from fvcomClass import FVCOM
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as Tri
import matplotlib.ticker as ticker
filename = '/home/wesley/ncfiles/smallcape_force_0001.nc'
test = FVCOM(filename)
trinodes = test.trinodes
# Wes Comment: ONly works with 1 point right now
X = -66.3385
Y = 44.277
index = test.closest_point([-66.3385],[44.277])[0]
newtri = Tri.Triangulation(test.lon[trinodes[index]],
test.lat[trinodes[index]], np.array([[0,1,2]]))
trif = newtri.get_trifinder()
trif.__call__(-66.3385, 44.277)
averEl = np.mean(test.el[:, trinodes[index]], axis=0)
inter = Tri.LinearTriInterpolator(newtri, averEl)
zi = inter(-66.3385, 44.277)
fig = plt.figure(figsize=(18,10))
ax = fig.add_subplot(111,aspect=(1.0/np.cos(np.mean(test.lat[trinodes[index]])*np.pi/180.0)))
import matplotlib.pyplot as plt
from shortest_element_path import shortest_element_path
from fvcomClass import FVCOM
import numexpr as ne
import scipy.io as sio
import matplotlib.tri as Tri
filename = '/EcoII/july_2012/output/dngrid_0001_03.nc'
filename = '/EcoII/june_2013/output/dngrid_0001_week2.nc'
filename = '/EcoII/july_2012/output/dngrid_0001_03.nc'
siglay = np.array([0.98999,0.94999,0.86999,0.74999,0.58999,0.41000,0.25000,0.13000,0.05000,0.01000])
ax = [-66.35, -66.325, 44.261, 44.272]
#data = FVCOM(filename)
data = FVCOM(filename, ax=ax)
data.el_region()
print 'Loading Data'
print 'U'
u = data.u[:, :, data.region_e[:,0]]
print 'V'
v = data.v[:, :, data.region_e[:,0]]
#ww = data.ww[:, :, data.region_e[:,0]]
#vel = ne.evaluate('sqrt(u**2 + v**2 + ww**2)')
vel = ne.evaluate('sqrt(u**2 + v**2)')
mean_vel = np.mean(vel, axis=0)
h = data.h
def main(filename, eastwest=True):
#filename = '/array2/data3/rkarsten/july_2012/output/dngrid_0001_02.nc'
#filename = '/EcoII/june_2013/output/dngrid_0001_week2.nc'
#filename = '/EcoII/july_2012/output/dngrid_0001_03.nc'
#eastwest = True
siglay = np.array([0.98999,0.94999,0.86999,0.74999,0.58999,0.41000,0.25000,0.13000,0.05000,0.01000])
data = FVCOM(filename)
if eastwest:
# East- West
ind = data.closest_point([-66.3419, -66.3324], [44.2778, 44.2778])
else:
# North-South
ind = data.closest_point([-66.3385, -66.3385], [44.2815, 44.2755])
short_path = shortest_element_path(data.lonc, data.latc,
data.lon, data.lat,
data.nv, data.h)
#short_path = shortest_element_path(data.xc, data.yc,
# data.x, data.y,
# data.nv, data.h)
el, _ = short_path.getTargets([ind])
#short_path.graphGrid()
#plt.show()
#saveName = './figures/e-wPath.png'
#plt.savefig(saveName, bbox_inches=0)
plt.clf()
print 'Path Saved'
t_slice = ['2014-02-02T06:45:00','2014-02-02T07:05:00']
t_slice = np.array(t_slice,dtype='datetime64[us]')
t = data.time.shape[0]
l = []
for i in range(t):
date = datetime.fromordinal(int(data.time[i]))+timedelta(days=data.time[i]%1)-timedelta(days=366)
l.append(date)
time = np.array(l,dtype='datetime64[us]')
if t_slice.shape[0] != 1:
argtime = np.argwhere((time>=t_slice[0])&(time<=t_slice[-1])).flatten()
#vel = np.sqrt(nc['u'][argtime,:,el]**2+nc['v'][argtime,:,el]**2+nc['ww'][argtime,:,el]**2)
#vel = np.sqrt(data.u[argtime,:,el]**2+data.u[argtime,:,el]**2+data.ww[argtime,:,el]**2)
print len(el[0])
print 'Loading timeseries'
#vel = np.sqrt(data.u[:, :, el[0]]**2 + data.v[:, :, el[0]]**2 + data.ww[:, :, el[0]]**2)
print 'U'
u = data.u[:, :, el[0]]
print 'V'
v = data.v[:, :, el[0]]
#ww = data.ww[:, :, el[0]]
print 'Calculating'
#vel = ne.evaluate('sqrt(u**2 + v**2 + ww**2)')
vel = ne.evaluate('sqrt(u**2 + v**2)')
mean_vel = np.mean(vel, axis=0)
lat = data.latc[el]
lon = data.lonc[el]
if eastwest:
line = lon
else:
line = lat
print data.time[0]
new = date2py(data.time[0])
print new
print vel.shape
print mean_vel.shape
print 'Calculating elc and hc'
size = data.trinodes.T[el].shape[0]
size1 = data.el.shape[0]
elc = np.zeros((size1, size))
hc = np.zeros((size))
for ind,value in enumerate(data.trinodes.T[el[0]]):
elc[:, ind] = np.mean(data.el[:, value-1], axis=1)
hc[ind] = np.mean(data.h[value-1])
print 'Saving mat file'
mat = {'u':u, 'v':v, 'latc':lat, 'lonc':lon, 'time':data.time,
'siglay':data.siglay, 'siglev':data.siglev, 'ua':data.ua[:, el[0]],
'va':data.va[:, el[0]],
'elc':elc, 'hc':hc}
if eastwest:
sio.savemat('east-west.mat', mat)
else:
sio.savemat('north-south.mat', mat)
vmax = 2.5
vmin = 0
print 'Plotting'
fig,ax = plt.subplots()
plt.rc('font',size='22')
levels = np.linspace(0,3.3,34)
cs = ax.contourf(line,siglay,mean_vel,levels=levels, cmap=plt.cm.jet)
ax.contour(line,siglay,mean_vel,cs.levels, colors='k')
cbar = fig.colorbar(cs,ax=ax)
cbar.set_label(r'Velocity $(m/s)$', rotation=-90,labelpad=30)
if eastwest:
ax.set_xlabel('Longitude')
else:
ax.set_xlabel('Latitude')
ax.set_title('vel_mean')
scale = 1
ticks = ticker.FuncFormatter(lambda lon, pos: '{0:g}'.format(lon/scale))
ax.xaxis.set_major_formatter(ticks)
ax.yaxis.set_major_formatter(ticks)
if eastwest:
saveName = './figures/eastwest/mean_vel.png'.format(i)
else:
saveName = './figures/northsouth/mean_vel.png'.format(i)
#plt.show()
plt.savefig(saveName, bbox_inches=0)
plt.clf()
for i in range(vel.shape[0]):
print i
fig,ax = plt.subplots()
plt.rc('font',size='12')
levels = np.linspace(0,3.3,34)
cs = ax.contourf(line,siglay,vel[i,:],levels=levels, cmap=plt.cm.jet)
ax.contour(line,siglay,vel[i,:],cs.levels,colors='k',hold='on')
cbar = fig.colorbar(cs,ax=ax)
cbar.set_label(r'Velocity $(m/s)$', rotation=-90,labelpad=30)
title = '{}'.format(date2py(data.time[i]))
ax.set_title(title)
if eastwest:
ax.set_xlabel('Longitude')
else:
ax.set_xlabel('Latitude')
scale = 1
ticks = ticker.FuncFormatter(lambda lon, pos: '{0:g}'.format(lon/scale))
ax.xaxis.set_major_formatter(ticks)
ax.yaxis.set_major_formatter(ticks)
if eastwest:
saveName = './figures/eastwest/figure{0:>04d}.png'.format(i)
else:
saveName = './figures/northsouth/figure{0:>04d}.png'.format(i)
#plt.show()
plt.savefig(saveName, bbox_inches=0)
plt.clf()
