def read_transect(transect):
"""
Find and load transects from a folder, calling tweak_sontek on each.
return list of xr.Dataset
"""
rivr_fns = glob.glob('%s/*.rivr' % transect) + glob.glob(
'%s/*.riv' % transect)
tran_dss = [
tweak_sontek(read_sontek.surveyor_to_xr(fn, proj='EPSG:26910'))
for fn in rivr_fns
]
return tran_dss
def convert_sontek(self, rivr_file):
self.ds = read_sontek.surveyor_to_xr(rivr_file)
# Set ADCPData members from self.ds
# Starts with z as a positive-down quantity
# use round(...,4) to drop some FP roundoff trash
z_in = self.ds.location.values
min_z = round(np.nanmin(z_in), 4)
min_dz = np.round(np.nanmin(np.diff(z_in, axis=1)), 4)
max_z = round(np.nanmax(z_in), 4)
nbins = 1 + int(round((max_z - min_z) / min_dz))
new_z = np.linspace(min_z, max_z, nbins)
def resamp(orig, axis=-1):
""" orig: [samples,cells].
interpolate each sample to from z_in[sample,:] to new_z
"""
n_samples = orig.shape[0]
new_A = np.zeros((n_samples, len(new_z)), np.float64)
for sample in range(n_samples):
new_A[sample, :] = np.interp(new_z,
z_in[sample, :],
orig[sample, :],
left=np.nan,
right=np.nan)
return new_A
self.n_ensembles = len(self.ds.sample)
self.velocity = np.array(
(resamp(self.ds.Ve.values), resamp(self.ds.Vn.values),
resamp(self.ds.Vu.values))).transpose(1, 2, 0)
self.bin_center_elevation = -new_z # make it negative downward
self.n_bins = len(new_z)
self.mtime = utils.to_dnum(self.ds.time.values)
self.rotation_angle = 0
self.rotation_axes = 0
self.lonlat = np.c_[self.ds.lon.values, self.ds.lat.values]
self.source = rivr_file
self.references = "Sontek M9"
# First go, just use the bottom track depth, not the per-beam data.
# Assemble a set of xyz from all above data.
# will add in progressive complexity:
# 1. all relative to transducer
# 2. Offset for transducer depth below water
# 3. Timeseries of water surface elevation
# 4. Multiple beams?
all_xyz = []
all_t = []
surface_to_transducer = 0.20 # [m]
for rivr_fn in rivr_fns:
print(rivr_fn)
ds = read_sontek.surveyor_to_xr(rivr_fn, proj="EPSG:26910")
x = ds.x_sample.values
y = ds.y_sample.values
z = -ds.depth_bt.values # positive up, relative to transducer
z -= surface_to_transducer
all_xyz.append(np.c_[x, y, z])
all_t.append(ds.time.values)
xyz_samples = np.concatenate(all_xyz)
t_samples = np.concatenate(all_t)
##
# Assume that the ADCP is in PST (that's probably an hour wrong, but
##
#fig_dir="figs-20180514"
#os.path.exists(fig_dir) or os.mkdir(fig_dir)
##
bt_dir = '040518_7_BTref'
gps_dir = '040518_7_GPSref'
rivr_fns = [os.path.basename(f) for f in glob.glob('%s/*.rivr' % bt_dir)]
all_bt = []
for fn in rivr_fns:
ds = read_sontek.surveyor_to_xr('%s/%s' % (bt_dir, fn), proj='EPSG:26910')
all_bt.append(ds)
##
@memoize()
def bathy():
from stompy.spatial import field
return field.GdalGrid(
'../bathy/OldRvr_at_SanJoaquinRvr2012_0104-utm-m_NAVD.tif')
##
# x-z of a single transect:
import six
from stompy import xr_transect
six.moves.reload_module(xr_transect)
six.moves.reload_module(read_sontek)
#---
# 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()