def test_transect_contour():
tran=xr.open_dataset('data/temp-transect.nc')
plt.figure(10).clf()
fig,axs=plt.subplots(2,1,num=10,sharex=True,sharey=True)
xr_transect.plot_scalar(tran,'temp',ax=axs[0])
xr_transect.plot_scalar_pcolormesh(tran,'temp',ax=axs[1])
xr_transect.contour(tran,'temp',np.linspace(5,27,28),
ax=axs[1],colors='k',linewidths=0.5)
def test_transect():
"""
Test loading a 'section_hydro' formatted transect, and resample to evenly
spaced z.
"""
untrim_sec=xr_transect.section_hydro_to_transect("data/section_hydro.txt",name="7B")
new_z=np.linspace( untrim_sec.z_bed.values.min(),
untrim_sec.z_surf.values.max(),
100)
untrim_sec_eq=xr_transect.resample_z(untrim_sec,new_z)
plt.figure(22).clf()
fig,axs=plt.subplots(2,1,num=22,sharex=True,sharey=True)
xr_transect.plot_scalar(untrim_sec,'Ve',ax=axs[0])
xr_transect.plot_scalar(untrim_sec_eq,'Ve',ax=axs[1])
axs[0].text(0.02,0.9,'Original',transform=axs[0].transAxes)
axs[1].text(0.02,0.9,'Resample',transform=axs[1].transAxes)
utils.path("../../field/adcp")
from stompy import xr_transect
import summarize_xr_transects
##
six.moves.reload_module(dflow_model)
six.moves.reload_module(sun_driver)
# plot a few a sections
run_dir = "runs/steady_036"
model = sun_driver.SuntansModel.load(run_dir)
tran_shp = "../../gis/model_transects.shp"
tran_geoms = wkb2shp.shp2geom(tran_shp)
for ti, t in enumerate(tran_geoms):
if t['name'] not in ['2018_05']:
continue # Focus on transect 5
break
xy = np.array(t['geom'])
tran = model.extract_transect(
xy=xy, time=-1, dx=2, vars=['uc', 'vc', 'Ac', 'dv', 'dzz', 'eta', 'nu_v'])
wet_samples = np.nonzero(np.abs(tran.z_dz.sum(dim='layer').values) > 0.01)[0]
sample_slice = slice(wet_samples[0], wet_samples[-1] + 1)
tran = tran.isel(sample=sample_slice)
plt.figure(2).clf()
coll = xr_transect.plot_scalar(tran, tran.nu_v, cmap='jet')
plt.colorbar(coll, label=r'$\nu_T$')
"""
import six
from stompy import xr_transect
six.moves.reload_module(xr_transect)
##
tran = xr.open_dataset('040518_BT/040518_5BTref-avg.nc')
##
xr_transect.add_rozovski(tran)
##
plt.figure(1).clf()
xr_transect.plot_scalar(tran, tran.Uroz.sel(roz='downstream'))
plt.plot(tran.d_sample, z_sgn * tran.z_bed, 'k-', lw=0.5)
##
six.moves.reload_module(xr_transect)
var_methods = [('Uroz', dict(roz=0), 'linear', 'constant'),
('Uroz', dict(roz=1), 'linear', 'constant'),
('U', dict(xy=0), 'linear', 'constant'),
('U', dict(xy=1), 'linear', 'constant')]
ds = xr_transect.extrapolate_vertical(tran, var_methods, eta=0)
##
# Choose 2, because it has the matlab output which is richer and has
# RiverSurveyor computed flows.
adcp_data_dir="040518_BT/040518_2BTref"
rivr_fns=glob.glob('%s/*.rivr'%adcp_data_dir)
all_ds=[ read_sontek.surveyor_to_xr(fn,proj='EPSG:26910',positive='up')
for fn in rivr_fns]
##
ds=all_ds[1]
plt.figure(1).clf()
fig,ax=plt.subplots(1,1,num=1)
coll=xr_transect.plot_scalar(ds,ds.Ve,ax=ax)
ax.plot(ds.d_sample,ds.z_bed,'k-')
##
@memoize()
def bathy():
from stompy.spatial import field
return field.GdalGrid('../bathy/OldRvr_at_SanJoaquinRvr2012_0104-utm-m_NAVD.tif')
##
# Transect averaging:
def transects_to_segment(trans,unweight=True,ax=None):
##
tr=xr.Dataset()
cc=g.cells_center()
tr['x_sample']=('sample',),cc[cells,0]
tr['y_sample']=('sample',),cc[cells,1]
tr['eta']=('sample',),ei[cells]
tr['z_dz']=('sample','layer'), (ei[cells]+sim.zi[cells])[:,None]
tr['z_ctr']=tr.eta-0.5*tr.z_dz
tr['U']=('sample','layer','xy'), ui[cells,:][:,None,:]
##
from stompy import xr_transect
xr_transect.add_rozovski(tr)
plt.figure(2).clf()
fig,axs=plt.subplots(2,1,sharex=True,num=2)
xr_transect.plot_scalar(tr,tr.Uroz.isel(roz=0),ax=axs[0])
axs[1].plot( tr.d_sample, tr.Uroz.isel(roz=0,layer=0) )
axs[0].set_title('SWAMPY transect 5. Downstream velocity. n=0.025')
axs[1].axis(ymin=0)
axs[1].set_ylabel('m/s')
fig.savefig('swampy-n0.025-transect_05.png')
##
fig=plt.figure(1)
fig.savefig('swampy-n0.025-planview.png')