{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Si Ratio 1\n", "Nutrient comparisons with edited dataset using surface instead of 2m for depth. (" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4 as nc\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import datetime\n", "import xarray as xr\n", "from salishsea_tools import tidetools, geo_tools, viz_tools, nc_tools\n", "import pytz\n", "import os\n", "import glob\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "HTML('''\n", "
''')\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#load model grid stuff\n", "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n", "bathy, X, Y = tidetools.get_bathy_data(grid)\n", "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:170: RuntimeWarning: invalid value encountered in greater\n", " (np.logical_and(model_lons > lon - tols[grid]['tol_lon'],\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:171: RuntimeWarning: invalid value encountered in less\n", " model_lons < lon + tols[grid]['tol_lon'])),\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:172: RuntimeWarning: invalid value encountered in greater\n", " (np.logical_and(model_lats > lat - tols[grid]['tol_lat'],\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:173: RuntimeWarning: invalid value encountered in less\n", " model_lats < lat + tols[grid]['tol_lat']))\n" ] } ], "source": [ "# Load CitSci data and clean it up\n", "nutrients_2015 = pd.read_csv(\n", " '/ocean/eolson/MEOPAR/obs/PSFCitSci/PSFbottledata2015_CN_edits_EOCor2.csv')\n", "Yinds = np.array([])\n", "Xinds = np.array([])\n", "for n in nutrients_2015.index:\n", " Yind, Xind = geo_tools.find_closest_model_point(nutrients_2015['lon'][n], \n", " nutrients_2015['lat'][n], \n", " X, Y, land_mask = bathy.mask)\n", " Yinds = np.append(Yinds, Yind)\n", " Xinds = np.append(Xinds, Xind)\n", "nutrients_2015 = nutrients_2015.assign(Yind = Yinds)\n", "nutrients_2015 = nutrients_2015.assign(Xind = Xinds)\n", "nutrients_2015 = nutrients_2015.dropna(subset=['Yind'])\n", "nutrients_2015 = nutrients_2015[~nutrients_2015.flagged]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(890, 13)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nutrients_2015.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load model data and load it into arrays\n", "model_nutrients = sorted(glob.glob(\n", " '/data/vdo/MEOPAR/completed-runs/SiRatio1/test*/SalishSea*1d*ptrc*'))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "times is done\n", "Si is done\n", "Nitrate is done\n" ] } ], "source": [ "with nc_tools.scDataset(model_nutrients) as f: #takes a while to run, prone to killing kernal\n", " times = f.variables['time_counter'][:]\n", " print('times is done')\n", " model_si = f.variables['silicon'][:, :19, ...]\n", " print('Si is done')\n", " model_n023 = f.variables['nitrate'][:, :19, ...]\n", " print('Nitrate is done')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h = nc.Dataset(model_nutrients[0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert into datetime\n", "converted_timesa = nc.num2date(times, h.variables['time_counter'].units)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# mask everything outside of daterange of modelled results \n", "# apply same mask to everything in CS data\n", "dates = nutrients_2015['date'].values\n", "dates = np.array([pd.to_datetime(dates[n]) for n in range(890)])\n", "dates = np.ma.masked_outside(dates, datetime.datetime(2015,1,31), datetime.datetime(2015,5,2))\n", "Yinds = np.ma.masked_array(nutrients_2015['Yind'].values, mask = dates.mask)\n", "Xinds = np.ma.masked_array(nutrients_2015['Xind'].values, mask = dates.mask)\n", "depths = np.ma.masked_array(nutrients_2015['depth'].values, mask = dates.mask)\n", "cs_si = np.ma.masked_array(nutrients_2015['si'].values, mask = dates.mask)\n", "cs_no23 = np.ma.masked_array(nutrients_2015['no23'].values, mask = dates.mask)\n", "stations = np.ma.masked_array(nutrients_2015['station'].values, mask = dates.mask)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "273" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ma.count(cs_si)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:19: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:11: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:12: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] } ], "source": [ "list_of_model_si = np.ma.masked_array(np.zeros((890)), mask = True)\n", "list_of_model_ni = np.ma.masked_array(np.zeros((890)), mask = True)\n", "t = 0\n", "for n in range(890):\n", " if dates.mask[n] == False:\n", " Yind = Yinds[n]\n", " Xind = Xinds[n]\n", " date = dates[n]\n", " if ((depths[n] == 20) and (mesh.variables['tmask'][0,18,Yind,Xind] == 1)):\n", " index = np.argmin(np.abs(converted_timesa - date))\n", " s_val = model_si[index, 18,Yind, Xind]\n", " n_val = model_n023[index, 18, Yind, Xind]\n", " list_of_model_si.mask[t] = False\n", " list_of_model_si[t] = s_val\n", " list_of_model_ni.mask[t] = False\n", " list_of_model_ni[t] = n_val\n", " if ((depths[n] == 2) and (mesh.variables['tmask'][0,0,Yind,Xind] == 1)):\n", " index = np.argmin(np.abs(converted_timesa - date))\n", " s_val = model_si[index, 0,Yind, Xind]\n", " n_val = model_n023[index, 0, Yind, Xind]\n", " list_of_model_si.mask[t] = False\n", " list_of_model_si[t] = s_val\n", " list_of_model_ni.mask[t] = False\n", " list_of_model_ni[t] = n_val\n", " t = t + 1" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cs_no23.mask = list_of_model_ni.mask\n", "cs_si.mask = list_of_model_si.mask" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stations = np.ma.masked_array(stations, mask = list_of_model_si.mask)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "267" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ma.count(cs_no23)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cb_model_si = np.array([])\n", "cb_model_ni = np.array([])\n", "cb_cs_si = np.array([])\n", "cb_cs_ni = np.array([])\n", "cb_depths = np.array([])\n", "for n in range(890):\n", " if stations.mask[n] == False:\n", " if stations[n][:2] == 'CB':\n", " cb_model_si = np.append(cb_model_si, list_of_model_si[n])\n", " cb_model_ni = np.append(cb_model_ni, list_of_model_ni[n])\n", " cb_cs_si = np.append(cb_cs_si, cs_si[n])\n", " cb_cs_ni = np.append(cb_cs_ni, cs_no23[n])\n", " cb_depths = np.append(cb_depths, depths[n])\n", " list_of_model_ni.mask[n] = True\n", " list_of_model_si.mask[n] = True\n", " cs_no23.mask[n] = True\n", " cs_si.mask[n] = True" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bias = 7.20042826461\n", "RMSE = 9.24450311859\n", "Willmott = 0.672373706015\n" ] }, { "data": { "image/png": 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CJKhdO1PyBlMKz6Ak/vPPMGAALFliWkWGDHE7ouwgiVyIUG60MdvVll7ZqdyP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\nJCIi9RTk0qzly0OIv/deCPGvfjXuikREJJeCXJr0yishxLd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (8,8))\n", "ax.plot(cs_no23, list_of_model_ni, 'r.')\n", "ax.plot(cb_cs_ni[cb_depths == 2], cb_model_ni[cb_depths == 2], 'b.', label = 'CB surface')\n", "ax.plot(cb_cs_ni[cb_depths == 20], cb_model_ni[cb_depths == 20], 'g.',label = 'CB 20 m' )\n", "ax.plot(np.arange(0,38), np.arange(0,38), 'b-')\n", "ax.grid('on')\n", "plt.legend()\n", "ax.set_title('Citizen Science Data vs Nowcast-green: Nitrate')\n", "ax.set_xlabel('Citizen Science')\n", "ax.set_ylabel('Nowcast-green');\n", "print('bias = ' + str(-np.mean(cs_no23) + np.mean(list_of_model_ni)))\n", "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_ni - cs_no23)**2) /\n", " 267)))\n", "xbar = np.mean(cs_no23)\n", "print('Willmott = ' + str(1-(np.sum((list_of_model_ni - cs_no23)**2) / \n", " np.sum((np.abs(list_of_model_ni - xbar) \n", " + np.abs(cs_no23 - xbar))**2))))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bias = 11.8493508666\n", "RMSE = 16.410903418\n", "Willmott = 0.527624984829\n" ] }, { "data": { "image/png": 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UyEVExBdVVa4lXl7ufmZ7/ZBevXoxf/78tJ4ba+02ddhhhzF37lx23nlnhgwZQnV1NdB4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\ntW95v68B3gN2Bk4AZnpPmwmcmKsYJDc2bICRI6F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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (8,8))\n", "ax.plot(cs_si, list_of_model_si, 'r.')\n", "ax.plot(cb_cs_si[cb_depths == 2], cb_model_si[cb_depths == 2], 'b.', label = 'CB surface')\n", "ax.plot(cb_cs_si[cb_depths == 20], cb_model_si[cb_depths == 20], 'g.',label = 'CB 20 m' )\n", "ax.plot(np.arange(0,56), np.arange(0,56), 'b-')\n", "ax.grid('on')\n", "plt.legend()\n", "ax.set_title('Citizen Science Data vs Nowcast-green: Si')\n", "ax.set_xlabel('Citizen Science')\n", "ax.set_ylabel('Nowcast-green');\n", "print('bias = ' + str(-np.mean(cs_si) + np.mean(list_of_model_si)))\n", "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_si - cs_si)**2) /\n", " 267)))\n", "xbar = np.mean(cs_si)\n", "print('Willmott = ' + str(1-(np.sum((list_of_model_si - cs_si)**2) / \n", " np.sum((np.abs(list_of_model_si - xbar) \n", " + np.abs(cs_si - xbar))**2))))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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86U9/OqV4DhW4Z511FuFwmKeffhoAn8/HwIEDeeaZZ4D0E5+bm5uZNWsW//Vf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\n1v8HvMyP+gPkQbQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,2, figsize = (16,8))\n", "ax[0].plot(cs_no23[nutrients_2015['depth'].values == 2], \n", " cs_si[nutrients_2015['depth'].values == 2], \n", " 'b.', alpha = 0.5)\n", "ax[0].plot(cs_no23[nutrients_2015['depth'].values == 20], \n", " cs_si[nutrients_2015['depth'].values == 20], \n", " 'g.', alpha = 0.5)\n", "ax[0].plot(cb_cs_ni[cb_depths == 20], cb_cs_si[cb_depths == 20], \n", " '.', alpha = 0.5, color = 'saddlebrown')\n", "ax[0].plot(cb_cs_ni[cb_depths == 2], cb_cs_si[cb_depths == 2], \n", " '.', alpha = 0.5, color = 'purple')\n", "ax[0].plot(np.unique(cs_no23), np.poly1d(np.polyfit(cs_no23, cs_si, 1))(np.unique(cs_no23)))\n", "x = np.arange(0,50)\n", "#ax[0].plot(x,x, 'r-', alpha = 0.3)\n", "ax[0].plot(x, 2*x, 'g-', alpha = 0.3)\n", "ax[0].plot(x, 2*x+30, 'y-', alpha = 0.3)\n", "ax[1].plot(list_of_model_ni[nutrients_2015['depth'].values == 2], \n", " list_of_model_si[nutrients_2015['depth'].values == 2], 'b.', \n", " alpha = 0.5, label = 'surface')\n", "ax[1].plot(list_of_model_ni[nutrients_2015['depth'].values == 20], \n", " list_of_model_si[nutrients_2015['depth'].values == 20], 'g.', \n", " alpha = 0.5, label = '20m')\n", "ax[1].plot(cb_model_ni[cb_depths == 2], cb_model_si[cb_depths == 2], \n", " '.', alpha = 0.5, color = 'purple', label = 'CB surface')\n", "ax[1].plot(cb_model_ni[cb_depths == 20], cb_model_si[cb_depths == 20], \n", " '.', alpha = 0.5, color = 'saddlebrown', label = 'CB 20m')\n", "ax[1].plot(np.unique(list_of_model_ni), \n", " np.poly1d(np.polyfit(list_of_model_ni, \n", " list_of_model_si, 1))(np.unique(list_of_model_ni)))\n", "x = np.arange(0,53)\n", "#ax[1].plot(x,x, 'r-', alpha = 0.3, label = 'slope = 1')\n", "ax[1].plot(x, 2*x, 'g-', alpha = 0.3, label = 'slope = 2')\n", "ax[1].plot(x, 2*x+30, 'y-', alpha = 0.3, label = '2*N + 30')\n", "ax[0].set_title('Citizen Science', fontsize = 16)\n", "ax[1].set_title('Model', fontsize = 16)\n", "for ax in ax:\n", " ax.grid('on')\n", " ax.set_ylabel('Si', fontsize = 14)\n", " ax.set_xlabel('N', fontsize = 14)\n", " ax.set_ylim(0,105)\n", " ax.set_xlim(0,40)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CitSci slope = 1.20089704365\n", "CitSci y int = 16.3134925754\n", "model slope = 1.71121703956\n", "model y int = 1.23905594628\n" ] } ], "source": [ "m1, b1 = np.polyfit(cs_no23, cs_si, 1)\n", "print('CitSci slope = ' + str(m1))\n", "print('CitSci y int = ' + str(b1))\n", "m2, b2 = np.polyfit(list_of_model_ni, list_of_model_si, 1)\n", "print('model slope = ' + str(m2))\n", "print('model y int = ' + str(b2))" ] }, { "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 }