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refactor: using new JSON dictionary format for model projections for #…
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Plot Antarctic Cotidal Charts\n", | ||
| "=============================\n", | ||
| "\n", | ||
| "Demonstrates plotting cotidal charts around Antarctica\n", | ||
| "\n", | ||
| "OTIS format tidal solutions provided by Oregon State University and ESR \n", | ||
| "- http://volkov.oce.orst.edu/tides/region.html \n", | ||
| "- https://www.esr.org/research/polar-tide-models/list-of-polar-tide-models/\n", | ||
| "- ftp://ftp.esr.org/pub/datasets/tmd/ \n", | ||
| "\n", | ||
| "Global Tide Model (GOT) solutions provided by Richard Ray at GSFC \n", | ||
| "\n", | ||
| "Finite Element Solution (FES) provided by AVISO \n", | ||
| "- https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes.html\n", | ||
| "\n", | ||
| "#### Python Dependencies\n", | ||
| " - [numpy: Scientific Computing Tools For Python](https://www.numpy.org) \n", | ||
| " - [scipy: Scientific Tools for Python](https://www.scipy.org/) \n", | ||
| " - [pyproj: Python interface to PROJ library](https://pypi.org/project/pyproj/) \n", | ||
| " - [netCDF4: Python interface to the netCDF C library](https://unidata.github.io/netcdf4-python/) \n", | ||
| " - [matplotlib: Python 2D plotting library](http://matplotlib.org/) \n", | ||
| " - [cartopy: Python package designed for geospatial data processing](https://scitools.org.uk/cartopy/docs/latest/) \n", | ||
| "\n", | ||
| "#### Program Dependencies\n", | ||
| "\n", | ||
| "- `arguments.py`: load the nodal corrections for tidal constituents \n", | ||
| "- `astro.py`: computes the basic astronomical mean longitudes \n", | ||
| "- `crs.py`: Coordinate Reference System (CRS) routines \n", | ||
| "- `io.model.py`: retrieves tide model parameters for named tide models\n", | ||
| "- `io.OTIS.py`: extract tidal harmonic constants from OTIS tide models \n", | ||
| "- `io.ATLAS.py`: extract tidal harmonic constants from ATLAS netcdf models \n", | ||
| "- `io.GOT.py`: extract tidal harmonic constants from GOT tide models \n", | ||
| "- `io.FES.py`: extract tidal harmonic constants from FES tide models \n", | ||
| "- `predict.py`: predict tidal values using harmonic constants \n", | ||
| "- `time.py`: utilities for calculating time operations\n", | ||
| "- `utilities.py`: download and management utilities for files\n", | ||
| "\n", | ||
| "This notebook uses Jupyter widgets to set parameters for calculating the cotidal maps. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Load modules" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pyproj\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib\n", | ||
| "matplotlib.rcParams['axes.linewidth'] = 2.0\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import cartopy.crs as ccrs\n", | ||
| "\n", | ||
| "# import tide programs\n", | ||
| "import pyTMD.io\n", | ||
| "import pyTMD.predict\n", | ||
| "import pyTMD.tools\n", | ||
| "import pyTMD.utilities\n", | ||
| "\n", | ||
| "# autoreload\n", | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Set parameters for program\n", | ||
| "\n", | ||
| "- Model directory \n", | ||
| "- Tide model " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# available model list\n", | ||
| "model_list = sorted(pyTMD.io.model.ocean_elevation())\n", | ||
| "# display widgets for setting directory and model\n", | ||
| "TMDwidgets = pyTMD.tools.widgets()\n", | ||
| "TMDwidgets.model.options = model_list\n", | ||
| "TMDwidgets.model.value = 'CATS2008'\n", | ||
| "TMDwidgets.VBox([\n", | ||
| " TMDwidgets.directory,\n", | ||
| " TMDwidgets.model,\n", | ||
| " TMDwidgets.compress,\n", | ||
| "])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Setup tide model parameters" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# get model parameters\n", | ||
| "model = pyTMD.io.model(TMDwidgets.directory.value,\n", | ||
| " compressed=TMDwidgets.compress.value\n", | ||
| " ).elevation(TMDwidgets.model.value)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Setup coordinates for calculating tidal amplitudes" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# create an image around Antarctica\n", | ||
| "xlimits = [-560.*5e3,560.*5e3]\n", | ||
| "ylimits = [-560.*5e3,560.*5e3]\n", | ||
| "spacing = [20e3,-20e3]\n", | ||
| "# x and y coordinates\n", | ||
| "x = np.arange(xlimits[0],xlimits[1]+spacing[0],spacing[0])\n", | ||
| "y = np.arange(ylimits[1],ylimits[0]+spacing[1],spacing[1])\n", | ||
| "xgrid,ygrid = np.meshgrid(x,y)\n", | ||
| "# x and y dimensions\n", | ||
| "nx = int((xlimits[1]-xlimits[0])/spacing[0])+1\n", | ||
| "ny = int((ylimits[0]-ylimits[1])/spacing[1])+1\n", | ||
| "# convert image coordinates from polar stereographic to latitude/longitude\n", | ||
| "crs1 = pyproj.CRS.from_epsg(3031)\n", | ||
| "crs2 = pyproj.CRS.from_epsg(4326)\n", | ||
| "transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)\n", | ||
| "lon,lat = transformer.transform(xgrid.flatten(), ygrid.flatten())" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Calculate tide map" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# read tidal constants and interpolate to grid points\n", | ||
| "if model.format in ('OTIS','ATLAS-compact','TMD3'):\n", | ||
| " amp,ph,D,c = pyTMD.io.OTIS.extract_constants(lon, lat, model.grid_file,\n", | ||
| " model.model_file, model.projection, type=model.type, crop=True,\n", | ||
| " method='spline', grid=model.file_format)\n", | ||
| "elif (model.format == 'ATLAS-netcdf'):\n", | ||
| " amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(lon, lat, model.grid_file,\n", | ||
| " model.model_file, type=model.type, crop=True, method='spline',\n", | ||
| " scale=model.scale, compressed=model.compressed)\n", | ||
| "elif model.format in ('GOT-ascii', 'GOT-netcdf'):\n", | ||
| " amp,ph,c = pyTMD.io.GOT.extract_constants(lon, lat, model.model_file,\n", | ||
| " grid=model.file_format, crop=True, method='spline', scale=model.scale,\n", | ||
| " compressed=model.compressed)\n", | ||
| "elif (model.format == 'FES-netcdf'):\n", | ||
| " amp,ph = pyTMD.io.FES.extract_constants(lon, lat, model.model_file,\n", | ||
| " type=model.type, version=model.version, crop=True,\n", | ||
| " method='spline', scale=model.scale, compressed=model.compressed)\n", | ||
| " c = model.constituents" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Select constituent for cotidal chart" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "TMDwidgets.constituents.options = c\n", | ||
| "TMDwidgets.constituents.value = c[0]\n", | ||
| "display(TMDwidgets.constituents)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Create cotidal chart" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# extract amplitude and phase for constituent\n", | ||
| "i = TMDwidgets.constituents.index\n", | ||
| "cons = TMDwidgets.constituents.value\n", | ||
| "amplitude = amp[:,i].reshape(ny,nx)\n", | ||
| "phase = ph[:,i].reshape(ny,nx)\n", | ||
| "\n", | ||
| "# plot Antarctic cotidal charts\n", | ||
| "projection = ccrs.Stereographic(central_longitude=0.0,\n", | ||
| " central_latitude=-90,true_scale_latitude=-71.0)\n", | ||
| "fig, ax = plt.subplots(num=1, figsize=(9,8),\n", | ||
| " subplot_kw=dict(projection=projection))\n", | ||
| "# plot tide amplitude\n", | ||
| "extent = (xlimits[0],xlimits[1],ylimits[0],ylimits[1])\n", | ||
| "im = ax.imshow(amplitude, interpolation='nearest',\n", | ||
| " vmin=0, vmax=amplitude.max(), transform=projection,\n", | ||
| " extent=extent, origin='upper')\n", | ||
| "# plot tide phase\n", | ||
| "contour_levels = np.arange(0, 360, 30)\n", | ||
| "contour = ax.contour(xgrid, ygrid, phase,\n", | ||
| " contour_levels, colors='0.2', linestyles='solid',\n", | ||
| " transform=projection)\n", | ||
| "# add high resolution cartopy coastlines\n", | ||
| "ax.coastlines('10m')\n", | ||
| "\n", | ||
| "# Add colorbar and adjust size\n", | ||
| "# pad = distance from main plot axis\n", | ||
| "# extend = add extension triangles to upper and lower bounds\n", | ||
| "# options: neither, both, min, max\n", | ||
| "# shrink = percent size of colorbar\n", | ||
| "# aspect = lengthXwidth aspect of colorbar\n", | ||
| "cbar = plt.colorbar(im, ax=ax, pad=0.025, extend='max',\n", | ||
| " extendfrac=0.0375, shrink=0.85, aspect=22.5, drawedges=False)\n", | ||
| "# rasterized colorbar to remove lines\n", | ||
| "cbar.solids.set_rasterized(True)\n", | ||
| "# Add label to the colorbar\n", | ||
| "cbar.ax.set_ylabel(f'{model.name} {cons} Tide Chart', labelpad=10, fontsize=13)\n", | ||
| "cbar.ax.set_title('cm', fontsize=13, va='bottom')\n", | ||
| "# ticks lines all the way across\n", | ||
| "cbar.ax.tick_params(which='both', width=1, length=20,\n", | ||
| " labelsize=13, direction='in')\n", | ||
| "\n", | ||
| "# set x and y limits\n", | ||
| "ax.set_xlim(xlimits)\n", | ||
| "ax.set_ylim(ylimits)\n", | ||
| "\n", | ||
| "# stronger linewidth on frame\n", | ||
| "ax.spines['geo'].set_linewidth(2.0)\n", | ||
| "ax.spines['geo'].set_capstyle('projecting')\n", | ||
| "# adjust subplot within figure\n", | ||
| "fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98)\n", | ||
| "\n", | ||
| "# show the plot\n", | ||
| "plt.show()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3.10.4 64-bit", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.14" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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