{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Matt's parcels tests"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# imports modules and renames them short names\n",
"import numpy as np\n",
"import xarray as xr\n",
"import os\n",
"from matplotlib import pyplot as plt, animation\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from IPython.display import HTML\n",
"from salishsea_tools import nc_tools, places\n",
"\n",
"# imports functions from the parcels module\n",
"from parcels import FieldSet, Field, VectorField, ParticleSet, JITParticle, ErrorCode, AdvectionRK4, AdvectionRK4_3D, plotTrajectoriesFile\n",
"\n",
"# makes the plots show up below the code cells\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Sets the default font size of matplotlib plots\n",
"plt.rcParams['font.size'] = 12"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Fieldset functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def fieldset_from_nemo(daterange, coords):\n",
" \"\"\"Generate a fieldset from a hourly SalishSeaCast forcing fields\n",
" over daterange.\n",
" \"\"\"\n",
"\n",
" # Generate sequential list of forcing file prefixes\n",
" prefixes = [\n",
" nc_tools.get_hindcast_prefix(daterange[0] + timedelta(days=d)) # This uses the get_hindcast_prefix function which I think is from SalishSeaTools and was made by the MOAD group. It sets the prefix with results/salishsea... etc. so you dont have to specify that\n",
" for d in range(np.diff(daterange)[0].days + 1)\n",
" ]\n",
"\n",
" # Predefine fieldset argument dictionaries\n",
" filenames, variables, dimensions = {}, {}, {}\n",
"\n",
" # Define dict fields for each variable. This might be where I would add other variables from the model\n",
" for var, name in zip(['U', 'V', 'W'], ['vozocrtx', 'vomecrty', 'vovecrtz']):\n",
"\n",
" # Dict of filenames containing the coordinate and forcing variables\n",
" datafiles = [prefix + f'_grid_{var}.nc' for prefix in prefixes]\n",
" filenames[var] = {'lon': coords, 'lat': coords, 'data': datafiles}\n",
"\n",
" # NEMO variable name\n",
" variables[var] = name\n",
"\n",
" # Dict of NEMO coordinate names (f-points)\n",
" dimensions[var] = {'lon': 'glamf', 'lat': 'gphif', 'time': 'time_counter'}\n",
" \n",
" # Add depth fields (f-points are on W grid)\n",
" filenames[var]['depth'] = prefixes[0] + '_grid_W.nc'\n",
" dimensions[var]['depth'] = 'depthw'\n",
"\n",
" # Load NEMO forcing into fieldset\n",
" field_set = FieldSet.from_nemo(filenames, variables, dimensions, allow_time_extrapolation=True)\n",
" \n",
" return field_set"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Kernels"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# I think this causes any particle that goes outside the boundary to be deleted\n",
"def DeleteParticle(particle, fieldset, time):\n",
" print(f'Particle {particle.id} lost !! [{particle.lon}, {particle.lat}, {particle.depth}, {particle.time}]')\n",
" particle.delete()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Simulations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Paths and filenames\n",
"paths = {\n",
" 'coords': '/data/SalishSeaCast/grid/coordinates_seagrid_SalishSea201702.nc',\n",
" 'mask': '/data/SalishSeaCast/grid/mesh_mask201702.nc',\n",
" 'results': '/ocean/mattmiller/MOAD/results/parcels/test',\n",
"}\n",
"\n",
"# Load coords and mask files and extract grid variables\n",
"coords, mask = [xr.open_dataset(paths[key], decode_times=False) for key in ('coords', 'mask')]\n",
"gridlon, gridlat = [coords[key][0, ...].values for key in ('glamt', 'gphit')]\n",
"tmask = mask.tmask[0, 0, ...].values\n",
"\n",
"# Define release parameters\n",
"location = 'Strait of Georgia' # there are predefined location names built into SalishSeaTools, under salishsea_tools.places.PLACES which can be found easily on github\n",
"n = 100 # number of particles\n",
"r = 50 # radius of particle cloud [m]\n",
"\n",
"# Start time, duration and timestep\n",
"start = datetime(2019, 1, 1, 12, 30, 0) # year, month, day, hour, minute, second\n",
"duration = timedelta(days=3) # the total duration of the simulation\n",
"dt = timedelta(seconds=90) # the timestep - toggle between + and - to run forwards or reverse\n",
"# is this the timestep that parcels uses or of the fieldset from SalishSeaCast?\n",
"\n",
"# Create Gaussian distribution around release point\n",
"mean, cov = [0, 0], [[r**2, 0], [0, r**2]]\n",
"x_offset, y_offset = np.random.multivariate_normal(mean, cov, n).T\n",
"lon, lat = places.PLACES[location]['lon lat']\n",
"lons = lon + x_offset / 111000 / np.cos(np.deg2rad(lat)) # this code might be used to convert from SalishSeaCast's grid to a lat lon grid, since it is an angled domain grid\n",
"lats = lat + y_offset / 111000\n",
"\n",
"# Forcing daterange (I add 1-day buffers)\n",
"daterange = [start - timedelta(days=1), start + duration + timedelta(days=1)]\n",
"\n",
"# Output file prefix\n",
"strings = [location] + [t.strftime('%Y%m%dT%H%M%S') for t in (start, start + duration)]\n",
"prefix = os.path.join(paths['results'], '_'.join(strings))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Particle simulations (3D)\n",
"\n",
"Build forcing fieldset"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: File /data/SalishSeaCast/grid/coordinates_seagrid_SalishSea201702.nc could not be decoded properly by xarray (version 2023.1.0).\n",
" It will be opened with no decoding. Filling values might be wrongly parsed.\n"
]
}
],
"source": [
"# Load SalishSeaCast results into fieldset\n",
"fieldset = fieldset_from_nemo(daterange, paths['coords'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Run simulation with NEMO forcing - only need to do once with current particle settings, then can load the particle file in the plot codes below"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Compiled ArrayJITParticleAdvectionRK4_3D ==> /tmp/parcels-2928/libcd4da4ae6c141826c9f262277affcb24_0.so\n",
"INFO: Output files are stored in /ocean/mattmiller/MOAD/analysis-matt/results/parcels/test/Strait of Georgia_20190101T123000_20190104T123000_NEMO_3D.zarr.\n",
"100%|██████████| 259200.0/259200.0 [01:21<00:00, 3169.17it/s]\n"
]
}
],
"source": [
"# Execute NEMO-only, 3D run, release at 5m\n",
"pset = ParticleSet.from_list(fieldset, JITParticle, lon=lons, lat=lats, depth=np.repeat(5, n), time=np.repeat(start, n))\n",
"kernel = AdvectionRK4_3D\n",
"output_file = pset.ParticleFile(name=prefix + '_NEMO_3D.zarr', outputdt=timedelta(hours=1))\n",
"pset.execute(\n",
" kernel, runtime=duration, dt=dt, output_file=output_file,\n",
" recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle},\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"\n",
"# Make initial figure\n",
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"cax = fig.add_axes([0.92, 0.15, 0.02, 0.7])\n",
"l = ax.scatter([], [], s=50, c=[], vmin=0, vmax=10, edgecolor='k') # \"s\" controls the size of the points\n",
"t = ax.text(0.02, 0.02, '', transform=ax.transAxes)\n",
"data = xr.open_dataset(prefix + '_NEMO_3D.zarr') # This line opens the dataset of the particles, so you don't have to run the \n",
"# particle simulation every time you come back to this notebook to play around with it, as long as you want to keep the particles the same\n",
"ax.contourf(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='gray')\n",
"ax.contour(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='k')\n",
"ax.set_xlim([-124.3, -123])\n",
"ax.set_ylim([48.7, 49.6])\n",
"ax.set_title('NEMO_3D')\n",
"ax.set_aspect(1/np.sin(np.deg2rad(49)))\n",
"fig.colorbar(l, cax=cax, label='Depth [m]')\n",
"\n",
"# Init function\n",
"def init():\n",
" t.set_text('')\n",
" l.set_offsets(np.empty((0, 2)))\n",
" l.set_array(np.empty(0))\n",
" return l, t,\n",
"\n",
"# Animate function\n",
"def animate(hour):\n",
" tstamp = data.time[0, hour].values.astype('datetime64[s]').astype(datetime)\n",
" t.set_text(tstamp.strftime('%Y-%b-%d %H:%M UTC'))\n",
" l.set_offsets(np.vstack([data.lon[:, hour], data.lat[:, hour]]).T)\n",
" l.set_array(data.z[:, hour])\n",
" return l, t,\n",
"\n",
"# Build animation\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, frames=73, interval=100, blit=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Render animation\n",
"HTML(anim.to_html5_video())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the end of what I did using Ben's notebook example\n",
"\n",
"From here on is what I have developed"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"\n",
"# Make initial vertical section profile figure\n",
"fig, ax = plt.subplots(figsize=(8, 8)) # names the figure fig and sets the figure size\n",
"cax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) # adds colour bar and sets its position\n",
"l = ax.scatter([data.lon], [data.z], s=50, c=[data.z], vmin=0, vmax=10, edgecolor='k')\n",
"t = ax.text(0.02, 0.02, '', transform=ax.transAxes)\n",
"data = xr.open_dataset(prefix + '_NEMO_3D.zarr')\n",
"#ax.contourf(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='gray') # don't need these because it's a vertical plot\n",
"#ax.contour(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='k')\n",
"ax.set_xlim([-124.3, -123])\n",
"ax.set_ylim([100, 0])\n",
"ax.set_title('NEMO_3D')\n",
"ax.set_ylabel('Depth [m]')\n",
"#ax.set_aspect(1/np.sin(np.deg2rad(49)))\n",
"fig.colorbar(l, cax=cax, label='Depth [m]')\n",
"\n",
"# Init function\n",
"def init():\n",
" t.set_text('')\n",
" l.set_offsets(np.empty((0, 2)))\n",
" l.set_array(np.empty(0))\n",
" return l, t,\n",
"\n",
"# Animate function\n",
"def animate(hour):\n",
" tstamp = data.time[0, hour].values.astype('datetime64[s]').astype(datetime)\n",
" t.set_text(tstamp.strftime('%Y-%b-%d %H:%M UTC'))\n",
" l.set_offsets(np.vstack([data.lon[:, hour], data.z[:, hour]]).T)\n",
" l.set_array(data.z[:, hour])\n",
" return l, t,\n",
"\n",
"# Build animation\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, frames=73, interval=100, blit=True)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Render animation\n",
"HTML(anim.to_html5_video())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Dimensions: (trajectory: 100, obs: 73)\n",
"Coordinates:\n",
" * obs (obs) int32 0 1 2 3 4 5 6 7 8 9 ... 64 65 66 67 68 69 70 71 72\n",
" * trajectory (trajectory) int64 0 1 2 3 4 5 6 7 8 ... 92 93 94 95 96 97 98 99\n",
"Data variables:\n",
" lat (trajectory, obs) float64 ...\n",
" lon (trajectory, obs) float64 ...\n",
" time (trajectory, obs) datetime64[ns] ...\n",
" z (trajectory, obs) float64 ...\n",
"Attributes:\n",
" Conventions: CF-1.6/CF-1.7\n",
" feature_type: trajectory\n",
" ncei_template_version: NCEI_NetCDF_Trajectory_Template_v2.0\n",
" parcels_mesh: spherical\n",
" parcels_version: 2.4.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mattmiller/conda_envs/analysis-matt/lib/python3.10/site-packages/xarray/backends/plugins.py:139: RuntimeWarning: 'netcdf4' fails while guessing\n",
" warnings.warn(f\"{engine!r} fails while guessing\", RuntimeWarning)\n",
"/home/mattmiller/conda_envs/analysis-matt/lib/python3.10/site-packages/xarray/backends/plugins.py:139: RuntimeWarning: 'scipy' fails while guessing\n",
" warnings.warn(f\"{engine!r} fails while guessing\", RuntimeWarning)\n"
]
}
],
"source": [
"data = xr.open_dataset(prefix + '_NEMO_3D.zarr')\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[1 values with dtype=float64]\n",
"Coordinates:\n",
" obs int32 0\n",
" trajectory int64 0\n",
"Attributes:\n",
" axis: X\n",
" long_name: \n",
" standard_name: longitude\n",
" units: degrees_east\n"
]
}
],
"source": [
"print(data.lon[0, 0])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Make side by side map and depth profile"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"\n",
"# Make initial figure\n",
"fig, axs = plt.subplots(1, 2, figsize=(17, 8), gridspec_kw={'wspace': 0.1})\n",
"cax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) # do I need this in this side by side?\n",
"l0 = axs[0].scatter([data.lon], [data.lat], s=50, c=[data.z], vmin=0, vmax=10, edgecolor='k')\n",
"l1 = axs[1].scatter([data.lon], [data.z], s=50, c=[data.z], vmin=0, vmax=10, edgecolor='k')\n",
"t0 = axs[0].text(0.02, 0.02, '', transform=axs[0].transAxes)\n",
"t1 = axs[1].text(0.02, 0.02, '', transform=axs[1].transAxes)\n",
"data = xr.open_dataset(prefix + '_NEMO_3D.zarr') # This line opens the dataset of the particles, so you don't have to run the \n",
"# particle simulation every time you come back to this notebook to play around with it, as long as you want to keep the particles the same\n",
"axs[0].contourf(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='gray')\n",
"axs[0].contour(gridlon, gridlat, tmask, levels=[-0.01, 0.01], colors='k')\n",
"axs[0].set_xlim([-124.4, -123])\n",
"axs[1].set_xlim([-124.4, -123])\n",
"axs[0].set_ylim([48.7, 49.8])\n",
"axs[1].set_ylim([100, 0])\n",
"axs[1].set_ylabel('Depth [m]')\n",
"axs[0].set_aspect(1/np.sin(np.deg2rad(49)))\n",
"fig.colorbar(l1, cax=cax, label='Depth [m]')\n",
"\n",
"# Init function\n",
"def init():\n",
" t0.set_text('')\n",
" t1.set_text('')\n",
" l0.set_offsets(np.empty((0, 2)))\n",
" l1.set_offsets(np.empty((0, 2)))\n",
" l0.set_array(np.empty(0))\n",
" l1.set_array(np.empty(0))\n",
" return l0, l1, t0, t1,\n",
"\n",
"# Animate function\n",
"def animate(hour):\n",
" tstamp = data.time[0, hour].values.astype('datetime64[s]').astype(datetime)\n",
" t0.set_text(tstamp.strftime('%Y-%b-%d %H:%M UTC'))\n",
" t1.set_text(tstamp.strftime('%Y-%b-%d %H:%M UTC'))\n",
" l0.set_offsets(np.vstack([data.lon[:, hour], data.lat[:, hour]]).T)\n",
" l1.set_offsets(np.vstack([data.lon[:, hour], data.z[:, hour]]).T)\n",
" l0.set_array(data.z[:, hour])\n",
" l1.set_array(data.z[:, hour])\n",
" return l0, l1, t0, t1,\n",
"\n",
"# Build animation\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, frames=73, interval=100, blit=True)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Render animation\n",
"HTML(anim.to_html5_video())"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Field.show() does not always correctly determine the domain for curvilinear grids. Use plotting with caution and perhaps use domain argument as in the NEMO 3D tutorial\n"
]
},
{
"data": {
"image/png": 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",
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