{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial Strat Files for SalishSeaLake\n", "### May 17, 2017" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from salishsea_tools import timeseries_tools" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Dimensions: (deptht: 40, time_counter: 1, x: 398, y: 898)\n", "Coordinates:\n", " nav_lat (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n", " nav_lon (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n", " * deptht (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n", " * time_counter (time_counter) float32 9.96921e+36\n", "Dimensions without coordinates: x, y\n", "Data variables:\n", " vosaline (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n", " votemper (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n", "Attributes:\n", " Conventions: CF-1.6\n", " title: Salinity Temperature Initial Conditions based on Mar 20, 20...\n", " institution: Dept of Earth, Ocean & Atmospheric Sciences, University of ...\n", " source: https://bitbucket.org/salishsea/tools/src/tip/FindTSforSmoo...\n", " references: REQUIRED\n", " comment: Salinity and Temperature conditions from Mar 20, 2016 23:0...\n", " history: [2016-07-06 Created]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dropped_variables = ['area','bounds_lon','bounds_lat','deptht_bounds','sossheig',\n", " 'time_centered_bounds','time_counter_bounds','buoy_n2','mixed_depth']\n", "insert_number_here = 3\n", "ones = np.ones((40,898,398))\n", "mesh_mask = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n", "t_mask = mesh_mask.tmask.squeeze('t')\n", "\n", "example = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/TS20mar2016DeepSmooth.nc')\n", "example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### January data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vdo/anaconda3/lib/python3.6/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide\n", " x = np.divide(x1, x2, out)\n" ] }, { "data": { "text/plain": [ "\n", "Dimensions: (deptht: 40, x: 398, y: 898)\n", "Coordinates:\n", " * deptht (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n", " nav_lat (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n", " nav_lon (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n", " time_counter int64 3\n", "Dimensions without coordinates: x, y\n", "Data variables:\n", " vosaline (deptht, y, x) float64 26.97 26.97 26.97 26.97 26.97 26.97 ...\n", " votemper (deptht, y, x) float64 6.055 6.055 6.055 6.055 6.055 6.055 ..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timerange1 = ['2017-01-01','2017-02-01']\n", "Jan_time_series = timeseries_tools.make_filename_list(timerange1,'T',model = 'nowcast-green',resolution='d')\n", "Jan_data = xr.open_mfdataset(Jan_time_series)\n", "averaged_in_time_Jan = Jan_data.mean(dim='time_counter')\n", "new1 = averaged_in_time_Jan.drop(dropped_variables)\n", "new3 = new1.assign_coords(time_counter = insert_number_here).expand_dims('time_counter',1)\n", "new4 = new3.squeeze('time_counter')\n", "new5 = new4.where(new4.vosaline != 0).mean(['x','y'],skipna = True)\n", "\n", "saline = new5.vosaline.values\n", "saline = np.expand_dims(saline,axis=1)\n", "saline = np.expand_dims(saline,axis=2)\n", "saline = saline*ones\n", "\n", "temp = new5.votemper.values\n", "temp= np.expand_dims(temp,axis=1)\n", "temp = np.expand_dims(temp,axis=2)\n", "temp = temp*ones\n", "\n", "nav_lon2 = new4.nav_lon.values\n", "nav_lat2 = new4.nav_lat.values\n", "deptht2 = new4.deptht.values\n", "\n", "new_winter = xr.Dataset({'vosaline':(['deptht','y','x'], saline),\n", " 'votemper':(['deptht','y','x'],temp)},\n", " coords={'deptht':(['deptht'], deptht2), \n", " 'nav_lat':(['y','x'], nav_lat2),\n", " 'nav_lon':(['y','x'], nav_lon2),\n", " 'time_counter': 3})\n", "\n", "new_winter" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "syncing\n" ] } ], "source": [ "new_winter.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter2017_notmasked.nc')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_winter = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter2017_notmasked.nc')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nav_lon2 = new_winter.nav_lon.values\n", "nav_lat2 = new_winter.nav_lat.values\n", "deptht2 = new_winter.deptht.values" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Jan_saline_array = new_winter.vosaline.values\n", "Jan_saline_array[t_mask.values == 0] = 0\n", "Jan_temp_array = new_winter.votemper.values\n", "Jan_temp_array[t_mask.values == 0] = 0\n", "\n", "final_winter = xr.Dataset({'vosaline':(['deptht','y','x'], Jan_saline_array),\n", " 'votemper':(['deptht','y','x'],Jan_temp_array)},\n", " coords={'deptht':(['deptht'], deptht2), \n", " 'nav_lat':(['y','x'], nav_lat2),\n", " 'nav_lon':(['y','x'], nav_lon2),\n", " 'time_counter': 3})" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "final_winter.votemper.isel(y=500).plot()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "syncing\n" ] } ], "source": [ "final_winter.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter2017_201702.nc')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "final_winter = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter2017.nc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### June data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vdo/anaconda3/lib/python3.6/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide\n", " x = np.divide(x1, x2, out)\n" ] }, { "data": { "text/plain": [ "\n", "Dimensions: (deptht: 40, x: 398, y: 898)\n", "Coordinates:\n", " * deptht (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n", " nav_lat (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n", " nav_lon (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n", " time_counter int64 3\n", "Dimensions without coordinates: x, y\n", "Data variables:\n", " vosaline (deptht, y, x) float64 23.37 23.37 23.37 23.37 23.37 23.37 ...\n", " votemper (deptht, y, x) float64 14.31 14.31 14.31 14.31 14.31 14.31 ..." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timerange2 = ['2016-06-01','2016-07-01']\n", "Jun_time_series = timeseries_tools.make_filename_list(timerange2,'T',model='nowcast-green',resolution='d')\n", "Jun_data = xr.open_mfdataset(Jun_time_series)\n", "averaged_in_time_Jun = Jun_data.mean(dim='time_counter')\n", "newa = averaged_in_time_Jun.drop(dropped_variables)\n", "newb = newa.assign_coords(time_counter = insert_number_here).expand_dims('time_counter',1)\n", "newc = newb.squeeze('time_counter')\n", "newd = newc.where(newc.vosaline != 0).mean(['x','y'],skipna = True)\n", "\n", "saline_Jun = newd.vosaline.values\n", "saline_Jun = np.expand_dims(saline_Jun,axis=1)\n", "saline_Jun = np.expand_dims(saline_Jun,axis=2)\n", "saline_Jun = saline_Jun*ones\n", "\n", "temp_Jun = newd.votemper.values\n", "temp_Jun = np.expand_dims(temp_Jun,axis=1)\n", "temp_Jun = np.expand_dims(temp_Jun,axis=2)\n", "temp_Jun = temp_Jun*ones\n", "\n", "nav_lon_Jun = newc.nav_lon.values\n", "nav_lat_Jun = newc.nav_lat.values\n", "deptht_Jun = newc.deptht.values\n", "\n", "new_summer = xr.Dataset({'vosaline':(['deptht','y','x'], saline_Jun),\n", " 'votemper':(['deptht','y','x'],temp_Jun)},\n", " coords={'deptht':(['deptht'], deptht_Jun), \n", " 'nav_lat':(['y','x'], nav_lat_Jun),\n", " 'nav_lon':(['y','x'], nav_lon_Jun),\n", " 'time_counter': 3})\n", "new_summer" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "syncing\n" ] } ], "source": [ "new_summer.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer2016_notmasked.nc')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_summer = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer2016_notmasked.nc')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "\n", "Dimensions: (deptht: 40, x: 398, y: 898)\n", "Coordinates:\n", " * deptht (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n", " nav_lat (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n", " nav_lon (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n", " time_counter int64 3\n", "Dimensions without coordinates: x, y\n", "Data variables:\n", " vosaline (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", " votemper (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nav_lon_Jun = new_summer.nav_lon.values\n", "nav_lat_Jun = new_summer.nav_lat.values\n", "deptht_Jun = new_summer.deptht.values\n", "\n", "Jun_saline_array = new_summer.vosaline.values\n", "Jun_saline_array[t_mask.values == 0] = 0\n", "Jun_temp_array = new_summer.votemper.values\n", "Jun_temp_array[t_mask.values == 0] = 0\n", "\n", "final_summer = xr.Dataset({'vosaline':(['deptht','y','x'], Jun_saline_array),\n", " 'votemper':(['deptht','y','x'],Jun_temp_array)},\n", " coords={'deptht':(['deptht'], deptht_Jun), \n", " 'nav_lat':(['y','x'], nav_lat_Jun),\n", " 'nav_lon':(['y','x'], nav_lon_Jun),\n", " 'time_counter': 3})\n", "\n", "final_summer" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "final_summer.votemper.isel(y=500).plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "syncing\n" ] } ], "source": [ "final_summer.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer2016_201702.nc')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Making initial strat files for SalishSeaLake. Links may be dead after new repos come out for seperated NEMO-forcing. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }