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+---
+author: "Eva Rifà"
+date: "01/02/2024"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteEngine{knitr::knitr}
+  %\VignetteIndexEntry{Data Storage and Retrieval}
+  %\usepackage[utf8]{inputenc}
+---
+
+Reticulate
+-----------------------------------------
+
+The reticulate package provides a comprehensive set of tools for interoperability between Python and R.
+
+Reticulate embeds a Python session within your R session. It calls Python from R being able to translate objects within both packages. All the information can be found in the package documentation: https://rstudio.github.io/reticulate/. 
+
+> NOTE: This script was used as an Ice-Breaker of the R User Meeting held at BSC on 4/5/2023.
+
+**How to use reticulate?**  
+There are a variety of ways to integrate Python code into R:  
+1. Importing Python modules
+2. Sourcing Python scripts
+3. Python REPL
+4. Python in R Markdown (not shown in this file)
+
+### 1. Importing Python modules
+The import() function can import any Python module and call it’s functions directly from R.  
+
+#### Example I
+```t
+# Import modules in a terminal
+module load matplotlib/3.2.2-foss-2015a-Python-3.7.3
+module load Python/3.7.3-foss-2015a
+module load R/4.1.2-foss-2015a-bare
+```
+
+```r
+library(reticulate)
+```
+```r
+matplotlib <- import("matplotlib")
+
+# Generate some data
+x <- c(1, 2, 3, 4, 5)
+y <- c(2, 4, 6, 8, 10)
+
+# Create a scatter plot using matplotlib
+plt <- matplotlib$pyplot
+plt$scatter(x, y)
+plt$xlabel("X")
+plt$ylabel("Y")
+plt$title("Scatter Plot")
+plt$savefig(fname = "plot1.png")
+```
+#### Example II
+
+In the next example, we load the necessary modules in a terminal:  
+```t
+# Import modules in a terminal
+module load xarray/0.20.0-foss-2015a-Python-3.7.3
+# Other dependencies of xarray
+module load netcdf4-python/1.5.3-foss-2015a-Python-3.7.3
+module load numpy/1.20.3-foss-2015a-Python-3.7.3
+```
+```r
+library(reticulate)
+# Example 2: Read Netcdf with xarray 
+xr <- import("xarray")
+
+obs_path <- "/esarchive/recon/ecmwf/erainterim/monthly_mean/tas_f6h/"
+obs_file <- paste0(obs_path, "tas_201811.nc")
+
+obs <- xr$open_dataset(obs_file)
+
+obs
+# <xarray.Dataset>
+# Dimensions:  (time: 1, lat: 256, lon: 512)
+```
+
+### 2. Sourcing Python scripts
+The source_python() function can source a Python script the same way as source() an R script. Here, we first need to save a python script:
+
+```py
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Data for plotting
+t = np.arange(0.0, 2.0, 0.01)
+s = 1 + np.sin(2 * np.pi * t)
+fig, ax = plt.subplots()
+ax.plot(t, s)
+ax.set(xlabel = 'time (s)', ylabel = 'x (m)',
+       title = 'Created with R')
+ax.grid()
+fig.savefig("plot2.png")
+```
+Then, we need to save the script. Let's call it ('test_reticulate_source.py').  
+
+Then, we source it from an R session:
+```r
+library(reticulate)
+
+source_python("test_reticulate_source.py")
+```
+
+### 3. Python REPL
+The repl_python() function creates an interactive Python console within R. Objects you create within Python are available to your R session (and vice-versa).  
+
+Inside an R session:  
+```r
+library(reticulate)
+repl_python()
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Data for plotting
+t = np.arange(0.0, 2.0, 0.01)
+s = 1 + np.sin(2 * np.pi * t)
+fig, ax = plt.subplots()
+ax.plot(t, s)
+ax.set(xlabel = 'time (s)', ylabel = 'x (m)',
+       title = 'Created with R')
+ax.grid()
+fig.savefig("plot2.png")
+```
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