Here we download and plot “humpback whale sightings” from the OBIS SEAMAP database.

First, download the data as a **.csv** file. Then use following code:

import csv
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# Open file
f = open('species_180530_points.csv', 'r')
# Read file
r = csv.DictReader(f)
# Initialize empty variables
lat, lon, num = [],[],[]
# Loop to parse data into our variables
errors = 0
for row in r:
try: # The "Try/except" routine, prevents the loop to break if there is an empty field
lat.append(float(row['latitude']))
lon.append(float(row['longitude']))
if not row['count'] == '':
num.append(float(row['count']))
else:
num.append(1) # If "count" is empty, replace it with a 1
except ValueError:
errors = errors + 1 # Simple error counter
# Set up projection and limits
m1 = Basemap(projection='mill',llcrnrlat=-90,urcrnrlat=90,\
llcrnrlon=-180,urcrnrlon=180,resolution='c')
# Given the projection, estimate plot coordinates from lats and lons
x,y = m1(lon, lat)
# Project data points into a grid
nbins = 200
H, xedges, yedges = np.histogram2d(x,y,bins=nbins,weights=num)
# Rotate and flip H...
H = np.rot90(H)
H = np.flipud(H)
# Mask zeros
Hmasked = np.ma.masked_where(H==0,H)
# Log H for better display
Hmasked = np.log10(Hmasked)
# Make map
fig1 = plt.figure()
cs = m1.pcolor(xedges,yedges,Hmasked,shading='flat')
m1.drawcoastlines()
m1.fillcontinents()
m1.drawmapboundary()
plt.title('Distribution of humpback whales')
cbar = plt.colorbar(orientation='horizontal', extend='both')
cbar.ax.set_xlabel('Number of whales')

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