An array of pandas Interval objects.
Returns
-------
mids : 1D numpy array
Midpoints of the intervals.
"""
return np.array([v.mid for v in intervals])
# %% [markdown]
# Load datasets.
# %%
up = xr.open_dataset("../proc/downstream_deep_upward_2018_enu.nc")
up = up.set_coords(["lon", "lat"])
up["time"] = utils.POSIX_to_datetime(up.time.values).astype(np.datetime64)
do = xr.open_dataset("../proc/downstream_deep_down_2018_enu.nc")
do = do.set_coords(["lon", "lat"])
do["time"] = utils.POSIX_to_datetime(do.time.values).astype(np.datetime64)
sbe51 = xr.open_dataset("../proc/downstream_deep_SBE37_10551_2018.nc")
sbe51 = sbe51.set_coords(["lon", "lat"])
sbe51["time"] = utils.POSIX_to_datetime(sbe51.time.values).astype(np.datetime64)
sbe52 = xr.open_dataset("../proc/downstream_deep_SBE37_10552_2018.nc")
sbe52 = sbe52.set_coords(["lon", "lat"])
sbe52["time"] = utils.POSIX_to_datetime(sbe52.time.values).astype(np.datetime64)
sbe53 = xr.open_dataset("../proc/downstream_deep_SBE37_10553_2018.nc")
sbe53 = sbe53.set_coords(["lon", "lat"])
An array of pandas Interval objects.
Returns
-------
mids : 1D numpy array
Midpoints of the intervals.
"""
return np.array([v.mid for v in intervals])
# %% [markdown]
# Load datasets and do some basic conversion of times and variables.
# %%
sV = xr.open_dataset("../proc/ABLE_sentinel_2018_enu.nc")
sV = sV.set_coords(["lon", "lat"])
sV["time"] = utils.POSIX_to_datetime(sV.time.values).astype(np.datetime64)
x, y, *_ = utm.from_latlon(sV.lat, sV.lon)
sV = sV.assign_coords({"x": x, "y": y})
virt = xr.open_dataset("../proc/ABLE_sentinel_RBRvirtuoso_2018.nc")
virt = virt.set_coords(["lon", "lat"])
virt["time"] = utils.POSIX_to_datetime(virt.time.values).astype(np.datetime64)
sbe = xr.open_dataset("../proc/ABLE_sentinel_SBE37_2018.nc")
sbe = sbe.set_coords(["lon", "lat"])
sbe["time"] = utils.POSIX_to_datetime(sbe.time.values).astype(np.datetime64)
# %% [markdown]
# Define some parameters and simple thresholds for processing.
# language: python
# name: lcpp-dev
# ---
# %%
import xarray as xr
import matplotlib.pyplot as plt
import utils
# %% [markdown]
# ## Downstream deep upward looking
# %%
ddu = xr.open_dataset("../proc/downstream_deep_upward_2018_enu.nc"
) #, time=slice(9900, -2300)).sel(distance=slice(0, 75))
ddu["time"] = utils.POSIX_to_datetime(ddu.time.values)
# %%
ddu
# %%
ddu.u.plot(vmin=-0.5, vmax=0.5, cmap="coolwarm")
# %%
ddu.v.plot(vmin=-0.5, vmax=0.5, cmap="coolwarm")
# %%
ddu.a1.plot()
# %%
ddu.g1.plot()
adps = [adp1, adp2, adp3]
ctds = [ctd1, ctd2, ctd3]
# %% [markdown]
# # Processing Loop
# %%
for i, (adp, ctd) in enumerate(zip(adps, ctds)):
print(f"Loading {adp}")
print(f"Loading {ctd}")
# Load data
a1 = xr.open_dataset(adp)
a1 = a1.set_coords(["lon", "lat"])
a1["time"] = utils.POSIX_to_datetime(a1.time.values).astype(np.datetime64)
x, y, *_ = utm.from_latlon(a1.lat, a1.lon)
a1 = a1.assign_coords({"x": x, "y": y})
c1 = xr.open_dataset(ctd)
try:
c1["time"] = utils.POSIX_to_datetime(c1.time.values).astype(
np.datetime64)
except TypeError:
print("No need to convert time")
# Estimate CTD depth
c1["z"] = (
c1.p.dims,
gsw.z_from_p(c1.p, c1.lat).data,
#
# First run the R notebook: `test_R_adp.ipynb`
#
# This generates the netcdf files.
# %%
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import utils
import scipy.stats as stats
from scipy.ndimage import label, gaussian_filter
# %%
enu = xr.open_dataset("enu.nc", decode_times=False)
enu["time"] = utils.POSIX_to_datetime(enu.time.values).astype(np.datetime64)
# %%
hvel_kwargs = dict(vmin=-0.3, vmax=0.3, cmap="coolwarm")
vvel_kwargs = dict(vmin=-0.1, vmax=0.1, cmap="coolwarm")
fig, axs = plt.subplots(4, 1, sharex=True, figsize=(22, 14))
enu.u.plot(ax=axs[0], **hvel_kwargs)
enu.v.plot(ax=axs[1], **hvel_kwargs)
enu.w.plot(ax=axs[2], **vvel_kwargs)
np.abs(enu.err).plot(ax=axs[3], vmin=0, vmax=0.2)
for ax in axs:
ax.set_xlabel("")
# %%