{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from salishsea_tools import viz_tools, places, visualisations\n", "import netCDF4 as nc # unless you prefer xarray\n", "import datetime as dt\n", "import os\n", "import glob\n", "import cmocean\n", "\n", "from IPython.display import Markdown, display\n", "%matplotlib inline" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "from IPython.display import HTML\n", "\n", "HTML('''\n", "\n", "
''')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load December files from the 201905 hindcast" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d0=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20071201_20071231.nc') #Use December 2007 for both 2007 and 2008 \"winter values " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "d1=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20081201_20081231.nc')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "d2=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20091201_20091231.nc')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "d3=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20101201_20101231.nc')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "d4=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20111201_20111231.nc')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "d5=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20121201_20121231.nc')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "d6=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20131201_20131231.nc')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "d7=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20141201_20141231.nc')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "d8=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20151201_20151231.nc')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "d9=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20161201_20161231.nc')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "d10=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20171201_20171231.nc')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "d11=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20181201_20181231.nc')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "d12=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20191201_20191231.nc')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "d13=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20201201_20201231.nc')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['time', 'depth', 'gridY', 'gridX', 'nitrate', 'ammonium', 'silicon', 'diatoms', 'flagellates', 'ciliates', 'microzooplankton', 'dissolved_organic_nitrogen', 'particulate_organic_nitrogen', 'biogenic_silicon', 'mesozooplankton'])\n" ] } ], "source": [ "print(d1.variables.keys())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "AllYears_N_Dec=((d0.variables['nitrate'][0,...])+\\\n", " (d0.variables['nitrate'][0,...])+\\\n", " (d1.variables['nitrate'][0,...])+\\\n", " (d2.variables['nitrate'][0,...])+\\\n", " (d3.variables['nitrate'][0,...])+\\\n", " (d4.variables['nitrate'][0,...])+\\\n", " (d5.variables['nitrate'][0,...])+\\\n", " (d6.variables['nitrate'][0,...])+\\\n", " (d7.variables['nitrate'][0,...])+\\\n", " (d8.variables['nitrate'][0,...])+\\\n", " (d9.variables['nitrate'][0,...])+\\\n", " (d10.variables['nitrate'][0,...])+\\\n", " (d11.variables['nitrate'][0,...])+\\\n", " (d12.variables['nitrate'][0,...]))/14\n", "\n", "### use 2007 values for both 2007 and 2008 because there are no December 2006 files" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "ColdYears_N_Dec=((d0.variables['nitrate'][0,...])+\\\n", " (d2.variables['nitrate'][0,...])+\\\n", " (d3.variables['nitrate'][0,...])+\\\n", " (d4.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "WarmYears_N_Dec=((d8.variables['nitrate'][0,...])+\\\n", " (d11.variables['nitrate'][0,...])+\\\n", " (d12.variables['nitrate'][0,...])+\\\n", " (d13.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "AllYears_Si_Dec=((d0.variables['silicon'][0,...])+\\\n", " (d0.variables['silicon'][0,...])+\\\n", " (d1.variables['silicon'][0,...])+\\\n", " (d2.variables['silicon'][0,...])+\\\n", " (d3.variables['silicon'][0,...])+\\\n", " (d4.variables['silicon'][0,...])+\\\n", " (d5.variables['silicon'][0,...])+\\\n", " (d6.variables['silicon'][0,...])+\\\n", " (d7.variables['silicon'][0,...])+\\\n", " (d8.variables['silicon'][0,...])+\\\n", " (d9.variables['silicon'][0,...])+\\\n", " (d10.variables['silicon'][0,...])+\\\n", " (d11.variables['silicon'][0,...])+\\\n", " (d12.variables['silicon'][0,...]))/14" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "ColdYears_Si_Dec=((d0.variables['silicon'][0,...])+\\\n", " (d2.variables['silicon'][0,...])+\\\n", " (d3.variables['silicon'][0,...])+\\\n", " (d4.variables['silicon'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "WarmYears_Si_Dec=((d8.variables['silicon'][0,...])+\\\n", " (d11.variables['silicon'][0,...])+\\\n", " (d12.variables['silicon'][0,...])+\\\n", " (d13.variables['silicon'][0,...]))/4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load January files from the 201905 hindcast" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "j0=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20070101_20070131.nc')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "j1=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20080101_20080131.nc')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "j2=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20090101_20090131.nc')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "j3=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20100101_20100131.nc')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "j4=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20110101_20110131.nc')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "j5=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20120101_20120131.nc')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "j6=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20130101_20130131.nc')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "j7=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20140101_20140131.nc')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "j8=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20150101_20150131.nc')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "j9=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20160101_20160131.nc')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "j10=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20170101_20170131.nc')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "j11=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20180101_20180131.nc')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "j12=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20190101_20190131.nc')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "j13=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20200101_20200131.nc')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "AllYears_N_Jan=((j0.variables['nitrate'][0,...])+\\\n", " (j1.variables['nitrate'][0,...])+\\\n", " (j2.variables['nitrate'][0,...])+\\\n", " (j3.variables['nitrate'][0,...])+\\\n", " (j4.variables['nitrate'][0,...])+\\\n", " (j5.variables['nitrate'][0,...])+\\\n", " (j6.variables['nitrate'][0,...])+\\\n", " (j7.variables['nitrate'][0,...])+\\\n", " (j8.variables['nitrate'][0,...])+\\\n", " (j9.variables['nitrate'][0,...])+\\\n", " (j10.variables['nitrate'][0,...])+\\\n", " (j11.variables['nitrate'][0,...])+\\\n", " (j12.variables['nitrate'][0,...])+\\\n", " (j13.variables['nitrate'][0,...]))/14\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "ColdYears_N_Jan=((j1.variables['nitrate'][0,...])+\\\n", " (j3.variables['nitrate'][0,...])+\\\n", " (j4.variables['nitrate'][0,...])+\\\n", " (j5.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "WarmYears_N_Jan=((j8.variables['nitrate'][0,...])+\\\n", " (j11.variables['nitrate'][0,...])+\\\n", " (j12.variables['nitrate'][0,...])+\\\n", " (j13.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "AllYears_Si_Jan=((j0.variables['silicon'][0,...])+\\\n", " (j1.variables['silicon'][0,...])+\\\n", " (j2.variables['silicon'][0,...])+\\\n", " (j3.variables['silicon'][0,...])+\\\n", " (j4.variables['silicon'][0,...])+\\\n", " (j5.variables['silicon'][0,...])+\\\n", " (j6.variables['silicon'][0,...])+\\\n", " (j7.variables['silicon'][0,...])+\\\n", " (j8.variables['silicon'][0,...])+\\\n", " (j9.variables['silicon'][0,...])+\\\n", " (j10.variables['silicon'][0,...])+\\\n", " (j11.variables['silicon'][0,...])+\\\n", " (j12.variables['silicon'][0,...])+\\\n", " (j13.variables['silicon'][0,...]))/14" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "ColdYears_Si_Jan=((j1.variables['silicon'][0,...])+\\\n", " (j3.variables['silicon'][0,...])+\\\n", " (j4.variables['silicon'][0,...])+\\\n", " (j5.variables['silicon'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "WarmYears_Si_Jan=((j8.variables['silicon'][0,...])+\\\n", " (j11.variables['silicon'][0,...])+\\\n", " (j12.variables['silicon'][0,...])+\\\n", " (j13.variables['silicon'][0,...]))/4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load February files from the 201905 hindcast" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "f0=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20070201_20070228.nc')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "f1=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20080201_20080229.nc')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "f2=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20090201_20090228.nc')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "f3=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20100201_20100228.nc')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "f4=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20110201_20110228.nc')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "f5=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20120201_20120229.nc')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "f6=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20130201_20130228.nc')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "f7=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20140201_20140228.nc')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "f8=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20150201_20150228.nc')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "f9=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20160201_20160229.nc')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "f10=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20170201_20170228.nc')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "f11=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20180201_20180228.nc')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "f12=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20190201_20190228.nc')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "f13=nc.Dataset('/results2/SalishSea/month-avg.201905/SalishSeaCast_1m_ptrc_T_20200201_20200229.nc')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['time', 'depth', 'gridY', 'gridX', 'nitrate', 'ammonium', 'silicon', 'diatoms', 'flagellates', 'ciliates', 'microzooplankton', 'dissolved_organic_nitrogen', 'particulate_organic_nitrogen', 'biogenic_silicon', 'mesozooplankton'])\n" ] } ], "source": [ "print(f1.variables.keys())" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "AllYears_N_Feb=((f0.variables['nitrate'][0,...])+\\\n", " (f1.variables['nitrate'][0,...])+\\\n", " (f2.variables['nitrate'][0,...])+\\\n", " (f3.variables['nitrate'][0,...])+\\\n", " (f4.variables['nitrate'][0,...])+\\\n", " (f5.variables['nitrate'][0,...])+\\\n", " (f6.variables['nitrate'][0,...])+\\\n", " (f7.variables['nitrate'][0,...])+\\\n", " (f8.variables['nitrate'][0,...])+\\\n", " (f9.variables['nitrate'][0,...])+\\\n", " (f10.variables['nitrate'][0,...])+\\\n", " (f11.variables['nitrate'][0,...])+\\\n", " (f12.variables['nitrate'][0,...])+\\\n", " (f13.variables['nitrate'][0,...]) )/14\n", "\n", "### use 2007 values for both 2007 and 2008 because there are no December 2006 files" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "ColdYears_N_Feb=((f1.variables['nitrate'][0,...])+\\\n", " (f3.variables['nitrate'][0,...])+\\\n", " (f4.variables['nitrate'][0,...])+\\\n", " (f5.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "WarmYears_N_Feb=((f8.variables['nitrate'][0,...])+\\\n", " (f11.variables['nitrate'][0,...])+\\\n", " (f12.variables['nitrate'][0,...])+\\\n", " (f13.variables['nitrate'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "AllYears_Si_Feb=((f0.variables['silicon'][0,...])+\\\n", " (f1.variables['silicon'][0,...])+\\\n", " (f2.variables['silicon'][0,...])+\\\n", " (f3.variables['silicon'][0,...])+\\\n", " (f4.variables['silicon'][0,...])+\\\n", " (f5.variables['silicon'][0,...])+\\\n", " (f6.variables['silicon'][0,...])+\\\n", " (f7.variables['silicon'][0,...])+\\\n", " (f8.variables['silicon'][0,...])+\\\n", " (f9.variables['silicon'][0,...])+\\\n", " (f10.variables['silicon'][0,...])+\\\n", " (f11.variables['silicon'][0,...])+\\\n", " (f12.variables['silicon'][0,...])+\\\n", " (f13.variables['silicon'][0,...]))/14" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "ColdYears_Si_Feb=((f1.variables['silicon'][0,...])+\\\n", " (f3.variables['silicon'][0,...])+\\\n", " (f4.variables['silicon'][0,...])+\\\n", " (f5.variables['silicon'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "WarmYears_Si_Feb=((f8.variables['silicon'][0,...])+\\\n", " (f11.variables['silicon'][0,...])+\\\n", " (f12.variables['silicon'][0,...])+\\\n", " (f13.variables['silicon'][0,...]))/4" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "AllYears_N_Winter=(AllYears_N_Dec+AllYears_N_Jan+AllYears_N_Feb)/3" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "AllYears_Si_Winter=(AllYears_Si_Dec+AllYears_Si_Jan+AllYears_Si_Feb)/3" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "ColdYears_N_Winter=(ColdYears_N_Dec+ColdYears_N_Jan+ColdYears_N_Feb)/3" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "WarmYears_N_Winter=(WarmYears_N_Dec+WarmYears_N_Jan+WarmYears_N_Feb)/3" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "ColdYears_Si_Winter=(ColdYears_Si_Dec+ColdYears_Si_Jan+ColdYears_Si_Feb)/3" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "WarmYears_Si_Winter=(WarmYears_Si_Dec+WarmYears_Si_Jan+WarmYears_Si_Feb)/3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Thalweg plot\n", " method using contour_thalweg from visualisations.py in tools repo" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "#open bathy file and meshmask\n", "fbathy=nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/bathymetry_201702.nc')\n", "fmesh=nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'Winter Nitrate Anomalies - Cold Years')" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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