{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import xarray as xr\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import os\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['image.cmap'] = 'Paired'\n",
"#sns.set(font_scale=2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Parent directory for the results of this run\n",
"group_results_directory = '/data/jpetrie/MEOPAR/SalishSea/results/remin_param_sweep_new_depth//'\n",
"tracer_file = 'SS5x5_1h_20150201_20150501_ptrc_T.nc'\n",
"mesh_mask_file = 'mesh_mask.nc'\n",
"individual_directories = []\n",
"param_vals = []\n",
"for file in os.listdir(group_results_directory):\n",
" if file.startswith(\"nampisrem_zz_remin_D_PON_\"):\n",
" val = float(file.split(\"nampisrem_zz_remin_D_PON_\")[1])\n",
" individual_directories.append(file)\n",
" param_vals.append(val)\n",
"\n",
"\n",
"tracer_datasets = [xr.open_dataset(group_results_directory + '/' + file +'/' + tracer_file) for file in individual_directories]\n",
"dataset_dict = dict(zip(param_vals, tracer_datasets))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"depths = dataset_dict[param_vals[0]].deptht.values\n",
"\n",
"min_depth_index = np.argmax(depths > 150)\n",
"max_depth_index = len(depths)\n",
"\n",
"#for param in param_vals:\n",
"t = np.array([float(x) for x in dataset_dict[param_vals[0]].time_centered.values]) \n",
"days = (t[:] - t[0])/10**9/3600/24\n",
"\n",
"min_day_index = np.argmax(days > 30)\n",
"max_day_index = len(days)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mesh_mask = xr.open_dataset(group_results_directory + individual_directories[0] + '/' + mesh_mask_file)\n",
"grid_heights = np.array(mesh_mask['e3t_1d'][0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NO3_quantity = ((dataset_dict[param_vals[0]].NO3.values)*(grid_heights.reshape((1,40,1,1))))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"
\n",
" \n",
" \n",
" | \n",
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