# This just lets the output of the following code samples
#  display inline on this page.%pylab inline
%pylab inline
from pylab import *

# Set default figure size for this notebook
pylab.rcParams['figure.figsize'] = (8.0, 6.4)

from pylab import *
plot([1,2,3])
show()

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='ortho', lat_0=50, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
 
plt.show()

###highlight=[12,13]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='ortho', lat_0=50, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
 
plt.show()

###highlight=[14]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='ortho', lat_0=50, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
plt.show()

###highlight=[16,17]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='ortho', lat_0=50, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
plt.show()

###highlight=[8,9]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='ortho', lat_0=0, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
plt.show()

###highlight=[8]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='robin', lat_0=0, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
plt.show()

###highlight=[10,11]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='merc', lat_0=57, lon_0=-135,
    resolution = 'l', area_thresh = 1000.0,
    llcrnrlon=-136.25, llcrnrlat=56,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
plt.show()

###highlight=[9]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='merc', lat_0=57, lon_0=-135,
    resolution = 'h', area_thresh = 1000.0,
    llcrnrlon=-136.25, llcrnrlat=56,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
plt.show()

###highlight=[9]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
# make sure the value of resolution is a lowercase L,
#  for 'low', not a numeral 1
map = Basemap(projection='merc', lat_0=57, lon_0=-135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
map.drawmapboundary()
 
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))

plt.show()

###highlight=[16,17,18,19]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 57, lon_0 = -135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56.0,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()
 
lon = -135.3318
lat = 57.0799
x,y = map(lon, lat)
map.plot(x, y, 'bo', markersize=12)
 
plt.show()

###highlight=[19]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 57, lon_0 = -135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56.0,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()
 
lons = [-135.3318, -134.8331, -134.6572]
lats = [57.0799, 57.0894, 56.2399]
x,y = map(lons, lats)
map.plot(x, y, 'bo', markersize=10)
 
plt.show()

###highlight=[21,22,23]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 57, lon_0 = -135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56.0,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()
 
lons = [-135.3318, -134.8331, -134.6572]
lats = [57.0799, 57.0894, 56.2399]
x,y = map(lons, lats)
map.plot(x, y, 'bo', markersize=10)
 
labels = ['Sitka', 'Baranof Warm Springs', 'Port Alexander']
for label, xpt, ypt in zip(labels, x, y):
    plt.text(xpt, ypt, label)
 
plt.show()

###highlight=[23]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 57, lon_0 = -135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56.0,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()
 
lons = [-135.3318, -134.8331, -134.6572]
lats = [57.0799, 57.0894, 56.2399]
x,y = map(lons, lats)
map.plot(x, y, 'bo', markersize=10)
 
labels = ['Sitka', 'Baranof Warm Springs', 'Port Alexander']
for label, xpt, ypt in zip(labels, x, y):
    plt.text(xpt+10000, ypt+5000, label)
 
plt.show()

###highlight=[21,22,23,24,25,26]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 57, lon_0 = -135,
    resolution = 'h', area_thresh = 0.1,
    llcrnrlon=-136.25, llcrnrlat=56.0,
    urcrnrlon=-134.25, urcrnrlat=57.75)
 
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()
 
lons = [-135.3318, -134.8331, -134.6572]
lats = [57.0799, 57.0894, 56.2399]
x,y = map(lons, lats)
map.plot(x, y, 'bo', markersize=10)
 
labels = ['Sitka', 'Baranof\n  Warm Springs', 'Port Alexander']
x_offsets = [10000, -20000, -25000]
y_offsets = [5000, -50000, -35000]

for label, xpt, ypt, x_offset, y_offset in zip(labels, x, y, x_offsets, y_offsets):
    plt.text(xpt+x_offset, ypt+y_offset, label)
 
plt.show()

data_file = open('datasets/earthquake_data.csv')
 
for index, line in enumerate(data_file.readlines()):
    print(line.split(','))
    if index == 1:
        break

# Open the earthquake data file.
data_file = open('datasets/earthquake_data.csv')

# Read each line in the file, and keep track of what line we are on.
for index, line in enumerate(data_file.readlines()):
    # Split the data into items in a list.
    split_line = line.split(',')
    
    # Count the number of fields in each line.
    number_fields = len(split_line)
    print(number_fields, split_line)
    
    # Stop after the 5th line of data.
    if index == 5:
        break

# Open the earthquake data file.
data_file = open('datasets/earthquake_data.csv')

# Create empty lists for the latitudes and longitudes.
lats, lons = [], []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
for index, line in enumerate(data_file.readlines()):
    if index > 0:
        lats.append(float(line.split(',')[1]))
        lons.append(float(line.split(',')[2]))

# Display the first 5 lats and lons.
print('lats', lats[0:5])
print('lons', lons[0:5])

import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the latitudes and longitudes.
lats, lons = [], []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        
# Display the first 5 lats and lons.
print('lats', lats[0:5])
print('lons', lons[0:5])

import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the latitudes and longitudes.
lats, lons = [], []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
 
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
x,y = map(lons, lats)
map.plot(x, y, 'ro', markersize=6)
 
plt.show()

###highlight=[9,24]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
 
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
x,y = map(lons, lats)
map.plot(x, y, 'ro', markersize=6)
 
plt.show()

###highlight=[40,41,42,43,44]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 2.5
for lon, lat, mag in zip(lons, lats, magnitudes):
    x,y = map(lon, lat)
    msize = mag * min_marker_size
    map.plot(x, y, 'ro', markersize=msize)
 
plt.show()

###highlight=[31,32,33,34,35,36,37,38,39,54,55]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

def get_marker_color(magnitude):
    # Returns green for small earthquakes, yellow for moderate
    #  earthquakes, and red for significant earthquakes.
    if magnitude < 3.0:
        return ('go')
    elif magnitude < 5.0:
        return ('yo')
    else:
        return ('ro')
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 2.5
for lon, lat, mag in zip(lons, lats, magnitudes):
    x,y = map(lon, lat)
    msize = mag * min_marker_size
    marker_string = get_marker_color(mag)
    map.plot(x, y, marker_string, markersize=msize)
 
plt.show()

###highlight=[10,26,59,60,61]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []
timestrings = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
        timestrings.append(row[0])
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

def get_marker_color(magnitude):
    # Returns green for small earthquakes, yellow for moderate
    #  earthquakes, and red for significant earthquakes.
    if magnitude < 3.0:
        return ('go')
    elif magnitude < 5.0:
        return ('yo')
    else:
        return ('ro')
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 2.5
for lon, lat, mag in zip(lons, lats, magnitudes):
    x,y = map(lon, lat)
    msize = mag * min_marker_size
    marker_string = get_marker_color(mag)
    map.plot(x, y, marker_string, markersize=msize)
    
title_string = "Earthquakes of Magnitude 1.0 or Greater\n"
title_string += "%s through %s" % (timestrings[-1], timestrings[0])
plt.title(title_string)
 
plt.show()

###highlight=[60,63,64]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []
timestrings = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
        timestrings.append(row[0])
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

def get_marker_color(magnitude):
    # Returns green for small earthquakes, yellow for moderate
    #  earthquakes, and red for significant earthquakes.
    if magnitude < 3.0:
        return ('go')
    elif magnitude < 5.0:
        return ('yo')
    else:
        return ('ro')
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'gray')
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 2.5
for lon, lat, mag in zip(lons, lats, magnitudes):
    x,y = map(lon, lat)
    msize = mag * min_marker_size
    marker_string = get_marker_color(mag)
    map.plot(x, y, marker_string, markersize=msize)
    
title_string = "Earthquakes of Magnitude 1.0 or Greater\n"
title_string += "%s through %s" % (timestrings[-1][:10], timestrings[0][:10])
plt.title(title_string)

# Make this final plot larger.
figsize(24, 18)
plt.show()

###highlight=[47,48,53]
import csv

# Open the earthquake data file.
filename = 'datasets/earthquake_data.csv'

# Create empty lists for the data we are interested in.
lats, lons = [], []
magnitudes = []
timestrings = []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        magnitudes.append(float(row[4]))
        timestrings.append(row[0])
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

def get_marker_color(magnitude):
    # Returns green for small earthquakes, yellow for moderate
    #  earthquakes, and red for significant earthquakes.
    if magnitude < 3.0:
        return ('go')
    elif magnitude < 5.0:
        return ('yo')
    else:
        return ('ro')
 
map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=0, lon_0=-130)
map.drawcoastlines()
map.drawcountries()
#map.fillcontinents(color = 'gray')
map.bluemarble()
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 2.25
for lon, lat, mag in zip(lons, lats, magnitudes):
    x,y = map(lon, lat)
    msize = mag * min_marker_size
    marker_string = get_marker_color(mag)
    map.plot(x, y, marker_string, markersize=msize)
    
title_string = "Earthquakes of Magnitude 1.0 or Greater\n"
title_string += "%s through %s" % (timestrings[-1][:10], timestrings[0][:10])
plt.title(title_string)
 
plt.show()