In [1]:

    

import pandas  as pd
%matplotlib inline
import matplotlib.pyplot as plt
import datetime as dt

import numpy as np


    

In [15]:

    

#Input the file name."JoeDupes3_forearth"
fname = 'hotspot_2015'

data = pd.read_csv(fname+".csv", parse_dates=['datetime'], usecols=['latitude','longitude','datetime', 'power'])
#Skip the 1st header row.
#data.next()
data.head()


    

    
Out[15]:






  

    

      

      
latitude
      
longitude
      
datetime
      
power
    
  
  

    

      
0
      
-15.0460
      
144.3795
      
2015-01-01 01:04:50
      
14.2
    
    

      
1
      
-15.8927
      
136.6096
      
2015-01-01 01:00:50
      
11.9
    
    

      
2
      
-17.1466
      
145.8501
      
2015-01-01 01:00:50
      
10.0
    
    

      
3
      
-18.0796
      
122.6971
      
2015-01-01 02:41:30
      
11.1
    
    

      
4
      
-18.0764
      
122.6902
      
2015-01-01 02:45:40
      
11.0
    
  

In [3]:

    

max(data.power)


    

    
Out[3]:





1810.5999999999999

In [4]:

    

binwidth = 5
plt.hist(data.power, bins=np.arange(min(data.power), 300, binwidth))


    

    
Out[4]:





(array([   189.,  31077.,  47088.,  33699.,  22275.,  15110.,  10847.,
          7921.,   6193.,   4849.,   3956.,   3268.,   2780.,   2257.,
          1889.,   1640.,   1526.,   1353.,   1158.,   1042.,    903.,
           795.,    710.,    645.,    611.,    492.,    494.,    445.,
           389.,    367.,    280.,    350.,    299.,    280.,    263.,
           240.,    222.,    216.,    197.,    178.,    167.,    159.,
           139.,    143.,    139.,    138.,    119.,    110.,     98.,
            92.,     77.,     90.,    115.,     87.,     90.,     79.,
            58.,     72.,     76.]),
 array([   0.,    5.,   10.,   15.,   20.,   25.,   30.,   35.,   40.,
          45.,   50.,   55.,   60.,   65.,   70.,   75.,   80.,   85.,
          90.,   95.,  100.,  105.,  110.,  115.,  120.,  125.,  130.,
         135.,  140.,  145.,  150.,  155.,  160.,  165.,  170.,  175.,
         180.,  185.,  190.,  195.,  200.,  205.,  210.,  215.,  220.,
         225.,  230.,  235.,  240.,  245.,  250.,  255.,  260.,  265.,
         270.,  275.,  280.,  285.,  290.,  295.]),
 <a list of 59 Patch objects>)






    

In [5]:

    

binwidth = 5
plt.hist(data.temp_kelvin, bins=np.arange(min(data.temp_kelvin), max(data.temp_kelvin), binwidth))


    

    
Out[5]:





(array([  2.85300000e+03,   9.18400000e+03,   1.63450000e+04,
          1.76640000e+04,   1.93230000e+04,   2.32530000e+04,
          2.84350000e+04,   3.19250000e+04,   2.77030000e+04,
          2.42970000e+04,   1.97850000e+04,   1.21530000e+04,
          8.39700000e+03,   6.03500000e+03,   4.46700000e+03,
          3.42600000e+03,   2.60400000e+03,   1.89800000e+03,
          1.52900000e+03,   1.22500000e+03,   9.71000000e+02,
          7.72000000e+02,   6.16000000e+02,   5.05000000e+02,
          4.03000000e+02,   2.97000000e+02,   2.77000000e+02,
          2.16000000e+02,   1.66000000e+02,   1.01000000e+02,
          1.16000000e+02,   9.70000000e+01,   6.40000000e+01,
          5.20000000e+01,   4.20000000e+01,   3.80000000e+01,
          3.00000000e+01,   2.40000000e+01,   1.90000000e+01,
          8.00000000e+00,   9.00000000e+00,   6.00000000e+00,
          7.00000000e+01]),
 array([ 288.,  293.,  298.,  303.,  308.,  313.,  318.,  323.,  328.,
         333.,  338.,  343.,  348.,  353.,  358.,  363.,  368.,  373.,
         378.,  383.,  388.,  393.,  398.,  403.,  408.,  413.,  418.,
         423.,  428.,  433.,  438.,  443.,  448.,  453.,  458.,  463.,
         468.,  473.,  478.,  483.,  488.,  493.,  498.,  503.]),
 <a list of 43 Patch objects>)






    

In [6]:

    

data.shape


    

    
Out[6]:





(444900, 23)

In [7]:

    

f = open(fname + '.kml', 'w')

#Writing the kml file.
f.write("<?xml version='1.0' encoding='UTF-8'?>\n")
f.write("<kml xmlns='http://earth.google.com/kml/2.1'>\n")
f.write("<Document>\n")
f.write("   <name>" + fname + '.kml' +"</name>\n")
for i, fid in enumerate(data.FID[:1000]):
    if data.power[i] < 50:
        continue
    f.write("   <Placemark>\n")
    f.write("       <name>" + str(data.temp_kelvin[i]  -273.15) + "C</name>\n")
    f.write("       <description>" +  str(data.datetime[i]) +"</description>\n")
    f.write("       <Point>\n")
    f.write("           <coordinates>" + str(data.longitude[i]) + "," + str(data.latitude[i]) +"</coordinates>\n")
    f.write("       </Point>\n")
    f.write("   </Placemark>\n")
f.write("</Document>\n")
f.write("</kml>\n")
f.close()


    

In [16]:

    

json = data[df.line_race != 0].to_json(orient="records")


    

In [19]:

    

f = open('data.js', 'w')
f.write('var data = ' + json + ";")
f.write('var data2 = ' + json2 + ";")
f.close()