{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## turn stats"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import netCDF4 as nc\n",
"import seaborn as sns\n",
"import matplotlib.colors as mcolors\n",
"import glob\n",
"import os\n",
"import xarray as xr\n",
"import datetime\n",
"from salishsea_tools import viz_tools, tidetools, geo_tools, gsw_calls\n",
"import pickle\n",
"from IPython.core.display import display, HTML\n",
"display(HTML(\"\"))\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\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": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_turn2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_turn2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_turn2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_turn2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"two = np.append(example1, example2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"three = np.append(two, example3)\n",
"full = np.append(three, example4)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"wnd_avg = np.array([])\n",
"wnd_min = np.array([])\n",
"wnd_max = np.array([])\n",
"wnd_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" wnd_avg = np.append(wnd_avg, full[start:end].mean())\n",
" wnd_min = np.append(wnd_min, full[start:end].min())\n",
" wnd_max = np.append(wnd_max, full[start:end].max())\n",
" wnd_std = np.append(wnd_std, full[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35064,)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two = np.append(example1[:,143,240], example2[:,143,240])\n",
"three = np.append(two, example3[:,143,240])\n",
"fullc = np.append(three, example4[:,143,240])\n",
"fullc.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"dates2 = np.array([datetime.datetime(2015,1,1,0,30) + datetime.timedelta(hours = i) for i in range(35064)])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"month_of_data = np.array([dates2[a].month for a in range(35064)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cur_avg = np.array([])\n",
"cur_min = np.array([])\n",
"cur_max = np.array([])\n",
"cur_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" cur_avg = np.append(cur_avg, fullc[start:end].mean())\n",
" cur_min = np.append(cur_min, fullc[start:end].min())\n",
" cur_max = np.append(cur_max, fullc[start:end].max())\n",
" cur_std = np.append(cur_std, fullc[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/salinity/3points/turn2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/salinity/3points/turn2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/salinity/3points/turn2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/salinity/3points/turn2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)\n",
"\n",
"two = np.append(example1, example2)\n",
"three = np.append(two, example3)\n",
"fulls = np.append(three, example4)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"sal_avg = np.array([])\n",
"sal_min = np.array([])\n",
"sal_max = np.array([])\n",
"sal_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" sal_avg = np.append(sal_avg, fulls[start:end].mean())\n",
" sal_min = np.append(sal_min, fulls[start:end].min())\n",
" sal_max = np.append(sal_max, fulls[start:end].max())\n",
" sal_std = np.append(sal_std, fulls[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"monthly_sal_avg = np.array([])\n",
"monthly_cur_avg = np.array([])\n",
"monthly_wnd_avg = np.array([])\n",
"for a in range(1,13):\n",
" monthly_sal_avg = np.append(monthly_sal_avg, fulls[month_of_data==a].mean())\n",
" monthly_cur_avg = np.append(monthly_cur_avg, fullc[month_of_data==a].mean())\n",
" monthly_wnd_avg = np.append(monthly_wnd_avg, full[month_of_data==a].mean())"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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