#Pandas method of importing data frame and getting extents

db_connect="dbname='reach_4a' user='root' host='localhost' port='9000'"

conn = psycopg2.connect(db_connect)

df = pd.read_sql_query('SELECT * from mb_may_2012_1m tt inner join ( SELECT s.easting, s.northing, s.texture, s.sidescan_intensity FROM ss_2012_05 s) ss on tt.easting=ss.easting and tt.northing=ss.northing;', con=conn)

minE = df['easting'].min()[0]

maxE = df['easting'].max()[0]

minN = df['northing'].min()[0]

maxN = df['northing'].max()[0]

conn.close()

print 'Done Importing Data from Database'

#Create grid for countourf plot

res = 1

grid_x, grid_y = np.meshgrid( np.arange(np.floor(minE), np.ceil(maxE), res), np.arange(np.floor(minN), np.ceil(maxN), res))

grid_lon, grid_lat = trans(grid_x,grid_y,inverse=True)

#Re-sampling procedure

m_lon, m_lat = trans(df['easting'].values.flatten(), df['northing'].values.flatten(), inverse=True)

orig_def = geometry.SwathDefinition(lons=m_lon, lats=m_lat)

target_def = geometry.SwathDefinition(lons=grid_lon.flatten(), lats=grid_lat.flatten())

print 'Now Resampling...'

result = kd_tree.resample_nearest(orig_def, df['sidescan_intensity'].values.flatten(), target_def, radius_of_influence=1, fill_value=None, nprocs = cpu_count())

print 'Done Resampling!!!'

#format side scan intensities grid for plotting

gridded_result = np.reshape(result,np.shape(grid_lon))

gridded_result = np.squeeze(gridded_result)

gridded_result[np.isinf(gridded_result)] = np.nan

gridded_result[gridded_result<=0] = np.nan

grid2plot = np.ma.masked_invalid(gridded_result)

print 'Now mapping...'

#Create Figure

fig = plt.figure(frameon=True)

ax = plt.subplot(1,1,1)

map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], llcrnrlon=np.min(grid_lon)-0.0009, llcrnrlat=np.min(grid_lat)-0.0009,urcrnrlon=np.max(grid_lon)+0.0009, urcrnrlat=np.max(grid_lat)+0.0009)

gx,gy = map.projtran(grid_lon,grid_lat)

map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=1200)

im = map.pcolormesh(gx, gy, grid2plot, cmap='gray',vmin=0.1, vmax=30)

divider = make_axes_locatable(ax)

cax = divider.append_axes("right", size="5%", pad=0.1)

cbr = plt.colorbar(im, cax=cax)

cbr.set_label('Sidescan Intensity [dBw]', size=8)

for t in cbr.ax.get_yticklabels():

t.set_fontsize(8)