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update example
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‎examples/07_cube_manipulation/pet_particles_drift.py‎

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"longitude",
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"latitude",
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"time",
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heigth="adt",
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unset=True
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)
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# %%
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# Get paths
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x0, y0 = meshgrid(arange(32, 35, 0.5), arange(32.5, 34.5, 0.5))
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x0, y0 = x0.reshape(-1), y0.reshape(-1)
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t, x, y = c.path(x0, y0, "u", "v", t_init=t0, **kw_p, nb_time=nb_time)
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t, x, y = c.path(x0, y0, h_name="adt", t_init=t0, **kw_p, nb_time=nb_time)
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# %%
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# Plot paths
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ax.plot(x, y, lw=3)
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ax.set_title("10 days particle paths")
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ax.set_xlim(31, 35), ax.set_ylim(32, 34.5)
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ax.grid()
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ax.grid()

‎notebooks/python_module/06_grid_manipulation/pet_advect.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\nGrid advection\n==============\n\nDummy advection which use only static geostrophic current, which didn't solve the complex circulation of the ocean.\n"
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"\n# Grid advection\n\nDummy advection which use only static geostrophic current, which didn't solve the complex circulation of the ocean.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Anim\n----\nParticles setup\n\n"
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"## Anim\nParticles setup\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"kwargs = dict(frames=arange(51), interval=100)\nkw_p = dict(nb_step=2, time_step=21600)\nframe_t = kw_p[\"nb_step\"] * kw_p[\"time_step\"] / 86400.0"
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"kwargs = dict(frames=arange(51), interval=100)\nkw_p = dict(u_name=\"u\", v_name=\"v\", nb_step=2, time_step=21600)\nframe_t = kw_p[\"nb_step\"] * kw_p[\"time_step\"] / 86400.0"
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]
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},
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{
@@ -152,7 +152,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Filament forward\n^^^^^^^^^^^^^^^^\nDraw 3 last position in one path for each particles.,\nit could be run backward with `backward=True` option in filament method\n\n"
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"### Filament forward\nDraw 3 last position in one path for each particles.,\nit could be run backward with `backward=True` option in filament method\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"p = g.filament(x, y, \"u\", \"v\", **kw_p, filament_size=3)\nfig, txt, l, t = anim_ax(lw=0.5)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(frame_t,))"
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"p = g.filament(x, y, **kw_p, filament_size=3)\nfig, txt, l, t = anim_ax(lw=0.5)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(frame_t,))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Particle forward\n^^^^^^^^^^^^^^^^^\nForward advection of particles\n\n"
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"### Particle forward\nForward advection of particles\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"p = g.advect(x, y, \"u\", \"v\", **kw_p)\nfig, txt, l, t = anim_ax(ls=\"\", marker=\".\", markersize=1)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(frame_t,))"
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"p = g.advect(x, y, **kw_p)\nfig, txt, l, t = anim_ax(ls=\"\", marker=\".\", markersize=1)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(frame_t,))"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"p = g.advect(x, y, \"u\", \"v\", **kw_p, backward=True)\nfig, txt, l, _ = anim_ax(ls=\"\", marker=\".\", markersize=1)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(-frame_t,))"
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"p = g.advect(x, y, **kw_p, backward=True)\nfig, txt, l, _ = anim_ax(ls=\"\", marker=\".\", markersize=1)\n_ = VideoAnimation(fig, update, **kwargs, fargs=(-frame_t,))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Particles stat\n--------------\n\n"
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"## Particles stat\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Time_step settings\n^^^^^^^^^^^^^^^^^^\nDummy experiment to test advection precision, we run particles 50 days forward and backward with different time step\nand we measure distance between new positions and original positions.\n\n"
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"### Time_step settings\nDummy experiment to test advection precision, we run particles 50 days forward and backward with different time step\nand we measure distance between new positions and original positions.\n\n"
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]
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{
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},
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"outputs": [],
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"source": [
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"fig = plt.figure()\nax = fig.add_subplot(111)\nkw = dict(\n bins=arange(0, 50, 0.001),\n cumulative=True,\n weights=ones(x0.shape) / x0.shape[0] * 100.0,\n histtype=\"step\",\n)\nfor time_step in (10800, 21600, 43200, 86400):\n x, y = x0.copy(), y0.copy()\n kw_advect = dict(nb_step=int(50 * 86400 / time_step), time_step=time_step)\n g.advect(x, y, \"u\", \"v\", **kw_advect).__next__()\n g.advect(x, y, \"u\", \"v\", **kw_advect, backward=True).__next__()\n d = ((x - x0) ** 2 + (y - y0) ** 2) ** 0.5\n ax.hist(d, **kw, label=f\"{86400. / time_step:.0f} time step by day\")\nax.set_xlim(0, 0.25), ax.set_ylim(0, 100), ax.legend(loc=\"lower right\"), ax.grid()\nax.set_title(\"Distance after 50 days forward and 50 days backward\")\nax.set_xlabel(\"Distance between original position and final position (in degrees)\")\n_ = ax.set_ylabel(\"Percent of particles with distance lesser than\")"
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"fig = plt.figure()\nax = fig.add_subplot(111)\nkw = dict(\n bins=arange(0, 50, 0.001),\n cumulative=True,\n weights=ones(x0.shape) / x0.shape[0] * 100.0,\n histtype=\"step\",\n)\nfor time_step in (10800, 21600, 43200, 86400):\n x, y = x0.copy(), y0.copy()\n kw_advect = dict(nb_step=int(50 * 86400 / time_step), time_step=time_step, u_name=\"u\", v_name=\"v\")\n g.advect(x, y, **kw_advect).__next__()\n g.advect(x, y, **kw_advect, backward=True).__next__()\n d = ((x - x0) ** 2 + (y - y0) ** 2) ** 0.5\n ax.hist(d, **kw, label=f\"{86400. / time_step:.0f} time step by day\")\nax.set_xlim(0, 0.25), ax.set_ylim(0, 100), ax.legend(loc=\"lower right\"), ax.grid()\nax.set_title(\"Distance after 50 days forward and 50 days backward\")\nax.set_xlabel(\"Distance between original position and final position (in degrees)\")\n_ = ax.set_ylabel(\"Percent of particles with distance lesser than\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Time duration\n^^^^^^^^^^^^^\nWe keep same time_step but change time duration\n\n"
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"### Time duration\nWe keep same time_step but change time duration\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"fig = plt.figure()\nax = fig.add_subplot(111)\ntime_step = 10800\nfor duration in (5, 50, 100):\n x, y = x0.copy(), y0.copy()\n kw_advect = dict(nb_step=int(duration * 86400 / time_step), time_step=time_step)\n g.advect(x, y, \"u\", \"v\", **kw_advect).__next__()\n g.advect(x, y, \"u\", \"v\", **kw_advect, backward=True).__next__()\n d = ((x - x0) ** 2 + (y - y0) ** 2) ** 0.5\n ax.hist(d, **kw, label=f\"Time duration {duration} days\")\nax.set_xlim(0, 0.25), ax.set_ylim(0, 100), ax.legend(loc=\"lower right\"), ax.grid()\nax.set_title(\n \"Distance after N days forward and N days backward\\nwith a time step of 1/8 days\"\n)\nax.set_xlabel(\"Distance between original position and final position (in degrees)\")\n_ = ax.set_ylabel(\"Percent of particles with distance lesser than \")"
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"fig = plt.figure()\nax = fig.add_subplot(111)\ntime_step = 10800\nfor duration in (5, 50, 100):\n x, y = x0.copy(), y0.copy()\n kw_advect = dict(nb_step=int(duration * 86400 / time_step), time_step=time_step, u_name=\"u\", v_name=\"v\")\n g.advect(x, y, **kw_advect).__next__()\n g.advect(x, y, **kw_advect, backward=True).__next__()\n d = ((x - x0) ** 2 + (y - y0) ** 2) ** 0.5\n ax.hist(d, **kw, label=f\"Time duration {duration} days\")\nax.set_xlim(0, 0.25), ax.set_ylim(0, 100), ax.legend(loc=\"lower right\"), ax.grid()\nax.set_title(\n \"Distance after N days forward and N days backward\\nwith a time step of 1/8 days\"\n)\nax.set_xlabel(\"Distance between original position and final position (in degrees)\")\n_ = ax.set_ylabel(\"Percent of particles with distance lesser than \")"
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]
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}
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],
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,

‎notebooks/python_module/06_grid_manipulation/pet_lavd.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\nLAVD experiment\n===============\n\nNaive method to reproduce LAVD(Lagrangian-Averaged Vorticity deviation) method with a static velocity field.\nIn the current example we didn't remove a mean vorticity.\n\nMethod are described here:\n\n - Abernathey, Ryan, and George Haller. \"Transport by Lagrangian Vortices in the Eastern Pacific\",\n Journal of Physical Oceanography 48, 3 (2018): 667-685, accessed Feb 16, 2021,\n https://doi.org/10.1175/JPO-D-17-0102.1\n - `Transport by Coherent Lagrangian Vortices`_,\n R. Abernathey, Sinha A., Tarshish N., Liu T., Zhang C., Haller G., 2019,\n Talk a t the Sources and Sinks of Ocean Mesoscale Eddy Energy CLIVAR Workshop\n\n https://usclivar.org/sites/default/files/meetings/2019/presentations/Aberernathey_CLIVAR.pdf\n"
18+
"\n# LAVD experiment\n\nNaive method to reproduce LAVD(Lagrangian-Averaged Vorticity deviation) method with a static velocity field.\nIn the current example we didn't remove a mean vorticity.\n\nMethod are described here:\n\n - Abernathey, Ryan, and George Haller. \"Transport by Lagrangian Vortices in the Eastern Pacific\",\n Journal of Physical Oceanography 48, 3 (2018): 667-685, accessed Feb 16, 2021,\n https://doi.org/10.1175/JPO-D-17-0102.1\n - `Transport by Coherent Lagrangian Vortices`_,\n R. Abernathey, Sinha A., Tarshish N., Liu T., Zhang C., Haller G., 2019,\n Talk a t the Sources and Sinks of Ocean Mesoscale Eddy Energy CLIVAR Workshop\n\n https://usclivar.org/sites/default/files/meetings/2019/presentations/Aberernathey_CLIVAR.pdf\n"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Data\n----\nTo compute vorticity ($\\omega$) we compute u/v field with a stencil and apply the following equation with stencil\nmethod :\n\n\\begin{align}\\omega = \\frac{\\partial v}{\\partial x} - \\frac{\\partial u}{\\partial y}\\end{align}\n\n"
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"## Data\nTo compute vorticity ($\\omega$) we compute u/v field with a stencil and apply the following equation with stencil\nmethod :\n\n\\begin{align}\\omega = \\frac{\\partial v}{\\partial x} - \\frac{\\partial u}{\\partial y}\\end{align}\n\n"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Particles\n---------\nParticles specification\n\n"
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"## Particles\nParticles specification\n\n"
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{
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"outputs": [],
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"source": [
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"step = 1 / 32\nx_g, y_g = arange(0, 36, step), arange(28, 46, step)\nx, y = meshgrid(x_g, y_g)\noriginal_shape = x.shape\nx, y = x.reshape(-1), y.reshape(-1)\nprint(f\"{len(x)} particles advected\")\n# A frame every 8h\nstep_by_day = 3\n# Compute step of advection every 4h\nnb_step = 2\nkw_p = dict(nb_step=nb_step, time_step=86400 / step_by_day / nb_step)\n# Start a generator which at each iteration return new position at next time step\nparticule = g.advect(x, y, \"u\", \"v\", **kw_p, rk4=True)"
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"step = 1 / 32\nx_g, y_g = arange(0, 36, step), arange(28, 46, step)\nx, y = meshgrid(x_g, y_g)\noriginal_shape = x.shape\nx, y = x.reshape(-1), y.reshape(-1)\nprint(f\"{len(x)} particles advected\")\n# A frame every 8h\nstep_by_day = 3\n# Compute step of advection every 4h\nnb_step = 2\nkw_p = dict(nb_step=nb_step, time_step=86400 / step_by_day / nb_step, u_name=\"u\", v_name=\"v\")\n# Start a generator which at each iteration return new position at next time step\nparticule = g.advect(x, y, **kw_p, rk4=True)"
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{
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"cell_type": "markdown",
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"source": [
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"LAVD\n----\n\n"
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"## LAVD\n\n"
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{
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"cell_type": "markdown",
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"Anim\n^^^^\nMovie of LAVD integration at each integration time step.\n\n"
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"### Anim\nMovie of LAVD integration at each integration time step.\n\n"
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{
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"Final LAVD\n^^^^^^^^^^\n\n"
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"### Final LAVD\n\n"
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{
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"outputs": [],
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"source": [
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"lavd = RegularGridDataset.with_array(\n coordinates=(\"lon\", \"lat\"),\n datas=dict(lavd=lavd.T, lon=x_g, lat=y_g,),\n centered=True,\n)"
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"lavd = RegularGridDataset.with_array(\n coordinates=(\"lon\", \"lat\"), datas=dict(lavd=lavd.T, lon=x_g, lat=y_g), centered=True\n)"
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,

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