In [1]:
from mpl_toolkits.basemap import Basemap
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import pandas as pd
import folium
from folium import IFrame
import base64
import matplotlib.pyplot as plt
In [2]:
#Section that creates the map
data4 = pd.DataFrame({
'lat':[111,140, -110, -135, -37,122, 123, 124,126,-9, 78,5,123,-175,-67],
'lon':[12,36, 22, 53, -14,24, 23, 22,33,55, 10,57,30,60,5],
'name':['Vietnam', 'Japan', 'Mexico', 'Canada', 'Brazil','Taiwan',
'Taiwan','Taiwan','Korea','Scotland','India','Norway','China','Arctic','Venezuela'],
'value':[2.2,0.14,0.15,0.144,0.25,1.82,0.78,0.114,0.125,0.24, 0.203,0.084,0.25,0.22,0.31]
})
data4
# Make an empty map
m4 = folium.Map(location=[20,0], tiles="Mapbox Bright",zoom_start=2)
# I can add marker one by one on the map
for i in range(0,len(data4)):
folium.Circle(
location=[data4.iloc[i]['lon'], data4.iloc[i]['lat']],
popup=str(data4.iloc[i]['value'])+' ppm',
radius=data4.iloc[i]['value']*300000.0,
color='limegreen',
fill=True,
fill_color='limegreen'
).add_to(m4)
In [3]:
#Section that creates the first chart
#Swai Basa
station = '42'
resolution, width, height = 75, 9.5, 5
elements = ['Sodium', 'Potassium', 'Cobalt', 'Bromine', 'Rubidium', 'Antimony',
'Cesium', 'Mercury']
ppm = [980.2, 2588.97, 0.043, 1.4, 2.22, 2.57, 0.21, 0.035]
sys_unc = [428.53, 1325.27, 0.022, 0.45, 1.056, 0.62, 0.061, 0.0043]
stat_unc = [5.66, 98.93, 0.0056, 0.07, 0.058, 0.17, 0.016, 0.0026]
x = np.linspace(1,len(ppm),len(ppm))
tot_unc = []
for i in x:
val = (sys_unc[int(i)-1]**2+stat_unc[int(i)-1]**2)**0.5
tot_unc.append(val)
mpl_fig = plt.figure(figsize=(9.25,3))
ax = mpl_fig.add_subplot(111)
w=0.25
p1 = ax.bar(x, ppm, w, color='powderblue', label='Element')
#reference values (based on averages)
ppm2 = [1120, 4912, 0.045, 13, 14.7, 1.5, 0.01, 0.314]
#regulatory limits for the elements
limits = [0, 0, 8.48, 0, 23, 0.1, 0, 1]
#reference graph
p2 = ax.bar(x+0.25, ppm2, w, color='lightsalmon', edgecolor='red', alpha=0.3, label='Reference')
#error bars
p3 = plt.errorbar(x, ppm, yerr=tot_unc, fmt='.', ecolor='steelblue', capthick=1, capsize=2)
#limits graph
p4 = ax.bar(x-0.25, limits, w, color='lightslategray', edgecolor='k', label='Limit')
plt.xlabel('Element')
plt.ylabel('Concentration in ppm')
plt.title('Element ppm of Sample 1: Swai Basa Fillet from Vietnam')
plt.legend()
plt.xticks(x, elements)
ax.set_yscale('log')
png = 'mpld3_{}.png'.format(station)
mpl_fig.savefig(png, dpi=resolution)
encoded = base64.b64encode(open(png, 'rb').read()).decode()
encoded2 = base64.b64encode(open('swaiBasa.jpeg', 'rb').read()).decode()
In [7]:
#The first pop up
html="""
<body style="border:3px; border-style:solid; border-color:navy; padding: 1em;">
<h2>Swai Basa </h2><br>
<img src="data:image/png;base64,{}">
<p>
<b>Location:</b> Vietnam</p>
<img src="data:image/jpeg;base64,{}" style="width:400px;height:200px;" alt="Vietnam Concentrations">
<p><b>Average size:</b> 2 kg</p>
<p><b>Concentration:</b> 2.22 ppm</p>
<p><b>Limit:</b> 23 ppm</p>
<p>
Rubidium is a non-toxic trace metal commonly found in marine life. While the concentration of rubidium
in Vietnam appears high, it does not exceed the reference limit nor does it pose a health threat. The
amount of rubidium in the fish can be explained by it's diet since rubidium mimics potassium in the body.
Overall, it appears that rubidium is popular in fish located in South East Asia so there is probably an
increased amount of rubidium sediments.
</p>
</body>
""".format
iframe = IFrame(html(encoded,encoded2), width=(width*resolution)+20, height=(height*resolution)+20)
popup = folium.Popup(iframe, max_width=2650)
folium.Marker([12,110], popup=popup).add_to(m4)
m4.save('RubidiumMap.html')
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