In [4]:
from math import radians, cos, sin, asin, sqrt
import matplotlib
%matplotlib inline
from matplotlib import colors,colorbar
from matplotlib.patches import PathPatch,Polygon
from matplotlib.path import Path
from mpl_toolkits.basemap import Basemap
from numpy import asarray, concatenate, ones
from shapely.geometry import Polygon, MultiPolygon, shape
from shapely.ops import transform
import bq
import csv
import fiona
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import time
client = bq.Client.Get()
In [2]:
# create a bounding box:
max_lat = 80
min_lat = -80
max_lon = 180
min_lon = -180
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 [71]:
def draw_screen_poly( outside_ring, m, color, linewidth, opacity):
lons = []
lats = []
for point in outside_ring:
lats.append(point[1])
l = point[0]
lons.append(l)
x, y = m( lons, lats )
xy = zip(x,y)
poly = Polygon( xy, facecolor=color,fill=False, alpha=1,lw=1.5, edgecolor="#222222" ,zorder=10)
plt.gca().add_patch(poly)
In [72]:
def makeMap(grid,fig_title,fig_min_value,fig_max_value):
plt.figure(figsize=(12, 8))
ax = plt.subplot(111)
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)
extra = 0
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 = fig_max_value # grid.max()
minimum = fig_min_value #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 = fig_title
plt.title(t, color = "#000000", fontsize=18)
theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
for p in theShapes:
geo = p['geometry']
sh = transform(m, shape(geo)) #transform the object to the projected map
if geo['type'] == 'Polygon':
draw_poly(sh)
elif geo['type'] == 'MultiPolygon':
for p in sh:
draw_poly(p)
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=7)
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 Hours per Quarter Degree Square',labelpad=-40, y=0.45, color = "#000000")
plt.savefig(fig_title+".png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [ ]:
In [74]:
# Map Fishing in High Seas
q = '''
SELECT
INTEGER(floor(lat*4)) lat_bin,
INTEGER(floor(lon*4)) lon_bin,
SUM(hours) hours
FROM
(select lat, lon, hours, measure_new_score, seg_id, eez, mmsi 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) 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)
and ( eez is null or eez = 'EEZ:Antarctica')
GROUP BY
lat_bin,
lon_bin
'''
# print q
fishing_grid = Query(q)
In [75]:
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] = float(row[2])
In [5]:
# Map Fishing Eve
q = '''
SELECT
INTEGER(floor(lat*4)) lat_bin,
INTEGER(floor(lon*4)) lon_bin,
SUM(hours) hours
FROM
(select lat, lon, hours, measure_new_score, seg_id, eez, mmsi 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)
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
'''
# print q
fishing_grid_all = Query(q)
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_all = np.zeros(shape=(num_lats,num_lons))
for row in fishing_grid_all:
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_all[lat_index][lon_index] = float(row[2])
In [27]:
grid2 = np.copy(grid).flatten()
# grid2[grid2 == 0] = np.nan
# the histogram of the data
n, bins, patches = plt.hist(np.log(grid2), 3000, normed=1, facecolor='green', alpha=0.75)
# add a 'best fit' line
# y = mlab.normpdf( bins, mu, sigma)
# l = plt.plot(bins, y, 'r--', linewidth=1)
plt.xlabel('Fishing Hours')
plt.ylabel('')
plt.title(r'Histogram of Fishing Hours by Quarter Degree Grid')
plt.axis([5, 1000, 0, 0.005])
plt.grid(True)
plt.show()
In [132]:
# makeMap(grid,"Fishing Effort in the High Seas, 2015",1,1000)
fig_title = "Fishing Effort in the High Seas, 2015"
file_name = 'all2'
fig_min_value = 1
fig_max_value = 1000
# plt.figure(figsize=(12, 8))
fig, [ax1, ax2] = plt.subplots(2,1,figsize=(12,8))
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)
m = Basemap(llcrnrlat=lastlat, urcrnrlat=firstlat,
llcrnrlon=lastlon, urcrnrlon=firstlon, lat_ts=0, projection='robin',resolution="h", lon_0=0)
m.drawmapboundary(fill_color='#111111')
m.fillcontinents('#353535',lake_color='#353535')#, 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)
maximum = fig_max_value # grid.max()
minimum = fig_min_value #1
norm = colors.LogNorm(vmin=fig_min_value, vmax=fig_max_value)
m.pcolormesh(converted_x, converted_y, grid_all, norm=norm, vmin=fig_min_value,
vmax=fig_max_value, cmap = plt.get_cmap('viridis'))
plt.title(fig_title, color = "#ffffff", fontsize=18)
# world = shape({"type": "Polygon", "coordinates": [[[-180,-90],[-180,90],[180,90],[180,-90],[-180,-90]]]})
# theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
# for p in theShapes:
# geo = p['geometry']
# geom = shape(geo)
# geom = geom.intersection(world)
# sh = transform(m, geom) #transform the object to the projected map
# if geo['type'] == 'Polygon':
# draw_poly(sh)
# elif geo['type'] == 'MultiPolygon':
# for p in sh:
# draw_poly(p)
ax = fig.add_axes([0.2, 0.1, 0.65, 0.02]) #x coordinate ,
norm = colors.LogNorm(vmin=minimum, vmax=maximum)
lvls = np.logspace(np.log10(minimum),np.log10(maximum),num=7)
cb = colorbar.ColorbarBase(ax,norm = norm, orientation='horizontal', ticks=lvls, cmap = plt.get_cmap('viridis'))
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours per Quarter Degree Square',labelpad=-40, y=0.45, color = "#000000")
plt.savefig(file_name+".png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1, facecolor="#000000")
plt.show()
In [31]:
plt.figure(figsize=(12, 8))
ax = plt.subplot(111)
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)
extra = 0
m = Basemap(llcrnrlat=lastlat-extra, urcrnrlat=firstlat+extra,
llcrnrlon=firstlon-extra, urcrnrlon=lastlon+extra, lat_ts=0, projection='mill',resolution="l")
m.drawmapboundary()#fill_color='#111111')
# m.drawcoastlines(linewidth=.2)
m.fillcontinents('#111111',lake_color='#111111')#, lake_color, ax, zorder, alpha)
plt.title("temp", color = "#000000", fontsize=18)
theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
for p in theShapes:
geo = p['geometry']
geom = shape(geo)
geom = geom.intersection(world)
sh = transform(m, geom) #transform the object to the projected map
if geo['type'] == 'Polygon':
draw_poly(sh,ax)
elif geo['type'] == 'MultiPolygon':
for p in sh:
draw_poly(p,ax)
# plt.savefig(fig_title+".png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [15]:
plt.figure(figsize=(12, 8))
ax = plt.subplot(111)
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)
lon_adjust = 0
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('#111111',lake_color='#111111')#, lake_color, ax, zorder, alpha)
plt.title("temp", color = "#000000", fontsize=18)
theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
for p in theShapes:
geo = p['geometry']
geom = shape(geo)
# geom = geom.intersection(world)
sh = transform(m, geom) #transform the object to the projected map
if geo['type'] == 'Polygon':
draw_poly(sh)
elif geo['type'] == 'MultiPolygon':
for p in sh:
draw_poly(p)
# plt.savefig(fig_title+".png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [27]:
'''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 [38]:
fig_min_value = 1
fig_max_value = 1000
lon_adjust = 0
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(grid[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(figsize=(12, 8))
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, grid, norm=norm, vmin=minimum, vmax=maximum, cmap = mycmap_redder)
plt.title("Fishing Effort in the High Seas, 2015", color = "#000000", fontsize=18)
ax4 = fig.add_axes([0.25, 0.1, 0.5, 0.025]) #x coordinate ,#plt.subplot(gs[4:6])
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=7)
cb = colorbar.ColorbarBase(ax4,norm = norm, orientation='horizontal',ticks=lvls, cmap = mycmap_redder)#
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours',labelpad=-45, y=0.45, color = "#000000")
plt.savefig("High_Seas_noEEZ.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [49]:
# plt.rcParams["figure.figsize"] = [12,8]
def draw_poly(poly, ax):
path = pathify(poly)
patch = PathPatch(path, facecolor='#000000', lw=.3, alpha = 1 ,zorder=1,fill=False) #787878
ax.add_patch(patch)
##########FUNCTIONS FOR DRAWING POLYGONS##########
def ring_coding(ob):
# The codes will be all "LINETO" commands, except for "MOVETO"s at the
# beginning of each subpath
n = len(ob.coords)
codes = ones(n, dtype=Path.code_type) * Path.LINETO
codes[0] = Path.MOVETO
return codes
def pathify(polygon):
# Convert coordinates to path vertices. Objects produced by Shapely's
# analytic methods have the proper coordinate order, no need to sort.
vertices = concatenate(
[asarray(polygon.exterior)]
+ [asarray(r) for r in polygon.interiors])
codes = concatenate(
[ring_coding(polygon.exterior)]
+ [ring_coding(r) for r in polygon.interiors])
return Path(vertices, codes)
In [76]:
fig_min_value = 1
fig_max_value = 1000
lon_adjust = 0
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(grid[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(figsize=(12, 8))
ax = plt.subplot(111)
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, grid, norm=norm, vmin=minimum, vmax=maximum, cmap = mycmap_redder)
plt.title("Fishing Effort in the High Seas, 2015", color = "#000000", fontsize=18)
theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
for p in theShapes:
if p['properties']['Sovereign'] != 'Antarctica':
geo = p['geometry']
geom = shape(geo)
# geom = geom.intersection(world)
sh = transform(m, geom) #transform the object to the projected map
if geo['type'] == 'Polygon':
draw_poly(sh,ax)
elif geo['type'] == 'MultiPolygon':
for p in sh:
draw_poly(p,ax)
ax4 = fig.add_axes([0.25, 0.1, 0.5, 0.025]) #x coordinate ,#plt.subplot(gs[4:6])
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=7)
cb = colorbar.ColorbarBase(ax4,norm = norm, orientation='horizontal',ticks=lvls, cmap = mycmap_redder)#
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours',labelpad=-45, y=0.45, color = "#000000")
plt.savefig("High_Seas_EEZ2.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [7]:
fig_min_value = 1
fig_max_value = 10000
firstlat = max_lat
lastlat = min_lat
firstlon = min_lon
lastlon = max_lon
scale = cellsize
the_cmap = 'vidris'
lon_adjust = 0
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(grid[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(figsize=(12, 8))
ax = plt.subplot(111)
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, grid_all, norm=norm, vmin=minimum, vmax=maximum, cmap = the_cmap)
plt.title("Fishing Effort in 2015", color = "#000000", fontsize=18)
# theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
# for p in theShapes:
# geo = p['geometry']
# geom = shape(geo)
# # geom = geom.intersection(world)
# sh = transform(m, geom) #transform the object to the projected map
# if geo['type'] == 'Polygon':
# draw_poly(sh,ax)
# elif geo['type'] == 'MultiPolygon':
# for p in sh:
# draw_poly(p,ax)
ax4 = fig.add_axes([0.25, 0.1, 0.5, 0.025]) #x coordinate ,#plt.subplot(gs[4:6])
norm = colors.LogNorm(vmin=1, vmax=1000)
lvls = np.logspace(np.log10(1),np.log10(1000),num=7)
cb = colorbar.ColorbarBase(ax4,norm = norm, orientation='horizontal',ticks=lvls, cmap = the_cmap)#
cb.ax.set_xticklabels([int(i) for i in lvls], fontsize=10, color = "#000000")
cb.set_label('Fishing Hours',labelpad=-45, y=0.45, color = "#000000")
plt.savefig("AllFishing.png",bbox_inches='tight',dpi=300,transparent=True,pad_inches=.1)
plt.show()
In [56]:
theShapes = fiona.open('../../data/geospatial/World_EEZ_v8_20140228_LR/World_EEZ_v8_2014.shp')
for p in theShapes:
geo = p['geometry']
if p['properties']['Sovereign'] == 'Antarctica':
print "wow"
In [ ]: