) +``` + +As you can see, we have a cube with two elements in each dimension. Cubes are difficult to visualise, as they are 3D, and our screen is 2D. Therefore, when trying to print one, R presents the cube in "slices" of matrices. + +Base Python can only achieve a similar effect using nested lists: +```{python, eval=FALSE} +WURcoords = [5.7, 52] +WURcoords +LocMat = [WURcoords, WURcoords] +LocMat +LocArray = [LocMat, LocMat] +LocArray +``` + +The structure is also a cube, but it is less structured, and therefore printing it does not make it obvious that it is a cube. + +Here we can also notice that R can perform vectorised arithmetics: `WURcoords + 1` added 1 to each value of the vector `WURcoords`, and `LocMat + 1` added 1 to each value of the matrix `LocMat`. Core Python does not allow doing so without writing a loop. However, since arrays and vectorised arithmetics are very useful, it has all been implemented in the package `NumPy`, which is now considered to be an essential package in Python: + +```{python, eval=FALSE} +import numpy + +npWURcoords = numpy.array(WURcoords) +npWURcoords +type(npWURcoords) + +npLocMat = numpy.array([npWURcoords, npWURcoords + 1]) +npLocMat +type(npLocMat) + +npLocArray = numpy.array([npLocMat, npLocMat + 1]) +npLocArray +type(npLocArray) +``` + +As we can see here, NumPy arrays are more structured, and when printing a cube, it also shown sliced, just like in the R example above. The NumPy output also includes square brackets, which you will notice are in the same order and quanity as the plain Python list example we had above, implying that it is ultimately still a list-of-lists. + +```{block, type="alert alert-success"} +> **Question 3**: Make the same tic-tac-toe board as in question 2 in Python. Hint: You may wish to use numpy. +``` + +Another useful data type in R is Data Frames. A Data Frame is a table, similar to a matrix, but with one key difference: while matrices require all values to be of the same type, a data.frame only requires each column to have a consistent type. The Data Frame concept comes from R's statistical background, where it is useful to have multiple variables that are being studied, as columns, and multiple records or observations of those values, as rows. For example: + +```{r} +WURbuildings = data.frame(name = c("Gaia", "Aurora"), x = c(5.665, 5.657), y = c(51.987, 51.982)) +WURbuildings +class(WURbuildings) +``` + +To know the type of each column, we can use the function `str` (structure): +```{r} +str(WURbuildings) +``` + +We see here that the `name` column is made of character strings, whereas `x` and `y` columns are floating point numbers. The Data Frame gives more structure than a plain list does, ensuring that the data has rows and columns and that the column types are consistent. + +In Python, Data Frames are implemented in the package `pandas`: + +```{python, eval=FALSE} +import pandas +WURbuildings = pandas.DataFrame({"name": ["Gaia", "Aurora"], "x": [5.665, 5.657], "y": [51.987, 51.982]}) +WURbuildings +type(WURbuildings) +WURbuildings.dtypes +``` + +The `.dtypes` accessor is equivalent to `str()` in R, though you can see one difference: the strings are reported as "objects". + +## Slicing and accessors + +When dealing with variables that hold multiple values, we often need to select some smaller subset of those values. This is called *slicing* an array. It is done using operators that are called *accessors*. We used three of them in the examples above: `[]` (both languages), `[[]]` (R) and `.` (Python). These are the main accessors, though there are a few others that the languages support. + +The `[]` accessor accepts indices of values to select. As we saw earlier, the indices can also be negative, or a combination of positive and negative. Both R and Python have a way to quickly create ranges for slicing arrays, using the `:` operator, but there are some differences in how the ranges behave: + +```{r} +Buildings = c("Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas") +Buildings[2:4] # We get the second, third and fourth elements +``` + +```{python, eval=FALSE} +Buildings = ["Gaia", "Lumen", "Forum", "Orion", "Aurora", "Atlas"] +Buildings[2:4] # We get the third and fourth elements +``` + +As we can see, slicing in R is inclusive, i.e. any number you mention will be included in the resulting slice. In Python, it is one-sided: the fist number of the range will be included, but the last number will be excluded. And, of course, Python counts from 0 whereas R counts from 1. + +Let's try to slice the first half and the second half of the string array in both languages: +```{r} +Buildings[1:3] +Buildings[4:6] +``` + +```{python, eval=FALSE} +Buildings[:3] # The fourth element is not included +Buildings[3:] # It is included here +``` + +As you see, Python interprets the missing number as the first or the last element of the array, depending on it's missing before or after the `:`. R does not, and requires you to enter `1` for the first element and `length(YourArray)` (e.g. `length(Buildings)`) for the last one. + +```{block, type="alert alert-success"} +> **Question 4**: Create a subset of `Buildings` with only `Gaia` and `Aurora`, using index slicing in R and Python. Hint: You may wish to use the functions `c()` for R. To vectorize a list in Python, you can use `numpy.array()`, but creating a plain list doesn’t require it :) +``` + +When slicing two-dimensional arrays (matrices or data frames), two indices are used, separated by a comma, in the order `[row, column]`: + +```{r} +LocMat +LocMat[2, 1] # Get y coordinate of WUR campus +``` + +```{python, eval=FALSE} +npLocMat +npLocMat[0, 1] # Get y coordinate of WUR campus +``` + +If you want to select the whole row/column, without manually specifying the size of your array, in Python you can use `:`, and in R you can omit the index: + +```{r} +LocMat[, 1] # Get both coordinates of WUR campus +``` + +```{python, eval=FALSE} +npLocMat[0, :] # Get both coordinates of WUR campus +``` + +Likewise, if we have an array with more dimensions, we can specify more indices (e.g. a three-dimensional array will accept three indices, `x`, `y`, `z`). + +Previously we saw another accessor in R, namely, the `[[]]` accessor. It is a list accessor, and generally is used to directly access a value, treating the variable as a list. The accessor accepts numbers and character strings as input. Another accessor popular in R is the `$` accessor, which allows accessing values by name. The `$` accessor is generally equivalent to the `[[]]` accessor with a string input. Here's an example of how these work with data frames: + +```{r} +WURbuildings +WURbuildings[1] # When accessed using a single number, it selects a column +class(WURbuildings[1]) # Single-column data.frame +WURbuildings[[1]] # Also selects a column +class(WURbuildings[[1]]) # But now the result is a vector! +WURbuildings[["name"]] # Column by name +WURbuildings$name # Same +``` + +Generally the `$` accessor is not recommended, because it does not allow the use of variables, it only works with literal strings. + +Note that in Python, the operator `[[]]` has a very different meaning: the outer `[]` is the accessor, and the inner `[]` is a list constructor, in other words, it's equivalent to the R `[c()]`: + +```{python, eval=FALSE} +npLocArray = numpy.array([npLocMat, npLocMat + 1, npLocMat + 2, npLocMat + 3]) +npLocArray +npLocArray[0,1] # Meaning "first layer, second row, all columns" +npLocArray[[0,2]] # Meaning "first and third layers, all rows and all columns" +``` + +Similar to the R `$` accessor, Python has the accessor `.` to select by literal string: + +```{python, eval=FALSE} +WURbuildings +WURbuildings["name"] +WURbuildings.name +``` + +Instead of supplying numbers or strings, we can also slice by using a boolean array of the same dimensions as what we are trying to slice. This is very handy, as we can make use of this to select by a rule: + +```{r} +LocMat +LocMat[LocMat > 50] # Get only the values above 50 +``` + +What happens here is that the inner part of the accessor generates a boolean array, which is subsequently used for slicing, as if it was a mask: + +```{r} +LocMat > 50 +``` + +The same applies in NumPy: + +```{python, eval=FALSE} +npLocMat +npLocMat[npLocMat > 50] +``` + +```{block, type="alert alert-success"} +> **Question 5**: In R and Python, slice `LocMat` to select all values below `6` and above `52`. Hint: You can combine conditions by using `&` (AND) or `|` (OR). Note: In Python, always use parentheses around each condition when combining them — otherwise, operator precedence may cause unexpected behavior. +``` + +## Conditionals and control flow + +We just saw that we can slice arrays based on conditions. Note that the boolean operators `&` and `|` (for *and* and *or* respectively) are valid in both R and Python, but they mean different things! + +In R, `&` is a *vectorised* form of `&&`, in other words, it can compare boolean vectors against each other: + +```{r} +c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE) +``` + +This is good in some cases, such as slicing arrays using boolean arrays (like in Question 5, where `&&` doesn't really work), and not so good in other cases, like `if` statements that control program flow. This is because `if` requires a single `TRUE` or `FALSE`, not an array. + +The conditionals `if` and `else` are used to structure our code, so that part of the code only runs when certain conditions are met. In this case, `if (c(TRUE, FALSE))` is impossible to determine and thus will result in an error. The form `&&` only compares one boolean with another boolean, which makes sure you do not accidentally use arrays where a single boolean is needed, and thus is suitable. + +Here's an example in R: + +```{r} +MyLatitude = 52 +GaiaLatitude = WURbuildings[WURbuildings[["name"]] == "Gaia", "y"] +AuroraLatitude = WURbuildings[WURbuildings[["name"]] == "Aurora", "y"] + +if (MyLatitude > GaiaLatitude && MyLatitude > AuroraLatitude) { + print("I am to the north of both Gaia and Aurora") +} else if (MyLatitude < GaiaLatitude && MyLatitude < AuroraLatitude) { + print("I am to the south of both Gaia and Aurora") +} else { + print("I am in between Gaia and Aurora") +} +``` + +The `&` operator can still work in if statements when both sides are single boolean values. However, it is less strict and may lead to unexpected behavior if vectors are accidentally passed. + +In Python, `&` is a *bitwise* comparison, that is, it does not work on booleans, but numbers. +It just so happens that Python implicitly converts booleans to numbers (`True` to 1 and `False` to 0), so it often works with booleans as well, but it is not intended for that. +This behavior makes them useful in contexts like Question 5, where we compare an array of values against numeric thresholds and want element-wise boolean logic. +Instead, the regular boolean comparison is done using `and`, the logical comparison. It expects the entire variable to represent a single boolean value (True or False). That’s why `and` or `or` cannot be used for element-wise slicing in Question 5. +So just like in R, when you need a single boolean result to control program flow in `if` statement, `and` is more appropriate than `&`. + +Equivalent example code in Python (note the syntax differs a bit between R and Python, e.g. the lack of parentheses, the words for `and` and `or`, and the special `elif` keyword): + +```{python, eval=FALSE} +MyLatitude = 52 +# Note that boolean conditionals only work with regular booleans, not with Series/DataFrames, hence the .values[0] +GaiaLatitude = WURbuildings.loc[WURbuildings["name"] == "Gaia", "y"].values[0] +AuroraLatitude = WURbuildings.loc[WURbuildings["name"] == "Aurora", "y"].values[0] + +if MyLatitude > GaiaLatitude and MyLatitude > AuroraLatitude: + print("I am to the north of both Gaia and Aurora") +elif MyLatitude < GaiaLatitude and MyLatitude < AuroraLatitude: + print("I am to the south of both Gaia and Aurora") +else: + print("I am in between Gaia and Aurora") +``` + +```{block, type="alert alert-success"} +> **Question 6**: Why don't we need parentheses here in this Python code? Hint: it's something to do with operator precedence. +``` + +## Objects and inheritance + +Both R and Python are object-oriented languages, and in this tutorial we have already worked with many objects. For example, data frames, lists and matrices are objects, and the `class()` or `type()` function prints what type of object it is, in other words, what is the class of the object. In R, `str()` allows investigating the structure of an object. Python does not have a unified function for this and different packages implement this functionality differently, if at all. + +```{r} +WUR = list("name"="Wageningen University", "x"=5.7, "y"=52) +str(WUR) # Show the structure of a list +WURbuildings +str(WURbuildings) # Show the structure of a data frame (which is also a type of list) +``` + +```{python, eval=FALSE} +WURbuildings # pandas data frame +WURbuildings.dtypes # Shows data types +vars(WURbuildings) # More generic, but does not work with lists and dicts +dir(WURbuildings) # All of the methods contained in the object +``` + +In the last example we can see that objects can contain other objects and/or functions. A class is a definition of an object, and a function contained inside an object is traditionally called a method. + +R and Python both support classes and objects, but their philosophy regarding them differs significantly. R is very lax and allows the users to freely (re)define object classes as they see fit. Python is a lot more formal and requires a formal class definition to define an object class. Python prefers self-contained objects that contain all the methods that can interact with the object embedded inside the object. In contrast, the R philosophy is to define global functions whose behaviour is different depending on the class the function is run on. + +As an example, we might want to define a class `building` which, when instantiated as an object, will contain a `name`, and a vector `coordinates`. We want to also have a function that prints these attributes in a nice to read way. This is how it would be done in R: + +```{r} +Gaia = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987)) +class(Gaia) # It is a list +class(Gaia) = "building" # And now it's a `building`! +class(Gaia) +Aurora = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982)) +class(Aurora) = "building" +class(Aurora) + +# Define a function to print buildings +# We use the "paste" function to format strings. +print.building = function(x) +{ + print(paste(x[["name"]], "is a building that is located at x:", + x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"])) +} +# Now we simply use print() and get our custom output: +print(Gaia) +print(Aurora) +``` + +The reason why this works is because R uses the concept of function signatures. When running `print()`, R will first check what is the class of the object you are calling the function on, and checks if there is a function defined that is called `print.class` (in our case `print.building`). On a match, it will run that function instead of the `print.default` function. + +In Python, we need to formally define a class and then instantiate it: +```{python, eval=FALSE} +class building: + def __init__(self, name, coordinates): + self.name = name + self.coordinates = coordinates + + def print(self): + # Note that we use "string formatting". If a "f" is put before a string quote, + # all text inside curly brackets will be executed as plain Python + print(f'{self.name} is a building that is located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}') + +# Instantiate the class by calling it as if it was its __init__ function +Gaia = building(name="Gaia", coordinates={"x": 5.665, "y": 51.987}) +Aurora = building(name="Aurora", coordinates={"x": 5.657, "y": 51.982}) +type(Gaia) +type(Aurora) +Gaia.print() +Aurora.print() +``` + +Note that we called the method `print()` that is inside our object, not the global function `print()`, which would give a different output: + +```{python, eval=FALSE} +print(Gaia) +print(Aurora) +``` + +A key concept in object-oriented programming is inheritance: a class can inherit properties and methods of its parent class. This allows us to make extensions of existing classes without having to duplicate a lot of work. Let's say we want to extend our `building` class with an attribute `purpose`, calling the new child class `purposeBuilding`. In R, inheritance works by making an object part of multiple classes: + +```{r} +GaiaPurpose = list("name"="Gaia", "coordinates"=c("x"=5.665, "y"=51.987), purpose="office") +class(GaiaPurpose) = c("purposeBuilding", "building") +print(GaiaPurpose) # We reuse the parent class `print()` + +AuroraPurpose = list("name"="Aurora", "coordinates"=c("x"=5.657, "y"=51.982), purpose="education") +class(AuroraPurpose) = c("purposeBuilding", "building") +print(AuroraPurpose) + +# Make a custom print function for purposeBuilding +print.purposeBuilding = function(x) +{ + print(paste(x[["name"]], "is an", x[["purpose"]], "building that is located at x:", + x[["coordinates"]]["x"], "y:", x[["coordinates"]]["y"])) +} + +print(GaiaPurpose) +print(AuroraPurpose) +``` + +In Python, inheritance is also formally declared in the definition of the new class: +```{python, eval=FALSE} +class purposeBuilding(building): + def __init__(self, name, coordinates, purpose): + super().__init__(name, coordinates) # Let the parent class handle these + self.purpose = purpose + +Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office") +Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education") +Gaia.print() +Aurora.print() +``` + +We can override methods by redeclaring them, but any instantiated objects will have to be reinstantiated for the changes to apply: + +```{python, eval=FALSE} +# Let's also override the print now: +class purposeBuilding(building): + def __init__(self, name, coordinates, purpose): + super().__init__(name, coordinates) # Let the parent class handle these + self.purpose = purpose + + def print(self): + print(f'{self.name} is a building used for {self.purpose} purposes, located at x: {self.coordinates["x"]}, y: {self.coordinates["y"]}') + + +# If we don't reinstantiate, the old definition applies: +Gaia.print() +Aurora.print() + +Gaia = purposeBuilding(name="Gaia", coordinates={"x": 5.665, "y": 51.987}, purpose="office") +Aurora = purposeBuilding(name="Aurora", coordinates={"x": 5.657, "y": 51.982}, purpose="education") +Gaia.print() +Aurora.print() +``` + +Note that R also has a more formalised type of classes, called S4 classes, that behave a bit more similar to the Python classes, but S4 classes are generally not recommended to use as they are further away from the R philosophy. + +## Scope and side effects + +Another difference between R and Python you may also have noticed in the examples above: R uses curly braces `{}` to denote scope, whereas Python uses a colon `:` and enforces indentation. As an example: + +```{python, eval=FALSE} +def scope(): + print("I am inside the scope of the function scope()!") + print("I am also inside the scope of the function scope()!") +print("I am outside the scope of the function scope()!") + +scope() +``` + +```{r} +scope = function() { + print("I am inside the scope of the function scope()!") + print("I am also inside the scope of the function scope()!") +} + print("I am outside the scope of the function scope()!") + +scope() +``` + +In addition, in R, typically anything that happens inside of a function scope stays in the function, i.e. it does not alter the global state. + +```{r} +location = "Gaia" +move = function(where) +{ + location = where + print(paste("Moved to", location)) +} + +move("Aurora") +print(location) +``` + +In Python it is also often true, but the list of exceptions is much longer. + +```{python, eval=FALSE} +location = "Gaia" +def move(where): + location = where + print("Moved to " + location) + +move("Aurora") +print(location) +``` + +For instance, lists are mutable even outside of the function scope: + +```{python, eval=FALSE} +locations = ["Gaia"] +def addLocation(where): + locations.append(where) + print(locations) + +addLocation("Aurora") +print(locations) +``` + +Such mutability that goes outside of the function scope is called a side effect. They are best avoided as much as possible, as it brings confusion to the users (and may even destroy their work)! Normally, you expect that if you as a user run a function, it will process your arguments and return a new, processed object, but will not change your global environment, or the object that you passed to the function itself. + +To avoid such side effects, in Python we need to explicitly copy mutable objects: + +```{python, eval=FALSE} +locations = ["Gaia"] +def addLocation(where): + result = locations.copy() + result.append(where) + print(result) + +addLocation("Aurora") +print(locations) +``` + +## Summary + +We have looked at some similarities and differences between R and Python. As you can see, the same or similar functionality is available in both languages, but the philosophies of the languages are sometimes different, so it is important to be aware of them. We will make extensive use of the basics in the upcoming tutorials, and will build further upon them to specifically handle spatial data. We will first go through the specifics of R, then the specifics of Python, but it's good to keep in mind throughout the course that both languages can do what we want, just in a slightly different way. You can use this tutorial as a reference for the basics if you get stuck in future tutorials. Calling R and Python from Bash can also be a very useful technique to combine them during the project at the end of the course. From 67d79029e837e0f673a841568e52c4b66cd73a0e Mon Sep 17 00:00:00 2001 From: Arno Timmer Date: Thu, 27 Aug 2026 14:23:38 +0200 Subject: [PATCH 3/3] there is no positron in this tutorial! --- index.qmd | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/index.qmd b/index.qmd index 57f5605..845da1f 100644 --- a/index.qmd +++ b/index.qmd @@ -1,6 +1,6 @@ --- pagetitle: "R and Python basics" -author: "Dainius Masiliūnas, Arno Timmer" +author: "Dainius Masiliūnas" date: today format: html: @@ -42,7 +42,7 @@ Both R and Python have extremely active communities maintaining a wealth of pack In practice, this means that while all geospatial data handling tasks can be done in R or Python, R is better integrated for this. In this course you will learn about the packages `terra` for raster handling and `sf` for vector handling, which are both integrated with each other and offer a full suite of processing tools, and all of the other packages that use vectors or rasters in R also make use of `sf` and `terra` package objects. In Python, raster reading is done using the `rasterio` package, but processing needs to be done using other packages, many of which do not support `rasterio` objects. Vector handling is done in for example `geopandas`, which has spatial processing tools, but once again they are not always integrated with raster object support. So rasters often need to be converted into number matrices for processing, then converted back into raster objects, by the user. -Both R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in `plot()` function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, `ggplot2` can be used. In Python, the standard plotting package is `matplotlib`. Spatial packages often implement their own plotting functions for their own objects, therefore putting multiple objects into one plot can often be more challenging than in R. +Both R and Python can plot geospatial data and have several frameworks and packages for that. R has a built-in `plot()` function that can visualise any type of data quickly, and all packages make use of it. For more advanced custom visualisation, `ggplot2` can be used. In Python, the standard plotting package is `matplotlib`. Ultimately, the choice of language to use is often decided by interoperability needs (e.g. if you work in a company that uses Python, you will be expected to also write in Python, so that your script can be used in a pipeline) and personal preference. @@ -143,9 +143,6 @@ WorldNr = 2 paste("Hello world #", WorldNr, "!", sep="") # The 'sep' argument avoids adding spaces in between ``` -```{block type="alert alert-info"} -**Note** that Python uses `=` to define a new variable or to give it a new value, as was done above for the variable `WorldNr`. On the other hand, R typically uses `<-` to assign a value to a variable. However R also accepts `=`. In this tutorial, we use `=` for R to keep it simple, but you will find later tutorials using `<-`. Keep in mind that either option is valid as long as it is kept consistent throughout your code! -``` Additionally, in Python, string formatting can be used, in for example a print function. To do this, we start a string with an `f` in front of the quote. ```{python, eval=FALSE} @@ -153,6 +150,11 @@ WorldNr = 2 print(f"Hello world # {WorldNr} !") ``` + +```{block type="alert alert-info"} +**Note** that Python uses `=` to define a new variable or to give it a new value, as was done above for the variable `WorldNr`. On the other hand, R typically uses `<-` to assign a value to a variable. However R also accepts `=`. In this tutorial, we use `=` for R to keep it simple, but you will find later tutorials using `<-`. Keep in mind that either option is valid as long as it is kept consistent throughout your code! +``` + A variable can also hold multiple values of a particular data type, or even mix data types. Python supports associative arrays, called dictionaries, that are created using curly braces: ```{python, eval=FALSE}