In [4]:
import bq
import time
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib
from matplotlib import colors,colorbar
import matplotlib
%matplotlib inline
import csv
import math
from math import radians, cos, sin, asin, sqrt
from scipy import stats
import matplotlib.dates as mdates
client = bq.Client.Get()
In [38]:
# create a bounding box:
max_lat = 3
min_lat = -17
max_lon = 61.5
min_lon = 40
distance_from_shore_filter = 10 # in nautical miles
EEZ = 'Seychelles'
In [39]:
def Query(q):
t0 = time.time()
answer = client.ReadTableRows(client.Query(q)['configuration']['query']['destinationTable'])
print 'Query time: ' + str(time.time() - t0) + ' seconds.'
return answer
In [48]:
q = '''
select count(distinct mmsi)
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > '''+str(min_lat)+'''
AND latitude <'''+str(max_lat)+'''
AND longitude > '''+str(min_lon)+'''
AND longitude < '''+str(max_lon)+'''
AND weight >=.5
AND JSON_EXTRACT(extra,"$.eez") = '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
'''
number_of_mmsi = Query(q)
In [49]:
print "Number of unique MMSI:",int(number_of_mmsi[0][0])
In [50]:
q = '''
select a.mmsi mmsi,
a.number number,
if(b.country is null, "invalid mmsi",b.country) country,
b.continent continent
from
(select mmsi,
integer(if(length(string(mmsi))= 9,LEFT(STRING(mmsi),3), '0')) code,
count(*) number from
(SELECT
mmsi,
date(timestamp) date,
first(latitude) lat,
first(longitude) lon
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > -15
AND latitude <-5
AND longitude > 55
AND longitude <65
AND weight >=.5
AND JSON_EXTRACT(extra,"$.eez") = '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
group by mmsi, date)
group by mmsi, code
)
a
left join [scratch_roan.country_code] b
on a.code = b.code
order by number desc'''
days_by_mmsi = Query(q)
In [51]:
for r in days_by_mmsi:
if "Taiwan" in r[2]:
r[2] = "Taiwan"
print "mmsi:", r[0]," fishing days:", r[1], " country: ", r[2].split(" (")[0]
In [52]:
# Now Group by Country
q = '''
SELECT
country,
sum(number) number
from
(select a.mmsi mmsi,
a.number number,
if(b.country is null, "invalid mmsi",b.country) country,
b.continent continent
from
(select mmsi,
integer(if(length(string(mmsi))= 9,LEFT(STRING(mmsi),3), '0')) code,
count(*) number from
(SELECT
mmsi,
date(timestamp) date,
first(latitude) lat,
first(longitude) lon
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > '''+str(min_lat)+'''
AND latitude <'''+str(max_lat)+'''
AND longitude > '''+str(min_lon)+'''
AND longitude < '''+str(max_lon)+'''
AND weight >=.5
AND JSON_EXTRACT(extra,"$.eez") = '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
group by mmsi, date)
group by mmsi, code
)
a
left join [scratch_roan.country_code] b
on a.code = b.code)
group by country
order by number desc'''
country_groups = Query(q)
In [53]:
print "days\tcountry"
for c in country_groups:
print c[1],'\t', c[0].split(" (")[0]
In [54]:
objects = [c[0].split(" (")[0] for c in country_groups]
y_pos = np.arange(len(objects))
performance = [c[1] for c in country_groups]
plt.figure(figsize=(12,4))
plt.bar(y_pos-4, performance, align='center', alpha=0.5,)
plt.xticks(y_pos-4+.2, objects, fontsize=12) # rotation=45
locs, labels = plt.xticks()
plt.setp(labels, rotation=45,ha='right')
plt.ylabel('Fishing Days',fontsize=12)
plt.title('Fishing Days by Country in Seychelles EEZ, Jan 2014 to July 2015',fontsize=15)
ax = plt.axes()
ax.xaxis.set_ticks_position('none')
plt.savefig("fishing_effort_seychelles_by_Country.png",bbox_inches='tight',dpi=150,transparent=True,pad_inches=.1)
plt.show()
In [55]:
# Now Group by Country
q = '''
SELECT
lat,
lon,
count(*) fishing_days
from
(SELECT
mmsi,
date(timestamp) date,
integer(first(latitude)*4) lat,
integer(first(longitude)*4) lon
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > '''+str(min_lat)+'''
AND latitude <'''+str(max_lat)+'''
AND longitude > '''+str(min_lon)+'''
AND longitude < '''+str(max_lon)+'''
AND weight >=.5
AND JSON_EXTRACT(extra,"$.eez") = '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
group by mmsi, date)
group by lat, lon
'''
fishing_grid = Query(q)
In [56]:
cellsize = .25
one_over_cellsize = 4
num_lats = (max_lat-min_lat)*one_over_cellsize+1
num_lons = (max_lon-min_lon)*one_over_cellsize+1
grid = np.zeros(shape=(num_lats,num_lons))
for row in fishing_grid:
lat = int(row[0])
lon = int(row[1])
lat_index = lat-min_lat*one_over_cellsize
lon_index = lon-min_lon*one_over_cellsize
grid[lat_index][lon_index] = int(row[2])
In [58]:
plt.rcParams["figure.figsize"] = [8,9]
firstlat = max_lat
lastlat = min_lat
firstlon = min_lon
lastlon = max_lon
scale = cellsize
numlats = int((firstlat-lastlat)/scale+.5)
numlons = int((lastlon-firstlon)/scale+.5)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
fig = plt.figure()
extra = 10
m = Basemap(llcrnrlat=lastlat-extra, urcrnrlat=firstlat+extra,
llcrnrlon=firstlon-extra, urcrnrlon=lastlon+extra, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary(fill_color='#111111')
m.drawcoastlines(linewidth=.2)
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
x = np.linspace(firstlon, lastlon, -(firstlon-lastlon)*one_over_cellsize+1)
y = np.linspace(lastlat, firstlat, (firstlat-lastlat)*one_over_cellsize+1)
x, y = np.meshgrid(x, y)
converted_x, converted_y = m(x, y)
from matplotlib import colors,colorbar
maximum = grid.max()
minimum = 1
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
m.pcolormesh(converted_x, converted_y, grid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('viridis'))
t = "Fishing Days Jan 2014 to July 2015 \n in Seychelles EEZ"
plt.title(t, color = "#000000", fontsize=18)
ax = fig.add_axes([0.2, 0.1, 0.65, 0.02]) #x coordinate ,
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
lvls = np.logspace(np.log10(minimum),np.log10(maximum),num=8)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='horizontal', ticks=lvls, cmap = plt.get_cmap('viridis'))
#cb.ax.set_xticklabels(["0" ,round(m3**.5,1), m3, round(m3**1.5,1), m3*m3,round(m3**2.5,1), str(round(m3**3,1))+"+"], fontsize=10)
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Days',labelpad=-40, y=0.45, color = "#000000")
plt.savefig("fishing_effort_seychelles.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]:
In [59]:
# Now fishing days outside the EEZ
q = '''
SELECT
lat,
lon,
count(*) fishing_days
from
(SELECT
mmsi,
date(timestamp) date,
integer(first(latitude)*4) lat,
integer(first(longitude)*4) lon
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > '''+str(min_lat)+'''
AND latitude <'''+str(max_lat)+'''
AND longitude > '''+str(min_lon)+'''
AND longitude < '''+str(max_lon)+'''
AND weight >=.5
AND JSON_EXTRACT(extra,"$.eez") != '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
group by mmsi, date)
group by lat, lon
'''
fishing_grid = Query(q)
In [60]:
cellsize = .25
one_over_cellsize = 4
num_lats = (max_lat-min_lat)*one_over_cellsize+1
num_lons = (max_lon-min_lon)*one_over_cellsize+1
grid = np.zeros(shape=(num_lats,num_lons))
for row in fishing_grid:
lat = int(row[0])
lon = int(row[1])
lat_index = lat-min_lat*one_over_cellsize
lon_index = lon-min_lon*one_over_cellsize
grid[lat_index][lon_index] = int(row[2])
In [61]:
plt.rcParams["figure.figsize"] = [8,9]
firstlat = max_lat
lastlat = min_lat
firstlon = min_lon
lastlon = max_lon
scale = cellsize
numlats = int((firstlat-lastlat)/scale+.5)
numlons = int((lastlon-firstlon)/scale+.5)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
fig = plt.figure()
extra = 10
m = Basemap(llcrnrlat=lastlat-extra, urcrnrlat=firstlat+extra,
llcrnrlon=firstlon-extra, urcrnrlon=lastlon+extra, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary(fill_color='#111111')
m.drawcoastlines(linewidth=.2)
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
x = np.linspace(firstlon, lastlon, -(firstlon-lastlon)*one_over_cellsize+1)
y = np.linspace(lastlat, firstlat, (firstlat-lastlat)*one_over_cellsize+1)
x, y = np.meshgrid(x, y)
converted_x, converted_y = m(x, y)
from matplotlib import colors,colorbar
maximum = grid.max()
minimum = 1
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
m.pcolormesh(converted_x, converted_y, grid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('viridis'))
t = "Fishing Days Jan 2014 to July 2015 \n Outside Seychelles EEZ"
plt.title(t, color = "#000000", fontsize=18)
ax = fig.add_axes([0.2, 0.1, 0.65, 0.02]) #x coordinate ,
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
lvls = np.logspace(np.log10(minimum),np.log10(maximum),num=8)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='horizontal', ticks=lvls, cmap = plt.get_cmap('viridis'))
#cb.ax.set_xticklabels(["0" ,round(m3**.5,1), m3, round(m3**1.5,1), m3*m3,round(m3**2.5,1), str(round(m3**3,1))+"+"], fontsize=10)
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Days',labelpad=-40, y=0.45, color = "#000000")
plt.savefig("fishing_effort_outside_seychelles.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]:
In [62]:
# Now fishing days outside the EEZ
q = '''
SELECT
lat,
lon,
count(*) fishing_days
from
(SELECT
mmsi,
date(timestamp) date,
integer(first(latitude)*4) lat,
integer(first(longitude)*4) lon
FROM
[tilesets.pipeline_2015_08_24_08_19_01]
WHERE
latitude > '''+str(min_lat)+'''
AND latitude <'''+str(max_lat)+'''
AND longitude > '''+str(min_lon)+'''
AND longitude < '''+str(max_lon)+'''
AND weight >=.5
//AND JSON_EXTRACT(extra,"$.eez") != '"EEZ:'''+EEZ+'''"'
and float(JSON_EXTRACT(EXTRA,"$.distance_to_shore")) > '''+str(distance_from_shore_filter)+'''
group by mmsi, date)
group by lat, lon
'''
fishing_grid = Query(q)
In [63]:
cellsize = .25
one_over_cellsize = 4
num_lats = (max_lat-min_lat)*one_over_cellsize+1
num_lons = (max_lon-min_lon)*one_over_cellsize+1
grid = np.zeros(shape=(num_lats,num_lons))
for row in fishing_grid:
lat = int(row[0])
lon = int(row[1])
lat_index = lat-min_lat*one_over_cellsize
lon_index = lon-min_lon*one_over_cellsize
grid[lat_index][lon_index] = int(row[2])
In [64]:
plt.rcParams["figure.figsize"] = [8,9]
firstlat = max_lat
lastlat = min_lat
firstlon = min_lon
lastlon = max_lon
scale = cellsize
numlats = int((firstlat-lastlat)/scale+.5)
numlons = int((lastlon-firstlon)/scale+.5)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
fig = plt.figure()
extra = 10
m = Basemap(llcrnrlat=lastlat-extra, urcrnrlat=firstlat+extra,
llcrnrlon=firstlon-extra, urcrnrlon=lastlon+extra, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary(fill_color='#111111')
m.drawcoastlines(linewidth=.2)
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
x = np.linspace(firstlon, lastlon, -(firstlon-lastlon)*one_over_cellsize+1)
y = np.linspace(lastlat, firstlat, (firstlat-lastlat)*one_over_cellsize+1)
x, y = np.meshgrid(x, y)
converted_x, converted_y = m(x, y)
from matplotlib import colors,colorbar
maximum = grid.max()
minimum = 1
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
m.pcolormesh(converted_x, converted_y, grid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('viridis'))
t = "Fishing Days Jan 2014 to July 2015 \n Around Seychelles"
plt.title(t, color = "#000000", fontsize=18)
ax = fig.add_axes([0.2, 0.1, 0.65, 0.02]) #x coordinate ,
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# norm = colors.Normalize(vmin=0, vmax=1000)
lvls = np.logspace(np.log10(minimum),np.log10(maximum),num=8)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='horizontal', ticks=lvls, cmap = plt.get_cmap('viridis'))
#cb.ax.set_xticklabels(["0" ,round(m3**.5,1), m3, round(m3**1.5,1), m3*m3,round(m3**2.5,1), str(round(m3**3,1))+"+"], fontsize=10)
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Days',labelpad=-40, y=0.45, color = "#000000")
plt.savefig("fishing_effort_outside_and_indside_seychelles.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]: