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erikvansebilleerikvansebille
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Updating homepage animation notebook to v4
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‎docs/user_guide/examples/documentation_homepage_animation.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# The homepage animation"
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"# 🎓 The homepage animation"
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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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"This notebook shows how to create the animation on the homepage of the Parcels documentation. It uses a dataset of virtual particles at the surface of the global ocean simulation that can be retrieved from the [Copernicus Marine Service](https://marine.copernicus.eu/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"raises-exception"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib qt\n",
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"import copy\n",
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"\n",
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"import cartopy\n",
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"import cartopy.crs as ccrs\n",
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"import matplotlib.animation as animation\n",
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"import copernicusmarine\n",
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"import matplotlib.gridspec as gridspec\n",
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"import matplotlib.image as mpimg\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import polars as pl\n",
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"import xarray as xr\n",
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"from matplotlib import colors\n",
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"from matplotlib.animation import FuncAnimation, PillowWriter, writers"
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"from matplotlib.animation import FuncAnimation, PillowWriter\n",
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"\n",
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"import parcels"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load in particle data, define the time range at which to plot (**`plottimes`**) and select the indices of the first timestep in the variable `'b'`.\n"
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"The cell below provides the code needed to run this simulation - but because it is a time-consuming run (~20 minutes) and requires a login on the Copernicus Marine Service, we also provide the output file for download directly at TODO"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"raises-exception"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"filename = \"medusarun.nc\"\n",
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"pfile = xr.open_dataset(str(filename), decode_cf=True)\n",
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"lon = np.ma.filled(pfile.variables[\"lon\"], np.nan)\n",
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"lat = np.ma.filled(pfile.variables[\"lat\"], np.nan)\n",
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"time = np.ma.filled(pfile.variables[\"time\"], np.nan)\n",
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"particle_filename = \"copernicusmarine_globalsurface.parquet\"\n",
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"\n",
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"\n",
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"def run_global_copernicusmarine():\n",
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" copernicusmarine.login()\n",
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"\n",
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"pfile.close()\n",
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" ds = copernicusmarine.open_dataset(\n",
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" dataset_id=\"cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m\",\n",
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" variables=[\"uo\", \"vo\"],\n",
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" start_datetime=\"2024-01-01\",\n",
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" end_datetime=\"2024-12-31\",\n",
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" minimum_depth=0.5,\n",
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" maximum_depth=0.5,\n",
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" service=\"arco-geo-series\",\n",
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" chunk_size_limit=1,\n",
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" )\n",
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" ds = ds.fillna(0)\n",
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"\n",
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"plottimes = np.arange(time[0, 0], np.nanmax(time), np.timedelta64(10, \"D\"))\n",
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"starttime = 20\n",
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"b = time == plottimes[0 + starttime]"
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" ds_wrap = xr.concat(\n",
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" [\n",
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" ds,\n",
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" ds.isel(longitude=slice(0, 1)).assign_coords(\n",
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" longitude=ds.longitude.isel(longitude=slice(0, 1)) + 360\n",
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" ),\n",
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" ],\n",
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" dim=\"longitude\",\n",
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" )\n",
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" ds = parcels.convert.copernicusmarine_to_sgrid(\n",
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" fields={\"U\": ds_wrap[\"uo\"], \"V\": ds_wrap[\"vo\"]}\n",
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" )\n",
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" fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n",
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" # fieldset.UV.interp_method = parcels.interpolators.XFreeslip()\n",
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" fieldset.to_windowed_arrays()\n",
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"\n",
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" lon, lat = np.meshgrid(np.arange(-179, 180, 2), np.arange(-89, 90, 2))\n",
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" lon = lon.flatten()\n",
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" lat = lat.flatten()\n",
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"\n",
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" # Filter out particles that are not in the ocean (i.e. where speed is zero)\n",
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" u, v = fieldset.UV[np.zeros_like(lon), np.zeros_like(lat), lat, lon]\n",
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" speed = np.sqrt(u**2 + v**2)\n",
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" inocean = speed > 0\n",
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"\n",
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" pset = parcels.ParticleSet(fieldset, x=lon[inocean], y=lat[inocean])\n",
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"\n",
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" oufile = parcels.ParticleFile(\n",
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" particle_filename,\n",
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" outputdt=np.timedelta64(5, \"D\"),\n",
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" )\n",
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"\n",
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" def AdvectionRK2_periodic(particles, fieldset): # pragma: no cover\n",
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" \"\"\"Advection of particles using second-order Runge-Kutta integration,\n",
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" keeping particles within the periodic domain [-180, 180].\n",
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" \"\"\"\n",
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" (u1, v1) = fieldset.UV[particles]\n",
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" x1 = particles.x + u1 * 0.5 * particles.dt\n",
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" x1 = ((x1 + 180) % 360) - 180\n",
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" y1 = particles.y + v1 * 0.5 * particles.dt\n",
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" (u2, v2) = fieldset.UV[\n",
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" particles.t + 0.5 * particles.dt, particles.z, y1, x1, particles\n",
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" ]\n",
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" particles.dx += u2 * particles.dt\n",
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" particles.dx = ((particles.dx + particles.x + 180) % 360) - (particles.x + 180)\n",
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" particles.dy += v2 * particles.dt\n",
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"\n",
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" pset.execute(\n",
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" [AdvectionRK2_periodic],\n",
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" dt=np.timedelta64(1, \"h\"),\n",
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" runtime=np.timedelta64(365, \"D\"),\n",
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" output_file=oufile,\n",
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" )\n",
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"\n",
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"\n",
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"# run_global_copernicusmarine()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The particle data in `medusarun.nc` is available at https://surfdrive.surf.nl/files/index.php/s/lwjW5w05jtHuYz9\n"
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"Now load in the particle data, and select which time indices to plot\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = parcels.read_particlefile(particle_filename)\n",
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"\n",
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"startframe = 20\n",
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"endframe = 41\n",
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"plottimes = sorted(np.unique(df[\"t\"]))[startframe:endframe]"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The animation consists of three figures: the northern hemisphere, the southern hemisphere and the oceanparcels logo. To organize their locations we use [matplotlib.gridspec](https://matplotlib.org/tutorials/intermediate/gridspec.html). The animation spans 12 frames and updates the particle positions based on the timestep in `plottimes`.\n"
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"The animation consists of two figures: the northern hemisphere and the southern hemisphere, using the [Cartopy package](https://cartopy.readthedocs.io/stable/) for map projections.\n"
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]
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},
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{
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"fig = plt.figure(figsize=(8, 4))\n",
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"gs = gridspec.GridSpec(ncols=8, nrows=4, figure=fig)\n",
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"\n",
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"### Northern Hemisphere\n",
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"ax1 = fig.add_subplot(\n",
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" gs[:, :4],\n",
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" projection=ccrs.NearsidePerspective(\n",
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" central_latitude=90, central_longitude=-30, satellite_height=15000000\n",
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" ),\n",
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")\n",
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"ax1.add_feature(cartopy.feature.LAND, zorder=1)\n",
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"ax1.add_feature(cartopy.feature.OCEAN, zorder=1)\n",
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"ax1.coastlines()\n",
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"scat1 = ax1.scatter(\n",
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" lon[b],\n",
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" lat[b],\n",
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" marker=\".\",\n",
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" s=25,\n",
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" c=\"#AB2200\",\n",
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" edgecolor=\"white\",\n",
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" linewidth=0.15,\n",
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" transform=ccrs.PlateCarree(),\n",
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")\n",
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"\n",
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"### Southern Hemisphere\n",
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"ax2 = fig.add_subplot(\n",
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" gs[:, 4:],\n",
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" projection=ccrs.NearsidePerspective(\n",
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" central_latitude=-90, central_longitude=-30, satellite_height=15000000\n",
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" ),\n",
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")\n",
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"ax2.add_feature(cartopy.feature.LAND, zorder=1)\n",
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"ax2.add_feature(cartopy.feature.OCEAN, zorder=1)\n",
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"ax2.coastlines()\n",
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"scat2 = ax2.scatter(\n",
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" lon[b],\n",
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" lat[b],\n",
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" marker=\".\",\n",
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" s=25,\n",
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" c=\"#AB2200\",\n",
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" edgecolor=\"white\",\n",
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" linewidth=0.15,\n",
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" transform=ccrs.PlateCarree(),\n",
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")\n",
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"\n",
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"frames = np.arange(0, 20)\n",
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"\n",
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"\n",
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"def animate(t):\n",
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" b = time == plottimes[t + starttime]\n",
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" scat1.set_offsets(np.vstack((lon[b], lat[b])).transpose())\n",
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" scat2.set_offsets(np.vstack((lon[b], lat[b])).transpose())\n",
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" return scat1, scat2\n",
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"\n",
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"\n",
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"anim = animation.FuncAnimation(fig, animate, frames=frames, interval=150, blit=True)\n",
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"anim\n",
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"\n",
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"# needed for tight_layout to work with cartopy\n",
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"fig.canvas.draw()\n",
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"plt.tight_layout()\n",
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"# writergif = PillowWriter(fps=6)\n",
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"# anim.save('homepageshort.gif', writer=writergif, savefig_kwargs={\"transparent\": True})"
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"scat = [None, None]\n",
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"particles = df.filter(pl.col(\"t\") == pl.lit(plottimes[0]))\n",
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"\n",
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"for i, central_latitude in enumerate([90, -90]):\n",
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" ax = fig.add_subplot(\n",
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" gs[:, :4] if central_latitude == 90 else gs[:, 4:],\n",
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" projection=ccrs.NearsidePerspective(\n",
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" central_latitude=central_latitude,\n",
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" central_longitude=-30,\n",
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" satellite_height=15000000,\n",
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" ),\n",
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" )\n",
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" ax.add_feature(cartopy.feature.LAND, zorder=1)\n",
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" ax.add_feature(cartopy.feature.OCEAN, zorder=1)\n",
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" ax.coastlines()\n",
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" scat[i] = ax.scatter(\n",
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" particles[\"x\"],\n",
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" particles[\"y\"],\n",
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" marker=\".\",\n",
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" s=25,\n",
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" c=\"#AB2200\",\n",
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" edgecolor=\"white\",\n",
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" linewidth=0.15,\n",
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" transform=ccrs.PlateCarree(),\n",
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" )\n",
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"\n",
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"\n",
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"def animate(i):\n",
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" particles = df.filter(pl.col(\"t\") == pl.lit(plottimes[i]))\n",
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" scat[0].set_offsets(np.c_[particles[\"x\"], particles[\"y\"]])\n",
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" scat[1].set_offsets(np.c_[particles[\"x\"], particles[\"y\"]])\n",
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" return scat[0], scat[1]\n",
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"\n",
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"\n",
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"plt.rcParams[\"animation.html\"] = \"jshtml\"\n",
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"anim = FuncAnimation(fig, animate, frames=len(plottimes), interval=150, blit=True)\n",
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"plt.close(fig)\n",
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"anim.save(\n",
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" particle_filename.replace(\".parquet\", \".gif\"),\n",
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" writer=PillowWriter(fps=6),\n",
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" savefig_kwargs={\"transparent\": True},\n",
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")"
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]
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},
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{
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"source": [
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"The resulting animation is then\n",
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"\n",
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"![gif](images/homepage.gif)"
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"![gif](copernicusmarine_globalsurface.gif)"
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]
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}
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],
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"metadata": {
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"celltoolbar": "Metagegevens bewerken",
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Parcels:docs (3.14.6)",
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"language": "python",
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"name": "python3"
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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": "3.6.11"
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"version": "3.14.6"
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}
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},
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"nbformat": 4,

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