In [1]:
import bq
import time
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
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
import matplotlib.gridspec as gridspec
client = bq.Client.Get()
In [2]:
# create a bounding box:
max_lat = 85
min_lat = -80
max_lon = 180
min_lon = -180
# for the mapping
firstlat = max_lat
lastlat = min_lat
firstlon = min_lon
lastlon = max_lon
# scale of the
cellsize = .25
scale = cellsize
one_over_cellsize = 4
# num_lats = (max_lat-min_lat)*one_over_cellsize+1
# num_lons = (max_lon-min_lon)*one_over_cellsize+1
numlats = int((firstlat-lastlat)/scale+.5)
numlons = int((lastlon-firstlon)/scale+.5)
In [3]:
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 [4]:
q = '''select
sum(a.hours) hours,
IF(b.country IS NULL, "invalid mmsi",b.country) country,
from
(SELECT
SUM(hours) hours,
INTEGER(IF(LENGTH(STRING(mmsi))= 9,LEFT(STRING(mmsi),3), '0')) code
FROM
TABLE_DATE_RANGE([pipeline_classify_logistic_715_fishing.], TIMESTAMP("2015-01-01"), TIMESTAMP("2015-12-31"))
WHERE
lat > '''+str(min_lat)+'''
AND lat <'''+str(max_lat)+'''
AND lon > '''+str(min_lon)+'''
AND lon < '''+str(max_lon)+'''
AND measure_new_score >=.5
AND eez is null
GROUP BY
code)a
left join
[scratch_roan.country_code] b
ON
a.code = b.code
group by country
order by hours desc'''
bycountry_hours = Query(q)
In [5]:
for c in bycountry_hours:
print int(float(c[0])),c[1]
In [6]:
tots = np.array([int(float(c[0])) for c in bycountry_hours])
print tots[0:5].sum()/float(tots.sum())
In [7]:
labels = country_names = ["China","Spain","Taiwan","Japan","Korea","All Other Nations"]
sizes = list(tots[0:5]) #+ [tots[5:].sum()]
sizes.append(tots[5:].sum())
thecolors = ['#2A4879', '#335691', '#3964A8', '#607FBA','#8DA1CA', '#B5C1DB']
thecolors = ["#4E000E","#701F29","#8E4143","#AF6864","#D59587","#FECBB5"]
plt.rcParams["figure.figsize"] = [5,5]
# explode = (0, 0, 0, 0) # proportion with which to offset each wedge
plt.pie(sizes, # data
# explode=explode, # offset parameters
labels=labels, # slice labels
colors=thecolors, # array of colours
autopct='%1.f%%', # print the values inside the wedges
# shadow=True, # enable shadow
startangle=270 # starting angle
)
plt.title("Fishing in the High Seas\nTop 5 Countries Account for 80% of Fishing")
plt.savefig("piechart.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [22]:
top_countries = ["China (People's Republic of)", "Spain",
"Taiwan (Province of China) - China (People's Republic of)",
"Japan", "Korea (Republic of)"]
country_names = ["China","Spain","Taiwan","Japan","Korea"]
country_query_string = '''("China (People's Republic of)", "Spain",
"Taiwan (Province of China) - China (People's Republic of)",
"Japan", "Korea (Republic of)")'''
In [10]:
def get_area(lat):
lat_degree = 69 # miles
# Convert latitude and longitude to
# spherical coordinates in radians.
degrees_to_radians = math.pi/180.0
# phi = 90 - latitude
phi = (lat+cellsize/2.)*degrees_to_radians #plus half a cell size to get the middle
lon_degree = math.cos(phi)*lat_degree
# return 69*69*2.6
return lat_degree*lon_degree* 2.58999 # miles to square km
In [15]:
get_area(lat*float(cellsize))*cellsize*cellsize
Out[15]:
In [16]:
def get_grid(c,allcountries=False):
if allcountries:
extra = 'where b.country not in '+ country_query_string
else:
extra = 'where b.country = "'+c+'"'
q = '''select
a.lat_bin lat_bin,
a.lon_bin lon_bin,
sum(a.hours) hours,
// IF(b.country IS NULL, "invalid mmsi",b.country) country,
from
(SELECT
INTEGER(FLOOR(lat*4)) lat_bin,
INTEGER(FLOOR(lon*4)) lon_bin,
SUM(hours*measure_new_score) hours,
INTEGER(IF(LENGTH(STRING(mmsi))= 9,LEFT(STRING(mmsi),3), '0')) code
FROM
(select lat, lon, mmsi, measure_new_score, hours, seg_id from
TABLE_DATE_RANGE([pipeline_classify_logistic_715_fishing.], TIMESTAMP("2015-01-01"), TIMESTAMP("2015-12-31"))
WHERE
lat > '''+str(min_lat)+'''
AND lat <'''+str(max_lat)+'''
AND lon > '''+str(min_lon)+'''
AND lon < '''+str(max_lon)+'''
AND measure_new_score >=.5
and eez is null)
where seg_id NOT IN ( // eliminate clearly bad segments
SELECT
seg_id
FROM
[scratch_david_seg_analysis_715.2015_segments]
WHERE
(point_count<20
AND terrestrial_positions = point_count)
OR ((min_lon >= 0 // these are almost definitely noise
AND max_lon <= 0.109225)
OR (min_lat >= 0
AND max_lat <= 0.109225) ))
and mmsi not in (412437961,412437962,412420502,412420503,412420576,412420574,412420789,412420871,
900025357,900025393,413322650,414203586,412211196,412440255,412440256,412440257,412440258,412440259,
412440261,150200162,412440077,412440078,412420805,412420421,412440377,412425706,412447093,412211691,
412420276,412420917,411041815, 525018188, 412420276,412420561,533180156)
GROUP BY
lat_bin,
lon_bin,
code)a
left join
[scratch_roan.country_code] b
ON
a.code = b.code
'''+extra+'''
group by lat_bin, lon_bin'''
# print q
fishing_bined = Query(q)
grid = np.zeros(shape=(numlats,numlons))
for row in fishing_bined:
lat = int(row[0])
lon = int(row[1])
lat_index = lat-min_lat*one_over_cellsize
lon_index = lon-min_lon*one_over_cellsize
area = get_area(lat*float(cellsize))*cellsize*cellsize
grid[lat_index][lon_index] = float(row[2])/ area
return grid
In [17]:
all_grids = []
for i in range(5):
all_grids.append(get_grid(top_countries[i]))
In [18]:
all_grids.append(get_grid("",True))
In [97]:
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
plt.figure(1)
fig_max_value = 1500
fig_min_value = .01
plt.rcParams["figure.figsize"] = [9,14]
# fig = plt.figure()
# st = fig.suptitle("Fishing in the High Seas by Country", fontsize="x-large")
for i in range(5):
fig_title = country_names[i]
thegrid = get_grid(top_countries[i])
plt.subplot(421+i)
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, thegrid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title(fig_title, color = "#000000", fontsize=12)
plt.grid(True)
fig_title = "Other Nations"
thegrid = get_grid("",True)
plt.subplot(426)
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, thegrid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title("Other Nations", color = "#000000", fontsize=12)
plt.grid(True)
# ax = fig.add_axes([0.2, 0.1, 0.6, 0.02]) #x coordinate ,
# norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# 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('Reds'))
# cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
# cb.set_label('Fishing Hours',labelpad=-40, y=0.45, color = "#000000")
# shift subplots down:
# st.set_y(0.95)
# fig.subplots_adjust(top=0.85)
plt.savefig("HighSeasFishing.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [20]:
'''Make a custom colormap'''
tm = 255
thecolors = [['#FDD0BD',0,0], # a small vaiation on 'Reds' colormap
['#620110',255,255]]
cdict = { 'red':tuple( (color[2]/tm, int(color[0][1:3],16)/256.0, int(color[0][1:3],16)/256.0) for color in thecolors ),
'green':tuple( (color[2]/tm, int(color[0][3:5],16)/256.0, int(color[0][3:5],16)/256.0) for color in thecolors ),
'blue':tuple( (color[2]/tm, int(color[0][5:7],16)/256.0, int(color[0][5:7],16)/256.0) for color in thecolors )}
mycmap = colors.LinearSegmentedColormap('my_colormap',cdict,256)
thecolors = [['#FDD0BD',0,0], # a small vaiation on 'Reds' colormap
['#62000C',255,255]]
cdict = { 'red':tuple( (color[2]/tm, int(color[0][1:3],16)/256.0, int(color[0][1:3],16)/256.0) for color in thecolors ),
'green':tuple( (color[2]/tm, int(color[0][3:5],16)/256.0, int(color[0][3:5],16)/256.0) for color in thecolors ),
'blue':tuple( (color[2]/tm, int(color[0][5:7],16)/256.0, int(color[0][5:7],16)/256.0) for color in thecolors )}
mycmap_redder = colors.LinearSegmentedColormap('my_colormap',cdict,256)
In [81]:
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
plt.figure(1)
fig_max_value = 1000
fig_min_value = .01
plt.rcParams["figure.figsize"] = [9,14]
# fig = plt.figure()
# st = fig.suptitle("Fishing in the High Seas by Country", fontsize="x-large")
for i in range(5):
fig_title = country_names[i]
thegrid = get_grid(top_countries[i])
plt.subplot(421+i)
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, thegrid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title(fig_title, color = "#000000", fontsize=12)
plt.grid(True)
fig_title = "Other Nations"
thegrid = get_grid("",True)
plt.subplot(426)
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="h")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, thegrid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title("Other Nations", color = "#000000", fontsize=12)
plt.grid(True)
# ax = fig.add_axes([0.2, 0.1, 0.6, 0.02]) #x coordinate ,
# norm = colors.LogNorm(vmin=minimum, vmax=maximum)
# 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('Reds'))
# cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
# cb.set_label('Fishing Hours',labelpad=-40, y=0.45, color = "#000000")
# shift subplots down:
# st.set_y(0.95)
# fig.subplots_adjust(top=0.85)
plt.savefig("HighSeasFishing2.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
Out[81]:
In [91]:
plt.rcParams["figure.figsize"] = [9,15]
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
plt.figure(1)
# linear
plt.subplot(421)
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.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("China")
plt.grid(True)
# log
plt.subplot(422)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# symmetric log
plt.subplot(423)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.05)
plt.title('symlog')
plt.grid(True)
# logit
plt.subplot(424)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# linear
plt.subplot(425)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
# log
plt.subplot(426)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# plt.grid(True)
plt.savefig("temp.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]:
plt.rcParams["figure.figsize"] = [10,10]
fig = plt.figure()
gs = gridspec.GridSpec(3, 2, wspace=0.05, hspace=0.10)
ax1 = plt.subplot(gs[0])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180+lon_offset, urcrnrlon=180+lon_offset, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("China")
ax2 = plt.subplot(gs[1])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180+lon_offset, urcrnrlon=180+lon_offset, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary(fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("U.S.")
ax3 = plt.subplot(gs[2])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180, urcrnrlon=180, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("Argentina")
ax4 = plt.subplot(gs[3])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180, urcrnrlon=180, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("South Africa")
ax3 = plt.subplot(gs[4])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180, urcrnrlon=180, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("Argentina")
ax4 = plt.subplot(gs[5])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180, urcrnrlon=180, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
plt.title("South Africa")
ax4 = fig.add_axes([0.95, 0.25, 0.025, 0.5]) #x coordinate ,#plt.subplot(gs[4:6])
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=6)
cb = colorbar.ColorbarBase(ax4,norm = norm, orientation='vertical',ticks=lvls, cmap = mycmap)#plt.get_cmap('Reds'))#
cb.ax.set_yticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Nice Colorbar',labelpad=-65, y=0.45, color = "#000000")
fig.suptitle("Beautiful Maps", fontsize=20)
plt.subplots_adjust(top=0.92)
plt.show()
In [56]:
fig_min_value = 1
fig_max_value = 1000
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
fig = plt.figure()
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, all_girds[0], norm=norm, vmin=minimum, vmax=maximum, cmap = mycmap)# plt.get_cmap('Reds'))
plt.title("China", color = "#000000", fontsize=12)
plt.show()
In [57]:
fig_min_value = 1
fig_max_value = 1000
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
fig = plt.figure()
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon, urcrnrlon=lastlon, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, all_girds[0], norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title("China", color = "#000000", fontsize=12)
plt.show()
In [23]:
country_names.append("All Other Nations")
In [43]:
fig_min_value = 1
fig_max_value = 1500
lon_offset = 0
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
plt.rcParams["figure.figsize"] = [10,10]
fig = plt.figure()
gs = gridspec.GridSpec(3, 2, wspace=0.05, hspace=0.10)
for i in range(6):
ax = plt.subplot(gs[i])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180+lon_offset, urcrnrlon=180+lon_offset, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, all_grids[i]*1000, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))# plt.get_cmap('Reds'))
plt.title(country_names[i], color = "#000000", fontsize=12)
ax = fig.add_axes([0.95, 0.25, 0.02, 0.5]) #x coordinate
norm = colors.LogNorm(vmin=fig_min_value, vmax=fig_max_value)
lvls = np.logspace(np.log10(fig_min_value),np.log10(fig_max_value),num=6)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='vertical',ticks=lvls, cmap = plt.get_cmap('Reds')) # plt.get_cmap('Reds'))
cb.ax.set_yticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours per 1000 $\mathregular{km^{2}}$',labelpad=-65, y=0.45, color = "#000000")
fig.suptitle("Fishing Effort in the High Seas", fontsize=20)
plt.subplots_adjust(top=0.92)
plt.savefig("HighSeasFishing_reds.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [30]:
all_fishing = np.zeros(shape=(numlats,numlons))
for g in all_grids:
all_fishing = np.add(g,all_fishing)
In [50]:
fig_min_value = 1
fig_max_value = 10000
lon_adjust = 0 # adjust to move the map
plt.rcParams["figure.figsize"] = [10,7]
x = np.linspace(firstlon+lon_adjust, lastlon+lon_adjust, -(firstlon-lastlon)*one_over_cellsize+1)
y = np.linspace(lastlat, firstlat, (firstlat-lastlat)*one_over_cellsize+1)
x, y = np.meshgrid(x, y)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon+lon_adjust,lastlon+lon_adjust,num=numlons,endpoint=False)
offset_grid = np.zeros((numlats,numlons))
i = 0
pixel_offset = lon_adjust*one_over_cellsize
offset_grid[:,0:len(all_fishing[0]) - pixel_offset] = all_fishing[:,pixel_offset:len(all_fishing[0])]
offset_grid[:,len(all_fishing[0]) - pixel_offset:len(all_fishing[0])] = all_fishing[:,0:pixel_offset]
fig = plt.figure()
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon+lon_adjust, urcrnrlon=lastlon+lon_adjust, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, offset_grid*1000, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title("Fishing Effort on the High Seas", color = "#000000", fontsize=12)
ax = fig.add_axes([0.25, 0.1, 0.5, 0.02]) #x coordinate ,
norm = colors.LogNorm(vmin=fig_min_value, vmax=fig_max_value)
lvls = np.logspace(np.log10(fig_min_value),np.log10(fig_max_value),num=5)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='horizontal',ticks=lvls, cmap = plt.get_cmap('Reds')) # plt.get_cmap('Reds'))
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours per 1000 $\mathregular{km^{2}}$',labelpad=-40, y=0.45, color = "#000000")
plt.show()
In [116]:
fig_min_value = 1
fig_max_value = 1000
lon_adjust = 205
x = np.linspace(firstlon+lon_adjust, lastlon+lon_adjust, -(firstlon-lastlon)*one_over_cellsize+1)
y = np.linspace(lastlat, firstlat, (firstlat-lastlat)*one_over_cellsize+1)
x, y = np.meshgrid(x, y)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon+lon_adjust,lastlon+lon_adjust,num=numlons,endpoint=False)
offset_grid = np.zeros((numlats,numlons))
i = 0
pixel_offset = lon_adjust*one_over_cellsize
offset_grid[:,0:len(all_grids[i][0]) - pixel_offset] = all_grids[i][:,pixel_offset:len(all_grids[i][0])]
offset_grid[:,len(all_grids[i][0]) - pixel_offset:len(all_grids[i][0])] = all_grids[i][:,0:pixel_offset]
fig = plt.figure()
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon+lon_adjust, urcrnrlon=lastlon+lon_adjust, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()#fill_color='#111111')
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, offset_grid, norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title("China", color = "#000000", fontsize=12)
plt.show()
In [ ]:
In [124]:
fig_min_value = 1
fig_max_value = 1000
lon_adjust = 205
x = np.linspace(firstlon+lon_adjust, lastlon+lon_adjust, -(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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon+lon_adjust,lastlon+lon_adjust,num=numlons,endpoint=False)
pixel_offset = lon_adjust*one_over_cellsize
plt.rcParams["figure.figsize"] = [10,10]
fig = plt.figure()
gs = gridspec.GridSpec(3, 2, wspace=0.05, hspace=0.10)
for i in range(6):
offset_grid = np.zeros((numlats,numlons))
offset_grid[:,0:len(all_grids[i][0]) - pixel_offset] = all_grids[i][:,pixel_offset:len(all_grids[i][0])]
offset_grid[:,len(all_grids[i][0]) - pixel_offset:len(all_grids[i][0])] = all_grids[i][:,0:pixel_offset]
ax = plt.subplot(gs[i])
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=firstlon+lon_adjust, urcrnrlon=lastlon+lon_adjust, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, offset_grid, norm=norm, vmin=minimum, vmax=maximum, cmap = mycmap_redder)# plt.get_cmap('Reds'))
plt.title(country_names[i], color = "#000000", fontsize=12)
ax = fig.add_axes([0.95, 0.25, 0.02, 0.5]) #x coordinate
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=7)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='vertical',ticks=lvls, cmap = mycmap_redder)#plt.get_cmap('Reds'))
cb.ax.set_yticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours',labelpad=-65, y=0.45, color = "#000000")
fig.suptitle("Fishing Effort in the High Seas", fontsize=20)
plt.subplots_adjust(top=0.92)
plt.savefig("HighSeasFishing_reds_oceans.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]:
fig_min_value = 1
fig_max_value = 1000
lon_offset = 0
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)
lat_boxes = np.linspace(lastlat,firstlat,num=numlats,endpoint=False)
lon_boxes = np.linspace(firstlon,lastlon,num=numlons,endpoint=False)
plt.rcParams["figure.figsize"] = [10,10]
fig = plt.figure()
gs = gridspec.GridSpec(3, 2, wspace=0.05, hspace=0.10)
for i in range(6):
ax = plt.subplot(gs[i])
m = Basemap(llcrnrlat=-85, urcrnrlat=85,
llcrnrlon=-180+lon_offset, urcrnrlon=180+lon_offset, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()
m.fillcontinents('#cccccc',lake_color='#cccccc')#, lake_color, ax, zorder, alpha)
converted_x, converted_y = m(x, y)
maximum = fig_max_value
minimum = fig_min_value
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
m.pcolormesh(converted_x, converted_y, all_grids[i], norm=norm, vmin=minimum, vmax=maximum, cmap = plt.get_cmap('Reds'))
plt.title(country_names[i], color = "#000000", fontsize=12)
ax = fig.add_axes([0.95, 0.25, 0.02, 0.5]) #x coordinate
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=7)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='vertical',ticks=lvls, cmap = plt.get_cmap('Reds'))
cb.ax.set_yticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours',labelpad=-65, y=0.45, color = "#000000")
fig.suptitle("Fishing Effort in the High Seas", fontsize=20)
plt.subplots_adjust(top=0.92)
plt.savefig("HighSeasFishing_reds.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()