{
"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, wind_tools\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": [
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"wind_grid = nc.Dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSaAtmosphereGridV1')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(116, 150)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_tools.find_closest_model_point(-123.24, 48.69, wind_grid['longitude'][:]-360, wind_grid['latitude'][:],\n",
" grid = 'GEM2.5')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"wind_data = xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSaSurfaceAtmosphereFieldsV1')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"time_slice = slice('2015-01-01 00:00:00', '2019-01-01 00:00:00')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"u_winds = wind_data.u_wind.isel(gridY=116, gridX=150).sel(time=time_slice).data\n",
"v_winds = wind_data.v_wind.isel(gridY=116, gridX=150).sel(time=time_slice).data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"times = wind_data.time.sel(time=time_slice).data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35065,)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"times.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35065,)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_winds.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"wind_speed, wind_dir = wind_tools.wind_speed_dir(u_winds, v_winds)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',\n",
" '2015-01-01T02:00:00.000000000'], dtype='datetime64[ns]')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"times[:3]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"wnd_dir_avg = np.array([])\n",
"wnd_dir_min = np.array([])\n",
"wnd_dir_max = np.array([])\n",
"wnd_dir_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" wnd_dir_avg = np.append(wnd_dir_avg, wind_dir[start:end].mean())\n",
" wnd_dir_min = np.append(wnd_dir_min, wind_dir[start:end].min())\n",
" wnd_dir_max = np.append(wnd_dir_max, wind_dir[start:end].max())\n",
" wnd_dir_std = np.append(wnd_dir_std, wind_dir[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"wnd_spd_avg = np.array([])\n",
"wnd_spd_min = np.array([])\n",
"wnd_spd_max = np.array([])\n",
"wnd_spd_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" wnd_spd_avg = np.append(wnd_spd_avg, wind_speed[start:end].mean())\n",
" wnd_spd_min = np.append(wnd_spd_min, wind_speed[start:end].min())\n",
" wnd_spd_max = np.append(wnd_spd_max, wind_speed[start:end].max())\n",
" wnd_spd_std = np.append(wnd_spd_std, wind_speed[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35064,)"
]
},
"execution_count": 17,
"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": 18,
"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": 19,
"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": 20,
"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": 21,
"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": 22,
"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": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35064,)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fullc.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35065,)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wind_dir.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"wind_dir2 = wind_dir[:-1]\n",
"wind_speed2 = wind_speed[:-1]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"monthly_sal_avg = np.array([])\n",
"monthly_cur_avg = np.array([])\n",
"monthly_wnd_dir_avg = np.array([])\n",
"monthly_wnd_spd_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_dir_avg = np.append(monthly_wnd_dir_avg, wind_dir2[month_of_data==a].mean())\n",
" monthly_wnd_spd_avg = np.append(monthly_wnd_spd_avg, wind_speed2[month_of_data==a].mean())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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