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feat: functions to read and interpolate from all constituents to address #91 (#137)
* feat: add functions to read and interpolate from all constituents to address #91 * test: switch interpolation test to soft tabs * test: add tests for io methods * feat: update forecast notebook with dynamic plotting
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‎doc/source/api_reference/io/ATLAS.rst

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.. autofunction:: pyTMD.io.ATLAS.extract_constants
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.. autofunction:: pyTMD.io.ATLAS.read_constants
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.. autofunction:: pyTMD.io.ATLAS.interpolate_constants
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.. autofunction:: pyTMD.io.ATLAS.read_netcdf_grid
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.. autofunction:: pyTMD.io.ATLAS.read_netcdf_elevation

‎doc/source/api_reference/io/FES.rst

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.. autofunction:: pyTMD.io.FES.extract_constants
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.. autofunction:: pyTMD.io.FES.read_constants
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.. autofunction:: pyTMD.io.FES.interpolate_constants
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.. autofunction:: pyTMD.io.FES.read_ascii_file
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.. autofunction:: pyTMD.io.FES.read_netcdf_file

‎doc/source/api_reference/io/GOT.rst

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.. autofunction:: pyTMD.io.GOT.extract_constants
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.. autofunction:: pyTMD.io.GOT.read_constants
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.. autofunction:: pyTMD.io.GOT.interpolate_constants
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.. autofunction:: pyTMD.io.GOT.read_ascii_file
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.. autofunction:: pyTMD.io.GOT.extend_array

‎doc/source/api_reference/io/OTIS.rst

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.. autofunction:: pyTMD.io.OTIS.extract_constants
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.. autofunction:: pyTMD.io.OTIS.read_constants
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.. autofunction:: pyTMD.io.OTIS.interpolate_constants
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.. autofunction:: pyTMD.io.OTIS.read_otis_grid
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.. autofunction:: pyTMD.io.OTIS.read_atlas_grid

‎doc/source/api_reference/io/constituents.rst

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===============
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io.constituents
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===============
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Basic tide model constituent class
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`Source code`__
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.. __: https://github.com/tsutterley/pyTMD/blob/main/pyTMD/io/constituents.py
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General Attributes and Methods
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==============================
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.. autoclass:: pyTMD.io.constituents
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:members:

‎doc/source/index.rst

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api_reference/io/FES.rst
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api_reference/io/GOT.rst
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api_reference/io/OTIS.rst
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api_reference/io/constituents.rst
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api_reference/io/ocean_pole_tide.rst
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api_reference/load_constituent.rst
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api_reference/load_nodal_corrections.rst

‎notebooks/Plot Tide Forecasts.ipynb

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{
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"cells": [
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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": [
@@ -35,8 +36,10 @@
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"- `load_nodal_corrections.py`: load the nodal corrections for tidal constituents \n",
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"- `model.py`: retrieves tide model parameters for named tide models\n",
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"- `io.OTIS.py`: extract tidal harmonic constants from OTIS tide models \n",
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"- `io.ATLAS.py`: extract tidal harmonic constants from ATLAS netcdf models \n",
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"- `io.ATLAS.py`: extract tidal harmonic constants from ATLAS netCDF4 tide models \n",
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"- `io.GOT.py`: extract tidal harmonic constants from GOT tide models \n",
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"- `io.FES.py`: extract tidal harmonic constants from FES tide models \n",
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"- `io.constituents.py`: basic tide model constituent class \n",
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"- `predict.py`: predict tidal values using harmonic constants \n",
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"- `time.py`: utilities for calculating time operations\n",
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"- `utilities.py`: download and management utilities for files\n",
@@ -120,6 +123,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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"\n",
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"# get model parameters\n",
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"model = pyTMD.model(TMDwidgets.directory.value,\n",
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" format=TMDwidgets.atlas.value,\n",
@@ -130,94 +135,122 @@
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"YMD = TMDwidgets.datepick.value\n",
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"# calculate a weeks forecast every minute\n",
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"minutes = np.arange(7*1440)\n",
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"# convert time from MJD to days relative to Jan 1, 1992 (48622 MJD)\n",
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"tide_time = pyTMD.time.convert_calendar_dates(YMD.year, YMD.month,\n",
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" YMD.day, minute=minutes)\n",
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"hours = minutes/60.0\n",
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"\n",
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"# create plot with tidal displacements, high and low tides and dates\n",
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"fig,ax1 = plt.subplots(num=1)\n",
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"xmax = np.ceil(hours[-1]).astype('i')\n",
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"l1, = ax1.plot([], [], 'k')\n",
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"l2, = ax1.plot([], [], 'r*')\n",
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"l3, = ax1.plot([], [], 'b*')\n",
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"for h in range(24,xmax,24):\n",
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" ax1.axvline(h,color='gray',lw=0.5,ls='dashed',dashes=(11,5))\n",
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"ax1.set_xlim(0,xmax)\n",
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"ax1.set_ylabel(f'{model.name} Tidal Displacement [cm]')\n",
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"args = (YMD.year,YMD.month,YMD.day)\n",
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"ax1.set_xlabel('Time from {0:4d}-{1:02d}-{2:02d} UTC [Hours]'.format(*args))\n",
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"ttl = ax1.set_title(None)\n",
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"fig.subplots_adjust(left=0.10,right=0.98,bottom=0.10,top=0.95)\n",
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"\n",
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"# delta time (TT - UT1) file\n",
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"delta_file = pyTMD.utilities.get_data_path(['data','merged_deltat.data'])\n",
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"\n",
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"# leaflet location\n",
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"LAT,LON = np.copy(m.marker.location)\n",
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"# verify longitudes\n",
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"LON = m.wrap_longitudes(LON)\n",
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"# read tidal constants and interpolate to leaflet points\n",
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"if model.format in ('OTIS','ATLAS','ESR'):\n",
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" amp,ph,D,c = pyTMD.io.OTIS.extract_constants(np.atleast_1d(LON),\n",
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" np.atleast_1d(LAT), model.grid_file, model.model_file,\n",
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" model.projection, type=model.type, method='spline',\n",
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" extrapolate=True, grid=model.format)\n",
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" constituents = pyTMD.io.OTIS.read_constants(\n",
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" model.grid_file, model.model_file,\n",
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" model.projection, type=model.type,\n",
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" grid=model.format)\n",
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" c = constituents.fields\n",
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" DELTAT = np.zeros_like(tide_time)\n",
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"elif (model.format == 'netcdf'):\n",
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" amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(np.atleast_1d(LON),\n",
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" np.atleast_1d(LAT), model.grid_file, model.model_file,\n",
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" type=model.type, method='spline', extrapolate=True,\n",
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" scale=model.scale, compressed=model.compressed)\n",
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" constituents = pyTMD.io.ATLAS.read_constants(\n",
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" model.grid_file, model.model_file,\n",
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" type=model.type, compressed=model.compressed)\n",
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" c = constituents.fields\n",
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" DELTAT = np.zeros_like(tide_time)\n",
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"elif (model.format == 'GOT'):\n",
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" amp,ph,c = pyTMD.io.GOT.extract_constants(np.atleast_1d(LON),\n",
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" np.atleast_1d(LAT), model.model_file, method='spline',\n",
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" extrapolate=True, scale=model.scale,\n",
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" compressed=model.compressed)\n",
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" constituents = pyTMD.io.GOT.read_constants(\n",
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" model.model_file, compressed=model.compressed)\n",
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" c = constituents.fields\n",
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" # interpolate delta times from calendar dates to tide time\n",
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" DELTAT = pyTMD.time.interpolate_delta_time(delta_file, tide_time)\n",
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"elif (model.format == 'FES'):\n",
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" amp,ph = pyTMD.io.FES.extract_constants(np.atleast_1d(LON),\n",
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" np.atleast_1d(LAT), model.model_file, type=model.type,\n",
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" version=model.version, method='spline', extrapolate=True,\n",
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" scale=model.scale, compressed=model.compressed)\n",
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" constituents = pyTMD.io.FES.read_constants(model.model_file,\n",
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" type=model.type, version=model.version,\n",
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" compressed=model.compressed)\n",
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" c = model.constituents\n",
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" # interpolate delta times from calendar dates to tide time\n",
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" DELTAT = pyTMD.time.interpolate_delta_time(delta_file, tide_time)\n",
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"\n",
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"# calculate complex phase in radians for Euler's\n",
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"cph = -1j*ph*np.pi/180.0\n",
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"# calculate constituent oscillation\n",
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"hc = amp*np.exp(cph)\n",
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"# update the tide prediction and plot\n",
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"def update_tide_prediction(*args):\n",
191+
" # leaflet location\n",
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" LAT,LON = np.copy(m.marker.location)\n",
193+
" # verify longitudes\n",
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" LON = m.wrap_longitudes(LON)\n",
195+
" if model.format in ('OTIS','ATLAS','ESR'):\n",
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" amp,ph,D = pyTMD.io.OTIS.interpolate_constants(\n",
197+
" np.atleast_1d(LON), np.atleast_1d(LAT),\n",
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" constituents, model.projection, type=model.type,\n",
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" method='spline', extrapolate=True)\n",
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" elif (model.format == 'netcdf'):\n",
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" amp,ph,D = pyTMD.io.ATLAS.interpolate_constants(\n",
202+
" np.atleast_1d(LON), np.atleast_1d(LAT),\n",
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" constituents, type=model.type, scale=model.scale,\n",
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" method='spline', extrapolate=True)\n",
205+
" elif (model.format == 'GOT'):\n",
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" amp,ph = pyTMD.io.GOT.interpolate_constants(\n",
207+
" np.atleast_1d(LON), np.atleast_1d(LAT),\n",
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" constituents, scale=model.scale,\n",
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" method='spline', extrapolate=True)\n",
210+
" elif (model.format == 'FES'):\n",
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" amp,ph = pyTMD.io.FES.interpolate_constants(\n",
212+
" np.atleast_1d(LON), np.atleast_1d(LAT),\n",
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" constituents, scale=model.scale,\n",
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" method='spline', extrapolate=True)\n",
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" # calculate complex phase in radians for Euler's\n",
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" cph = -1j*ph*np.pi/180.0\n",
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" # calculate constituent oscillation\n",
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" hc = amp*np.exp(cph)\n",
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" # predict tidal elevations at time 1 and infer minor corrections\n",
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" TIDE = pyTMD.predict.time_series(tide_time, hc, c,\n",
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" deltat=DELTAT, corrections=model.format)\n",
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" MINOR = pyTMD.predict.infer_minor(tide_time, hc, c,\n",
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" deltat=DELTAT, corrections=model.format)\n",
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" TIDE.data[:] += MINOR.data[:]\n",
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" # convert to centimeters\n",
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" TIDE.data[:] *= 100.0\n",
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"\n",
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"# convert time from MJD to days relative to Jan 1, 1992 (48622 MJD)\n",
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"# predict tidal elevations at time 1 and infer minor corrections\n",
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"TIDE = pyTMD.predict.time_series(tide_time, hc, c,\n",
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" deltat=DELTAT, corrections=model.format)\n",
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"MINOR = pyTMD.predict.infer_minor(tide_time, hc, c,\n",
182-
" deltat=DELTAT, corrections=model.format)\n",
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"TIDE.data[:] += MINOR.data[:]\n",
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"# convert to centimeters\n",
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"TIDE.data[:] *= 100.0\n",
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"\n",
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"# differentiate to calculate high and low tides\n",
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"diff = np.zeros_like(tide_time, dtype=np.float64)\n",
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"# forward differentiation for starting point\n",
190-
"diff[0] = TIDE.data[1] - TIDE.data[0]\n",
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"# backward differentiation for end point\n",
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"diff[-1] = TIDE.data[-1] - TIDE.data[-2]\n",
193-
"# centered differentiation for all others\n",
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"diff[1:-1] = (TIDE.data[2:] - TIDE.data[0:-2])/2.0\n",
195-
"# indices of high and low tides\n",
196-
"htindex, = np.nonzero((np.sign(diff[0:-1]) >= 0) & (np.sign(diff[1:]) < 0))\n",
197-
"ltindex, = np.nonzero((np.sign(diff[0:-1]) <= 0) & (np.sign(diff[1:]) > 0))\n",
228+
" # differentiate to calculate high and low tides\n",
229+
" diff = np.zeros_like(tide_time, dtype=np.float64)\n",
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" # forward differentiation for starting point\n",
231+
" diff[0] = TIDE.data[1] - TIDE.data[0]\n",
232+
" # backward differentiation for end point\n",
233+
" diff[-1] = TIDE.data[-1] - TIDE.data[-2]\n",
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" # centered differentiation for all others\n",
235+
" diff[1:-1] = (TIDE.data[2:] - TIDE.data[0:-2])/2.0\n",
236+
" # indices of high and low tides\n",
237+
" htindex, = np.nonzero((np.sign(diff[0:-1]) >= 0) & (np.sign(diff[1:]) < 0))\n",
238+
" ltindex, = np.nonzero((np.sign(diff[0:-1]) <= 0) & (np.sign(diff[1:]) > 0))\n",
239+
" # update plot data\n",
240+
" l1.set_data(hours, TIDE.data)\n",
241+
" l2.set_data(hours[htindex], TIDE.data[htindex])\n",
242+
" l3.set_data(hours[ltindex], TIDE.data[ltindex])\n",
243+
" # update plot title\n",
244+
" ttl.set_text(u'{0:0.6f}\\u00b0N {1:0.6f}\\u00b0W'.format(LAT,LON))\n",
245+
" ax1.relim()\n",
246+
" ax1.autoscale_view()\n",
247+
" fig.canvas.draw()\n",
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"\n",
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"# create plot with tidal displacements, high and low tides and dates\n",
200-
"fig,ax1 = plt.subplots(num=1)\n",
201-
"xmax = np.ceil(hours[-1]).astype('i')\n",
202-
"ax1.plot(hours, TIDE.data, 'k')\n",
203-
"ax1.plot(hours[htindex], TIDE.data[htindex], 'r*')\n",
204-
"ax1.plot(hours[ltindex], TIDE.data[ltindex], 'b*')\n",
205-
"for h in range(24,xmax,24):\n",
206-
" ax1.axvline(h,color='gray',lw=0.5,ls='dashed',dashes=(11,5))\n",
207-
"ax1.set_xlim(0,xmax)\n",
208-
"ax1.set_ylabel(f'{model.name} Tidal Displacement [cm]')\n",
209-
"args = (YMD.year,YMD.month,YMD.day)\n",
210-
"ax1.set_xlabel('Time from {0:4d}-{1:02d}-{2:02d} UTC [Hours]'.format(*args))\n",
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"ax1.set_title(u'{0:0.6f}\\u00b0N {1:0.6f}\\u00b0W'.format(LAT,LON))\n",
212-
"fig.subplots_adjust(left=0.10,right=0.98,bottom=0.10,top=0.95)"
249+
"# run tide prediction at initial location\n",
250+
"update_tide_prediction()\n",
251+
"# watch marker location for changes\n",
252+
"m.marker_text.observe(update_tide_prediction)"
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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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}
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],
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"metadata": {

‎pyTMD/compute_tide_corrections.py

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@@ -292,12 +292,14 @@ def compute_tide_corrections(x, y, delta_time, DIRECTORY=None, MODEL=None,
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model.model_file, model.projection, type=model.type,
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method=METHOD, extrapolate=EXTRAPOLATE, cutoff=CUTOFF,
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grid=model.format, apply_flexure=APPLY_FLEXURE)
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# use delta time at 2000.0 to match TMD outputs
295296
deltat = np.zeros_like(t)
296297
elif (model.format == 'netcdf'):
297298
amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(lon, lat, model.grid_file,
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model.model_file, type=model.type, method=METHOD,
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extrapolate=EXTRAPOLATE, cutoff=CUTOFF, scale=model.scale,
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compressed=model.compressed)
302+
# use delta time at 2000.0 to match TMD outputs
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deltat = np.zeros_like(t)
302304
elif (model.format == 'GOT'):
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amp,ph,c = pyTMD.io.GOT.extract_constants(lon, lat, model.model_file,

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