%%html
<style>
div.input {
width: 105ex; /* about 80 chars + buffer */
}
 
div.text_cell {
width: 105ex /* instead of 100%, */
}
 
div.text_cell_render {
/*font-family: sans-serif;*/
font-family: serif; /* Make non-code text serif. */
line-height: 145%; /* added for some line spacing of text. */
width: 105ex; /* instead of 'inherit' for shorter lines */
}
 
/* Set the size of the headers */
div.text_cell_render h1 {
font-size: 18pt;
}
 
div.text_cell_render h2 {
font-size: 14pt;
}
 
.CodeMirror {
font-family: monospace;
}
</style>

%%javascript
IPython.OutputArea.auto_scroll_threshold = -1;

%matplotlib inline
import math
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
from scipy import stats
#
figuresize = 20
plt.rcParams['figure.figsize'] = (1+math.sqrt(5))/2*figuresize, figuresize
plt.rcParams['figure.dpi'] = 200
plt.rcParams['font.size'] = 20
plt.rcParams['font.family'] = "serif"
plt.rcParams['text.usetex'] = "True"
#
land_color = 'gray'
water_color = 'white'

import geoip2.database
reader = geoip2.database.Reader('./geolocate_ip_data/GeoLite2-City.mmdb')

with open('./geolocate_ip_data/hacker_ips') as f:
    IPs = f.read().splitlines()

x = []
y = []
missing = 0
for IP in IPs:
    try:
        response = reader.city(IP)
        x.append(float(response.location.longitude))
        y.append(float(response.location.latitude))
    except:
        print("IP address not found.")
        missing += 1
found = len(x) - missing

# Set map axis limits.
map_min_lat =  -70
map_max_lat =   80
map_min_lon = -180
map_max_lon =  180

# Create map axes & base map.
fig, ax = plt.subplots()
map = Basemap(projection='merc', llcrnrlat=map_min_lat, urcrnrlat=map_max_lat, 
              llcrnrlon=map_min_lon, urcrnrlon=map_max_lon, resolution='l')


# Remap x and y coordinates into the map space projection.
xm,ym = map(x,y)

xmin, ymin = map(map_min_lon, map_min_lat)
xmax, ymax = map(map_max_lon, map_max_lat)

# Perform kernel density estimation calculation.
X, Y = np.mgrid[xmin:xmax:500j, ymin:ymax:500j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([xm, ym])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)

# Create plots.
map.imshow(np.flipud(np.rot90(Z)), cmap=plt.cm.Reds, 
           extent=[xmin, xmax, ymin, ymax], alpha=0.6)
map.fillcontinents(color=land_color, lake_color=water_color,zorder=0)
map.plot(xm, ym,'k.',markersize=5)

plt.title(r"Point \& Relative Density Plot of " + str(found) 
          + r" SSH Penetration Attempts")
fig = plt.gcf()

plt.show()
fig.savefig("test.png", bbox_inches=None, pad_inches=0.1)

reader.close()

import geoip2.database
reader = geoip2.database.Reader('./geolocate_ip_data/GeoLite2-City.mmdb')

x = []
y = []
missing = 0
found = 0
for IP in IPs:
    try:
        response = reader.city(IP)
        if(response.country.name == 'Canada'):
            x.append(float(response.location.longitude))
            y.append(float(response.location.latitude))
            found = found + 1
    except:
        print("IP address not found.")
        missing += 1
print(found)

# Set map axis limits.
map_min_lat =   40
map_max_lat =   83
map_min_lon = -150
map_max_lon =  -50

# Create map axes & base map.
fig, ax = plt.subplots()
map = Basemap(projection='merc', llcrnrlat=map_min_lat, urcrnrlat=map_max_lat, 
              llcrnrlon=map_min_lon, urcrnrlon=map_max_lon, resolution='l')

# Remap x and y coordinates into the map space projection.
xm,ym = map(x,y)

xmin, ymin = map(map_min_lon, map_min_lat)
xmax, ymax = map(map_max_lon, map_max_lat)

# Perform kernel density estimation calculation.
X, Y = np.mgrid[xmin:xmax:500j, ymin:ymax:500j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([xm, ym])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)

# Create plots.
map.imshow(np.flipud(np.rot90(Z)), cmap=plt.cm.Reds, 
           extent=[xmin, xmax, ymin, ymax], alpha=0.6)
map.fillcontinents(color=land_color, lake_color=water_color,zorder=0)
map.plot(xm, ym,'k.',markersize=5)

# Create label on point of interest.
for i, xmm in enumerate(xm):
    if(y[i] > 55):
        plt.text(xmm, ym[i], str(x[i]) + ", " + str(y[i]))

plt.title(r"Point \& Relative Density Plot of " + str(found) 
          + r" SSH Penetration Attempts")
fig = plt.gcf()

plt.show()
fig.savefig("test2.png", bbox_inches=None, pad_inches=0.1)

reader.close()