From 7499e6e3ca04bace427f567e9390110f8bebbad1 Mon Sep 17 00:00:00 2001 From: Aymeric Stamm Date: Mon, 13 Jun 2022 12:21:23 +0200 Subject: [PATCH 1/6] First draft for r-interface tutos. --- .gitignore | 1 + r-interface/.gitignore | 1 + r-interface/README.md | 77 + r-interface/README.qmd | 84 + r-interface/_quarto.yml | 13 + r-interface/r-interface.Rproj | 16 + r-interface/reticulate/reticulate.html | 445 +++++ r-interface/reticulate/reticulate.md | 235 +++ r-interface/reticulate/reticulate.qmd | 205 ++ .../figure-gfm/unnamed-chunk-10-1.png | Bin 0 -> 50277 bytes .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 114343 bytes .../libs/bootstrap/bootstrap-icons.css | 1704 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 137124 bytes .../libs/bootstrap/bootstrap.min.css | 10 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 167 ++ .../libs/quarto-html/quarto.js | 664 +++++++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + 22 files changed, 3654 insertions(+) create mode 100644 r-interface/.gitignore create mode 100644 r-interface/README.md create mode 100644 r-interface/README.qmd create mode 100644 r-interface/_quarto.yml create mode 100644 r-interface/r-interface.Rproj create mode 100644 r-interface/reticulate/reticulate.html create mode 100644 r-interface/reticulate/reticulate.md create mode 100644 r-interface/reticulate/reticulate.qmd create mode 100644 r-interface/reticulate/reticulate_files/figure-gfm/unnamed-chunk-10-1.png create mode 100644 r-interface/reticulate/reticulate_files/figure-html/unnamed-chunk-10-1.png create mode 100644 r-interface/reticulate/reticulate_files/libs/bootstrap/bootstrap-icons.css create mode 100644 r-interface/reticulate/reticulate_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 r-interface/reticulate/reticulate_files/libs/bootstrap/bootstrap.min.css create mode 100644 r-interface/reticulate/reticulate_files/libs/bootstrap/bootstrap.min.js create mode 100644 r-interface/reticulate/reticulate_files/libs/clipboard/clipboard.min.js create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/anchor.min.js create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/popper.min.js create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/quarto.js create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/tippy.css create mode 100644 r-interface/reticulate/reticulate_files/libs/quarto-html/tippy.umd.min.js diff --git a/.gitignore b/.gitignore index 41b77e6..9a05a1f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .DS_Store .ipynb_checkpoints __pycache__ +.Rproj.user diff --git a/r-interface/.gitignore b/r-interface/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/r-interface/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/r-interface/README.md b/r-interface/README.md new file mode 100644 index 0000000..86975a9 --- /dev/null +++ b/r-interface/README.md @@ -0,0 +1,77 @@ + + + +# Setup instructions for using GUDHI from R + + + + +## The [**reticulate**](https://rstudio.github.io/reticulate/) package + +The [**reticulate**](https://rstudio.github.io/reticulate/) package +provides a comprehensive set of tools for interoperability between +Python and R. The package includes facilities for: + +- Calling Python from R in a variety of ways including R Markdown, + sourcing Python scripts, importing Python modules, and using Python + interactively within an R session. +- Translation between R and Python objects (for example, between R and + Pandas data frames, or between R matrices and NumPy arrays). +- Flexible binding to different versions of Python including virtual + environments and Conda environments. + +[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python +session within your R session, enabling seamless, high-performance +interoperability. If you are an R developer that uses Python for some of +your work or a member of data science team that uses both languages, +[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically +streamline your workflow. + +## Bring GUDHI into R + +- First, you’ll want to install and load the + [**reticulate**](https://rstudio.github.io/reticulate/)package into + your session: + + ``` r + # install.packages("reticulate") + library(reticulate) + ``` + +- Next, the fastest way to get you set up to use Python from R with + [**reticulate**](https://rstudio.github.io/reticulate/) is to use + the `install_miniconda()` utility function which is included in the + [**reticulate**](https://rstudio.github.io/reticulate/) package. In + details, use something like: + + ``` r + version <- "3.9.6" + install_miniconda() + conda_create("r-reticulate", python_version = version) + ``` + +- Next, you can seamlessly install all the Python packages you need by + calling the `conda_install()` function. For instance, to use Gudhi, + you would do something like: + + ``` r + conda_install("scikit-learn", envname = "r-reticulate") + conda_install("gudhi", envname = "r-reticulate") + ``` + +This is a setup that you ought to do only once (unless you want to +change your Python version for some reason). + +If you want to check your Python configuration, you can do: + +``` r +py_config() +#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9 +#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib +#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate +#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ] +#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy +#> numpy_version: 1.22.4 +#> +#> NOTE: Python version was forced by RETICULATE_PYTHON +``` diff --git a/r-interface/README.qmd b/r-interface/README.qmd new file mode 100644 index 0000000..53d1859 --- /dev/null +++ b/r-interface/README.qmd @@ -0,0 +1,84 @@ +--- +format: gfm +--- + + + +```{r} +#| label: setup +#| include: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +# Setup instructions for using GUDHI from R + + + + + +## The [**reticulate**](https://rstudio.github.io/reticulate/) package + +The [**reticulate**](https://rstudio.github.io/reticulate/) package +provides a comprehensive set of tools for interoperability between +Python and R. The package includes facilities for: + +- Calling Python from R in a variety of ways including R Markdown, + sourcing Python scripts, importing Python modules, and using Python + interactively within an R session. +- Translation between R and Python objects (for example, between R and + Pandas data frames, or between R matrices and NumPy arrays). +- Flexible binding to different versions of Python including virtual + environments and Conda environments. + +[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python +session within your R session, enabling seamless, high-performance +interoperability. If you are an R developer that uses Python for some of +your work or a member of data science team that uses both languages, +[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically +streamline your workflow. + +## Bring GUDHI into R + +- First, you'll want to install and load the + [**reticulate**](https://rstudio.github.io/reticulate/)package into + your session: + + ```{r} + # install.packages("reticulate") + library(reticulate) + ``` + +- Next, the fastest way to get you set up to use Python from R with + [**reticulate**](https://rstudio.github.io/reticulate/) is to use + the `install_miniconda()` utility function which is included in the + [**reticulate**](https://rstudio.github.io/reticulate/) package. In + details, use something like: + + ```{r} + #| eval: false + version <- "3.9.6" + install_miniconda() + conda_create("r-reticulate", python_version = version) + ``` + +- Next, you can seamlessly install all the Python packages you need by + calling the `conda_install()` function. For instance, to use Gudhi, + you would do something like: + + ```{r} + #| eval: false + conda_install("scikit-learn", envname = "r-reticulate") + conda_install("gudhi", envname = "r-reticulate") + ``` + +This is a setup that you ought to do only once (unless you want to +change your Python version for some reason). + +If you want to check your Python configuration, you can do: + +```{r} +py_config() +``` diff --git a/r-interface/_quarto.yml b/r-interface/_quarto.yml new file mode 100644 index 0000000..2adc850 --- /dev/null +++ b/r-interface/_quarto.yml @@ -0,0 +1,13 @@ +project: + title: "r-interface" + +author: "Aymeric Stamm" +title-block-banner: true +editor: visual + +toc: true +number-sections: true + +format: + html: default + gfm: default diff --git a/r-interface/r-interface.Rproj b/r-interface/r-interface.Rproj new file mode 100644 index 0000000..e83436a --- /dev/null +++ b/r-interface/r-interface.Rproj @@ -0,0 +1,16 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +AutoAppendNewline: Yes +StripTrailingWhitespace: Yes diff --git a/r-interface/reticulate/reticulate.html b/r-interface/reticulate/reticulate.html new file mode 100644 index 0000000..96e0c0a --- /dev/null +++ b/r-interface/reticulate/reticulate.html @@ -0,0 +1,445 @@ + + + + + + + + + + + +Using up-to-date Gudhi library in R + + + + + + + + + + + + + + + + + + + + +
+
+
+

Using up-to-date Gudhi library in R

+
+
+ + +
+ +
+
Author
+
+

Aymeric Stamm

+
+
+ +
+
Published
+
+

June 13, 2022

+
+
+ +
+ + +
+ +
+ + + + +
+

1 The reticulate package

+

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

+
    +
  • Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
    • +
  • Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
    • +
  • Flexible binding to different versions of Python including virtual environments and Conda environments.
    • +
+

reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow.

+

Let us start by loading the reticulate package into the environment, along with a suite of useful packages for data science:

+
+
# install.packages("tidyverse")
+library(tidyverse)
+
+
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
+
+
+
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
+✔ tibble  3.1.7     ✔ dplyr   1.0.9
+✔ tidyr   1.2.0     ✔ stringr 1.4.0
+✔ readr   2.1.2     ✔ forcats 0.5.1
+
+
+
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+✖ dplyr::filter() masks stats::filter()
+✖ dplyr::lag()    masks stats::lag()
+
+
# install.packages("reticulate")
+library(reticulate)
+
+

The fastest way to get you set up to use Python from R with reticulate is to use the install_python() and install_miniconda() utility functions that are included in the reticulate package. In details, use something like:

+
+
version <- "3.9.6"
+install_miniconda()
+conda_create("r-reticulate", python_version = version)
+
+

This is a setup that you ought to do only once (unless you want to change your Python version for some reason).

+

Next, you can seamlessly install all the Python packages you need by calling the conda_install() function. For instance, to use Gudhi, you would do something like:

+
+
conda_install("scikit-learn", envname = "r-reticulate")
+conda_install("gudhi", envname = "r-reticulate")
+conda_install("jupyter", envname = "r-reticulate")
+conda_install("r" , envname = "r-reticulate")
+conda_install("r-irkernel" , envname = "r-reticulate")
+install.packages("IRkernel")
+IRkernel::installspec()
+
+

Again, installation of Python packages is to be done only once.

+

You finally need to tell your R markdown document which conda environment to use for interpreting Python code. This is done with:

+
+
use_condaenv("r-reticulate")
+
+
Warning: The request to `use_python("/Users/stamm-a/Library/r-miniconda/envs/
+r-reticulate/bin/python")` will be ignored because the environment variable
+RETICULATE_PYTHON is set to "/Users/stamm-a/Library/r-miniconda/envs/r-
+reticulate/bin/python3.9"
+
+
+

If you want to check your Python configuration, you can do:

+
+
py_config()
+
+
python:         /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9
+libpython:      /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib
+pythonhome:     /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate
+version:        3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15)  [Clang 11.1.0 ]
+numpy:          /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy
+numpy_version:  1.22.4
+
+NOTE: Python version was forced by RETICULATE_PYTHON
+
+
+

You can now create chunks of Python code that will be correctly interpreted when knitting your document into an HTML file. Let us make a first Python chunk of code to import numpy and gudhi:

+
+
import numpy as np
+import gudhi as gd
+import gudhi.representations
+
+
+
+

2 Full example

+
+

2.1 Data model

+

First, we will generate persistence diagrams with orbits of dynamical systems. This data set is very common in TDA. We use the following system, which depends on a parameter \(r > 0\):

+

\[ +\begin{cases} +x_{n+1} = x_n + r y_n (1 - y_n) \mbox{ mod } 1 \\ +y_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \mbox{ mod } 1 +\end{cases} +\]

+

Let’s first see what the point cloud looks like for a given choice of \(r\).

+

We first define a Python function to compute a single point cloud according to the above orbit model and then use the Gudhi library to compute the persistence diagrams from its alpha complex and turn it into its silhouette representation. This can be achieved by a function that looks like:

+
+
def orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):
+  X = np.empty([num_pts, 2])
+  x, y = np.random.uniform(), np.random.uniform()
+  for i in range(num_pts):
+    X[i, :] = [x, y]
+    x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.
+    y = (X[i, 1] + r * x * (1 - x)) % 1.
+  acX = gd.AlphaComplex(points = X).create_simplex_tree()
+  dgmX = acX.persistence()
+  SH = gd.representations.Silhouette(
+    resolution = resolution, 
+    weight = lambda x: np.power(x[1] - x[0], 1)
+  )
+  sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])
+  return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])
+
+
+ +
+

This function returns a Python list of size 2. The first element is the abscissa grid and the second element is the silhouette value on the grid.

+

Python lists are automatically converted into atomic vectors in R. Automatic conversions of data types between R and Python languages are achieved according to the following table:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RPythonExamples
Single-element vectorScalar1, 1L, TRUE, "foo"
Multi-element vectorListc(1.0, 2.0, 3.0), c(1L, 2L, 3L)
List of multiple typesTuplelist(1L, TRUE, "foo")
Named listDictlist(a = 1L, b = 2.0), dict(x = x_data)
Matrix/ArrayNumPy ndarraymatrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)
Data FramePandas DataFramedata.frame(x = c(1, 2, 3), y = c("a", "b", "c"))
FunctionPython functionfunction(x) x + 1
NULL, TRUE, FALSENone, True, FalseNULL, TRUE, FALSE
+
+
+

2.2 Sample generation

+

Now we can go back in R to use this Python function and generate two samples of silhouette profiles. To access any Python object my_py_object created in a previous chunk from an R chunk, use py$my_py_object. Following this logic, the Python functionorbit_silhouette() created in the above Python chunk is accessible from any subsequent R chunk as py$orbit_silhouette():

+
+
n1 <- 10
+n2 <- 10
+# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE)
+# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE)
+x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)
+x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)
+
+t1 <- x1 |> map(1) |> map(~ c(0, .x))
+x1 <- x1 |> map(2) |> map(~ c(0, .x))
+t2 <- x2 |> map(1) |> map(~ c(0, .x))
+x2 <- x2 |> map(2) |> map(~ c(0, .x))
+
+df <- bind_rows(
+  tibble(id = 1:n1, x = t1, y = x1) |> 
+    unnest(cols = c(x, y)) |> 
+    mutate(r = "1.9"), 
+  tibble(id = 1:n2, x = t2, y = x2) |> 
+    unnest(cols = c(x, y)) |> 
+    mutate(r = "2.1")
+) |> 
+  mutate(
+    id = as_factor(id), 
+    r = as_factor(r)
+  )
+df |> 
+  ggplot(aes(x, y, color = r, group = interaction(id, r))) +
+  geom_line() + 
+  scale_color_viridis_d()
+
+

+
+
+
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/r-interface/reticulate/reticulate.md b/r-interface/reticulate/reticulate.md new file mode 100644 index 0000000..e5be75f --- /dev/null +++ b/r-interface/reticulate/reticulate.md @@ -0,0 +1,235 @@ +Using up-to-date Gudhi library in R +================ +Aymeric Stamm +2022-06-13 + +- 1 1 The + reticulate package +- 2 2 Full example + - 2.1 2.1 Data model + - 2.2 2.2 Sample generation + +## 1 The [**reticulate**](https://rstudio.github.io/reticulate/) package + +The [**reticulate**](https://rstudio.github.io/reticulate/) package +provides a comprehensive set of tools for interoperability between +Python and R. The package includes facilities for: + +- Calling Python from R in a variety of ways including R Markdown, + sourcing Python scripts, importing Python modules, and using Python + interactively within an R session. +- Translation between R and Python objects (for example, between R and + Pandas data frames, or between R matrices and NumPy arrays). +- Flexible binding to different versions of Python including virtual + environments and Conda environments. + +[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python +session within your R session, enabling seamless, high-performance +interoperability. If you are an R developer that uses Python for some of +your work or a member of data science team that uses both languages, +[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically +streamline your workflow. + +Let us start by loading the +[**reticulate**](https://rstudio.github.io/reticulate/) package into the +environment, along with a suite of useful packages for data science: + +``` r +# install.packages("tidyverse") +library(tidyverse) +``` + + ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── + + ✔ ggplot2 3.3.6 ✔ purrr 0.3.4 + ✔ tibble 3.1.7 ✔ dplyr 1.0.9 + ✔ tidyr 1.2.0 ✔ stringr 1.4.0 + ✔ readr 2.1.2 ✔ forcats 0.5.1 + + ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── + ✖ dplyr::filter() masks stats::filter() + ✖ dplyr::lag() masks stats::lag() + +``` r +# install.packages("reticulate") +library(reticulate) +``` + +The fastest way to get you set up to use Python from R with +[**reticulate**](https://rstudio.github.io/reticulate/) is to use the +`install_python()` and `install_miniconda()` utility functions that are +included in the [**reticulate**](https://rstudio.github.io/reticulate/) +package. In details, use something like: + +``` r +version <- "3.9.6" +install_miniconda() +conda_create("r-reticulate", python_version = version) +``` + +This is a setup that you ought to do only once (unless you want to +change your Python version for some reason). + +Next, you can seamlessly install all the Python packages you need by +calling the `conda_install()` function. For instance, to use Gudhi, you +would do something like: + +``` r +conda_install("scikit-learn", envname = "r-reticulate") +conda_install("gudhi", envname = "r-reticulate") +conda_install("jupyter", envname = "r-reticulate") +conda_install("r" , envname = "r-reticulate") +conda_install("r-irkernel" , envname = "r-reticulate") +install.packages("IRkernel") +IRkernel::installspec() +``` + +Again, installation of Python packages is to be done only once. + +You finally need to tell your R markdown document which conda +environment to use for interpreting Python code. This is done with: + +``` r +use_condaenv("r-reticulate") +``` + + Warning: The request to `use_python("/Users/stamm-a/Library/r-miniconda/envs/ + r-reticulate/bin/python")` will be ignored because the environment variable + RETICULATE_PYTHON is set to "/Users/stamm-a/Library/r-miniconda/envs/r- + reticulate/bin/python3.9" + +If you want to check your Python configuration, you can do: + +``` r +py_config() +``` + + python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9 + libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib + pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate + version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ] + numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy + numpy_version: 1.22.4 + + NOTE: Python version was forced by RETICULATE_PYTHON + +You can now create chunks of Python code that will be correctly +interpreted when knitting your document into an HTML file. Let us make a +first Python chunk of code to import **numpy** and **gudhi**: + +``` python +import numpy as np +import gudhi as gd +import gudhi.representations +``` + +## 2 Full example + +### 2.1 Data model + +First, we will generate persistence diagrams with orbits of dynamical +systems. This data set is very common in TDA. We use the following +system, which depends on a parameter $r > 0$: + +$$ +\begin{cases} +x_{n+1} = x_n + r y_n (1 - y_n) \mbox{ mod } 1 \\ +y_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \mbox{ mod } 1 +\end{cases} +$$ + +Let’s first see what the point cloud looks like for a given choice of +$r$. + +We first define a **Python** function to compute a single point cloud +according to the above orbit model and then use the +[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence +diagrams from its alpha complex and turn it into its silhouette +representation. This can be achieved by a function that looks like: + +``` python +def orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2): + X = np.empty([num_pts, 2]) + x, y = np.random.uniform(), np.random.uniform() + for i in range(num_pts): + X[i, :] = [x, y] + x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1. + y = (X[i, 1] + r * x * (1 - x)) % 1. + acX = gd.AlphaComplex(points = X).create_simplex_tree() + dgmX = acX.persistence() + SH = gd.representations.Silhouette( + resolution = resolution, + weight = lambda x: np.power(x[1] - x[0], 1) + ) + sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)]) + return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]]) +``` + +This function returns a Python list of size 2. The first element is the +abscissa grid and the second element is the silhouette value on the +grid. + +Python lists are automatically converted into atomic vectors in R. +Automatic conversions of data types between R and Python languages are +achieved according to the following table: + +| R | Python | Examples | +|------------------------|-------------------|----------------------------------------------------| +| Single-element vector | Scalar | `1`, `1L`, `TRUE`, `"foo"` | +| Multi-element vector | List | `c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)` | +| List of multiple types | Tuple | `list(1L, TRUE, "foo")` | +| Named list | Dict | `list(a = 1L, b = 2.0)`, `dict(x = x_data)` | +| Matrix/Array | NumPy ndarray | `matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)` | +| Data Frame | Pandas DataFrame | `data.frame(x = c(1, 2, 3), y = c("a", "b", "c"))` | +| Function | Python function | `function(x) x + 1` | +| NULL, TRUE, FALSE | None, True, False | `NULL`, `TRUE`, `FALSE` | + +### 2.2 Sample generation + +Now we can go back in **R** to use this **Python** function and generate +two samples of silhouette profiles. To access any Python object +`my_py_object` created in a previous chunk from an R chunk, use +`py$my_py_object`. Following this logic, the Python +function`orbit_silhouette()` created in the above Python chunk is +accessible from any subsequent R chunk as `py$orbit_silhouette()`: + +``` r +n1 <- 10 +n2 <- 10 +# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE) +# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE) +x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE) +x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE) + +t1 <- x1 |> map(1) |> map(~ c(0, .x)) +x1 <- x1 |> map(2) |> map(~ c(0, .x)) +t2 <- x2 |> map(1) |> map(~ c(0, .x)) +x2 <- x2 |> map(2) |> map(~ c(0, .x)) + +df <- bind_rows( + tibble(id = 1:n1, x = t1, y = x1) |> + unnest(cols = c(x, y)) |> + mutate(r = "1.9"), + tibble(id = 1:n2, x = t2, y = x2) |> + unnest(cols = c(x, y)) |> + mutate(r = "2.1") +) |> + mutate( + id = as_factor(id), + r = as_factor(r) + ) +df |> + ggplot(aes(x, y, color = r, group = interaction(id, r))) + + geom_line() + + scale_color_viridis_d() +``` + +![](reticulate_files/figure-gfm/unnamed-chunk-10-1.png) diff --git a/r-interface/reticulate/reticulate.qmd b/r-interface/reticulate/reticulate.qmd new file mode 100644 index 0000000..a95c907 --- /dev/null +++ b/r-interface/reticulate/reticulate.qmd @@ -0,0 +1,205 @@ +--- +title: "Using up-to-date Gudhi library in R" +author: "Aymeric Stamm" +date: 2022-06-13 +--- + +```{r} +#| label: setup +#| include: false +knitr::opts_chunk$set(echo = TRUE) +``` + +## The [**reticulate**](https://rstudio.github.io/reticulate/) package + +The [**reticulate**](https://rstudio.github.io/reticulate/) package +provides a comprehensive set of tools for interoperability between +Python and R. The package includes facilities for: + +- Calling Python from R in a variety of ways including R Markdown, + sourcing Python scripts, importing Python modules, and using Python + interactively within an R session. +- Translation between R and Python objects (for example, between R and + Pandas data frames, or between R matrices and NumPy arrays). +- Flexible binding to different versions of Python including virtual + environments and Conda environments. + +[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python +session within your R session, enabling seamless, high-performance +interoperability. If you are an R developer that uses Python for some of +your work or a member of data science team that uses both languages, +[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically +streamline your workflow. + +Let us start by loading the +[**reticulate**](https://rstudio.github.io/reticulate/) package into the +environment, along with a suite of useful packages for data science: + +```{r} +# install.packages("tidyverse") +library(tidyverse) +# install.packages("reticulate") +library(reticulate) +``` + +The fastest way to get you set up to use Python from R with +[**reticulate**](https://rstudio.github.io/reticulate/) is to use the +`install_python()` and `install_miniconda()` utility functions that are +included in the [**reticulate**](https://rstudio.github.io/reticulate/) +package. In details, use something like: + +```{r} +#| eval: false +version <- "3.9.6" +install_miniconda() +conda_create("r-reticulate", python_version = version) +``` + +This is a setup that you ought to do only once (unless you want to +change your Python version for some reason). + +Next, you can seamlessly install all the Python packages you need by +calling the `conda_install()` function. For instance, to use Gudhi, you +would do something like: + +```{r} +#| eval: false +conda_install("scikit-learn", envname = "r-reticulate") +conda_install("gudhi", envname = "r-reticulate") +conda_install("jupyter", envname = "r-reticulate") +conda_install("r" , envname = "r-reticulate") +conda_install("r-irkernel" , envname = "r-reticulate") +install.packages("IRkernel") +IRkernel::installspec() +``` + +Again, installation of Python packages is to be done only once. + +You finally need to tell your R markdown document which conda +environment to use for interpreting Python code. This is done with: + +```{r} +use_condaenv("r-reticulate") +``` + +If you want to check your Python configuration, you can do: + +```{r} +py_config() +``` + +You can now create chunks of Python code that will be correctly +interpreted when knitting your document into an HTML file. Let us make a +first Python chunk of code to import **numpy** and **gudhi**: + +```{python} +import numpy as np +import gudhi as gd +import gudhi.representations +``` + +## Full example + +### Data model + +First, we will generate persistence diagrams with orbits of dynamical +systems. This data set is very common in TDA. We use the following +system, which depends on a parameter $r > 0$: + +$$ +\begin{cases} +x_{n+1} = x_n + r y_n (1 - y_n) \mbox{ mod } 1 \\ +y_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \mbox{ mod } 1 +\end{cases} +$$ + +Let's first see what the point cloud looks like for a given choice of +$r$. + +We first define a **Python** function to compute a single point cloud +according to the above orbit model and then use the +[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence +diagrams from its alpha complex and turn it into its silhouette +representation. This can be achieved by a function that looks like: + +```{python} +def orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2): + X = np.empty([num_pts, 2]) + x, y = np.random.uniform(), np.random.uniform() + for i in range(num_pts): + X[i, :] = [x, y] + x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1. + y = (X[i, 1] + r * x * (1 - x)) % 1. + acX = gd.AlphaComplex(points = X).create_simplex_tree() + dgmX = acX.persistence() + SH = gd.representations.Silhouette( + resolution = resolution, + weight = lambda x: np.power(x[1] - x[0], 1) + ) + sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)]) + return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]]) +``` + +```{r} +#| echo: false +# source_python("orbit_silhouette.py") +``` + +This function returns a Python list of size 2. The first element is the +abscissa grid and the second element is the silhouette value on the +grid. + +Python lists are automatically converted into atomic vectors in R. +Automatic conversions of data types between R and Python languages are +achieved according to the following table: + +| R | Python | Examples | +|-------------------|-------------------|----------------------------------| +| Single-element vector | Scalar | `1`, `1L`, `TRUE`, `"foo"` | +| Multi-element vector | List | `c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)` | +| List of multiple types | Tuple | `list(1L, TRUE, "foo")` | +| Named list | Dict | `list(a = 1L, b = 2.0)`, `dict(x = x_data)` | +| Matrix/Array | NumPy ndarray | `matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)` | +| Data Frame | Pandas DataFrame | `data.frame(x = c(1, 2, 3), y = c("a", "b", "c"))` | +| Function | Python function | `function(x) x + 1` | +| NULL, TRUE, FALSE | None, True, False | `NULL`, `TRUE`, `FALSE` | + +### Sample generation + +Now we can go back in **R** to use this **Python** function and generate +two samples of silhouette profiles. To access any Python object +`my_py_object` created in a previous chunk from an R chunk, use +`py$my_py_object`. Following this logic, the Python +function`orbit_silhouette()` created in the above Python chunk is +accessible from any subsequent R chunk as `py$orbit_silhouette()`: + +```{r} +n1 <- 10 +n2 <- 10 +# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE) +# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE) +x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE) +x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE) + +t1 <- x1 |> map(1) |> map(~ c(0, .x)) +x1 <- x1 |> map(2) |> map(~ c(0, .x)) +t2 <- x2 |> map(1) |> map(~ c(0, .x)) +x2 <- x2 |> map(2) |> map(~ c(0, .x)) + +df <- bind_rows( + tibble(id = 1:n1, x = t1, y = x1) |> + unnest(cols = c(x, y)) |> + mutate(r = "1.9"), + tibble(id = 1:n2, x = t2, y = x2) |> + unnest(cols = c(x, y)) |> + mutate(r = "2.1") +) |> + mutate( + id = as_factor(id), + r = as_factor(r) + ) +df |> + ggplot(aes(x, y, color = r, group = interaction(id, r))) + + geom_line() + + scale_color_viridis_d() +``` 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_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}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||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,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_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(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["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:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),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=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_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"function"==typeof t?t.call(this._element):t}_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:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const 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file mode 100644 index 0000000..0bba767 --- /dev/null +++ b/r-interface/reticulate/reticulate_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,167 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; +} + +code span.at { + color: #657422; +} + +code span.ss { + color: #20794D; +} + +code span.an { + color: #5E5E5E; +} + +code span.fu { + color: #4758AB; +} + +code span.st { + color: #20794D; +} + +code span.cf { + color: #003B4F; +} + +code span.op { + color: #5E5E5E; +} + +code span.er { + color: #AD0000; +} + +code span.bn { + color: #AD0000; +} + +code span.al { + color: #AD0000; +} + +code span.va { + color: #111111; +} + +code span.pp { + color: #AD0000; +} + +code span.in { + color: #5E5E5E; +} + +code span.vs { + color: #20794D; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; +} + +code span.ch { + color: #20794D; +} + +code span.dt { + color: #AD0000; +} + +code span.fl { + color: #AD0000; +} + +code span.co { + color: #5E5E5E; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; +} + +code span.sc { + color: #5E5E5E; +} + +code span.dv { + color: #AD0000; +} + +code span.kw { + color: #003B4F; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + 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); + }); + + // 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(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${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() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + 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) { + if ( + item === currentPagePath || + item === 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; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.display = "none"; + } + + 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.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.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + 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; + } + }; + + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + const elRect = el.getBoundingClientRect(); + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + + // 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(() => { + positionToggle(); + }, 50) + ); + positionToggle(); + + // 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; + clone.style.display = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + 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; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + let lastBottom = 0; + for (const marginChild of marginChildren) { + const top = marginChild.getBoundingClientRect().top; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + lastBottom = top + marginChild.getBoundingClientRect().height; + } + + // 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 top = + el.getBoundingClientRect().top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightSideConflictEls)); + sidebarScrollVisiblity(toRegions(leftSideConflictEls)); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility(toRegions(leftSideConflictEls)); + } + }; + + 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; + + // 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 (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (depth === 1 || 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) { + walk(tocEl, 0); + updateActiveLink(); + } + + // 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 (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +function throttle(func, wait) { + let 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Stamm Date: Mon, 13 Jun 2022 14:26:52 +0200 Subject: [PATCH 2/6] Discard hmtl files from git versioning. --- r-interface/.gitignore | 1 + r-interface/reticulate/reticulate.html | 445 ------------------ r-interface/reticulate/reticulate.md | 92 +--- r-interface/reticulate/reticulate.qmd | 89 +--- .../figure-gfm/unnamed-chunk-7-1.png | Bin 0 -> 66376 bytes .../figure-html/unnamed-chunk-10-1.png | Bin 114343 -> 0 bytes 6 files changed, 24 insertions(+), 603 deletions(-) delete mode 100644 r-interface/reticulate/reticulate.html create mode 100644 r-interface/reticulate/reticulate_files/figure-gfm/unnamed-chunk-7-1.png delete mode 100644 r-interface/reticulate/reticulate_files/figure-html/unnamed-chunk-10-1.png diff --git a/r-interface/.gitignore b/r-interface/.gitignore index 075b254..2e8708c 100644 --- a/r-interface/.gitignore +++ b/r-interface/.gitignore @@ -1 +1,2 @@ /.quarto/ +*html diff --git a/r-interface/reticulate/reticulate.html b/r-interface/reticulate/reticulate.html deleted file mode 100644 index 96e0c0a..0000000 --- a/r-interface/reticulate/reticulate.html +++ /dev/null @@ -1,445 +0,0 @@ - - - - - - - - - - - -Using up-to-date Gudhi library in R - - - - - - - - - - - - - - - - - - - - -
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Using up-to-date Gudhi library in R

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Author
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Aymeric Stamm

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Published
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June 13, 2022

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1 The reticulate package

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The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

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  • Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
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  • Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
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  • Flexible binding to different versions of Python including virtual environments and Conda environments.
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reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow.

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Let us start by loading the reticulate package into the environment, along with a suite of useful packages for data science:

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# install.packages("tidyverse")
-library(tidyverse)
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── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
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✔ ggplot2 3.3.6     ✔ purrr   0.3.4
-✔ tibble  3.1.7     ✔ dplyr   1.0.9
-✔ tidyr   1.2.0     ✔ stringr 1.4.0
-✔ readr   2.1.2     ✔ forcats 0.5.1
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── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
-✖ dplyr::filter() masks stats::filter()
-✖ dplyr::lag()    masks stats::lag()
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# install.packages("reticulate")
-library(reticulate)
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The fastest way to get you set up to use Python from R with reticulate is to use the install_python() and install_miniconda() utility functions that are included in the reticulate package. In details, use something like:

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version <- "3.9.6"
-install_miniconda()
-conda_create("r-reticulate", python_version = version)
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This is a setup that you ought to do only once (unless you want to change your Python version for some reason).

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Next, you can seamlessly install all the Python packages you need by calling the conda_install() function. For instance, to use Gudhi, you would do something like:

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conda_install("scikit-learn", envname = "r-reticulate")
-conda_install("gudhi", envname = "r-reticulate")
-conda_install("jupyter", envname = "r-reticulate")
-conda_install("r" , envname = "r-reticulate")
-conda_install("r-irkernel" , envname = "r-reticulate")
-install.packages("IRkernel")
-IRkernel::installspec()
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Again, installation of Python packages is to be done only once.

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You finally need to tell your R markdown document which conda environment to use for interpreting Python code. This is done with:

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use_condaenv("r-reticulate")
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Warning: The request to `use_python("/Users/stamm-a/Library/r-miniconda/envs/
-r-reticulate/bin/python")` will be ignored because the environment variable
-RETICULATE_PYTHON is set to "/Users/stamm-a/Library/r-miniconda/envs/r-
-reticulate/bin/python3.9"
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If you want to check your Python configuration, you can do:

-
-
py_config()
-
-
python:         /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9
-libpython:      /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib
-pythonhome:     /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate
-version:        3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15)  [Clang 11.1.0 ]
-numpy:          /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy
-numpy_version:  1.22.4
-
-NOTE: Python version was forced by RETICULATE_PYTHON
-
-
-

You can now create chunks of Python code that will be correctly interpreted when knitting your document into an HTML file. Let us make a first Python chunk of code to import numpy and gudhi:

-
-
import numpy as np
-import gudhi as gd
-import gudhi.representations
-
-
-
-

2 Full example

-
-

2.1 Data model

-

First, we will generate persistence diagrams with orbits of dynamical systems. This data set is very common in TDA. We use the following system, which depends on a parameter \(r > 0\):

-

\[ -\begin{cases} -x_{n+1} = x_n + r y_n (1 - y_n) \mbox{ mod } 1 \\ -y_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \mbox{ mod } 1 -\end{cases} -\]

-

Let’s first see what the point cloud looks like for a given choice of \(r\).

-

We first define a Python function to compute a single point cloud according to the above orbit model and then use the Gudhi library to compute the persistence diagrams from its alpha complex and turn it into its silhouette representation. This can be achieved by a function that looks like:

-
-
def orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):
-  X = np.empty([num_pts, 2])
-  x, y = np.random.uniform(), np.random.uniform()
-  for i in range(num_pts):
-    X[i, :] = [x, y]
-    x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.
-    y = (X[i, 1] + r * x * (1 - x)) % 1.
-  acX = gd.AlphaComplex(points = X).create_simplex_tree()
-  dgmX = acX.persistence()
-  SH = gd.representations.Silhouette(
-    resolution = resolution, 
-    weight = lambda x: np.power(x[1] - x[0], 1)
-  )
-  sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])
-  return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])
-
-
- -
-

This function returns a Python list of size 2. The first element is the abscissa grid and the second element is the silhouette value on the grid.

-

Python lists are automatically converted into atomic vectors in R. Automatic conversions of data types between R and Python languages are achieved according to the following table:

- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
RPythonExamples
Single-element vectorScalar1, 1L, TRUE, "foo"
Multi-element vectorListc(1.0, 2.0, 3.0), c(1L, 2L, 3L)
List of multiple typesTuplelist(1L, TRUE, "foo")
Named listDictlist(a = 1L, b = 2.0), dict(x = x_data)
Matrix/ArrayNumPy ndarraymatrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)
Data FramePandas DataFramedata.frame(x = c(1, 2, 3), y = c("a", "b", "c"))
FunctionPython functionfunction(x) x + 1
NULL, TRUE, FALSENone, True, FalseNULL, TRUE, FALSE
-
-
-

2.2 Sample generation

-

Now we can go back in R to use this Python function and generate two samples of silhouette profiles. To access any Python object my_py_object created in a previous chunk from an R chunk, use py$my_py_object. Following this logic, the Python functionorbit_silhouette() created in the above Python chunk is accessible from any subsequent R chunk as py$orbit_silhouette():

-
-
n1 <- 10
-n2 <- 10
-# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE)
-# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE)
-x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)
-x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)
-
-t1 <- x1 |> map(1) |> map(~ c(0, .x))
-x1 <- x1 |> map(2) |> map(~ c(0, .x))
-t2 <- x2 |> map(1) |> map(~ c(0, .x))
-x2 <- x2 |> map(2) |> map(~ c(0, .x))
-
-df <- bind_rows(
-  tibble(id = 1:n1, x = t1, y = x1) |> 
-    unnest(cols = c(x, y)) |> 
-    mutate(r = "1.9"), 
-  tibble(id = 1:n2, x = t2, y = x2) |> 
-    unnest(cols = c(x, y)) |> 
-    mutate(r = "2.1")
-) |> 
-  mutate(
-    id = as_factor(id), 
-    r = as_factor(r)
-  )
-df |> 
-  ggplot(aes(x, y, color = r, group = interaction(id, r))) +
-  geom_line() + 
-  scale_color_viridis_d()
-
-

-
-
-
-
- -
- - -
- - - - \ No newline at end of file diff --git a/r-interface/reticulate/reticulate.md b/r-interface/reticulate/reticulate.md index e5be75f..7c76991 100644 --- a/r-interface/reticulate/reticulate.md +++ b/r-interface/reticulate/reticulate.md @@ -3,10 +3,9 @@ Using up-to-date Gudhi library in R Aymeric Stamm 2022-06-13 -- 1 1 The - reticulate package + class="header-section-number">1 Initial setup - 2 2 Full example @@ -17,32 +16,14 @@ Aymeric Stamm class="toc-section-number">2.2 2.2 Sample generation -## 1 The [**reticulate**](https://rstudio.github.io/reticulate/) package - -The [**reticulate**](https://rstudio.github.io/reticulate/) package -provides a comprehensive set of tools for interoperability between -Python and R. The package includes facilities for: - -- Calling Python from R in a variety of ways including R Markdown, - sourcing Python scripts, importing Python modules, and using Python - interactively within an R session. -- Translation between R and Python objects (for example, between R and - Pandas data frames, or between R matrices and NumPy arrays). -- Flexible binding to different versions of Python including virtual - environments and Conda environments. - -[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python -session within your R session, enabling seamless, high-performance -interoperability. If you are an R developer that uses Python for some of -your work or a member of data science team that uses both languages, -[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically -streamline your workflow. +## 1 Initial setup Let us start by loading the [**reticulate**](https://rstudio.github.io/reticulate/) package into the environment, along with a suite of useful packages for data science: ``` r +library(reticulate) # install.packages("tidyverse") library(tidyverse) ``` @@ -58,44 +39,9 @@ library(tidyverse) ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() -``` r -# install.packages("reticulate") -library(reticulate) -``` - -The fastest way to get you set up to use Python from R with -[**reticulate**](https://rstudio.github.io/reticulate/) is to use the -`install_python()` and `install_miniconda()` utility functions that are -included in the [**reticulate**](https://rstudio.github.io/reticulate/) -package. In details, use something like: - -``` r -version <- "3.9.6" -install_miniconda() -conda_create("r-reticulate", python_version = version) -``` - -This is a setup that you ought to do only once (unless you want to -change your Python version for some reason). - -Next, you can seamlessly install all the Python packages you need by -calling the `conda_install()` function. For instance, to use Gudhi, you -would do something like: - -``` r -conda_install("scikit-learn", envname = "r-reticulate") -conda_install("gudhi", envname = "r-reticulate") -conda_install("jupyter", envname = "r-reticulate") -conda_install("r" , envname = "r-reticulate") -conda_install("r-irkernel" , envname = "r-reticulate") -install.packages("IRkernel") -IRkernel::installspec() -``` - -Again, installation of Python packages is to be done only once. - -You finally need to tell your R markdown document which conda -environment to use for interpreting Python code. This is done with: +Let us inform the R session that we want to use the virtual environment +`r-reticulate` where we installed all the required Python package (see +`README` at the root of the project). This is done with: ``` r use_condaenv("r-reticulate") @@ -179,33 +125,21 @@ grid. Python lists are automatically converted into atomic vectors in R. Automatic conversions of data types between R and Python languages are -achieved according to the following table: - -| R | Python | Examples | -|------------------------|-------------------|----------------------------------------------------| -| Single-element vector | Scalar | `1`, `1L`, `TRUE`, `"foo"` | -| Multi-element vector | List | `c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)` | -| List of multiple types | Tuple | `list(1L, TRUE, "foo")` | -| Named list | Dict | `list(a = 1L, b = 2.0)`, `dict(x = x_data)` | -| Matrix/Array | NumPy ndarray | `matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)` | -| Data Frame | Pandas DataFrame | `data.frame(x = c(1, 2, 3), y = c("a", "b", "c"))` | -| Function | Python function | `function(x) x + 1` | -| NULL, TRUE, FALSE | None, True, False | `NULL`, `TRUE`, `FALSE` | +implemented in reticulate: see +. ### 2.2 Sample generation Now we can go back in **R** to use this **Python** function and generate two samples of silhouette profiles. To access any Python object `my_py_object` created in a previous chunk from an R chunk, use -`py$my_py_object`. Following this logic, the Python -function`orbit_silhouette()` created in the above Python chunk is -accessible from any subsequent R chunk as `py$orbit_silhouette()`: +`py$my_py_object`. Following this logic, the Python function +`orbit_silhouette()` created in the above Python chunk is accessible +from any subsequent R chunk as `py$orbit_silhouette()`: ``` r n1 <- 10 n2 <- 10 -# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE) -# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE) x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE) x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE) @@ -232,4 +166,4 @@ df |> scale_color_viridis_d() ``` -![](reticulate_files/figure-gfm/unnamed-chunk-10-1.png) +![](reticulate_files/figure-gfm/unnamed-chunk-7-1.png) diff --git a/r-interface/reticulate/reticulate.qmd b/r-interface/reticulate/reticulate.qmd index a95c907..7598e03 100644 --- a/r-interface/reticulate/reticulate.qmd +++ b/r-interface/reticulate/reticulate.qmd @@ -10,73 +10,21 @@ date: 2022-06-13 knitr::opts_chunk$set(echo = TRUE) ``` -## The [**reticulate**](https://rstudio.github.io/reticulate/) package - -The [**reticulate**](https://rstudio.github.io/reticulate/) package -provides a comprehensive set of tools for interoperability between -Python and R. The package includes facilities for: - -- Calling Python from R in a variety of ways including R Markdown, - sourcing Python scripts, importing Python modules, and using Python - interactively within an R session. -- Translation between R and Python objects (for example, between R and - Pandas data frames, or between R matrices and NumPy arrays). -- Flexible binding to different versions of Python including virtual - environments and Conda environments. - -[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python -session within your R session, enabling seamless, high-performance -interoperability. If you are an R developer that uses Python for some of -your work or a member of data science team that uses both languages, -[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically -streamline your workflow. +## Initial setup Let us start by loading the [**reticulate**](https://rstudio.github.io/reticulate/) package into the environment, along with a suite of useful packages for data science: ```{r} +library(reticulate) # install.packages("tidyverse") library(tidyverse) -# install.packages("reticulate") -library(reticulate) -``` - -The fastest way to get you set up to use Python from R with -[**reticulate**](https://rstudio.github.io/reticulate/) is to use the -`install_python()` and `install_miniconda()` utility functions that are -included in the [**reticulate**](https://rstudio.github.io/reticulate/) -package. In details, use something like: - -```{r} -#| eval: false -version <- "3.9.6" -install_miniconda() -conda_create("r-reticulate", python_version = version) ``` -This is a setup that you ought to do only once (unless you want to -change your Python version for some reason). - -Next, you can seamlessly install all the Python packages you need by -calling the `conda_install()` function. For instance, to use Gudhi, you -would do something like: - -```{r} -#| eval: false -conda_install("scikit-learn", envname = "r-reticulate") -conda_install("gudhi", envname = "r-reticulate") -conda_install("jupyter", envname = "r-reticulate") -conda_install("r" , envname = "r-reticulate") -conda_install("r-irkernel" , envname = "r-reticulate") -install.packages("IRkernel") -IRkernel::installspec() -``` - -Again, installation of Python packages is to be done only once. - -You finally need to tell your R markdown document which conda -environment to use for interpreting Python code. This is done with: +Let us inform the R session that we want to use the virtual environment +`r-reticulate` where we installed all the required Python package (see +`README` at the root of the project). This is done with: ```{r} use_condaenv("r-reticulate") @@ -140,44 +88,27 @@ def orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2): return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]]) ``` -```{r} -#| echo: false -# source_python("orbit_silhouette.py") -``` - This function returns a Python list of size 2. The first element is the abscissa grid and the second element is the silhouette value on the grid. Python lists are automatically converted into atomic vectors in R. Automatic conversions of data types between R and Python languages are -achieved according to the following table: - -| R | Python | Examples | -|-------------------|-------------------|----------------------------------| -| Single-element vector | Scalar | `1`, `1L`, `TRUE`, `"foo"` | -| Multi-element vector | List | `c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)` | -| List of multiple types | Tuple | `list(1L, TRUE, "foo")` | -| Named list | Dict | `list(a = 1L, b = 2.0)`, `dict(x = x_data)` | -| Matrix/Array | NumPy ndarray | `matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)` | -| Data Frame | Pandas DataFrame | `data.frame(x = c(1, 2, 3), y = c("a", "b", "c"))` | -| Function | Python function | `function(x) x + 1` | -| NULL, TRUE, FALSE | None, True, False | `NULL`, `TRUE`, `FALSE` | +implemented in reticulate: see +. ### Sample generation Now we can go back in **R** to use this **Python** function and generate two samples of silhouette profiles. To access any Python object `my_py_object` created in a previous chunk from an R chunk, use -`py$my_py_object`. Following this logic, the Python -function`orbit_silhouette()` created in the above Python chunk is -accessible from any subsequent R chunk as `py$orbit_silhouette()`: +`py$my_py_object`. Following this logic, the Python function +`orbit_silhouette()` created in the above Python chunk is accessible +from any subsequent R chunk as `py$orbit_silhouette()`: ```{r} n1 <- 10 n2 <- 10 -# x1 <- replicate(n1, orbit_silhouette(r = 1.9), simplify = FALSE) -# x2 <- replicate(n2, orbit_silhouette(r = 2.1), simplify = FALSE) x1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE) x2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE) diff --git a/r-interface/reticulate/reticulate_files/figure-gfm/unnamed-chunk-7-1.png b/r-interface/reticulate/reticulate_files/figure-gfm/unnamed-chunk-7-1.png new file mode 100644 index 0000000000000000000000000000000000000000..29811881300c6e500e87886a504962a5c04a4a86 GIT binary patch literal 66376 zcmZs@1yoe+*FHQm3=J|MAt2pIN=gnL(y1VzAT6OZ5<^ILcXuibq99#@(xtR?hjh+& z=J~zxeCz$MHLf*lm@{YJJFk81eGJ#qP$I;m!UKUogeuDNIv@}v4+H`);a~xuct_Hs&cPH&vv={mo%uvD^iw0vi4p`#=V0*OXP>zmj-)F+k7{96Brc_?V2 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t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;et===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_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)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_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}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||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,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_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(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["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:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),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=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_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"function"==typeof t?t.call(this._element):t}_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:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const 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++++++-------- .../figure-gfm/unnamed-chunk-7-1.png | Bin 29698 -> 49551 bytes r-interface/_quarto.yml | 2 +- r-interface/reticulate/reticulate.md | 27 +++++++----------- .../figure-gfm/unnamed-chunk-7-1.png | Bin 29698 -> 49551 bytes 5 files changed, 19 insertions(+), 31 deletions(-) diff --git a/r-interface/README.md b/r-interface/README.md index b2bf1e0..92bdf72 100644 --- a/r-interface/README.md +++ b/r-interface/README.md @@ -1,26 +1,21 @@ Aymeric Stamm - 1 1. Setup instructions for using - GUDHI from R - - 1.1 1.1 The + id="toc-setup-instructions-for-using-gudhi-from-r">Setup instructions + for using GUDHI from R + - The reticulate package - - 1.2 1.2 Bring GUDHI into R + - Bring GUDHI + into R -# 1. Setup instructions for using GUDHI from R +# Setup instructions for using GUDHI from R -## 1.1 The [**reticulate**](https://rstudio.github.io/reticulate/) package +## The [**reticulate**](https://rstudio.github.io/reticulate/) package The [**reticulate**](https://rstudio.github.io/reticulate/) package provides a comprehensive set of tools for interoperability between @@ -41,7 +36,7 @@ your work or a member of data science team that uses both languages, [**reticulate**](https://rstudio.github.io/reticulate/) can dramatically streamline your workflow. -## 1.2 Bring GUDHI into R +## Bring GUDHI into R - First, you’ll want to install and load the [**reticulate**](https://rstudio.github.io/reticulate/) package into diff --git a/r-interface/_freeze/reticulate/reticulate/figure-gfm/unnamed-chunk-7-1.png b/r-interface/_freeze/reticulate/reticulate/figure-gfm/unnamed-chunk-7-1.png index 0a8b4c25809fa91684e1cf3b470a58a2d457ba9c..615cdca952f7d8e04a459fba92aca4fc08220334 100644 GIT binary patch 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This data set is very common in TDA. We use the following @@ -129,7 +122,7 @@ Automatic conversions of data types between R and Python languages are implemented in reticulate: see . -### 2.2 Sample generation +### Sample generation Now we can go back in **R** to use this **Python** function and generate two samples of silhouette profiles. 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zZ{Xu)0o@Zy`{CEgtUyQhEP;fqoKyL{EGYb=7E?q8HV(ZUJU1mk%(Z2H*v&6p{J1+N~)9Q`?OsC9|Me0XR z0PBTNLRan8=^EFT-Q>2e1QEN6pCIN58SQT~(i3m3jp9HX_}Sa=_6-bJ5sAc+(NVil zm1Z7ZUgxJzo2hTe1_L|_c(p5}4euo`ST}+fDd*if!o%x5mZJlY5Bk#-e617}6~_c_ zajSqv+0Q$d@CR@IWK4*Rc8g+dq8d%j3R$>HB5NMKA2}!W8-fI&=qu`|LABL z>Hdxj60+!Ppl^yqY5~yh?{n^gPFGWYeD}Wt<^Nhz1_=wvQ@Z~S0%Z;p@Z65WX!-xv jC0O|`sNVlknUnJjVO?^}@Pz~6&=+ILG-p1k^B1aS Date: Mon, 13 Jun 2022 15:39:34 +0200 Subject: [PATCH 5/6] Remove section numbering everywhere, and author from README. --- r-interface/README.md | 9 --------- r-interface/README.qmd | 2 ++ .../_freeze/README/execute-results/md.json | 4 ++-- .../reticulate/execute-results/html.json | 4 ++-- .../reticulate/execute-results/md.json | 4 ++-- .../figure-gfm/unnamed-chunk-7-1.png | Bin 49551 -> 61019 bytes r-interface/_quarto.yml | 2 -- r-interface/reticulate/reticulate.md | 6 +++--- r-interface/reticulate/reticulate.qmd | 6 +++--- .../figure-gfm/unnamed-chunk-7-1.png | Bin 49551 -> 61019 bytes 10 files changed, 14 insertions(+), 23 deletions(-) diff --git a/r-interface/README.md b/r-interface/README.md index 92bdf72..d04198c 100644 --- a/r-interface/README.md +++ b/r-interface/README.md @@ -1,12 +1,3 @@ -Aymeric Stamm - -- Setup instructions - for using GUDHI from R - - The - reticulate package - - Bring GUDHI - into R diff --git a/r-interface/README.qmd b/r-interface/README.qmd index 343d853..3d404cb 100644 --- a/r-interface/README.qmd +++ b/r-interface/README.qmd @@ -1,5 +1,6 @@ --- format: gfm +toc: false --- @@ -90,3 +91,4 @@ If you want to check your Python configuration, you can do: ```{r} py_config() ``` + diff --git a/r-interface/_freeze/README/execute-results/md.json b/r-interface/_freeze/README/execute-results/md.json index f0db3cb..addcb9e 100644 --- a/r-interface/_freeze/README/execute-results/md.json +++ b/r-interface/_freeze/README/execute-results/md.json @@ -1,7 +1,7 @@ { - "hash": "941a1dac304470fad84c027d539dae0d", + "hash": "76edff0cb54ae50302fe5f65c9a6517e", "result": { - "markdown": "---\nformat: gfm\n---\n\n\n\n\n\n\n\n\n# Setup instructions for using GUDHI from R\n\n\n\n\n\n## The [**reticulate**](https://rstudio.github.io/reticulate/) package\n\nThe [**reticulate**](https://rstudio.github.io/reticulate/) package\nprovides a comprehensive set of tools for interoperability between\nPython and R. The package includes facilities for:\n\n- Calling Python from R in a variety of ways including R Markdown,\n sourcing Python scripts, importing Python modules, and using Python\n interactively within an R session.\n- Translation between R and Python objects (for example, between R and\n Pandas data frames, or between R matrices and NumPy arrays).\n- Flexible binding to different versions of Python including virtual\n environments and Conda environments.\n\n[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python\nsession within your R session, enabling seamless, high-performance\ninteroperability. If you are an R developer that uses Python for some of\nyour work or a member of data science team that uses both languages,\n[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically\nstreamline your workflow.\n\n## Bring GUDHI into R\n\n- First, you'll want to install and load the\n [**reticulate**](https://rstudio.github.io/reticulate/) package into\n your session:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n # install.packages(\"reticulate\")\n library(reticulate)\n ```\n :::\n\n\n- Next, the fastest way to get you set up to use Python from R with\n [**reticulate**](https://rstudio.github.io/reticulate/) is to use\n the `install_miniconda()` utility function which is included in the\n [**reticulate**](https://rstudio.github.io/reticulate/) package. In\n details, use something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n install_miniconda()\n ```\n :::\n\n\n- Next, it is recommended that you create a virtual conda environment\n in which you'll install all required Python packages. This can be\n achieved using the `reticulate::conda_create()` function as follows:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n version <- \"3.9.6\"\n conda_create(\"r-reticulate\", python_version = version)\n ```\n :::\n\n\n- Next, you can seamlessly install all the Python packages you need by\n calling the `conda_install()` function. For instance, to use Gudhi,\n you would do something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n conda_install(\"scikit-learn\", envname = \"r-reticulate\")\n conda_install(\"gudhi\", envname = \"r-reticulate\")\n ```\n :::\n\n\nThis is a setup that you ought to do only once (unless you want to\nchange your Python version for some reason).\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\n#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\n#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\n#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\n#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\n#> numpy_version: 1.22.4\n#> \n#> NOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n", + "markdown": "---\nformat: gfm\ntoc: false\n---\n\n\n\n\n\n\n\n\n# Setup instructions for using GUDHI from R\n\n\n\n\n\n## The [**reticulate**](https://rstudio.github.io/reticulate/) package\n\nThe [**reticulate**](https://rstudio.github.io/reticulate/) package\nprovides a comprehensive set of tools for interoperability between\nPython and R. The package includes facilities for:\n\n- Calling Python from R in a variety of ways including R Markdown,\n sourcing Python scripts, importing Python modules, and using Python\n interactively within an R session.\n- Translation between R and Python objects (for example, between R and\n Pandas data frames, or between R matrices and NumPy arrays).\n- Flexible binding to different versions of Python including virtual\n environments and Conda environments.\n\n[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python\nsession within your R session, enabling seamless, high-performance\ninteroperability. If you are an R developer that uses Python for some of\nyour work or a member of data science team that uses both languages,\n[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically\nstreamline your workflow.\n\n## Bring GUDHI into R\n\n- First, you'll want to install and load the\n [**reticulate**](https://rstudio.github.io/reticulate/) package into\n your session:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n # install.packages(\"reticulate\")\n library(reticulate)\n ```\n :::\n\n\n- Next, the fastest way to get you set up to use Python from R with\n [**reticulate**](https://rstudio.github.io/reticulate/) is to use\n the `install_miniconda()` utility function which is included in the\n [**reticulate**](https://rstudio.github.io/reticulate/) package. In\n details, use something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n install_miniconda()\n ```\n :::\n\n\n- Next, it is recommended that you create a virtual conda environment\n in which you'll install all required Python packages. This can be\n achieved using the `reticulate::conda_create()` function as follows:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n version <- \"3.9.6\"\n conda_create(\"r-reticulate\", python_version = version)\n ```\n :::\n\n\n- Next, you can seamlessly install all the Python packages you need by\n calling the `conda_install()` function. For instance, to use Gudhi,\n you would do something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n conda_install(\"scikit-learn\", envname = \"r-reticulate\")\n conda_install(\"gudhi\", envname = \"r-reticulate\")\n ```\n :::\n\n\nThis is a setup that you ought to do only once (unless you want to\nchange your Python version for some reason).\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\n#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\n#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\n#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\n#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\n#> numpy_version: 1.22.4\n#> \n#> NOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/r-interface/_freeze/reticulate/reticulate/execute-results/html.json b/r-interface/_freeze/reticulate/reticulate/execute-results/html.json index 344cb0e..1ad6345 100644 --- a/r-interface/_freeze/reticulate/reticulate/execute-results/html.json +++ b/r-interface/_freeze/reticulate/reticulate/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "2a83547c2b2bbde7b49edc0ab0d00347", + "hash": "4092a74eee6aad07fbcefc465f58277e", "result": { - "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [tidyverse](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in reticulate: see\n.\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: {.cell-output-display}\n![](reticulate_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::\n", + "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [**tidyverse**](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in [**reticulate**](https://rstudio.github.io/reticulate/):\nsee .\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: {.cell-output-display}\n![](reticulate_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::\n", "supporting": [ "reticulate_files/figure-html" ], diff --git a/r-interface/_freeze/reticulate/reticulate/execute-results/md.json b/r-interface/_freeze/reticulate/reticulate/execute-results/md.json index 6600a56..974e80e 100644 --- a/r-interface/_freeze/reticulate/reticulate/execute-results/md.json +++ b/r-interface/_freeze/reticulate/reticulate/execute-results/md.json @@ -1,7 +1,7 @@ { - "hash": "2a83547c2b2bbde7b49edc0ab0d00347", + "hash": "4092a74eee6aad07fbcefc465f58277e", "result": { - "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [tidyverse](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\n\nIf you want to check your Python configuration, you can do:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in reticulate: see\n.\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: {.cell-output-display}\n![](reticulate_files/figure-gfm/unnamed-chunk-7-1.png)\n:::\n:::\n", + "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [**tidyverse**](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\n\nIf you want to check your Python configuration, you can do:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in [**reticulate**](https://rstudio.github.io/reticulate/):\nsee .\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: 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b/r-interface/reticulate/reticulate.md @@ -13,7 +13,7 @@ Aymeric Stamm Let us start by loading the [**reticulate**](https://rstudio.github.io/reticulate/) package into the -environment, along with the [tidyverse](https://www.tidyverse.org), +environment, along with the [**tidyverse**](https://www.tidyverse.org), which is a suite of useful packages for data science: ``` r @@ -119,8 +119,8 @@ grid. Python lists are automatically converted into atomic vectors in R. Automatic conversions of data types between R and Python languages are -implemented in reticulate: see -. +implemented in [**reticulate**](https://rstudio.github.io/reticulate/): +see . ### Sample generation diff --git a/r-interface/reticulate/reticulate.qmd b/r-interface/reticulate/reticulate.qmd index 5fb67c5..70336a9 100644 --- a/r-interface/reticulate/reticulate.qmd +++ b/r-interface/reticulate/reticulate.qmd @@ -14,7 +14,7 @@ knitr::opts_chunk$set(echo = TRUE) Let us start by loading the [**reticulate**](https://rstudio.github.io/reticulate/) package into the -environment, along with the [tidyverse](https://www.tidyverse.org), +environment, along with the [**tidyverse**](https://www.tidyverse.org), which is a suite of useful packages for data science: ```{r} @@ -95,8 +95,8 @@ grid. Python lists are automatically converted into atomic vectors in R. 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z>EBTYFglcJcrPvF-#S_UMiIaVDD~anQY5C}&1K8YcH3~6_fJpxHtU^Btxn~7ng2mn zq_pohiRdy|QtzG|&X3*cP#0Kps%_4dX>LFWUdhU_$^;-){RJn4Ziu#`S$al_e<#2a z%+LMia5I9wlef$J<^2A_phB|+StJ@)?q89fdM|O|9H868rUsKx&7h(4IygZp`< z)!+5-OB4hiSv-$+2pRZ=DXhCdEW!3*hV0!Sfd+&F4_EuMJDf)He}^x?0Ply$)~+hZ z2mvOx9U1i4xf)nI$#h!bwj7vlfAurA%hXczhCXxJN}7`v3#$Y>KMr@ zDhfrdQWZHPa?-P%&fF<{@x&8N>>wbrHw7w+i|7$t%_<-ez#gs<=6BPnD?Hzid zcgIQnul4-@H9?ex-54hSU;8b1&xV>Ype6xI^8BBrL8-puMF?BN_ktP!2jF;TNX~s~ XhKP7M^v0<^06$_v(t=g|y8izUk9DV$ From f96d4c09f6cc80a943d2df50f5fcb8026416dbad Mon Sep 17 00:00:00 2001 From: Aymeric Stamm Date: Mon, 13 Jun 2022 16:44:52 +0200 Subject: [PATCH 6/6] Updated README. --- r-interface/README.md | 49 +++++++++++++++++++ r-interface/README.qmd | 48 ++++++++++++++++++ .../_freeze/README/execute-results/md.json | 8 +-- 3 files changed, 102 insertions(+), 3 deletions(-) diff --git a/r-interface/README.md b/r-interface/README.md index d04198c..180914d 100644 --- a/r-interface/README.md +++ b/r-interface/README.md @@ -82,3 +82,52 @@ py_config() #> #> NOTE: Python version was forced by RETICULATE_PYTHON ``` + +# Organisation + +## File hierarchy + +This folder contains a number of [Quarto](https://quarto.org) documents +contributed with the purpose of showing how +[`R`](https://www.r-project.org) can be used to enhance statistical +analysis of topological data produced via +[Gudhi](https://gudhi.inria.fr). Each tutorial is hosted in its own +folder which is organised as follows: + +- The Quarto document with extension `.qmd` which is the main file + that a developer is supposed to contribute. In this document, there + is a YAML header in which the contributor can specify various output + formats by adding + + ``` yaml + #| eval: false + format: + html: default + gfm: default + pdf: default + ``` + + The complete list of possible output formats: + . + +- A Github-flavored markdown (GMF) document with extension `.md` which + enables nice visualization of the notebook directly in the Github + web interface. + +- A folder containing useful libs and resources to display the GMF + document. + +## Contributing + +If you want to contribute, you should + +1. Fork the `TDA-tutorials` repo +2. Open the RStudio project in + `TDA-tutorials/r-interface/r-interface.Rproj`. +3. When opened in RStudio, the working directory should now be + `TDA-tutorials/r-interface/` in which you can now create a folder + with evocative name to put the Quarto document describing the + analysis you want to share. + - Note: you can use the same YAML header as in + `reticulate/reticulate.qmd` as a number of presets for rendering + are made into the `_quarto.yml` file. diff --git a/r-interface/README.qmd b/r-interface/README.qmd index 3d404cb..7b953d6 100644 --- a/r-interface/README.qmd +++ b/r-interface/README.qmd @@ -92,3 +92,51 @@ If you want to check your Python configuration, you can do: py_config() ``` +# Organisation + +## File hierarchy + +This folder contains a number of [Quarto](https://quarto.org) documents +contributed with the purpose of showing how +[`R`](https://www.r-project.org) can be used to enhance statistical +analysis of topological data produced via +[Gudhi](https://gudhi.inria.fr). Each tutorial is hosted in its own +folder which is organised as follows: + +- The Quarto document with extension `.qmd` which is the main file + that a developer is supposed to contribute. In this document, there + is a YAML header in which the contributor can specify various output + formats by adding + + ``` yaml + #| eval: false + format: + html: default + gfm: default + pdf: default + ``` + + The complete list of possible output formats: + . + +- A Github-flavored markdown (GMF) document with extension `.md` which + enables nice visualization of the notebook directly in the Github + web interface. + +- A folder containing useful libs and resources to display the GMF + document. + +## Contributing + +If you want to contribute, you should + +1. Fork the `TDA-tutorials` repo +2. Open the RStudio project in + `TDA-tutorials/r-interface/r-interface.Rproj`. +3. When opened in RStudio, the working directory should now be + `TDA-tutorials/r-interface/` in which you can now create a folder + with evocative name to put the Quarto document describing the + analysis you want to share. + - Note: you can use the same YAML header as in + `reticulate/reticulate.qmd` as a number of presets for rendering + are made into the `_quarto.yml` file. diff --git a/r-interface/_freeze/README/execute-results/md.json b/r-interface/_freeze/README/execute-results/md.json index addcb9e..43dd3bf 100644 --- a/r-interface/_freeze/README/execute-results/md.json +++ b/r-interface/_freeze/README/execute-results/md.json @@ -1,8 +1,10 @@ { - "hash": "76edff0cb54ae50302fe5f65c9a6517e", + "hash": "9629af67199513e45263219d8338c33e", "result": { - "markdown": "---\nformat: gfm\ntoc: false\n---\n\n\n\n\n\n\n\n\n# Setup instructions for using GUDHI from R\n\n\n\n\n\n## The [**reticulate**](https://rstudio.github.io/reticulate/) package\n\nThe [**reticulate**](https://rstudio.github.io/reticulate/) package\nprovides a comprehensive set of tools for interoperability between\nPython and R. The package includes facilities for:\n\n- Calling Python from R in a variety of ways including R Markdown,\n sourcing Python scripts, importing Python modules, and using Python\n interactively within an R session.\n- Translation between R and Python objects (for example, between R and\n Pandas data frames, or between R matrices and NumPy arrays).\n- Flexible binding to different versions of Python including virtual\n environments and Conda environments.\n\n[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python\nsession within your R session, enabling seamless, high-performance\ninteroperability. If you are an R developer that uses Python for some of\nyour work or a member of data science team that uses both languages,\n[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically\nstreamline your workflow.\n\n## Bring GUDHI into R\n\n- First, you'll want to install and load the\n [**reticulate**](https://rstudio.github.io/reticulate/) package into\n your session:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n # install.packages(\"reticulate\")\n library(reticulate)\n ```\n :::\n\n\n- Next, the fastest way to get you set up to use Python from R with\n [**reticulate**](https://rstudio.github.io/reticulate/) is to use\n the `install_miniconda()` utility function which is included in the\n [**reticulate**](https://rstudio.github.io/reticulate/) package. In\n details, use something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n install_miniconda()\n ```\n :::\n\n\n- Next, it is recommended that you create a virtual conda environment\n in which you'll install all required Python packages. This can be\n achieved using the `reticulate::conda_create()` function as follows:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n version <- \"3.9.6\"\n conda_create(\"r-reticulate\", python_version = version)\n ```\n :::\n\n\n- Next, you can seamlessly install all the Python packages you need by\n calling the `conda_install()` function. For instance, to use Gudhi,\n you would do something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n conda_install(\"scikit-learn\", envname = \"r-reticulate\")\n conda_install(\"gudhi\", envname = \"r-reticulate\")\n ```\n :::\n\n\nThis is a setup that you ought to do only once (unless you want to\nchange your Python version for some reason).\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\n#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\n#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\n#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\n#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\n#> numpy_version: 1.22.4\n#> \n#> NOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n", - "supporting": [], + "markdown": "---\nformat: gfm\ntoc: false\n---\n\n\n\n\n\n\n\n\n# Setup instructions for using GUDHI from R\n\n\n\n\n\n## The [**reticulate**](https://rstudio.github.io/reticulate/) package\n\nThe [**reticulate**](https://rstudio.github.io/reticulate/) package\nprovides a comprehensive set of tools for interoperability between\nPython and R. The package includes facilities for:\n\n- Calling Python from R in a variety of ways including R Markdown,\n sourcing Python scripts, importing Python modules, and using Python\n interactively within an R session.\n- Translation between R and Python objects (for example, between R and\n Pandas data frames, or between R matrices and NumPy arrays).\n- Flexible binding to different versions of Python including virtual\n environments and Conda environments.\n\n[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python\nsession within your R session, enabling seamless, high-performance\ninteroperability. If you are an R developer that uses Python for some of\nyour work or a member of data science team that uses both languages,\n[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically\nstreamline your workflow.\n\n## Bring GUDHI into R\n\n- First, you'll want to install and load the\n [**reticulate**](https://rstudio.github.io/reticulate/) package into\n your session:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n # install.packages(\"reticulate\")\n library(reticulate)\n ```\n :::\n\n\n- Next, the fastest way to get you set up to use Python from R with\n [**reticulate**](https://rstudio.github.io/reticulate/) is to use\n the `install_miniconda()` utility function which is included in the\n [**reticulate**](https://rstudio.github.io/reticulate/) package. In\n details, use something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n install_miniconda()\n ```\n :::\n\n\n- Next, it is recommended that you create a virtual conda environment\n in which you'll install all required Python packages. This can be\n achieved using the `reticulate::conda_create()` function as follows:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n version <- \"3.9.6\"\n conda_create(\"r-reticulate\", python_version = version)\n ```\n :::\n\n\n- Next, you can seamlessly install all the Python packages you need by\n calling the `conda_install()` function. For instance, to use Gudhi,\n you would do something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n conda_install(\"scikit-learn\", envname = \"r-reticulate\")\n conda_install(\"gudhi\", envname = \"r-reticulate\")\n ```\n :::\n\n\nThis is a setup that you ought to do only once (unless you want to\nchange your Python version for some reason).\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\n#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\n#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\n#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\n#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\n#> numpy_version: 1.22.4\n#> \n#> NOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n\n\n# Organisation\n\n## File hierarchy\n\nThis folder contains a number of [Quarto](https://quarto.org) documents\ncontributed with the purpose of showing how\n[`R`](https://www.r-project.org) can be used to enhance statistical\nanalysis of topological data produced via\n[Gudhi](https://gudhi.inria.fr). Each tutorial is hosted in its own\nfolder which is organised as follows:\n\n- The Quarto document with extension `.qmd` which is the main file\n that a developer is supposed to contribute. In this document, there\n is a YAML header in which the contributor can specify various output\n formats by adding\n\n ``` yaml\n #| eval: false\n format:\n html: default\n gfm: default\n pdf: default\n ```\n\n The complete list of possible output formats:\n .\n\n- A Github-flavored markdown (GMF) document with extension `.md` which\n enables nice visualization of the notebook directly in the Github\n web interface.\n\n- A folder containing useful libs and resources to display the GMF\n document.\n\n## Contributing\n\nIf you want to contribute, you should\n\n1. Fork the `TDA-tutorials` repo\n2. Open the RStudio project in\n `TDA-tutorials/r-interface/r-interface.Rproj`.\n3. When opened in RStudio, the working directory should now be\n `TDA-tutorials/r-interface/` in which you can now create a folder\n with evocative name to put the Quarto document describing the\n analysis you want to share.\n - Note: you can use the same YAML header as in\n `reticulate/reticulate.qmd` as a number of presets for rendering\n are made into the `_quarto.yml` file.\n", + "supporting": [ + "README_files" + ], "filters": [ "rmarkdown/pagebreak.lua" ],

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Setup instructions for using + GUDHI from R + - 1.1 1.1 The + reticulate package + - 1.2 1.2 Bring GUDHI into R -# Setup instructions for using GUDHI from R +# 1. Setup instructions for using GUDHI from R -## The [**reticulate**](https://rstudio.github.io/reticulate/) package +## 1.1 The [**reticulate**](https://rstudio.github.io/reticulate/) package The [**reticulate**](https://rstudio.github.io/reticulate/) package provides a comprehensive set of tools for interoperability between @@ -27,10 +41,10 @@ your work or a member of data science team that uses both languages, [**reticulate**](https://rstudio.github.io/reticulate/) can dramatically streamline your workflow. -## Bring GUDHI into R +## 1.2 Bring GUDHI into R - First, you’ll want to install and load the - [**reticulate**](https://rstudio.github.io/reticulate/)package into + [**reticulate**](https://rstudio.github.io/reticulate/) package into your session: ``` r @@ -45,8 +59,15 @@ streamline your workflow. details, use something like: ``` r - version <- "3.9.6" install_miniconda() + ``` + +- Next, it is recommended that you create a virtual conda environment + in which you’ll install all required Python packages. This can be + achieved using the `reticulate::conda_create()` function as follows: + + ``` r + version <- "3.9.6" conda_create("r-reticulate", python_version = version) ``` diff --git a/r-interface/README.qmd b/r-interface/README.qmd index 53d1859..343d853 100644 --- a/r-interface/README.qmd +++ b/r-interface/README.qmd @@ -43,7 +43,7 @@ streamline your workflow. ## Bring GUDHI into R - First, you'll want to install and load the - [**reticulate**](https://rstudio.github.io/reticulate/)package into + [**reticulate**](https://rstudio.github.io/reticulate/) package into your session: ```{r} @@ -59,8 +59,16 @@ streamline your workflow. ```{r} #| eval: false - version <- "3.9.6" install_miniconda() + ``` + +- Next, it is recommended that you create a virtual conda environment + in which you'll install all required Python packages. This can be + achieved using the `reticulate::conda_create()` function as follows: + + ```{r} + #| eval: false + version <- "3.9.6" conda_create("r-reticulate", python_version = version) ``` diff --git a/r-interface/_freeze/README/execute-results/md.json b/r-interface/_freeze/README/execute-results/md.json new file mode 100644 index 0000000..f0db3cb --- /dev/null +++ b/r-interface/_freeze/README/execute-results/md.json @@ -0,0 +1,14 @@ +{ + "hash": "941a1dac304470fad84c027d539dae0d", + "result": { + "markdown": "---\nformat: gfm\n---\n\n\n\n\n\n\n\n\n# Setup instructions for using GUDHI from R\n\n\n\n\n\n## The [**reticulate**](https://rstudio.github.io/reticulate/) package\n\nThe [**reticulate**](https://rstudio.github.io/reticulate/) package\nprovides a comprehensive set of tools for interoperability between\nPython and R. The package includes facilities for:\n\n- Calling Python from R in a variety of ways including R Markdown,\n sourcing Python scripts, importing Python modules, and using Python\n interactively within an R session.\n- Translation between R and Python objects (for example, between R and\n Pandas data frames, or between R matrices and NumPy arrays).\n- Flexible binding to different versions of Python including virtual\n environments and Conda environments.\n\n[**reticulate**](https://rstudio.github.io/reticulate/) embeds a Python\nsession within your R session, enabling seamless, high-performance\ninteroperability. If you are an R developer that uses Python for some of\nyour work or a member of data science team that uses both languages,\n[**reticulate**](https://rstudio.github.io/reticulate/) can dramatically\nstreamline your workflow.\n\n## Bring GUDHI into R\n\n- First, you'll want to install and load the\n [**reticulate**](https://rstudio.github.io/reticulate/) package into\n your session:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n # install.packages(\"reticulate\")\n library(reticulate)\n ```\n :::\n\n\n- Next, the fastest way to get you set up to use Python from R with\n [**reticulate**](https://rstudio.github.io/reticulate/) is to use\n the `install_miniconda()` utility function which is included in the\n [**reticulate**](https://rstudio.github.io/reticulate/) package. In\n details, use something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n install_miniconda()\n ```\n :::\n\n\n- Next, it is recommended that you create a virtual conda environment\n in which you'll install all required Python packages. This can be\n achieved using the `reticulate::conda_create()` function as follows:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n version <- \"3.9.6\"\n conda_create(\"r-reticulate\", python_version = version)\n ```\n :::\n\n\n- Next, you can seamlessly install all the Python packages you need by\n calling the `conda_install()` function. For instance, to use Gudhi,\n you would do something like:\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n conda_install(\"scikit-learn\", envname = \"r-reticulate\")\n conda_install(\"gudhi\", envname = \"r-reticulate\")\n ```\n :::\n\n\nThis is a setup that you ought to do only once (unless you want to\nchange your Python version for some reason).\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n#> python: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\n#> libpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\n#> pythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\n#> version: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\n#> numpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\n#> numpy_version: 1.22.4\n#> \n#> NOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/r-interface/_freeze/reticulate/reticulate/execute-results/html.json b/r-interface/_freeze/reticulate/reticulate/execute-results/html.json new file mode 100644 index 0000000..344cb0e --- /dev/null +++ b/r-interface/_freeze/reticulate/reticulate/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "2a83547c2b2bbde7b49edc0ab0d00347", + "result": { + "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [tidyverse](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\nIf you want to check your Python configuration, you can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in reticulate: see\n.\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: {.cell-output-display}\n![](reticulate_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::\n", + "supporting": [ + "reticulate_files/figure-html" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/r-interface/_freeze/reticulate/reticulate/execute-results/md.json b/r-interface/_freeze/reticulate/reticulate/execute-results/md.json new file mode 100644 index 0000000..6600a56 --- /dev/null +++ b/r-interface/_freeze/reticulate/reticulate/execute-results/md.json @@ -0,0 +1,16 @@ +{ + "hash": "2a83547c2b2bbde7b49edc0ab0d00347", + "result": { + "markdown": "---\ntitle: \"Using up-to-date Gudhi library in R\"\nauthor: \"Aymeric Stamm\"\ndate: 2022-06-13\n---\n\n\n\n\n\n## Initial setup\n\nLet us start by loading the\n[**reticulate**](https://rstudio.github.io/reticulate/) package into the\nenvironment, along with the [tidyverse](https://www.tidyverse.org),\nwhich is a suite of useful packages for data science:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(reticulate)\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n✔ ggplot2 3.3.6 ✔ purrr 0.3.4\n✔ tibble 3.1.7 ✔ dplyr 1.0.9\n✔ tidyr 1.2.0 ✔ stringr 1.4.0\n✔ readr 2.1.2 ✔ forcats 0.5.1\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\n```\n:::\n:::\n\n\n\nLet us inform the R session that we want to use the virtual environment\n`r-reticulate` where we installed all the required Python packages (see\n`README` at the root of the project). This is done with:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nuse_condaenv(\"r-reticulate\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The request to `use_python(\"/Users/stamm-a/Library/r-miniconda/envs/\nr-reticulate/bin/python\")` will be ignored because the environment variable\nRETICULATE_PYTHON is set to \"/Users/stamm-a/Library/r-miniconda/envs/r-\nreticulate/bin/python3.9\"\n```\n:::\n:::\n\n\n\nIf you want to check your Python configuration, you can do:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npy_config()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\npython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/bin/python3.9\nlibpython: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/libpython3.9.dylib\npythonhome: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate:/Users/stamm-a/Library/r-miniconda/envs/r-reticulate\nversion: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:36:15) [Clang 11.1.0 ]\nnumpy: /Users/stamm-a/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/numpy\nnumpy_version: 1.22.4\n\nNOTE: Python version was forced by RETICULATE_PYTHON\n```\n:::\n:::\n\n\n\nYou can now create chunks of Python code that will be correctly\ninterpreted when knitting your document into an HTML file. Let us make a\nfirst Python chunk of code to import **numpy** and **gudhi**:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nimport numpy as np\nimport gudhi as gd\nimport gudhi.representations\n```\n:::\n\n\n\n## Full example\n\n### Data model\n\nFirst, we will generate persistence diagrams with orbits of dynamical\nsystems. This data set is very common in TDA. We use the following\nsystem, which depends on a parameter $r > 0$:\n\n\n\n$$\n\\begin{cases}\nx_{n+1} = x_n + r y_n (1 - y_n) \\mbox{ mod } 1 \\\\\ny_{n+1} = y_n + r x_{n+1} (1 - x_{n+1}) \\mbox{ mod } 1\n\\end{cases}\n$$\n\n\n\nLet's first see what the point cloud looks like for a given choice of\n$r$.\n\nWe first define a **Python** function to compute a single point cloud\naccording to the above orbit model and then use the\n[**Gudhi**](https://gudhi.inria.fr) library to compute the persistence\ndiagrams from its alpha complex and turn it into its silhouette\nrepresentation. This can be achieved by a function that looks like:\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndef orbit_silhouette(num_pts = 1000, resolution = 1000, r = 2):\n X = np.empty([num_pts, 2])\n x, y = np.random.uniform(), np.random.uniform()\n for i in range(num_pts):\n X[i, :] = [x, y]\n x = (X[i, 0] + r * X[i, 1] * (1 - X[i, 1])) % 1.\n y = (X[i, 1] + r * x * (1 - x)) % 1.\n acX = gd.AlphaComplex(points = X).create_simplex_tree()\n dgmX = acX.persistence()\n SH = gd.representations.Silhouette(\n resolution = resolution, \n weight = lambda x: np.power(x[1] - x[0], 1)\n )\n sh = SH.fit_transform([acX.persistence_intervals_in_dimension(1)])\n return([np.linspace(SH.sample_range[0], SH.sample_range[1], resolution), sh[0]])\n```\n:::\n\n\n\nThis function returns a Python list of size 2. The first element is the\nabscissa grid and the second element is the silhouette value on the\ngrid.\n\nPython lists are automatically converted into atomic vectors in R.\nAutomatic conversions of data types between R and Python languages are\nimplemented in reticulate: see\n.\n\n### Sample generation\n\nNow we can go back in **R** to use this **Python** function and generate\ntwo samples of silhouette profiles. To access any Python object\n`my_py_object` created in a previous chunk from an R chunk, use\n`py$my_py_object`. Following this logic, the Python function\n`orbit_silhouette()` created in the above Python chunk is accessible\nfrom any subsequent R chunk as `py$orbit_silhouette()`:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn1 <- 10\nn2 <- 10\nx1 <- replicate(n1, py$orbit_silhouette(r = 1.9), simplify = FALSE)\nx2 <- replicate(n2, py$orbit_silhouette(r = 2.1), simplify = FALSE)\n\nt1 <- x1 |> map(1) |> map(~ c(0, .x))\nx1 <- x1 |> map(2) |> map(~ c(0, .x))\nt2 <- x2 |> map(1) |> map(~ c(0, .x))\nx2 <- x2 |> map(2) |> map(~ c(0, .x))\n\ndf <- bind_rows(\n tibble(id = 1:n1, x = t1, y = x1) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"1.9\"), \n tibble(id = 1:n2, x = t2, y = x2) |> \n unnest(cols = c(x, y)) |> \n mutate(r = \"2.1\")\n) |> \n mutate(\n id = as_factor(id), \n r = as_factor(r)\n )\ndf |> \n ggplot(aes(x, y, color = r, group = interaction(id, r))) +\n geom_line() + \n scale_color_viridis_d()\n```\n\n::: 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