{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## salmon 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_salmon2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_salmon2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_salmon2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_salmon2018.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[:,56,258], example2[:,56,258])\n",
"three = np.append(two, example3[:,56,258])\n",
"fullc = np.append(three, example4[:,56,258])\n",
"fullc.shape"
]
},
{
"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/salmon2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/salinity/3points/salmon2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/salinity/3points/salmon2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/salinity/3points/salmon2018.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": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1450,)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"\n",
"dates = np.array([datetime.date(2015,1,1) + datetime.timedelta(i) for i in range(1450)])\n",
"dates.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"dates2 = np.array([datetime.datetime(2015,1,1,0,30) + datetime.timedelta(hours = i) for i in range(35064)])\n",
"month_of_data = np.array([dates2[a].month for a in range(35064)])\n",
"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": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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