1 Introduction to GIA models

A central aspect of my postdoc project pertains to investigating the relationship between archaeological sites and past sea-level change. I’m therefore in the process of getting a better grasp of the mechanisms that drive sea-level change and the ways in which this can be drawn on to reconstruct past sea-levels. My work with sea-level change has so far been based on empirical data and the shoreline displacement curves that have been derived from these (Roalkvam 2023, Romundset et al. 2026). These map changes in the relative sea-level over time. That is, they concern changes to the elevation of the sea relative to land. However, the relative sea-level is (predominantly) the result of two combined effects. The first is eustatic changes to the global mean sea-level resulting from exchange of water between ice sheets and ocean. The other is isostatic changes to the surface elevation of the Earth following from its response to the redistribution of weight caused by melting and forming ice sheets. An important endeavour within the field of geophysics and geodynamics is to combine physics-based models with data on ice history and Earth rheology in so-called GIA models to predict how the sea-level and surface elevation respond to changing ice loads over time, thereby making it possible to isolate the distinct effects of eustatic and isostatic adjustments (see e.g. Peltier 1998 for a fairly accessible account).

To be able to unpick the relationship between sea-level change as induced by forming or melting ice sheets that impact the global mean sea-level, and the adjustments to the surface of the Earth that follow from its viscoelsatic response to the redistribution of weight, these models are typically global in scope, although they might be focused towards specific regions. Despite being global in scope they can nonetheless be very valuable in conjunction with empirically based local reconstructions of sea-level change. For my purposes, this especially pertains to how empirically derived displacement curves are often based on fragmented data associated with considerable temporal as well as spatial uncertainties, meaning that the GIA models can provide an important frame of reference against which to evaluate or condition purely empirical reconstructions. As GIA models provide a model for glaciation history that is physically coherent given the global history of glacaiation and deglacation, any local deviation from these expected patterns can potentially offer valuable information on local conditions. Ultimately, GIA models can only be approximations of a complex real world system, but if properly validated against geomorphological records of past conditions they hold the potential to provide plausible estimates for future geodynamical change.

2 GIA-models and ICE-7G_NA (VM7)

The precise model output that I will work with here comes from ICE-7G_NA (VM7). This, along with the result from other variations of the ICE-xG models is available from the website of W.R. Peltier: https://www.atmosp.physics.utoronto.ca/~peltier/data.php.

To briefly present the core components of this model, the “ICE-7G” part of the name of the model refers to a specific ice thickness history. The extension “NA” refers to the fact that this model has been fine-tuned against geophysical observations in North America and then been evaluated in terms of its global exportability (see Roy and Peltier 2018). The parenthesis after the name of the ice history model refers to this data having been derived when employing the radial viscosity profile named VM7.

I will not attempt to lay out further details of how these models are calculated here, but in addition to the references given above, some important literature are the first formulations of the so-called sea-level equation (Farrell and Clark 1976; Clark et al. 1978) which forms the numerical foundation for GIA models, and a series of important refinements and further developments of the sea-level equation underlie current GIA models (see e.g. Peltier and Tushingham 1989; Mitrovica and Milne 2003; and Whitehouse 2018 for a historical account). In addition to these references, papers related to the previous versions of the iCE-xG models are also useful (e.g. Peltier et al. 2015, 2018; Purcell et al. 2016), in addition to those associated with other GIA models (e.g. Lambeck et al. 2014, 2017). Finally, while this post only deals with using existing results from a GIA model to plot RSL-change, open software is available to implement these models, allowing for the exploration of different input variables and parameter settings, and to adjust temporal and spatial resolutions (Spada and Melini 2019).

Returning to the GIA model we are dealing with here, the files representing the output from ICE-7G_NA (VM7) number a total of 48 NetCDF-files. They are named following the structure “I7G_NA.VM7_1deg.26.nc”, “I7G_NA.VM7_1deg.25.nc”, etc. You will notice that only the number just before “.nc” is changing between these. In these file names “I7G_NA.VM7” denotes the ice history and radial viscosity used in the model that produced the files. The part that reads “1deg” signifies that the data comes at a spatial resolution of 1x1 degrees. The number after this denotes the time-slice that a specific file pertains to, given in thousand years (ka) BP. In the examples above 26 and 25 ka BP, respectively. Finally, “nc” is the file extension for the NetCDF (Network Common Data Form) format.

3 NetCDF and xarray in Python

To precisely recreate the steps and output to follow, you will need to download the ICE-7G_NA files from Peltier’s website linked to above. In total, these have a size of about 38 MB. However, the code will also run with a smaller subset of the files if you only want to explore some of the functionality of the code. As stated in the beginning, I will work through this in Python in some detail, but a condensed version to achieve the same in R can be found towards the end. To read in NetCDF data in Python we can use the xarray library (Hoyer and Hamman 2017). We’ll start by reading in and exploring one of these files. I’ve chosen the one pertaining to 12 ka BP for this:

import os
import xarray as xr

# Replace file path to where you have stored your NetCDF file(s) to load the data
ds = xr.open_dataset("../../../ICE-7G_NA(VM7)/I7G_NA.VM7_1deg.12.nc")

# Print the structure of the xarray dataset
print(ds)

<xarray.Dataset> Size: 782kB
Dimensions:    (lat: 180, lon: 360)
Coordinates:
  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
Data variables:
    stgit      (lat, lon) float32 259kB ...
    Topo       (lat, lon) float32 259kB ...
    Topo_Diff  (lat, lon) float32 259kB ...
Attributes:
    Model:            ICE=ICE-7G_NA, Viscosity=VM7
    Date:             December 17, 2019
    Author:           W.R. Peltier, Dept of Physics, Univ of Toronto,Canada
    Acknowledgement:  Please cite following papers [PAPER1 and PAPER2 investi...
    PAPER1:           Roy, Keven, and W. R. Peltier - Glacial isostatic adjus...
    PAPER2:           Roy, Keven, and W. R. Peltier - Space-geodetic and wate...
    PAPER3:           Roy, Keven, and W. R. Peltier - Relative sea level in t...

This tells us that the data has two coordinates given in latitude (lat) and longitude (lon), and three data variables, the values of which are mapped to these coordinates. At the bottom we also get some metadata associated with the file. The first of the three data variables is ice thickness (stgit). That is, the values for stgit for a given coordinate pair gives the thickness of ice cover at that location at 12 ka BP. The second variable gives the topography (Topo), which is the surface elevation of land, ocean bottom (bathymetry) and ice cover, with 0 m representing the present-day mean sea-level. The final variable is the difference between the topography at this time slice and the present topography (Topo_Diff). To find the relative sea-level for a given location we will use the Topo_Diff variable.

Where ice is present the difference to the present surface elevation will be defined by the thickness of the ice and not the sea. So for locations covered by ice the RSL is undefined. In other areas where Topo_Diff is positive this indicates that the surface was higher than what it was at 12 ka BP, meaning that the relative sea-level was lower. Conversely, negative values for Topo_Diff indicates that the surface was lower in the past, and consequently that the RSL was higher. This therefore means that by flipping the sign of Topo_Diff we effectively find the relative sea-level for any given location, provided it was not covered by ice at that point in time. (Note that the output of other GIA models might look different from this, and sometimes already hold a variable for RSL).

# Change the sign of Topo_Diff and exclude areas covered by ice
rsl = -ds["Topo_Diff"].where(ds["stgit"] == 0)

4 Global map of ice cover and RSL at 12 ka BP

As both a sanity check and to see what the global pattern for ice cover and relative sea-level looks like according to ICE-7G_NA at 12 ka BP we can plot this on a map:

# Import libraries for plotting and handling coordinate reference systems
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Set up plot
fig = plt.figure(figsize = (13, 6))

# Provide a coordinate reference system that is often used with global data
ax = plt.axes(projection = ccrs.PlateCarree())

# Ice cover and relative sea level
pcm = plt.pcolormesh(ds.lon, ds.lat, rsl,
    shading = "auto", cmap = "RdBu_r",
    # Center the colour scale and limit its range to +/- 100 m
    norm = TwoSlopeNorm(vmin = -100, vcenter = 0, vmax = 100))

# Plot the outline of the ice covers
ax.contour(ds.lon, ds.lat, ds.stgit, levels = [0],
    colors = "black", linewidths = 0.7)

# Plot labels and title
plt.colorbar(pcm, label = "Relative sea-level (m)")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title("ICE-7G_NA (VM7): Global ice cover and relative sea-level at 12 ka BP");

This looks sensible. Areas closer to glaciated areas tend to have a higher RSL relative to the present, and areas further afield have a smaller or negative RSL. We can now use all of the NetCDF files to find the GIA predicted trajectory for RSL change over time at some specific locations.

5 RSL-change for specific locations

As my postdoc project deals with the region of Kattegat and Skagerrak, I will choose locations from this area, but you can replace the coordinates in the code below to whatever location you are interested in.

First we will need to read in all of the NetCDF files to be able to combine the RSLs for each time-slice into a time-series. This could be done by a for-loop that reads in each individual file and extracts the time and RSL at the location of interest. For a bit more stability and functionality, and to make it easier to extract RSL-change for other locations later on, we will instead read in the NetCDF files and combine these in a xarray stack and assign each element a time coordinate to help us keep track of them.

# Import some additional libraries to facilitate reading in multiple files and
# manipulating these
import re
import os
import numpy as np

# As above, replace this file path to where you have stored your NetCDF files
path = "../../../ICE-7G_NA(VM7)"

# Get a list all the files in the directory
files = os.listdir(path)

# Exlude anything but the NetCDF files 
# (just in case some temporary files or similar are in there)
files_nc = [i for i in files if i.endswith('.nc')]

# Define a regex function to find the time assigned to each file, which
# falls between the string elements "1deg." and ".nc"
def get_agebp(file):
    return(float(re.search(r'1deg.(.*?).nc', file).group(1)))

# Get age ka BP for each file, sort these from oldest to youngest and store them
# so that they can be assigned to the data when it has been loaded
times_ka = sorted(np.array([get_agebp(f) for f in files_nc]), reverse = True)

# Use the function again to get a list of file names sorted from oldest to youngest
files_sorted = sorted(files_nc, key = get_agebp, reverse = True)

# Append the full path to these chronologically sorted file names
files_full = [os.path.join(path, name) for name in files_sorted]

# Read in files in a single xarray stack with a time dimension
ds = xr.open_mfdataset(
    files_full,
    combine = "nested",
    concat_dim = "time") 

# The time dimension is currently just an index going from 0-48, so here we'll assign 
# the actual ages ka BP.
ds = ds.assign_coords(time = times_ka)
ds["time"].attrs["units"] = "ka BP"
ds["time"].attrs["long_name"] = "Model age"


# Print the structure of the data
print(ds)

<xarray.Dataset> Size: 37MB
Dimensions:    (time: 48, lat: 180, lon: 360)
Coordinates:
  * time       (time) float64 384B 26.0 25.0 24.0 23.0 22.0 ... 1.5 1.0 0.5 0.0
  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
Data variables:
    stgit      (time, lat, lon) float32 12MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    Topo       (time, lat, lon) float32 12MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    Topo_Diff  (time, lat, lon) float32 12MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes:
    Model:            ICE=ICE-7G_NA, Viscosity=VM7
    Date:             December 17, 2019
    Author:           W.R. Peltier, Dept of Physics, Univ of Toronto,Canada
    Acknowledgement:  Please cite following papers [PAPER1 and PAPER2 investi...
    PAPER1:           Roy, Keven, and W. R. Peltier - Glacial isostatic adjus...
    PAPER2:           Roy, Keven, and W. R. Peltier - Space-geodetic and wate...
    PAPER3:           Roy, Keven, and W. R. Peltier - Relative sea level in t...

You can see that time is now a third dimension and that this has 48 unique values, representing the 48 NetCDF files and their corresponding time-slice ages.

# Coordinates for Oslo, Göteborg and Aarhus
osl_lat = 59.90
osl_lon = 10.75 % 360
gbg_lat = 57.70
gbg_lon = 11.97 % 360
aar_lat = 56.14
aar_lon = 10.20 % 360

# Time-slices to plot
ds_12ka = ds.sel(time = 12)
ds_lgm  = ds.sel(time = 21) # LGM at approx. 21 ka BP

# RSL where there is not ice at 12 ka BP
rsl_12ka = -ds_12ka["Topo_Diff"].where(ds_12ka["stgit"] == 0)
# Mask layer to colour ice white
ice_12ka = ds_12ka["stgit"].where(ds_12ka["stgit"] > 0)

# Projection for Fennoscandia
proj = ccrs.LambertConformal(central_longitude = 15, central_latitude = 62,
              standard_parallels = (55, 70))

# Set up plot with projection
fig = plt.figure(figsize = (9, 7))
ax = plt.axes(projection = proj)

# Set map extent to Fennoscandia
ax.set_extent([2, 42, 50, 75], crs = ccrs.PlateCarree())

# RSL layer
pcm = ax.pcolormesh(ds.lon, ds.lat, rsl_12ka, transform = ccrs.PlateCarree(),
    cmap = "RdBu_r", norm = TwoSlopeNorm(vmin = -100, vcenter = 0, vmax = 100),
    shading = "auto", zorder = 1)

# Ice mask
ax.pcolormesh(ds.lon, ds.lat, ice_12ka, transform = ccrs.PlateCarree(),
    cmap = "gray", shading = "auto", vmin = 0, vmax = 1, zorder = 3)

# LGM ice cover outline
ax.contour(ds.lon, ds.lat, ds_lgm["stgit"], levels = [0], colors="red",
    linewidths = 1.5, transform = ccrs.PlateCarree(), zorder = 4)

# Add points for Oslo, Göteborg and Aarhus
ax.plot(osl_lon, osl_lat, marker="o", markersize = 6, color = "white",
markeredgecolor = "black", transform = ccrs.PlateCarree(), zorder = 5)
ax.plot(gbg_lon, gbg_lat, marker="o", markersize = 6, color = "white",
markeredgecolor = "black", transform = ccrs.PlateCarree(), zorder = 6)
ax.plot(aar_lon, aar_lat, marker="o", markersize = 6, color = "white",
markeredgecolor = "black", transform = ccrs.PlateCarree(), zorder = 7)

# Add coastline for areas not covered by ice
ax.coastlines(resolution="50m", linewidth = 0.8, zorder = 2)

# RSL with colour gradient legend
plt.colorbar(pcm, ax = ax, shrink = 0.7, pad = 0.05,
            label = "Relative sea-level (m)")

# Title
ax.set_title("ICE-7G_NA (VM7): Fennoscandia at 12 ka BP");

# Retrieve the data for the point representing Göteborg, using nearest neighbour
# to interpolate the values to this point.
point_gbg = ds.sel(lat = gbg_lat, lon = gbg_lon, method = "nearest")
    
# Retrieve the RSL values by flipping the Topo_Diff sign and exclude periods when
# Göteborg was covered by ice
rsl_gbg = -point_gbg["Topo_Diff"].where(point_gbg["stgit"] == 0)

# Transform this into a pandas series amenable for plotting
rsl_gbg_ts = rsl_gbg.to_series()

# Repeat for Oslo
point_osl = ds.sel(lat = osl_lat, lon = osl_lon, method = "nearest")
rsl_osl = -point_osl["Topo_Diff"].where(point_osl["stgit"] == 0)
rsl_osl_ts = rsl_osl.to_series()

# Repeat for Aarhus
point_aar = ds.sel(lat = aar_lat, lon = aar_lon, method = "nearest")
rsl_aar = -point_aar["Topo_Diff"].where(point_aar["stgit"] == 0)
rsl_aar_ts = rsl_aar.to_series()

# Call to plot
plt.figure(figsize=(8,5))
plt.plot(point_osl["time"], rsl_osl_ts, color = '#27b376', label = 'Oslo')
plt.plot(point_gbg["time"], rsl_gbg_ts, color = '#bf212f', label = 'Göteborg')
plt.plot(point_osl["time"], rsl_aar_ts, color = '#264b96', label = 'Aarhus')
plt.legend()
plt.xlabel("ka BP")
plt.ylabel("Relative sea-level (m)")
plt.title("Post LGM RSL-change based on ICE-7G_NA (VM7)")
plt.gca().invert_xaxis() 
plt.xlim(20, 0);

6 Rough outline in R

# Uncomment the lines below to install the packages used in the script
# install.packages("terra")
# install.packages("stringr")
# install.packages("here")
library(terra) # For handling NetCDF files and spatial data
library(stringr) # For wokring with strings when loading files
library(here) # For file paths relative to the directory where the R script is located

# Replace file path to where you have stored the NetCDF files
files <- list.files(path = here("../ICE-7G_NA(VM7)"),
                     pattern = "\\.nc$", full.names = TRUE)

# Get the age of the time-slice of each file
ages_kBP <- as.numeric(str_extract(files, "(?<=1deg\\.)[0-9]+(\\.[0-9]+)?"))

# Order the list of file names based on this
files_sorted <- files[order(ages_kBP, decreasing = TRUE)]

# And get the sorted numbers to assign to a time dimension of the rasters in the stack.
# Using the full age BP instead of ka BP, due to how terra handles the time dimension
ages_kBP <- sort(ages_kBP, decreasing = TRUE) * 1000

# Read in the rasters as a stack and rotate() to change longitude convention from
# going from 0 to 260 to -180 to 180
topodiff_stack <- rotate(rast(files_sorted, subds = "Topo_Diff"))
ice_stack <- rotate(rast(files_sorted, subds = "stgit"))

# Assign ages to the stacks
time(topodiff_stack) <- ages_kBP
time(ice_stack) <- ages_kBP

# Retrieve RSL and ice cover at 12 ka BP for plot
i_12ka <- which.min(abs(time(topodiff_stack) - 12000))

# RSL at 12 ka BP
rsl_12ka <- -topodiff_stack[[i_12ka]]

# Ice cover at 12 ka BP, set to NA where ice is not present
ice_12ka <- ice_stack[[i_12ka]]
ice_12ka[ice_12ka <= 0] <- NA

# Point vector features for Oslo, Göteborg and Aarhus
osl <- vect(data.frame(lon = 10.75, lat = 59.90),
            geom = c("lon", "lat"), crs = "EPSG:4326")
gbg <- vect(data.frame(lon = 11.97, lat = 57.70),
            geom = c("lon", "lat"), crs = "EPSG:4326")
aar <- vect(data.frame(lon = 10.20, lat = 56.14),
            geom = c("lon", "lat"), crs = "EPSG:4326")

# Simple map plot for a sanity check (not reproduced here)
plot(rsl_12ka,
     main = "ICE-7G_NA (VM7): RSL and ice cover at 12 ka BP")
plot(ice_12ka, col = "white", legend = FALSE, add = TRUE)
plot(osl, cex = 0.5, add = TRUE)
plot(gbg, cex = 0.5, add = TRUE)
plot(aar, cex = 0.5, add = TRUE)

# Flip sign of Topo_Diff and extract this for Oslo.
# Remove the ID associated with the values using [1, -1]
rsl_osl <- extract(-topodiff_stack, osl)[1, -1]
# Extract ice cover for Oslo
ice_osl <- extract(ice_stack, osl)[1, -1]
# Remove RSL for when Oslo was covered by ice, as this is undefined
rsl_osl <- as.numeric(ifelse(ice_osl > 0, NA, rsl_osl))

# Repeat for Göteborg
rsl_gbg <- extract(-topodiff_stack, gbg)[1, -1]
ice_gbg <- extract(ice_stack, gbg)[1, -1]
rsl_gbg <- as.numeric(ifelse(ice_gbg > 0, NA, rsl_gbg))

# Repeat for Aarhus
rsl_aar <- extract(-topodiff_stack, aar)[1, -1]
ice_aar <- extract(ice_stack, aar)[1, -1]
rsl_aar <- as.numeric(ifelse(ice_aar > 0, NA, rsl_aar))

# Create data frames for plotting
rsl_osl <- data.frame(BP = time(topodiff_stack), rsl = rsl_osl)
rsl_gbg <- data.frame(BP = time(topodiff_stack), rsl = rsl_gbg)
rsl_aar <- data.frame(BP = time(topodiff_stack), rsl = rsl_aar)

# Call to plot
plot(rsl_osl$BP, rsl_osl$rsl, type = "n", xlim = c(20000, 0), ylim = c(-20, 120),
     xlab = "Years BP", ylab = "Relative sea level (m)",
     main = "Post LGM RSL-change based on ICE-7G_NA (VM7)")
lines(rsl_osl$BP, rsl_osl$rsl, type = "l", col = "#27b376")
lines(rsl_gbg$BP, rsl_gbg$rsl, type = "l", col = "#bf212f")
lines(rsl_aar$BP, rsl_aar$rsl, type = "l", col = "#264b96")
legend("topright", legend = c("Oslo", "Göteborg", "Aarhus"),
       col = c("#27b376", "#bf212f", "#264b96"), lty = 1)

7 References

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Some techincal details

Some readers (including future me) might be interested in reading a bit more about the technical details and the motivation for how I chose to set up the site. On the extreme ends of the scale I debated whether I could build the whole site from scratch using HTML/CSS or whether to use a GUI-based platform such as Wordpress. As for Wordpress, I quickly decided against this. For one, I already have experience setting up and maintaining a website for my late mother’s artwork (https://jmhk.no) using Wordpress. It works well enough for the purposes of that simple site, but there are a lot of annoyances with the lack of control Wordpress provides, and I am contemplating migrating that site somewhere else as well. On the other hand, while attempting to build the site from scratch would have given complete control and undoubtedly have been a great learning experience, I also decided against trying this because the result would likely have been worse, it would have taken a lot more time, and given my limited experience with web development, a more user-friendly framework will likely provide more than enough learning opportunities.

For these reasons I went with the Quarto Blog framework. I’m already comfortable with the use of R and Quarto and so this was easy to set up. Furthermore, as Quarto allows you to write literate programming documents that can include text, images, code and rendered output such as figures (see https://github.com/quarto-dev/quarto-cli#quarto) it fits perfectly with my intended use, as I will be including all of these elements in my posts. I currently develop the site in the integrated development environment (IDE) Positron, which I’m experimenting with as a possible replacement for RStudio.

A static and open source website

I have my domain from host1, a Norwegian hosting company, where I have already paid for a one-year hosting plan as well. However, after first dipping my toes into web-development I have become aware of some very interesting initiatives for both developing, hosting and serving websites. One of these is homebrewserver.club which promotes setting up and running websites in light of social, political and economic needs, goals and principles. This involves using free and open source software and to host using low-power solutions from home, rather than from data centers. The hosting-the-website-myself part of this will remain an ambition for now, not least because I already paid for the hosting plan at host1, but the open and minimal footprint aspects as related to how the content of this site are served are with me from the start. Some related initiatives to homebrewserver.club is Sustainable Web Design, the Feminist Server Manifesto, as well as the concept of Minimal Computing that I was recently made aware of in a Mastodon thread initiated by Joe Roe.

As for the open aspect of the website, all of the code that goes into creating this site is fully and freely available here https://codeberg.org/isakro/iroalkvam and everything on the website is explicitly released under the license CC BY-NC 4.0. Another principle that homebreserver.club recommends is to use static websites, which this site also is. What this means is that all of the content on this is site is rendered from static files that are stored on the server-side of the site, meaning the content is not dynamically generated with new visitors or as a visitor interacts with the site. That requires more energy. Although I have included code, for example to generate the images that I will use for the thumbnails of my posts (see below), this code has been run once by me to generate the files that I need. These are then served as static images and the code is never run again.

Finally then, just to give an example of how Quarto can be used to display and show the results of code, I’ve included the code chunk below, written in the R programming language. This generates the image that is also used for the thumbnail for this post. I feel my site needs some light-weight graphics, and since I’m interested in exploring the world(s) of creative data visualisation and generative art, I used the R package aRtsy for this. aRtsy makes a series of algorithms for generative art readily available for novices to explore (see https://koenderks.github.io/aRtsy/).

# Load aRtsy (uncomment and run the install.packages() line if it's not installed)
# install.packages("aRtsy")
library(aRtsy)

# Fix the random seed to create the same figure on each run
set.seed(1)

# Create a Flow Field visualisation (after having played around with the
# parameters a bit)
flow <- canvas_flow(outline = "circle",
            background = "#F5F5F5",
            lines = 8000,
            lwd = 0.05,
            stepmax = 0.005,
            colors = colorPalette("dark2"))

# Save the resulting figure as a jpg so that it can be served as a static file and
# be used as the thumbnail for this post
saveCanvas(flow,
       filename = "flow.jpg",
       width = 5,
       height = 5)