""" from sunpy import Grid import matplotlib.pyplot as plt import numpy as np from hybridgrid import HybridGrid ### ncfile = 'grid' outpath = 'grid/hex' sun = Grid(ncfile) sun.neigh = sun.neigh.astype(int) # Load the data into a hybrid grid class grd = HybridGrid(sun.xp, sun.yp, sun.cells, nfaces=sun.nfaces) grd.create_dual_grid(minfaces=5, outpath=outpath) sun = Grid(outpath) plt.figure() sun.plotmesh(facecolors='none', linewidths=0.2) plt.plot(sun.xv, sun.yv, 'm.') plt.figure() sun.plothist() plt.show()
maskr_gulf = g_gulf.variables['mask_rho'][:]
# Gulf grid
dx = 1; dy = 1;
ax_gulf.plot(xr_gulf2[::dx], yr_gulf2[::dy],
xr_gulf2[::dx].T, yr_gulf2[::dy].T, 'grey',alpha=.2)
if doshelf:
# Shelf grid
dx = 1; dy = 1;
# ax_gulf.plot(xr2_shelf[::dx], yr2_shelf[::dy], \
# xr2_shelf[::dx].T, yr2_shelf[::dy].T, color='darkcyan',alpha=.1)
if dobay:
# Plot Galveston grid
grd.plotmesh(ax=ax_gulf, edgecolors=('grey',), facecolors=('None',), zorder=9)
# if dogulf:
# "Gulf"
# plt.text(0.5, 0.07, 'Gulf', transform = ax_gulf.transAxes,
# axes=ax_gulf, fontsize=16, color='k', alpha=0.8)
if doshelf:
### Shelf
ax_shelf = zoomed_inset_axes(ax_gulf, 1.6, loc=2) # zoom = 6
basemap_gulf.drawcoastlines(ax=ax_shelf)
basemap_gulf.fillcontinents('0.8',ax=ax_shelf)
basemap_gulf.drawstates()
basemap_gulf.drawcountries()
""" Plots a hybrid grid """ from sunpy import Grid import matplotlib.pyplot as plt ### # #ncfile = 'rundata/Plume.nc.0' ncfile = 'grids/hybrid' sun = Grid(ncfile) plt.figure() #sun.plot() sun.plotmesh(facecolors='none') plt.show()
""" Example of how to plot the grid and open boundaries in python """ from sunpy import Grid import matplotlib.pyplot as plt from maptools import Polygon2GIS grdfolder = 'rundata/' kmlfile = 'gis/sfbay_suntans_grid.kml' shpfile = 'gis/sfbay_suntans_grid.shp' utmzone = 10 # Load the grid into a sunpy object sun = Grid(grdfolder) # save the grid to a google earth file #Polygon2GIS(sun.xy,kmlfile,utmzone) # the grid polygons are stored in the 'xy' attribute # save the grid to a gis shapefile #Polygon2GIS(sun.xy,shpfile,utmzone) # Plot the mesh plt.figure() sun.plotmesh() # Plot the boundary conditions sun.plotBC() plt.show()
""" Plots a hybrid grid """ from sunpy import Grid import matplotlib.pyplot as plt ### # #ncfile = 'rundata/Plume.nc.0' ncfile = 'grids/hybrid' sun = Grid(ncfile) plt.figure() #sun.plot() sun.plotmesh(facecolors='none') plt.show()
def run():
# Location of TXLA model output
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
# Matt's
d = netCDF.MFDataset('tracks/matt/2010-05-*.nc', aggdim='ntrac')
p = pyproj.Proj(proj='utm', zone='15')
lonm,latm = p(d.variables['xp'][:], d.variables['yp'][:], inverse=True)
# want one simulation to have a single set of the starting points
d1 = netCDF.MFDataset('tracks/matt/2010-05-23T00.nc', aggdim='ntrac')
lonmstart, latmstart = p(d1.variables['xp'][:,0], d1.variables['yp'][:,0], inverse=True)
# Kristen's
db = netCDF.MFDataset('tracks/galv_b/2010-05-*.nc',aggdim='ntrac')
lonb = np.fliplr(db.variables['lonp'][:]) # flip to be forward in time
latb = np.fliplr(db.variables['latp'][:]) # flip to be forward in time
df = netCDF.MFDataset('tracks/galv_f/2010-05-*.nc',aggdim='ntrac')
lonf = df.variables['lonp'][:]
latf = df.variables['latp'][:]
# Combined
lonk = np.concatenate((lonb,lonf),axis=1)
latk = np.concatenate((latb,latf),axis=1)
grid = tracpy.inout.readgrid(loc)
## Shelf tracks ##
# Eliminate drifters in the bay and outside Matt's domain
# lonvert = np.array([-94.44896376, -94.38868859, -94.38625664, -94.35380894,
# -94.35357294, -94.32114683, -94.32099761, -94.2839534 ,
# -94.28371841, -94.32073196, -94.32015033, -94.35247582,
# -94.35185901, -94.38873039, -94.3886522 , -94.41861373,
# -94.41854639, -94.45541454, -94.45530469, -94.52433946,
# -94.52422355, -94.59322436, -94.59308138, -94.65976418,
# -94.65956549, -94.76770875, -94.76763167, -94.96778311,
# -94.96789969, -95.07604071, -95.07642981, -95.17770396,
# -95.17810623, -95.2795571 , -95.2798657 , -95.31901876,
# -95.3199739 , -95.14404646, -95.14434603, -95.0427632 ,
# -95.04318363, -95.00854345, -95.00884843, -94.9349127 ,
# -94.935271 , -94.90060651, -94.90089718, -94.83385365,
# -94.83415664, -94.76476871, -94.76501578, -94.72799532,
# -94.7285101 , -94.69609675, -94.69634997, -94.62918694,
# -94.62940326, -94.59001667, -94.59019564, -94.45345669, -94.44896376])
# latvert = np.array([ 29.51080435, 29.51099607, 29.48073304, 29.48082425,
# 29.41424186, 29.41432502, 29.3679228 , 29.36800802,
# 29.28529434, 29.28520919, 29.10363677, 29.10355388,
# 28.92804192, 28.92793764, 28.90776056, 28.90766817,
# 28.89152842, 28.89140532, 28.86720038, 28.85483934,
# 28.8326488 , 28.81822849, 28.7940243 , 28.78361653,
# 28.75336181, 28.75277136, 28.74268359, 28.74135471,
# 28.75345628, 28.75261073, 28.78892442, 28.78805142,
# 28.82234232, 28.83349224, 28.85769558, 28.85730671,
# 28.92992664, 29.06675435, 29.0929739 , 29.09382103,
# 29.13417612, 29.134447 , 29.16470469, 29.16525227,
# 29.20358522, 29.20382761, 29.23610029, 29.23654313,
# 29.27286272, 29.273285 , 29.30556746, 29.30577777,
# 29.37638645, 29.37656202, 29.41287395, 29.41321234,
# 29.44751927, 29.44770176, 29.47796222, 29.47850429, 29.51080435])
# # xvert, yvert = grid['basemap'](lonvert, latvert)
# # These are the x,y locations for the normal projection, but want to save in lon/lat
# # xvert = np.array([422614, 428447, 428671, 431812, 431812, 434953, 434953, 438543,
# # 438543, 434953, 434953, 431812, 431812, 428223, 428223, 425306,
# # 425306, 421716, 421716, 414986, 414986, 408255, 408255, 401749,
# # 401749, 391204, 391204, 371685, 371685, 361140, 361140, 351268,
# # 351268, 341397, 341397, 337583, 337583, 354858, 354858, 364730,
# # 364730, 368095, 368095, 375275, 375275, 378640, 378640, 385146,
# # 385146, 391877, 391877, 395467, 395467, 398608, 398608, 405114,
# # 405114, 408928, 408928, 422165, 422614])
# # yvert = np.array([772846, 772846, 769480, 769480, 762076, 762076, 756916, 756916,
# # 747718, 747718, 727526, 727526, 708007, 708007, 705763, 705763,
# # 703968, 703968, 701276, 699930, 697462, 695891, 693199, 692077,
# # 688712, 688712, 687590, 687590, 688936, 688936, 692975, 692975,
# # 696789, 698135, 700827, 700827, 708904, 723936, 726852, 726852,
# # 731340, 731340, 734705, 734705, 738968, 738968, 742557, 742557,
# # 746596, 746596, 750186, 750186, 758038, 758038, 762076, 762076,
# # 765891, 765891, 769256, 769256, 772846])
# # xyverts = np.vstack((xvert,yvert))
# verts = np.vstack((lonvert,latvert))
# # Form path
# path = Path(verts.T)
# # Loop through the drifters as represented by their initial location for lonf,latf
# # Establish a set of indices of the drifters that are outside the desired domain
# ind = np.zeros(lonf.shape[0])
# for i in xrange(lonf.shape[0]):
# if path.contains_point(np.vstack((lonf[i,0],latf[i,0]))):
# ind[i] = 1
# # These only contain tracks that go through the starting points of Matt's runs
# lonknew = lonk[ind.astype(bool),:]
# latknew = latk[ind.astype(bool),:]
# Do plot
# Find initial points (since this is a backward run) to plot as starting points
# moving forward
lonki = []
latki = []
for idrift in xrange(lonk.shape[0]):
# pdb.set_trace()
# print idrift
# Find last non-nan and make sure it is in the desired month start time
ind3 = ~np.isnan(lonk[idrift,:])
#pdb.set_trace()
# only plot if last non-nan (back in time) is in 1 month period
# in order to plot the tracks that "started" in the plotted month
if np.sum(ind3) > 1: # don't want tracks that start on land
# This is for if we care when the drifter stopped
# if t[find(ind3)[-1]] >= datetime(year,startMonth,startDay,0) and \
# t[find(ind3)[-1]] <= datetime(year,startMonth+1,startDay,0):
# ind2 = ~np.isnan(lonk[idrift,:])
if np.sum(np.isnan(lonk[idrift,:])) > 0 and np.sum(np.isnan(lonk[idrift,:])) < lonk.shape[1]: # if there is a nan
# ax.plot(lonk[idrift,find(ind2)[-1]].T,latk[idrift,find(ind2)[-1]].T,'o',color='orange',liidth=.5,label='_nolegend_')
lonki.append(lonk[idrift,find(ind3)[0]])
latki.append(latk[idrift,find(ind3)[0]])
else:
# ax.plot(lonk[idrift,0].T,latk[idrift,0].T,'o',color='orange',liidth=.5,label='_nolegend_')
lonki.append(lonk[idrift,find(ind3)[0]])
latki.append(latk[idrift,find(ind3)[0]])
xki, yki = grid['basemap'](lonki,latki)
# Overall shelf tracks
# Backward to forward
tracpy.plotting.tracks(lonk, latk, 'matt/shelf', grid=grid)
plot(xki,yki,'go',alpha=.4)
savefig('figures/matt/shelftracks.png',bbox_inches='tight')
# Backward to Bay opening only
tracpy.plotting.tracks(lonb, latb, 'matt/shelf_backonly', grid=grid)
plot(xki,yki,'go',alpha=.4)
savefig('figures/matt/shelf_backonlytracks.png',bbox_inches='tight')
# Bay forward only
tracpy.plotting.tracks(lonf, latf, 'matt/shelf_forwardonly', grid=grid)
plot(lonf[:,0],latf[:,0],'go',alpha=.4)
savefig('figures/matt/shelf_forwardonlytracks.png',bbox_inches='tight')
## Galveston histograms ##
# Smaller basemap parameters.
llcrnrlon=-95.6; llcrnrlat=28.4; urcrnrlon=-93.8; urcrnrlat=29.8;
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
smallgrid = tracpy.inout.readgrid(loc, llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat,
urcrnrlat=urcrnrlat, urcrnrlon=urcrnrlon)
# Matt histogram
tracpy.plotting.hist(lonm, latm, 'matt/matt', grid=smallgrid, which='hexbin', bins=(160,160))
xmstart, ymstart = smallgrid['basemap'](lonmstart,latmstart)
f = gcf()
ax = f.axes[0]
# last axis was for colorbar so grab inital one
ax.plot(xmstart,ymstart,'go',alpha=.3)
# add grid
from sunpy import Grid # suntans code
p_utm = np.loadtxt('projects/suntans/points_utm.dat')
lonp, latp = p(p_utm[:,0], p_utm[:,1], inverse=True)
xp, yp = smallgrid['basemap'](lonp, latp)
p_lcc = np.zeros(p_utm.shape)
p_lcc[:,0] = xp
p_lcc[:,1] = yp
np.savetxt('projects/suntans/points.dat', p_lcc)
c_utm = np.loadtxt('projects/suntans/cells_utm.dat')
c_lcc = c_utm.copy()
lonp, latp = p(c_utm[:,0], c_utm[:,1], inverse=True)
xp, yp = smallgrid['basemap'](lonp, latp)
c_lcc[:,0] = xp
c_lcc[:,1] = yp
np.savetxt('projects/suntans/cells.dat', c_lcc)
grd = Grid('projects/suntans')
grd.plotmesh(ax=ax, edgecolors=('lightgrey',), facecolors=('None',), zorder=0)
# plt.show()
# triang = Triangulation(xp,yp)
# f.axes[0].triplot(triang)
savefig('figures/matt/matthisthexbin.png',bbox_inches='tight')
# Matt tracks
tracpy.plotting.tracks(lonm, latm, 'matt/matt', grid=smallgrid)
# Shelf:
# Need to stop drifters as his domain boundary BUT not from going into the Bay
# x and y verts on smallgrid projection, but this projection might change over time
# xvertbig = np.array([ 113263.66720431, 119096.66652423, 119320.66686604,
# 122461.66678215, 122461.66685854, 125602.66643451,
# 125602.66642388, 129192.66658589, 129192.66626941,
# 125602.66643373, 125602.66631186, 122461.66719913,
# 122461.66712714, 118872.66642492, 118872.66647544,
# 115955.66697976, 115955.6668092 , 112365.66628673,
# 112365.66670544, 105635.66662346, 105635.66692937,
# 98904.66691465, 98904.66683249, 92398.66693207,
# 92398.66669606, 81853.66708999, 81853.66694612,
# 62334.66648085, 62334.66659896, 51789.66706805,
# 51789.66695047, 41917.66679772, 41917.66677504,
# 32046.66715647, 32046.66652647, 28232.66691345,
# 28232.66655248, 28232.66655248, 97180. ,
# 113263.66720431])
# yvertbig = np.array([ 133653.2476132 , 133653.24740824, 130287.24684561,
# 130287.24661835, 122883.24689633, 122883.24719082,
# 117723.24701227, 117723.247473 , 108525.24747157,
# 108525.24679182, 88333.24664775, 88333.24703864,
# 68814.24678444, 68814.2469479 , 66570.24736787,
# 66570.24692043, 64775.24764515, 64775.2469847 ,
# 62083.24742793, 60737.24763316, 58269.24688878,
# 56698.24684586, 54006.24684154, 52884.24719558,
# 49519.24754775, 49519.24728893, 48397.24693971,
# 48397.24676136, 49743.24761656, 49743.24760865,
# 53782.24766385, 53782.24756101, 57596.24732823,
# 58942.24741941, 61634.24691447, 61634.24696074,
# 69711.24711498, 136328. , 169068. ,
# 133653.2476132 ])
# lonvertbig, latvertbig = smallgrid['basemap'](xvertbig, yvertbig, inverse=True)
lonvertbig = np.array([-94.44896376, -94.38868859, -94.38625664, -94.35380894,
-94.35357294, -94.32114683, -94.32099761, -94.2839534 ,
-94.28371841, -94.32073196, -94.32015033, -94.35247582,
-94.35185901, -94.38873039, -94.3886522 , -94.41861373,
-94.41854639, -94.45541454, -94.45530469, -94.52433946,
-94.52422355, -94.59322436, -94.59308138, -94.65976418,
-94.65956549, -94.76770875, -94.76763167, -94.96778311,
-94.96789969, -95.07604071, -95.07642981, -95.17770396,
-95.17810623, -95.2795571 , -95.2798657 , -95.31901876,
-95.3199739 , -95.32790465, -94.61713345, -94.44896376])
latvertbig = np.array([ 29.51080435, 29.51099607, 29.48073304, 29.48082425,
29.41424186, 29.41432502, 29.3679228 , 29.36800802,
29.28529434, 29.28520919, 29.10363677, 29.10355388,
28.92804192, 28.92793764, 28.90776056, 28.90766817,
28.89152842, 28.89140532, 28.86720038, 28.85483934,
28.8326488 , 28.81822849, 28.7940243 , 28.78361653,
28.75336181, 28.75277136, 28.74268359, 28.74135471,
28.75345628, 28.75261073, 28.78892442, 28.78805142,
28.82234232, 28.83349224, 28.85769558, 28.85730671,
28.92992664, 29.52892904, 29.82861514, 29.51080435])
vertsbig = np.vstack((lonvertbig,latvertbig))
# Form path
pathbig = Path(vertsbig.T)
# # Only use drifters starting where Matt started them
# lonfnew = lonf[ind.astype(bool),:]
# latfnew = latf[ind.astype(bool),:]
# update lonfnew to stop drifters that exit matt's domain (but they can enter bay)
for i in xrange(lonf.shape[0]):
for t in xrange(lonf.shape[1]):
if not pathbig.contains_point(np.vstack((lonf[i,t],latf[i,t]))):
lonf[i,t:] = np.nan
latf[i,t:] = np.nan
break
# Shelf histogram
tracpy.plotting.hist(lonf, latf, 'matt/kristen', grid=smallgrid, which='hexbin', bins=(160,160))
xf, yf = smallgrid['basemap'](lonf,latf)
f = gcf()
# plot grid
xr = np.ma.masked_where(smallgrid['mask']==0,smallgrid['xr'])
yr = np.ma.masked_where(smallgrid['mask']==0,smallgrid['yr'])
f.axes[0].plot(xr,yr,xr.T,yr.T,color='lightgrey',zorder=0)
# last axis was for colorbar so grab inital one
f.axes[0].plot(xf[0:384,0],yf[0:384,0],'go',alpha=.3) # just plot them once
savefig('figures/matt/kristenhisthexbin.png',bbox_inches='tight')
# Shelf tracks
tracpy.plotting.tracks(lonf, latf, 'matt/kristen', grid=smallgrid)
""" Example of how to plot the grid and open boundaries in python """ from sunpy import Grid import matplotlib.pyplot as plt from maptools import Polygon2GIS grdfolder = 'rundata/' kmlfile = 'gis/sfbay_suntans_grid.kml' shpfile = 'gis/sfbay_suntans_grid.shp' utmzone = 10 # Load the grid into a sunpy object sun = Grid(grdfolder) # save the grid to a google earth file #Polygon2GIS(sun.xy,kmlfile,utmzone) # the grid polygons are stored in the 'xy' attribute # save the grid to a gis shapefile #Polygon2GIS(sun.xy,shpfile,utmzone) # Plot the mesh plt.figure() sun.plotmesh() # Plot the boundary conditions sun.plotBC() plt.show()
