from __future__ import division
import numpy as np
import scipy.io as sio

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.patches as patches


topo_datastruct = sio.loadmat('/ocean/rich/more/mmapbase/bcgeo/PNW.mat')
coast={}
coast['lat'] = topo_datastruct['ncst'][:,1]
coast['lon'] = topo_datastruct['ncst'][:,0]
k=topo_datastruct['k']
Area=topo_datastruct['Area']

print topo_datastruct['data_source']

def separate_polygons(a):
    return [a[s] for s in np.ma.clump_unmasked(np.ma.masked_invalid(a))]

poly_lats = separate_polygons(coast['lat'])
poly_lons = separate_polygons(coast['lon'])

fig,ax=plt.subplots(1,1)
for x,y in zip(poly_lons,poly_lats):
    poly=zip(x,y)
    ax.add_patch(patches.Polygon(poly,closed=True,facecolor='burlywood'))
ax.set_xlim([-126,-121])
ax.set_ylim([48,51])

fig.savefig('test_all.svg')
! ls -lh test_all.svg

fig.savefig('test_all.png')
! ls -lh test_all.png

print coast['lon'][k[1]-2]


fig,ax=plt.subplots(1,1)
for ks,ke,A in zip(k[0:-1],k[1:],Area[0,:]):
    if A > 1e-4:
        poly=zip(coast['lon'][ks:ke-2],coast['lat'][ks:ke-2])
        ax.add_patch(patches.Polygon(poly,closed=True,facecolor='burlywood',rasterized=True))
ax.set_xlim([-126,-121])
ax.set_ylim([48,51])

fig.savefig('test.svg')
! ls -lh test.svg

import SVGCompress

SVGCompress.compress_by_method('test_all.svg','merge',outputfile='test_all_compress.svg')

ls -lh test_all.svg

from salishsea_tools.nowcast import figures
import netCDF4 as nc

grid_B=nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')

reload(figures)

fig,ax=plt.subplots(1,1)
figures.plot_map(ax,grid_B,topo_datastruct,'full',0,0)

fig.savefig('test2.svg')
! ls -lh test2.svg