File was created with py-eddy-tracker version '3.4.0+39.gf81199a.dirty' but software version is '3.6'
File was created with py-eddy-tracker version '3.2.0+67.g1a490b3.dirty' but software version is '3.6'
We assume pixel position of grid is centered for D:\pyton\Lib\site-packages\pyeddytracker-3.6.1-py3.11.egg\py_eddy_tracker\data\nrt_global_allsat_phy_l4_20190223_20190226.nc
Traceback (most recent call last):
File "D:\eddy\1\py-eddy-tracker-3.6.1\examples\06_grid_manipulation\pet_okubo_weiss.py", line 60, in
u_x = g.compute_stencil(g.grid("ugos"))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\pyton\Lib\site-packages\pyeddytracker-3.6.1-py3.11.egg\py_eddy_tracker\dataset\grid.py", line 1740, in compute_stencil
g, m = compute_stencil(
^^^^^^^^^^^^^^^^
File "D:\pyton\Lib\site-packages\numba-0.58.0rc1-py3.11-win-amd64.egg\numba\core\dispatcher.py", line 733, in typeof_pyval
tp = typeof(val, Purpose.argument)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\pyton\Lib\site-packages\numba-0.58.0rc1-py3.11-win-amd64.egg\numba\core\typing\typeof.py", line 33, in typeof
ty = typeof_impl(val, c)
^^^^^^^^^^^^^^^^^^^
File "D:\pyton\Lib\functools.py", line 909, in wrapper
return dispatch(args[0].class)(*args, **kw)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\pyton\Lib\site-packages\numba-0.58.0rc1-py3.11-win-amd64.egg\numba\core\typing\typeof.py", line 244, in _typeof_ndarray
raise errors.NumbaTypeError(msg)
numba.core.errors.NumbaTypeError: Unsupported array type: numpy.ma.MaskedArray.
Hello, I used the latest version of PyEddyTrack (V3.6.1) and ran the built-in example. The example code is as follows: (pet_okubo_weiss.py) ` from matplotlib import pyplot as plt from numpy import arange, ma, where from py_eddy_tracker import data from py_eddy_tracker.dataset.grid import RegularGridDataset from py_eddy_tracker.observations.observation import EddiesObservations def start_axes(title, zoom=False): fig = plt.figure(figsize=(12, 6)) axes = fig.add_axes([0.03, 0.03, 0.90, 0.94]) axes.set_xlim(0, 360), axes.set_ylim(-80, 80) if zoom: axes.set_xlim(270, 340), axes.set_ylim(20, 50) axes.set_aspect("equal") axes.set_title(title) return axes def update_axes(axes, mappable=None): axes.grid() if mappable: plt.colorbar(mappable, cax=axes.figure.add_axes([0.94, 0.05, 0.01, 0.9])) a = EddiesObservations.load_file(data.get_demo_path("Anticyclonic_20190223.nc")) c = EddiesObservations.load_file(data.get_demo_path("Cyclonic_20190223.nc")) g = RegularGridDataset( data.get_demo_path("nrt_global_allsat_phy_l4_20190223_20190226.nc"), "longitude", "latitude", ) ax = start_axes("ADT (cm)") m = g.display(ax, "adt", vmin=-120, vmax=120, factor=100) update_axes(ax, m) plt.show() u_x = g.compute_stencil(g.grid("ugos")) u_y = g.compute_stencil(g.grid("ugos"), vertical=True) v_x = g.compute_stencil(g.grid("vgos")) v_y = g.compute_stencil(g.grid("vgos"), vertical=True) ow = g.vars["ow"] = (u_x - v_y) 2 + (v_x + u_y) 2 - (v_x - u_y) 2 ax = start_axes("Okubo weis") m = g.display(ax, "ow", vmin=-1e-10, vmax=1e-10, cmap="bwr") update_axes(ax, m) ax = start_axes("Okubo weis, Gulf stream", zoom=True) m = g.display(ax, "ow", vmin=-1e-10, vmax=1e-10, cmap="bwr") kw_ed = dict(intern_only=True, color="k", lw=1) a.display(ax, kw_ed), c.display(ax, *kw_ed) update_axes(ax, m) ax = start_axes("Okubo weis, Gulf stream", zoom=True) threshold = ow.std() -0.2 ow = ma.array(ow, mask=ow > threshold) m = g.display(ax, ow, vmin=-1e-10, vmax=1e-10, cmap="bwr") a.display(ax, kw_ed), c.display(ax, kw_ed) update_axes(ax, m) plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax.set_xlabel("Okubo-Weiss parameter") kw_hist = dict(bins=arange(-20e-10, 20e-10, 50e-12), histtype="step") for method in ("mean", "center", "min"): kw_interp = dict(gridobject=g, varname="ow", method=method, intern=True) , _, m = ax.hist( a.interp_grid(kw_interp), label=f"Anticyclonic - OW {method}", kw_hist ) ax.hist( c.interp_grid(kw_interp), label=f"Cyclonic - OW {method}", color=m[0].get_edgecolor(), ls="--", kw_hist, ) ax.axvline(threshold, color="r") ax.set_yscale("log") ax.grid() ax.set_ylim(1, 1e4) ax.set_xlim(-15e-10, 15e-10) ax.legend() ax = start_axes("Eddies with a min OW in speed contour over threshold") ow_min = a.interp_grid(kw_interp) a_bad_ow = a.index(where(ow_min > threshold)[0]) a_bad_ow.display(ax, color="r", label="Anticyclonic") ow_min = c.interp_grid(kw_interp) c_bad_ow = c.index(where(ow_min > threshold)[0]) c_bad_ow.display(ax, color="b", label="Cyclonic") ax.legend() fig = plt.figure(figsize=(12, 5)) fig.suptitle( "Parameter distribution (solid line) and cumulative distribution (dashed line)" ) ax_amp, ax_rad = fig.add_subplot(121), fig.add_subplot(122) ax_amp_c, ax_rad_c = ax_amp.twinx(), ax_rad.twinx() ax_amp_c.set_ylim(0, 1), ax_rad_c.set_ylim(0, 1) kw_a = dict(xname="amplitude", bins=arange(0, 2, 0.002).astype("f4")) kw_r = dict(xname="radius_s", bins=arange(0, 500e6, 2e3).astype("f4")) for d, label, color in ( (a, "Anticyclonic all", "r"), (a_bad_ow, "Anticyclonic bad OW", "orange"), (c, "Cyclonic all", "blue"), (c_bad_ow, "Cyclonic bad OW", "lightblue"), ): x, y = d.bins_stat(kw_a) ax_amp.plot(x 100, y, label=label, color=color) ax_amp_c.plot( x 100, y.cumsum() / y.sum(), label=label, color=color, ls="-.", lw=0.5 ) x, y = d.bins_stat(kw_r) ax_rad.plot(x 1e-3, y, label=label, color=color) ax_rad_c.plot( x 1e-3, y.cumsum() / y.sum(), label=label, color=color, ls="-.", lw=0.5 )
ax_amp.set_xlim(0, 12.5), ax_amp.grid(), ax_amp.set_ylim(0), ax_amp.legend() ax_rad.set_xlim(0, 120), ax_rad.grid(), ax_rad.set_ylim(0) ax_amp.set_xlabel("Amplitude (cm)"), ax_amp.set_ylabel("Nb eddies") ax_rad.set_xlabel("Speed radius (km)") `
The results show：