Contents

Plot Global Map of Samples

Plot Global Map of Samples

Introduction

The publicly available Pf7 dataset for Plasmodium falciparum comprises 20,864 samples, collected by 82 partner studies from 33 countries in Africa, Asia, South America and Oceania between 1984 and 2018. In this notebook, we will generate Figure 1 (Panel A) from the Pf7 paper. This figure shows where the samples of the Pf7 dataset were collected on a global map, highlighting the 97 first-level administrative divisions from which samples were taken within countries. On this figure, points represent sampling locations, coloured according to the major sub-population to which the location is assigned. This notebook will demonstrate the code required to generate this figure panel. As one of the Python packages used to generate this panel is now depreciated, this notebook will also demonstrate an alternative method to generate this panel, and output this as both a .png and .pdf file.

This notebook should take around 2 minutes to run.

Setup

Install the MalariaGEN, basemap, and cartopy packages:

!pip install -q --no-warn-conflicts malariagen_data
!pip install -q --no-warn-conflicts basemap
!pip install -q --no-warn-conflicts cartopy

Load the required Python libraries:

import malariagen_data
import pandas as pd
import numpy as np
import collections
import sys
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.image import imread
from mpl_toolkits.basemap import Basemap
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from google.colab import drive

Data Access

Firstly, load the Pf7 metadata. We then check the ‘shape’ of the data to make sure it is what we were expecting. This is the full metadata for the Pf7 dataset, which includes 20864 samples, with 17 variables of associated metadata.

# Load Pf7 data
release_data = malariagen_data.Pf7()
sample_metadata = release_data.sample_metadata()

# View the 'shape' of the data - i.e. how many rows and columns does it have?
print(sample_metadata.shape)

(20864, 17)

Often when working with dataframes such as these, it is useful to check the first few rows of the dataset to make sure it is the correct data and in the format we are expecting. You can see each sample has an ID, information on when and where it was collected, and details on their related genetic data.

# View the top few rows of the metadata
sample_metadata.head()
Sample Study Country Admin level 1 Country latitude Country longitude Admin level 1 latitude Admin level 1 longitude Year ENA All samples same case Population % callable QC pass Exclusion reason Sample type Sample was in Pf6
0 FP0008-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081237 FP0008-C AF-W 82.16 True Analysis_set gDNA True
1 FP0009-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081238 FP0009-C AF-W 88.85 True Analysis_set gDNA True
2 FP0010-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889621 FP0010-CW AF-W 86.46 True Analysis_set sWGA False
3 FP0011-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889624 FP0011-CW AF-W 86.35 True Analysis_set sWGA False
4 FP0012-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889627 FP0012-CW AF-W 89.74 True Analysis_set sWGA False

Preparing the data for the figure

In the figure we would like to do three things:

  1. Show a world map

  2. Highlight the locations from which samples were taken. In our case, this will be as circular points.

  3. Colour each point by the sub-population to which the samples were assigned.

To do this, we will start by generating a dictionary object which we can use as a legend for the plot, with the full title for each population:

#You might have noticed the 'Population' variable in the above meta-data does not contain the full name of each population, only an abbreviation.
#Here, we generate a dictionary which contains the full name of each population. This will be useful later in creating a legend for the plot.
Population_legend = collections.OrderedDict()
Population_legend['SA']       = "South America"
Population_legend['AF-W']     = "Africa - West"
Population_legend['AF-C']     = "Africa - Central"
Population_legend['AF-NE']    = "Africa - Northeast"
Population_legend['AF-E']     = "Africa - East"
Population_legend['AS-S-E']   = "Asia - South - East"
Population_legend['AS-S-FE']  = "Asia - South - Far East"
Population_legend['AS-SE-W']  = "Asia - Southeast - West"
Population_legend['AS-SE-E']  = "Asia - Southeast - East"
Population_legend['OC-NG']    = "Oceania - New Guinea"

We would then like to assign each population a colour:

# We create a second dictionary object, where each population is given a hexadecimal colour e.g. #4daf4a
Population_colours = collections.OrderedDict()
Population_colours['SA']      = "#4daf4a"
Population_colours['AF-W']    = "#e31a1c"
Population_colours['AF-C']    = "#fd8d3c"
Population_colours['AF-NE']   = "#bb8129"
Population_colours['AF-E']    = "#fecc5c"
Population_colours['AS-S-E']  = "#dfc0eb"
Population_colours['AS-S-FE'] = "#984ea3"
Population_colours['AS-SE-W'] = "#9ecae1"
Population_colours['AS-SE-E'] = "#3182bd"
Population_colours['OC-NG']   = "#f781bf"

# Then we generate a new variable - 'population colour' onto the metadata, and impute the assigned colours using the dictionary
sample_metadata['Population_colour'] = sample_metadata['Population'].map(Population_colours)

# Note we now have an extra colomn
print(sample_metadata.shape)

# Lets check the above has worked by outputting the first few rows of the data, now with the new Population_colour variable
# you will see each population has now been assigned their corresponding colour
sample_metadata.head()

(20864, 18)
Sample Study Country Admin level 1 Country latitude Country longitude Admin level 1 latitude Admin level 1 longitude Year ENA All samples same case Population % callable QC pass Exclusion reason Sample type Sample was in Pf6 Population_colour
0 FP0008-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081237 FP0008-C AF-W 82.16 True Analysis_set gDNA True #e31a1c
1 FP0009-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081238 FP0009-C AF-W 88.85 True Analysis_set gDNA True #e31a1c
2 FP0010-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889621 FP0010-CW AF-W 86.46 True Analysis_set sWGA False #e31a1c
3 FP0011-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889624 FP0011-CW AF-W 86.35 True Analysis_set sWGA False #e31a1c
4 FP0012-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889627 FP0012-CW AF-W 89.74 True Analysis_set sWGA False #e31a1c

Next, we would like to count the number of samples from each location. Note that we have three levels of information for each location, making it a little tricky. To get counts for each location, we would like each of three category levels to be included:

  1. The population the sample came from (e.g. East Africa)

  2. The country the sample came from (e.g. Kenya)

  3. The administrative level within each country (e.g. Kilifi)

We therefore need to count the number of samples within each sub-category (e.g. East Africa - Kenya - Kilifi). To do this we can create a new dataframe, where we count the number of samples in each sub-category:

# Here, we create the new dataframe 'sample_metadata_admin1'.
sample_metadata_admin1 = (
    pd.DataFrame(
        sample_metadata                   # we first take the previous dataframe from above
        .groupby(['Population',           # then group the data by several variables (Population, Country & Admin level 1)
                  'Country',
                  'Admin level 1',
                  'Population_colour',    # we also want to include some of the other variables from the previous dataframe (population colour and latitude/longitude)
                  'Admin level 1 latitude',
                  'Admin level 1 longitude'])
        .size()
        .reset_index(name='Number of samples')                       # Then count samples within each subgroup (Population > Country > Admin level 1)
    )

    .set_index(['Country', 'Admin level 1']) # we then set Country and Admin level 1 as the new index
)

# Lets check the above has worked correctly by outputting the new dataframe
pd.options.display.max_rows = 100 # display a maximum of 100 rows
sample_metadata_admin1
Population Population_colour Admin level 1 latitude Admin level 1 longitude Number of samples
Country Admin level 1
Democratic Republic of the Congo Kinshasa AF-C #fd8d3c -4.436710 15.904241 573
Kenya Kilifi AF-E #fecc5c -3.174404 39.686888 662
Madagascar Fianarantsoa AF-E #fecc5c -21.856236 46.862308 1
Mahajanga AF-E #fecc5c -16.536991 46.684670 24
Malawi Chikwawa AF-E #fecc5c -16.164374 34.708946 319
Zomba AF-E #fecc5c -15.398794 35.426953 52
Mozambique Gaza AF-E #fecc5c -23.320472 32.804083 91
Tanzania Kagera AF-E #fecc5c -1.880386 31.453920 61
Kigoma AF-E #fecc5c -4.764340 30.402827 199
Lindi AF-E #fecc5c -9.480658 38.406946 79
Morogoro AF-E #fecc5c -7.922830 36.989147 34
Tanga AF-E #fecc5c -5.217996 38.281878 324
Ethiopia Amhara AF-NE #bb8129 11.562464 38.047312 15
Oromia AF-NE #bb8129 7.507237 38.765442 19
Kenya Kisumu AF-NE #bb8129 -0.163033 34.834400 64
Sudan Blue Nile AF-NE #bb8129 11.268474 34.125379 66
Kassala AF-NE #bb8129 15.988691 35.750775 13
Khartoum AF-NE #bb8129 15.843679 32.801797 124
Uganda Apac AF-NE #bb8129 2.119374 32.571842 15
Benin Atlantique AF-W #e31a1c 6.611916 2.221643 57
Littoral AF-W #e31a1c 6.364695 2.411790 277
Burkina Faso Haut-Bassins AF-W #e31a1c 11.367824 -4.332121 58
Cameroon Sud-Ouest AF-W #e31a1c 5.198150 9.290182 294
Côte d'Ivoire Abidjan AF-W #e31a1c 5.420295 -4.064976 71
Gabon Wouleu-Ntem AF-W #e31a1c 1.401591 11.952537 59
Gambia North Bank AF-W #e31a1c 13.507441 -16.008547 252
Upper River AF-W #e31a1c 13.393779 -14.160274 760
Western AF-W #e31a1c 13.245396 -16.401559 235
Ghana Ashanti AF-W #e31a1c 6.803616 -1.448573 286
Brong Ahafo AF-W #e31a1c 7.706088 -1.657978 69
Central AF-W #e31a1c 5.560964 -1.197226 175
Eastern AF-W #e31a1c 6.412757 -0.450589 21
Greater Accra AF-W #e31a1c 5.821748 0.065873 198
Upper East AF-W #e31a1c 10.777652 -0.820531 3300
Volta AF-W #e31a1c 7.269002 0.392035 41
Guinea Faranah AF-W #e31a1c 10.490027 -10.658152 60
Nzerekore AF-W #e31a1c 8.437316 -8.899060 139
Mali Bamako AF-W #e31a1c 12.611017 -7.979135 215
Kayes AF-W #e31a1c 13.872913 -10.230416 379
Koulikoro AF-W #e31a1c 13.625339 -7.645231 991
Mopti AF-W #e31a1c 14.705153 -3.511409 9
Segou AF-W #e31a1c 13.811435 -5.697849 49
Sikasso AF-W #e31a1c 11.432507 -6.558964 161
Mauritania Guidimaka AF-W #e31a1c 15.377000 -12.125584 23
Hodh ech Chargui AF-W #e31a1c 18.193701 -7.090179 40
Hodh el Gharbi AF-W #e31a1c 16.565426 -9.832345 41
Nigeria Kwara AF-W #e31a1c 8.982546 4.372565 8
Lagos AF-W #e31a1c 6.527058 3.564947 132
Senegal Dakar AF-W #e31a1c 14.754651 -17.265418 93
Sedhiou AF-W #e31a1c 12.893239 -15.586912 62
India Odisha AS-S-E #dfc0eb 20.505428 84.418059 122
West Bengal AS-S-E #dfc0eb 23.815952 87.980900 122
Bangladesh Chittagong AS-S-FE #984ea3 22.712080 91.730275 1658
India Tripura AS-S-FE #984ea3 23.744503 91.741765 72
Cambodia Battambang AS-SE-E #3182bd 12.979183 103.101657 65
Koh Kong AS-SE-E #3182bd 11.464804 103.467717 5
Pailin AS-SE-E #3182bd 12.878391 102.633336 286
Preah Vihear AS-SE-E #3182bd 13.797960 105.007949 216
Pursat AS-SE-E #3182bd 12.365194 103.636298 671
Ratanakiri AS-SE-E #3182bd 13.792914 107.087046 420
Stueng Traeng AS-SE-E #3182bd 13.803394 106.176850 60
Laos Attapeu AS-SE-E #3182bd 14.793855 106.945076 210
Champasak AS-SE-E #3182bd 14.771686 105.960564 218
Salavan AS-SE-E #3182bd 15.848394 106.273702 147
Savannakhet AS-SE-E #3182bd 16.553723 105.705702 452
Sekong AS-SE-E #3182bd 15.582019 107.039114 25
Thailand Sisakhet AS-SE-E #3182bd 14.855947 104.371389 112
Vietnam Bac Lieu AS-SE-E #3182bd 9.313846 105.489220 4
Binh Phuoc AS-SE-E #3182bd 11.755317 106.908543 751
Binh Thuan AS-SE-E #3182bd 11.117520 108.048195 11
Dak Lak AS-SE-E #3182bd 12.824588 108.206988 112
Dak Nong AS-SE-E #3182bd 12.227999 107.688640 73
Gia Lai AS-SE-E #3182bd 13.797699 108.240959 376
Khanh Hoa AS-SE-E #3182bd 12.328065 109.038169 66
Ninh Thuan AS-SE-E #3182bd 11.705161 108.869176 205
Quang Nam AS-SE-E #3182bd 15.589768 107.959958 95
Quang Tri AS-SE-E #3182bd 16.745896 106.930243 40
Myanmar Bago AS-SE-W #9ecae1 18.298181 96.130844 124
Kachin AS-SE-W #9ecae1 26.073837 97.347428 28
Kayin AS-SE-W #9ecae1 17.141314 97.797344 760
Mandalay AS-SE-W #9ecae1 21.209223 96.003494 120
Rakhine AS-SE-W #9ecae1 19.720378 93.735927 19
Sagaing AS-SE-W #9ecae1 24.142075 95.336510 93
Shan AS-SE-W #9ecae1 21.721974 98.126917 65
Tanintharyi AS-SE-W #9ecae1 12.661494 98.745189 51
Thailand Ranong AS-SE-W #9ecae1 9.966645 98.700506 27
Tak AS-SE-W #9ecae1 16.714952 98.791050 967
Indonesia Papua OC-NG #f781bf -4.662878 138.691107 133
Papua New Guinea East Sepik OC-NG #f781bf -4.311829 143.171785 166
Madang OC-NG #f781bf -5.160796 145.287638 55
Milne Bay OC-NG #f781bf -9.959220 150.845963 30
Colombia Cauca SA #4daf4a 2.386240 -76.857248 146
Choco SA #4daf4a 5.987357 -76.971119 3
Nariño SA #4daf4a 1.563412 -77.867314 7
Valle del Cauca SA #4daf4a 3.868855 -76.531745 3
Peru Loreto SA #4daf4a -4.123815 -74.424073 21
Venezuela Bolivar SA #4daf4a 6.213313 -63.486330 2

Plotting figure 1A from the Pf7 paper

We can now plot figure 1A from the Pf7 paper. Firstly, we set the basic plotting parameters for the plot, such as the font sizes. We then set the range of latitude and longitude we would like to include in the plot, as well as the total size of the image.

# Set basic plotting parameters (this is required regardless of whether the 'Basemap' or 'Cartopy' package is used)
rcParams = plt.rcParams
base_font_size = 8
rcParams['font.size'] = base_font_size
rcParams['axes.labelsize'] = base_font_size
rcParams['xtick.labelsize'] = base_font_size
rcParams['ytick.labelsize'] = base_font_size
rcParams['legend.fontsize'] = base_font_size
rcParams['axes.linewidth'] = .5
rcParams['lines.linewidth'] = .5
rcParams['patch.linewidth'] = .5
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
rcParams['lines.markeredgewidth'] = .5
rcParams['figure.max_open_warning'] = 1000

# Define the maximum and minimum latitude and longitude for the map
min_lat = int(sample_metadata_admin1['Admin level 1 latitude'].min()) - 3
max_lat = int(sample_metadata_admin1['Admin level 1 latitude'].max()) + 3
min_long = int(sample_metadata_admin1['Admin level 1 longitude'].min()) - 3
max_long = int(sample_metadata_admin1['Admin level 1 longitude'].max()) + 3

# Then define the total size of the image
map_size = ((int(sample_metadata_admin1['Admin level 1 longitude'].max()) + 3  - int(sample_metadata_admin1['Admin level 1 longitude'].min()) - 3) / 10 ,
(int(sample_metadata_admin1['Admin level 1 latitude'].max()) + 3  -  int(sample_metadata_admin1['Admin level 1 latitude'].min()) - 3 ) / 10 )

Plotting using the ‘Basemap’ package

In this first code chunk, we use the Basemap plotting function from the mpl_toolkits package. This code will recreate the image from the published Pf7 paper. However, this package is now deprecated. In future, the map can be recreated using a different map building package - ‘cartopy’. The code to generate this plot is in the following code chunk (see below).

# 1. Define the figure and axes
fig = plt.figure(figsize=map_size)

alpha = 1.0

# 2. Draw the map background
m = Basemap(projection='cyl', resolution='c',
            llcrnrlat=min_lat, urcrnrlat=max_lat,
            llcrnrlon=min_long, urcrnrlon=max_long
          )

m.shadedrelief(alpha=0.4)
m.drawcountries(color='gray')

# 3. Show admin 1 levels, with color reflecting population
m.scatter(
    sample_metadata_admin1['Admin level 1 longitude'],
    sample_metadata_admin1['Admin level 1 latitude'],
    c=sample_metadata_admin1['Population_colour'],
    edgecolors='black',
    s=40,
    alpha=alpha,
    latlon=True
)

# 4. Make legend with dummy points
for population in Population_colours:
    n = np.count_nonzero(sample_metadata['Population'] == population)
    plt.scatter(
        [],
        [],
        c=Population_colours[population],
        edgecolors='black',
        alpha=alpha,
        s=50,
        label=f"{Population_legend[population]} (n={n:,})"
    )

plt.legend(scatterpoints=1, frameon=False, labelspacing=.7, loc=(0.22, 0.04))
plt.text(-27.5, 4, 'Population')

# 5. Add title (optional)
#plt.title("Figure 1A: Sampling locations")

plt.show()
../../_images/global-sample-map_19_0.png

Recreating the plot using the ‘Cartopy’ package

Next, we recreate the plot using the ‘Cartopy’. While the code used is not identical to that used for the original plot, it uses the same coordinates and plots the sampling locations of the points to generate a near identical plot. This code should be used in future to generate the figures.

To do this, we first need to download the map image we will use. This first code chunk downloads an image to use from the Natural Earth Data website, this will be used to generate the plot.

# First we need to download the map image file via a web link:
!wget https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/raster/HYP_50M_SR_W.zip

# Unzip the file and define the path to the image file:
!unzip './HYP_50M_SR_W.zip'
image = './HYP_50M_SR_W.tif'

--2023-11-15 17:24:25--  https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/raster/HYP_50M_SR_W.zip
Resolving www.naturalearthdata.com (www.naturalearthdata.com)... 50.87.253.14
Connecting to www.naturalearthdata.com (www.naturalearthdata.com)|50.87.253.14|:443... connected.
HTTP request sent, awaiting response... 302 Moved Temporarily
Location: https://naciscdn.org/naturalearth/50m/raster/HYP_50M_SR_W.zip [following]
--2023-11-15 17:24:25--  https://naciscdn.org/naturalearth/50m/raster/HYP_50M_SR_W.zip
Resolving naciscdn.org (naciscdn.org)... 18.65.229.31, 18.65.229.84, 18.65.229.10, ...
Connecting to naciscdn.org (naciscdn.org)|18.65.229.31|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 102197904 (97M) [application/zip]
Saving to: ‘HYP_50M_SR_W.zip.3’

HYP_50M_SR_W.zip.3  100%[===================>]  97.46M   280MB/s    in 0.3s    

2023-11-15 17:24:26 (280 MB/s) - ‘HYP_50M_SR_W.zip.3’ saved [102197904/102197904]

Archive:  ./HYP_50M_SR_W.zip
replace HYP_50M_SR_W.README.html? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
  inflating: HYP_50M_SR_W.README.html  
replace HYP_50M_SR_W.VERSION.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
 extracting: HYP_50M_SR_W.VERSION.txt  
replace HYP_50M_SR_W.prj? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
  inflating: HYP_50M_SR_W.prj        
replace HYP_50M_SR_W.tfw? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
  inflating: HYP_50M_SR_W.tfw        
replace HYP_50M_SR_W.tif? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
  inflating: HYP_50M_SR_W.tif        y

'./HYP_50M_SR_W.tif'

# 1. Define the figure and axes
fig = plt.figure(figsize=map_size)
alpha = 1.0

# 2. Draw the map background with identical parameters
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([min_long, max_long,min_lat, max_lat], crs=ccrs.PlateCarree())
ax.imshow(imread(image), origin='upper', alpha = 0.4, transform= ccrs.PlateCarree(),
          extent=[-180, 180, -90, 90])
ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.6, edgecolor='grey')

# 3. Show admin 1 levels, with color reflecting population
ax.scatter(
    sample_metadata_admin1['Admin level 1 longitude'],
    sample_metadata_admin1['Admin level 1 latitude'],
    c=sample_metadata_admin1['Population_colour'],
    edgecolors='black',
    s=40,
    alpha=alpha,
    transform=ccrs.PlateCarree()
)

# 4. Make legend with dummy points
for population in Population_colours:
    n = np.count_nonzero(sample_metadata['Population'] == population)
    plt.scatter(
        [],
        [],
        c=Population_colours[population],
        edgecolors='black',
        alpha=alpha,
        s=50,
        label=f"{Population_legend[population]} (n={n:,})"
    )

plt.legend(scatterpoints=1, frameon=False, labelspacing=.7, loc=(0.22, 0.04))
plt.text(-27.5, 4.1, 'Population')

# 5. Add title (optional)
#plt.title("Figure 1A: Sampling locations")

plt.show()
../../_images/global-sample-map_22_0.png

Save the figure:

# Mount Google Drive
drive.mount('/content/drive')

# Save sampling locations plot
# This will send the file to your Google Drive, where you can download it from if needed
# Note, this code will only save the updated 'cartopy' version of the plot. If you would like to save the original version instead, you will have to comment out the code chunk which generates the map using cartopy (the chunk directly above this one).
# Change the file path if you wish to send the file to a specific location
# Change the file name if you wish to call it something else
file_path = '/content/drive/My Drive/'
file_name = 'Pf7_figure_1A_sample_collections_map'

# We save as both .png and .PDF files
fig.savefig(f'{file_path}{file_name}.png', dpi=250)
fig.savefig(f'{file_path}{file_name}.pdf')

Conclusion

In summary, in this notebook, we accessed the MalariaGEN data available on the cloud for the latest Plasmodium falciparum data release (Pf7). We then recreated the Figure 1A plot from the Pf7 paper using the ‘Basemap’ package. As this package is now depreciated, we then updated the plot for future data releases using the cartopy package, and output this as both a .png and .pdf file.

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