"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"da.plot(marker='o')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"加上维度名字"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array([9, 0, 2, 1, 0])\n",
"Dimensions without coordinates: x"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"da = xr.DataArray([9, 0, 2, 1, 0], dims=['x'])\n",
"da"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0kmfWN9DWoaKmaOroClDf0tbna/Utbfxk5XY21bcS0LXt4mJhB7UxZjDwR+Bua+2HypWttY9Za33WWp/H44nkjAmp0uflaHsXL25udHqUhGStZeXWAyx49NV+t0lPNTzyl23c9JPXmfXDldyzbAPPvddIa1tnDCcVObu0cDYyxqQTDOnfWWufjO5IyeEjYwq4oCCbJav3cdv0UqfHSSg7mo7yveVbeXWbn7GeHD43dwz//dbeDxz+yEpP5cHbL+KyCYW8us1PVa2fP285wBNr6khNMcwcNZT5ZUXML/dQNnwIxhgH/4sk2ZmzXXlggt+hvwYOWWvvDueT+nw+W11dff7TJbh/f3k7D7+0jVfunceoYTlOjxP3Wk908ujKbfzmrT1kZ6Ry99UT+eQlo0hPTeHpdfUsXlFLQ0sbxflZ3LugjIXTP3iqpas7wIa6Fqpq/FTVNrG5IfiL48i8TOaVeZhXVsSc8YUMHhTW/o3IgBhj1lhrfX2+FkZQXwa8BmwEAqF3f8Na+3x/H6OgDk9jaxtzHnqZf5g3nnsWlDk9Ttzq6g7wh9X7eOSlWlrbOvnorAv4yjUTGTZ40Hl93gNHTvJKbTC0X9t+kGPtXaSnGmaNKWB+WRHzyooY58nR3rZExHkF9blQUIfv0796l5rGo7xx35WkpugHfqDe3HGQ7y3fQs3+o8weW8C3b5rMpOLciH+dzu4A1bsPs6q2iVW1fmoPHAXAW5AVPERSVsTsscPIykiN+NeW5KCgdrHnNzbyD79by68+U8H8siKnx4kbe5tP8MPnt/Li5v2UDs3iX268kAWTR8Rs77a+pY1VtU1U1fh5Y8dB2jq7GZSWwuyxw5hf5mF+eZEOZ8mAKKhdrKMrwOwHVzJ7bAE/++uZTo/jesfau/hZ1Q4ef30XaSmGL84fz2cvG0NmunN7su1d3by76xBVNX5W1Tax8+BxAMYW5jAvdEJy1pgCBqVpb1v6p6B2ue8v38Jv3trN2/dfdd7HVRNVIGB5al09//piDU1H27l9Rglfv66c4bmZTo/2IXuaj7MqdGz7rfebae8KkJWeypzxhcwL7W2X5Gc5Paa4jILa5Wr3H2XBo6/yrZsm8dnLxjg9juus3XuY7z67hQ37Wpjqzef/3DyJ6RcMdXqssLR1dPP2zmaqapt4uaaJusPBm28mDh/ce0LSN3oo6anqR0t2Cuo4cOtP3+BkRzcv3n25riII2d96kn99sYan1tVTNGQQ911fzsJpJaTE6UlXay3v+4/3npB8Z1cznd2WIYPSuGxCIfPLiriizOPK3xIk+s4U1Log1CUqfaV886lNbKhrZZo33+lxHHWys5v/9+pOfrbqfbqt5Uvzx/OFeePIifPrl40xjC8azPiiwfzt5WM51t7FmzsOUlUbPLb9wqb9AEwamcv8cg/zy4qY5s0nTXvbSU971C5x5GQnsx74C7fPKOWHt13k9DiOsNbywqb9PPDcVupb2rh+ygi+ccOFeAuynR4t6qy11B442nuzzZo9h+kOWPKy0pk70cP8Mg9zJ3oo1DmMhKU96jiQm5nODVNG8uz6Br5146Skux53c0Mr33t2C+/sOkT5iCH8/u8+wqXjCp0eK2aMMZSPyKV8RC5fmDeO1rZOXt9+kKrQYZJnNzRgDFxcms+8icETkheX5MXtYSAZGO1Ru8jbO5u567G3eaRyKrfPSI7+j+Zj7Tz80jaWrN5LXlY69ywo466KC3TzzykCAcuWxiNU1TRRVdvEun0tWAvDcjK4YqKHeeVFzJ1QSH52htOjynnQycQ4Ya1l3sOrGJGbyZK/v8TpcaKqoyvAb97azY9Wbqeto5tPXjKaL181gbzsdKdHc71Dxzt4bbufqpomXtnm5/CJTlIMzLhgKPPLi5hX5mHSyFydlI4zCuo48tOqHSxeUcuqe+YxujAx72yrqm3i+8u3sNN/nCsmevjWTZMYXzTY6bHiUnfAsqGuhVWhE5Lv1QWfGlQ0ZFDwmu2yIuZMKCQ3U/8DdDsFdRzZ33qSSx9ayRfmjePeBeVOjxNR7/uP8YPlW6iq9TO2MIdv3TSJ+eW6bT6S/EfbeWVb8ITkq9v8HD3ZRVqKwTe6p7a1iAlFg7W37UIK6jjzmV+9y5bGI7x531UJcay2ta2TH6/czq/f3E1WeipfvnoCn7xkNBlpuuwsmrq6A6zd20JVbRNVNU3U7A8WSZXkZ3FFaG/70nHD4v6yx0ShoI4zL25q5PO/XcuvPl0R13uc3QHLktX7ePilWg6f6OCuCi9fvbZMl5g5pLG1rbe29fXtBzne0U1GagofGVsQ7CQp8zCmMFjbGk5/t0SWgjrOdHQFuOTBlcwaU8B/fDw+i5reer+Z7y3fwtbGI8waU8C3b5rElJI8p8eSkI6uANW7DwX3tmv97Gg6BsCoYdlcUJDNOzsP0dEd6N2+54k4Cuvo0XXUcSYjLYXbppfw67d203ysPa6KmvYdOsGDL2zl+Y37KcnP4qcfm8ENF8WuflTCk5GWwqXjC7l0fCHfvDH477Zqm59VNU2srGn60PY9T21XUDtDBwldqrLCS2d3sDEuHhxv7+LhFbVc9cgrVNX4+eo1E1n51Su48eKRCuk44C3I5hOzR/GLT1fQ379WfUsbP1i+hTd2HKS9q7ufrSQatEftUhOHD2GaN58lq/fx2cvGuDbsAgHLnzbU89ALNRw40s7CacV8/fpyRuapxjNeFednUd/S9qH3D0pL4Tdv7eHx13eRk9FT2xq8brtYta1RpaB2sUqfl288tZH1+1pcWeu5fl8L3312M+v2tnBxaR4/++sZzBxV4PRYcp7uXVDG/U9u7POp7ddOHs6bO5p7b21/acsBAMpHDOk9ITljlGpbI00nE13s6MlOKh74C7dNL+XB291T1HTgSLB+9Mm19XiGDOLr15Vz+/T4rR+VDwvnqg9rLTuajoUu//OzevchugKWIZlpzJ3gYV6ZhyvKPBQNUW1rOHTVRxz7ytL1vLT5AO9+8yqyM5z9BehkZze/eH0XP63aQVe35bOXj+GL88czWNfhCsEdizd2HOxtAGw62g7AlJLc3ockTPPmJ8S9AdGgoI5j7+xsZtFjb/N/75zKX810pqjJWsuKzQd44Pkt7DvUxrWThvPNGy/Uw1ulX9ZatjYeDR0iCda2BiwMze6pbS1i7kQPBTkqkuqhoI5j1lrmP7yKotxMljpQ1FSz/wjfe3YLb77fTNnwIXz75knMGZ889aMSGS0nOngtVNv6Sq2f5uMdGAPTvPnBW9vLiphcnJvUh88U1HGup6ip6p55jIlRUdOh4x088udafv/OXnKz0vnqNRP56KwL9LQROW+BgGVjfWvvzTbv1QVrWwsHD+KKiR7ml3u4fIKHvKzkKpJSUMe5nqKmz18xjq9dF92ips7uAL99ew//9udtHO/o5hOzR3H31RPUdSxR03ysnVe3+6mq8fPKNj+tbZ2kphhmXjCUeaFHkpWPGOLaS1QjRUGdAP7mv1azuaGVN75+ZdT2al/Z5uf7y7ewo+kYl08o5Ns3TWLC8CFR+VoifenqDrChrqX3hOTmhiMAjMjNZH65hysmFnHZhMKEPIGtoE4AL27az+d/u4ZfftrHleXDI/q5d/qP8cBzW1lZ08ToYdn8y42TuOrCooTfgxH3O3Dk5AeKpI62d5GeaqgYXRCqbfUwzpMYta0K6gTQU9TkGz2Un3+iz3/LATtyspOfrNzOf725m0FpqfzTVeP51KWjGZSWXM9rlPjQ2R1gzZ7DwStJavzUHgjWtpYOzeoN7UvGFsbt80ZVypQAMtJSuH1GCb96YzcHj7WfV1Vod8CyrHofi1fUcuhEB5UzvdyzoAzPkPgpf5Lkk56awuyxw5g9dhj3X38h9S1trArdbPPEmjr+++09ZKQFt5kf6ttOlKckaY86jmw/cJRr/u1VvnnDhfzd3LHn9Dne3XWI7z67mc0NR/CNGsp3bp7MRaWqH5X41t7Vzbu7DlFVE3wk2c6DxwEYU5jT+0iyWWMKyEx37962Dn0kkNt+9gbHTnbx0j/PHdBxubrDJ3jwhRqee6+R4rxM7r/hQm5Ss50kqD3Nx1kVOrb91vvNtHcFyEpPZc74Yb1FUqVDs50e8wN06COBVPq83P/kRtbta2FGGEVNJzq6+M9XdvLzV97HGLj76gn8/dxxcXscTyQco4bl8KlLc/jUpaNp6+jm7Z3BIqmXa5r4y9Zg3/bE4YN7Q9s3qsDVj4bTHnWcOXqyk1kPrGTh9GIevP3ifrez1vLMhgYeeqGGxtaT3Dy1mPuuL6dEdZSSxKy1vO8/zqpQ+987u5rp7LYMHpTGZeMLmV/uYV5ZEcNzY18kpT3qBDIkM50bLhrJsxsa+dZNk/osanqvroXvPruFNXsOM6Uklx9/dDoVo1U/KmKMYXzRYMYXDeZvLx/LsfYu3txxkKra4LHtFzfvB2DSyFzmh262mebNd/yOXO1Rx6F3dx2i8udv8fCdU7njlKKmpqMnWfxiLU+srWNYTgZfW1DOHTNLk7o/QSRc1lpqDxztvdlmzZ7DdAcsuZlpvUVSV5R5ovZw5vM+mWiMuQ74EZAKPG6tfehM2yuoo8taS8UDf+FIWxed3QFG5mUyc9RQXq5poqM7wN/MGcOXrhzPkMzk6koQiaTWtp7a1mAnycFj7RgDF5fkBR+SUF7ExSV5pKRE5qnt5xXUxphUYBtwDVAHrAY+aq3d0t/HKKij6+l19dyzbANdgQ/+200uzuXfPzYjZsVNIskiELBsaTwSCu0m1u0LFkkNy8lgTGE2G+pa6ez+35/Hc3lq+/keo54F7LDW7gx9sv8BbgX6DWqJrsUraj8U0gAtJzoV0iJRkJJimFKSx5SSPP7xqgkcOt7Ba9v9VNU08cyGBk7/cYz0U9vDOUJeAuw75e91ofd9gDHmc8aYamNMtd/vj8hw0reGPh48eqb3i0hkFeRkcOu0Eh69azr9HZSI5M9jOEHd15moD41mrX3MWuuz1vo8Hs/5Tyb96u+Jz3oStEjsxeLnMZygrgO8p/y9FGiI2AQyYPcuKCPrtFths9JTuXdBmUMTiSSvWPw8hnOMejUwwRgzBqgH7gI+FrEJZMB6jnud71lmETl/sfh5DPfyvBuARwlenvdLa+0DZ9peV32IiAzMed+ZaK19Hng+olOJiEhY3NtCIiIigIJaRMT1FNQiIi6noBYRcbmotOcZY/zAnnP88ELgYATHiRTNNTCaa2A018Ak4lyjrLV93i0YlaA+H8aY6v4uUXGS5hoYzTUwmmtgkm0uHfoQEXE5BbWIiMu5Magfc3qAfmiugdFcA6O5Biap5nLdMWoREfkgN+5Ri4jIKRTUIiIu50hQG2OuM8bUGmN2GGPu6+N1Y4z5cej194wxM1wy1zxjTKsxZn3oz7djNNcvjTFNxphN/bzu1HqdbS6n1strjKkyxmw1xmw2xny5j21ivmZhzhXzNTPGZBpj3jXGbAjN9d0+tnFivcKZy5HvsdDXTjXGrDPGLO/jtciul7U2pn8IVqW+D4wFMoANwKTTtrkBeIHg02VmA++4ZK55wHIH1mwuMAPY1M/rMV+vMOdyar1GAjNCbw8h+HBmN3yPhTNXzNcstAaDQ2+nA+8As12wXuHM5cj3WOhrfwX4fV9fP9Lr5cQede/Dcq21HUDPw3JPdSvwGxv0NpBvjBnpgrkcYa19FTh0hk2cWK9w5nKEtbbRWrs29PZRYCsffs5nzNcszLliLrQGx0J/TQ/9Of0qAyfWK5y5HGGMKQVuBB7vZ5OIrpcTQR3Ow3LDeqCuA3MBXBL6VewFY8zkKM8ULifWK1yOrpcxZjQwneDe2KkcXbMzzAUOrFno1/j1QBPwZ2utK9YrjLnAme+xR4GvAYF+Xo/oejkR1OE8LDesB+pGWDhfcy3B+/GnAj8Bno7yTOFyYr3C4eh6GWMGA38E7rbWHjn95T4+JCZrdpa5HFkza223tXYawWeizjLGTDltE0fWK4y5Yr5expibgCZr7ZozbdbH+855vZwI6nAeluvEA3XP+jWttUd6fhWzwafepBtjCqM8Vzhc+QBiJ9fLGJNOMAx/Z619so9NHFmzs83l9PeYtbYFWAVcd9pLjn6P9TeXQ+s1B7jFGLOb4CHSK40xvz1tm4iulxNB3fuwXGNMBsGH5T5z2jbPAJ8MnTmdDbRaaxudnssYM8IYY0JvzyK4fs1RniscTqzXWTm1XqGv+Qtgq7X2kX42i/mahTOXE2tmjPEYY/JDb2cBVwM1p23mxHqddS4n1stae7+1ttRaO5pgTrxsrf34aZtFdL3CemZiJFlru4wxXwJW8L8Py91sjPl86PX/JPh8xhuAHcAJ4DMumesO4AvGmC6gDbjLhk7xRpMx5g8Ez24XGmPqgO8QPLHi2HqFOZcj60Vwj+cTwMbQ8U2AbwAXnDKbE2sWzlxOrNlI4NfGmFSCQbfUWrvc6Z/JMOdy6nvsQ6K5XrqFXETE5XRnooiIyymoRURcTkEtIuJyCmoREZdTUIuIuJyCWkTE5RTUIiIup6CWhGeMqQh1AmcaY3JMsNv49M4IEdfSDS+SFIwxPwAygSygzlr7oMMjiYRNQS1JIdTfsho4CVxqre12eCSRsOnQhySLAmAwwSerZDo8i8iAaI9akoIx5hmClZRjgJHW2i85PJJI2GLenicSa8aYTwJd1trfh5rY3jTGXGmtfdnp2UTCoT1qERGX0zFqERGXU1CLiLicglpExOUU1CIiLqegFhFxOQW1iIjLKahFRFzu/wM1dHhetyZLoAAAAABJRU5ErkJggg==\n",
"text/plain": [
"
\n",
"
"
],
"text/plain": [
"<xarray.DataArray (x: 5)>\n",
"array([9, 0, 2, 1, 0])\n",
"Dimensions without coordinates: xxarray.DataArray
- x: 5
- 9 0 2 1 0
array([9, 0, 2, 1, 0])
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"da.plot(marker='o')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"如果加上坐标，情况会如何呢？"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array([9, 0, 2, 1, 0])\n",
"Coordinates:\n",
" * year (year) datetime64[ns] 2010-01-01 2011-01-01 ... 2014-01-01"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"da = xr.DataArray([9, 0, 2, 1, 0],\n",
" dims=['year'],\n",
" coords={'year':pd.to_datetime(['2010','2011','2012','2013','2014'])})\n",
"da"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
\n",
"
"
],
"text/plain": [
"<xarray.DataArray (year: 5)>\n",
"array([9, 0, 2, 1, 0])\n",
"Coordinates:\n",
" * year (year) datetime64[ns] 2010-01-01 2011-01-01 ... 2014-01-01xarray.DataArray
- year: 5
- 9 0 2 1 0
array([9, 0, 2, 1, 0])
- year(year)datetime64[ns]2010-01-01 ... 2014-01-01
array(['2010-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',\n", " '2012-01-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',\n", " '2014-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f,ax = plt.subplots()\n",
"da.plot(ax=ax,marker='o')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 多维 DataArray"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array([[1, 2, 4],\n",
" [5, 6, 7]])\n",
"Coordinates:\n",
" * level (level) int64 0 1\n",
" * date (date) datetime64[ns] 2015-09-01 2015-10-01 2015-11-01"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S = [[1,2,4],[5,6,7]]\n",
"levels = [0,1]\n",
"date = pd.to_datetime(['2015-09-01','2015-10-01','2015-11-01'])\n",
"\n",
"da = xr.DataArray(S, dims=['level', 'date'],\n",
" coords={'level': levels,\n",
" 'date': date},\n",
" name='da')\n",
"da"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"`dataarray`和`dataset`可以转换为`pandas.dataframe`。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"[3869000 values with dtype=float32]\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp.air"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 2. DataSet"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 坐标 vs. 变量"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 2.412e+04 2.425e+04 ... 2.957e+04\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp*100"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"操作是针对数据变量的，坐标本身不变。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"如果要改变坐标，需要用 `.assign_coords` 方法："
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 -160.0 -157.5 -155.0 -152.5 ... -35.0 -32.5 -30.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp_new = air_temp.assign_coords({'lon':(((air_temp.lon+180)%360)-180)})\n",
"# 或者\n",
"# air_temp_new = air_temp.assign_coords(lon = (((air_temp.lon+180)%360)-180))\n",
"air_temp_new"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 3. 数组访问方法"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array(241.2, dtype=float32)\n",
"Coordinates:\n",
" lat float32 75.0\n",
" lon float32 200.0\n",
" time datetime64[ns] 2013-01-01\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp.air[0,0,0]\n",
"# 等同于 \n",
"#air_temp.air.isel(lat=0,lon=0,time=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array(265.4, dtype=float32)\n",
"Coordinates:\n",
" lat float32 50.0\n",
" lon float32 300.0\n",
" time datetime64[ns] 2013-12-01\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp.air.sel(lat=50,lon=300,time='2013-12-01T00:00:00')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array([265.4 , 267.79 , 268.19998, 269.6 , 268.79 ], dtype=float32)\n",
"Coordinates:\n",
" lat float32 50.0\n",
" lon float32 300.0\n",
" * time (time) datetime64[ns] 2013-12-01 2013-12-01T06:00:00 ... 2013-12-02\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# slice 选取坐标范围\n",
"air_temp.air.sel(lat=50,lon=300,time=slice('2013-12-01T00:00:00','2013-12-02T00:00:00'))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array(259.69998, dtype=float32)\n",
"Coordinates:\n",
" lat float32 72.5\n",
" lon float32 300.0\n",
" time datetime64[ns] 2013-12-02\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp.air.sel(lat=73,lon=300,time='2013-12-02T00:00:00',method='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"`method` 参数的选项: \n",
"* `None` (default): only exact matches\n",
"* `pad` / `ffill`: propagate last valid index value forward\n",
"* `backfill` / `bfill`: propagate next valid index value backward\n",
"* `nearest`: use nearest valid index value"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"`.sel` 对整个数据集也可以使用, 注意数据中 `lat` 是倒序的"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 11)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 60.0 57.5 55.0 52.5 50.0\n",
" lon float32 300.0\n",
" time datetime64[ns] 2013-12-02\n",
"Data variables:\n",
" air (lat) float32 253.8 259.7 254.4 253.1 ... 262.3 258.3 259.2 268.8\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# \n",
"air_temp.sel(lat=slice(75,50),lon=300,time='2013-12-02T00:00:00')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 4. 简单绘图"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
\n",
"
"
],
"text/plain": [
"<xarray.DataArray 'da' (level: 2, date: 3)>\n",
"array([[1, 2, 4],\n",
" [5, 6, 7]])\n",
"Coordinates:\n",
" * level (level) int64 0 1\n",
" * date (date) datetime64[ns] 2015-09-01 2015-10-01 2015-11-01xarray.DataArray
'da'
- level: 2
- date: 3
- 1 2 4 5 6 7
array([[1, 2, 4],\n", " [5, 6, 7]]) - level(level)int640 1
array([0, 1])
- date(date)datetime64[ns]2015-09-01 2015-10-01 2015-11-01
array(['2015-09-01T00:00:00.000000000', '2015-10-01T00:00:00.000000000',\n", " '2015-11-01T00:00:00.000000000'], dtype='datetime64[ns]')
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" da\n",
"level date \n",
"0 2015-09-01 1\n",
" 2015-10-01 2\n",
" 2015-11-01 4\n",
"1 2015-09-01 5\n",
" 2015-10-01 6\n",
" 2015-11-01 7"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"da.to_dataframe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"#air_temp = xr.tutorial.load_dataset('air_temperature')\n",
"air_temp = xr.open_dataset('air_temperature.nc')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | \n", " | da | \n", "
|---|---|---|
| level | \n", "date | \n", "\n", " |
| 0 | \n", "2015-09-01 | \n", "1 | \n", "
| 2015-10-01 | \n", "2 | \n", "|
| 2015-11-01 | \n", "4 | \n", "|
| 1 | \n", "2015-09-01 | \n", "5 | \n", "
| 2015-10-01 | \n", "6 | \n", "|
| 2015-11-01 | \n", "7 | \n", "
\n",
"
"
],
"text/plain": [
"<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
"[3869000 values with dtype=float32]\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]xarray.DataArray
'air'
- time: 2920
- lat: 25
- lon: 53
- ...
[3869000 values with dtype=float32]
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
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- units :
- degrees_north
- axis :
- Y
array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- axis :
- X
array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
- time
- long_name :
- Time
array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- long_name :
- 4xDaily Air temperature at sigma level 995
- units :
- degK
- precision :
- 2
- GRIB_id :
- 11
- GRIB_name :
- TMP
- var_desc :
- Air temperature
- dataset :
- NMC Reanalysis
- level_desc :
- Surface
- statistic :
- Individual Obs
- parent_stat :
- Other
- actual_range :
- [185.16 322.1 ]
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
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- units :
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- axis :
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array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
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array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
- time
- long_name :
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array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float32...
- long_name :
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- units :
- degK
- precision :
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- GRIB_id :
- 11
- GRIB_name :
- TMP
- var_desc :
- Air temperature
- dataset :
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- level_desc :
- Surface
- statistic :
- Individual Obs
- parent_stat :
- Other
- actual_range :
- [185.16 322.1 ]
[3869000 values with dtype=float32]
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 2.412e+04 2.425e+04 ... 2.957e+04\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
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- long_name :
- Latitude
- units :
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- axis :
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array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
- longitude
- long_name :
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- units :
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- axis :
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array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
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- long_name :
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array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float322.412e+04 2.425e+04 ... 2.957e+04
array([[[24120. , 24250. , 24350. , ..., 23279.998, 23550. ,\n", " 23860. ],\n", " [24379.998, 24450. , 24470. , ..., 23279.998, 23529.998,\n", " 23929.998],\n", " [25000. , 24979.998, 24889. , ..., 23320. , 23639. ,\n", " 24170. ],\n", " ...,\n", " [29660. , 29619.998, 29640. , ..., 29540. , 29510. ,\n", " 29469.998],\n", " [29590. , 29619.998, 29679. , ..., 29590. , 29590. ,\n", " 29519.998],\n", " [29629. , 29679. , 29710. , ..., 29690. , 29679. ,\n", " 29660. ]],\n", "\n", " [[24210. , 24270. , 24310. , ..., 23200. , 23360. ,\n", " 23579.998],\n", " [24360. , 24410. , 24420. , ..., 23100. , 23250. ,\n", " 23570. ],\n", " [25320. , 25289. , 25210. , ..., 23079.998, 23339. ,\n", " 23850. ],\n", "...\n", " [29369. , 29388.998, 29538.998, ..., 29509. , 29469. ,\n", " 29429. ],\n", " [29629. , 29719. , 29759. , ..., 29529. , 29509. ,\n", " 29438.998],\n", " [29779. , 29838.998, 29849. , ..., 29569. , 29549. ,\n", " 29519. ]],\n", "\n", " [[24509. , 24429. , 24329. , ..., 24168.998, 24149. ,\n", " 24179. ],\n", " [24989. , 24929. , 24839. , ..., 23959. , 24029. ,\n", " 24168.998],\n", " [26299. , 26219. , 26138.998, ..., 23989. , 24259. ,\n", " 24629. ],\n", " ...,\n", " [29379. , 29369. , 29509. , ..., 29529. , 29509. ,\n", " 29469. ],\n", " [29609. , 29688.998, 29719. , ..., 29569. , 29569. ,\n", " 29519. ],\n", " [29769. , 29809. , 29809. , ..., 29649. , 29619. ,\n", " 29569. ]]], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
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" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
- Latitude
- units :
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- axis :
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array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32-160.0 -157.5 ... -32.5 -30.0
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- standard_name :
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array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float32241.2 242.5 243.5 ... 296.2 295.7
- long_name :
- 4xDaily Air temperature at sigma level 995
- units :
- degK
- precision :
- 2
- GRIB_id :
- 11
- GRIB_name :
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- var_desc :
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- level_desc :
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- statistic :
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- parent_stat :
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- actual_range :
- [185.16 322.1 ]
array([[[241.2 , 242.5 , ..., 235.5 , 238.59999],\n", " [243.79999, 244.5 , ..., 235.29999, 239.29999],\n", " ...,\n", " [295.9 , 296.19998, ..., 295.9 , 295.19998],\n", " [296.29 , 296.79 , ..., 296.79 , 296.6 ]],\n", "\n", " [[242.09999, 242.7 , ..., 233.59999, 235.79999],\n", " [243.59999, 244.09999, ..., 232.5 , 235.7 ],\n", " ...,\n", " [296.19998, 296.69998, ..., 295.5 , 295.1 ],\n", " [296.29 , 297.19998, ..., 296.4 , 296.6 ]],\n", "\n", " ...,\n", "\n", " [[245.79 , 244.79 , ..., 243.98999, 244.79 ],\n", " [249.89 , 249.29 , ..., 242.48999, 244.29 ],\n", " ...,\n", " [296.29 , 297.19 , ..., 295.09 , 294.38998],\n", " [297.79 , 298.38998, ..., 295.49 , 295.19 ]],\n", "\n", " [[245.09 , 244.29 , ..., 241.48999, 241.79 ],\n", " [249.89 , 249.29 , ..., 240.29 , 241.68999],\n", " ...,\n", " [296.09 , 296.88998, ..., 295.69 , 295.19 ],\n", " [297.69 , 298.09 , ..., 296.19 , 295.69 ]]], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\n",
"
"
],
"text/plain": [
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"Attributes:\n",
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" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
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" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]xarray.DataArray
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- lat()float3275.0
- standard_name :
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"text/plain": [
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"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
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'air'
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- lat()float3250.0
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- standard_name :
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- long_name :
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\n",
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"text/plain": [
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" lon float32 300.0\n",
" * time (time) datetime64[ns] 2013-12-01 2013-12-01T06:00:00 ... 2013-12-02\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [185.16 322.1 ]xarray.DataArray
'air'
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- lat()float3250.0
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- lon()float32300.0
- standard_name :
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- units :
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- time(time)datetime64[ns]2013-12-01 ... 2013-12-02
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array(['2013-12-01T00:00:00.000000000', '2013-12-01T06:00:00.000000000',\n", " '2013-12-01T12:00:00.000000000', '2013-12-01T18:00:00.000000000',\n", " '2013-12-02T00:00:00.000000000'], dtype='datetime64[ns]')
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\n",
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],
"text/plain": [
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"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
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" actual_range: [185.16 322.1 ]xarray.DataArray
'air'
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- lat()float3272.5
- standard_name :
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- units :
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array(72.5, dtype=float32)
- lon()float32300.0
- standard_name :
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- long_name :
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array(300., dtype=float32)
- time()datetime64[ns]2013-12-02
- standard_name :
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- long_name :
- Time
array('2013-12-02T00:00:00.000000000', dtype='datetime64[ns]')
- long_name :
- 4xDaily Air temperature at sigma level 995
- units :
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- precision :
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- GRIB_name :
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- var_desc :
- Air temperature
- dataset :
- NMC Reanalysis
- level_desc :
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- statistic :
- Individual Obs
- parent_stat :
- Other
- actual_range :
- [185.16 322.1 ]
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 11)\n",
"Coordinates:\n",
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" lon float32 300.0\n",
" time datetime64[ns] 2013-12-02\n",
"Data variables:\n",
" air (lat) float32 253.8 259.7 254.4 253.1 ... 262.3 258.3 259.2 268.8\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 11
- lat(lat)float3275.0 72.5 70.0 ... 55.0 52.5 50.0
- standard_name :
- latitude
- long_name :
- Latitude
- units :
- degrees_north
- axis :
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array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. ],\n", " dtype=float32) - lon()float32300.0
- standard_name :
- longitude
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- units :
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- axis :
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array(300., dtype=float32)
- time()datetime64[ns]2013-12-02
- standard_name :
- time
- long_name :
- Time
array('2013-12-02T00:00:00.000000000', dtype='datetime64[ns]')
- air(lat)float32253.8 259.7 254.4 ... 259.2 268.8
- long_name :
- 4xDaily Air temperature at sigma level 995
- units :
- degK
- precision :
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- GRIB_id :
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- GRIB_name :
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- var_desc :
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- dataset :
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- level_desc :
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- statistic :
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- parent_stat :
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- actual_range :
- [185.16 322.1 ]
array([253.79999, 259.69998, 254.39 , 253.09999, 259.79 , 264.79 ,\n", " 264.6 , 262.29 , 258.29 , 259.19998, 268.79 ], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"\n",
"air_temp.air.sel(lat=70,lon=250,time='2014-01').plot()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"air_temp.air.sel(time='2014-12-01T00:00:00').plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 5. 基本算术运算"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 261.2 262.5 263.5 ... 316.5 316.2 315.7\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp + 20.0"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 265.3 266.8 267.9 ... 326.1 325.8 325.3\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_temp * 1.1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 0.6462 -0.5625 -0.9996 ... 0.7709 0.3712\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sin(air_temp)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 6. 聚合运算"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 261.2 262.5 263.5 ... 316.5 316.2 315.7\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
- Latitude
- units :
- degrees_north
- axis :
- Y
array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- axis :
- X
array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
- time
- long_name :
- Time
array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float32261.2 262.5 263.5 ... 316.2 315.7
array([[[261.2 , 262.5 , 263.5 , ..., 252.79999, 255.5 ,\n", " 258.59998],\n", " [263.8 , 264.5 , 264.7 , ..., 252.79999, 255.29999,\n", " 259.3 ],\n", " [270. , 269.8 , 268.89 , ..., 253.2 , 256.39 ,\n", " 261.7 ],\n", " ...,\n", " [316.6 , 316.19998, 316.4 , ..., 315.4 , 315.1 ,\n", " 314.69998],\n", " [315.9 , 316.19998, 316.79 , ..., 315.9 , 315.9 ,\n", " 315.19998],\n", " [316.29 , 316.79 , 317.1 , ..., 316.9 , 316.79 ,\n", " 316.6 ]],\n", "\n", " [[262.09998, 262.7 , 263.09998, ..., 252. , 253.59999,\n", " 255.79999],\n", " [263.59998, 264.09998, 264.2 , ..., 251. , 252.5 ,\n", " 255.7 ],\n", " [273.2 , 272.89 , 272.09998, ..., 250.79999, 253.39 ,\n", " 258.5 ],\n", "...\n", " [313.69 , 313.88998, 315.38998, ..., 315.09 , 314.69 ,\n", " 314.29 ],\n", " [316.29 , 317.19 , 317.59 , ..., 315.29 , 315.09 ,\n", " 314.38998],\n", " [317.79 , 318.38998, 318.49 , ..., 315.69 , 315.49 ,\n", " 315.19 ]],\n", "\n", " [[265.09 , 264.28998, 263.28998, ..., 261.69 , 261.49 ,\n", " 261.78998],\n", " [269.89 , 269.28998, 268.39 , ..., 259.59 , 260.28998,\n", " 261.69 ],\n", " [282.99 , 282.19 , 281.38998, ..., 259.89 , 262.59 ,\n", " 266.28998],\n", " ...,\n", " [313.79 , 313.69 , 315.09 , ..., 315.29 , 315.09 ,\n", " 314.69 ],\n", " [316.09 , 316.88998, 317.19 , ..., 315.69 , 315.69 ,\n", " 315.19 ],\n", " [317.69 , 318.09 , 318.09 , ..., 316.49 , 316.19 ,\n", " 315.69 ]]], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 265.3 266.8 267.9 ... 326.1 325.8 325.3\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
- Latitude
- units :
- degrees_north
- axis :
- Y
array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- axis :
- X
array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
- time
- long_name :
- Time
array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float32265.3 266.8 267.9 ... 325.8 325.3
array([[[265.32 , 266.75 , 267.85 , ..., 256.08 , 259.05002,\n", " 262.46 ],\n", " [268.18 , 268.95 , 269.17 , ..., 256.08 , 258.83 ,\n", " 263.22998],\n", " [275. , 274.78 , 273.779 , ..., 256.52 , 260.029 ,\n", " 265.87 ],\n", " ...,\n", " [326.26 , 325.81998, 326.04 , ..., 324.94 , 324.61002,\n", " 324.16998],\n", " [325.49 , 325.81998, 326.46902, ..., 325.49 , 325.49 ,\n", " 324.72 ],\n", " [325.919 , 326.46902, 326.81003, ..., 326.59 , 326.46902,\n", " 326.26 ]],\n", "\n", " [[266.31 , 266.97 , 267.41 , ..., 255.20001, 256.96 ,\n", " 259.38 ],\n", " [267.96 , 268.51 , 268.62 , ..., 254.1 , 255.75 ,\n", " 259.27 ],\n", " [278.52 , 278.17902, 277.31 , ..., 253.87999, 256.729 ,\n", " 262.35 ],\n", "...\n", " [323.05902, 323.279 , 324.929 , ..., 324.599 , 324.159 ,\n", " 323.71902],\n", " [325.919 , 326.909 , 327.349 , ..., 324.81903, 324.599 ,\n", " 323.82898],\n", " [327.56903, 328.229 , 328.339 , ..., 325.259 , 325.039 ,\n", " 324.709 ]],\n", "\n", " [[269.599 , 268.719 , 267.619 , ..., 265.85898, 265.639 ,\n", " 265.969 ],\n", " [274.879 , 274.219 , 273.229 , ..., 263.549 , 264.319 ,\n", " 265.85898],\n", " [289.289 , 288.409 , 287.529 , ..., 263.879 , 266.849 ,\n", " 270.919 ],\n", " ...,\n", " [323.169 , 323.05902, 324.599 , ..., 324.81903, 324.599 ,\n", " 324.159 ],\n", " [325.699 , 326.57898, 326.909 , ..., 325.259 , 325.259 ,\n", " 324.709 ],\n", " [327.459 , 327.89902, 327.89902, ..., 326.139 , 325.80902,\n", " 325.259 ]]], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\n",
"
"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 0.6462 -0.5625 -0.9996 ... 0.7709 0.3712\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.Dataset
- lat: 25
- lon: 53
- time: 2920
- lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
- standard_name :
- latitude
- long_name :
- Latitude
- units :
- degrees_north
- axis :
- Y
array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- axis :
- X
array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
- time
- long_name :
- Time
array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
- air(time, lat, lon)float320.6462 -0.5625 ... 0.7709 0.3712
- long_name :
- 4xDaily Air temperature at sigma level 995
- units :
- degK
- precision :
- 2
- GRIB_id :
- 11
- GRIB_name :
- TMP
- var_desc :
- Air temperature
- dataset :
- NMC Reanalysis
- level_desc :
- Surface
- statistic :
- Individual Obs
- parent_stat :
- Other
- actual_range :
- [185.16 322.1 ]
array([[[ 0.6462326 , -0.5624663 , -0.9996471 , ..., 0.31658906,\n", " 0.11916517, -0.16035552],\n", " [-0.9471524 , -0.51775694, -0.33747196, ..., 0.31658906,\n", " 0.3140551 , 0.51323175],\n", " [-0.970528 , -0.9990596 , -0.6474089 , ..., 0.6609925 ,\n", " -0.6965303 , 0.20125344],\n", " ...,\n", " [ 0.96091723, 0.7772431 , 0.88675845, ..., 0.09016185,\n", " -0.20816974, -0.5726443 ],\n", " [ 0.55659735, 0.7772431 , 0.9959079 , ..., 0.55659735,\n", " 0.55659735, -0.10950769],\n", " [ 0.830664 , 0.9959079 , 0.9760064 , ..., 0.9998101 ,\n", " 0.9959079 , 0.96091723]],\n", "\n", " [[-0.19607785, -0.71551615, -0.93107796, ..., -0.45987672,\n", " 0.90102834, -0.17955962],\n", " [-0.99200195, -0.81004924, -0.7474598 , ..., -0.9956842 ,\n", " 0.02214182, -0.08046086],\n", " [ 0.95480645, 0.9999663 , 0.6979841 , ..., -0.99427325,\n", " 0.7908172 , -0.25808707],\n", "...\n", " [-0.9988041 , -0.98861045, 0.08018851, ..., -0.21794963,\n", " -0.58079666, -0.8519514 ],\n", " [ 0.830664 , 0.952487 , 0.7586938 , ..., -0.01969962,\n", " -0.21794963, -0.7954352 ],\n", " [ 0.61413825, 0.06127954, -0.03867849, ..., 0.37119257,\n", " 0.17930582, -0.1194213 ]],\n", "\n", " [[ 0.04575338, -0.6847304 , -0.9832225 , ..., 0.21104816,\n", " 0.4010329 , 0.112403 ],\n", " [-0.9911176 , -0.89309275, -0.20276402, ..., 0.7372255 ,\n", " 0.99912465, 0.21104816],\n", " [-0.78567886, -0.99117064, -0.5954245 , ..., 0.90396696,\n", " -0.63450044, 0.94764113],\n", " ...,\n", " [-0.9986949 , -0.9988041 , -0.21794963, ..., -0.01969962,\n", " -0.21794963, -0.58079666],\n", " [ 0.70348334, 0.99995506, 0.952487 , ..., 0.37119257,\n", " 0.37119257, -0.1194213 ],\n", " [ 0.6898629 , 0.353496 , 0.353496 , ..., 0.92471296,\n", " 0.7709256 , 0.37119257]]], dtype=float32)
- Conventions :
- COARDS
- title :
- 4x daily NMC reanalysis (1948)
- description :
- Data is from NMC initialized reanalysis\n", "(4x/day). These are the 0.9950 sigma level values.
- platform :
- Model
- references :
- http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"air_temp.air.mean(dim='time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IzqnH04jofb20OY0N+0/7PDekfT3Ex1r/2aBvqwtd2/8Y7j2Z2kqPanJ2/uJahUe+2aypXfWq9vJpOW7j+uB6/hoUA3iYmTsA6ANgHBF1BPAygGeYORXAFHW/jN3MnKr+3aujNkfxztLdPs9Z2RXVHXef8rsHtMJ7t3QPeM0HK3z/v4XwWbBFe4GjmjYzClNsnFpFN6PAzFnMvEHdzgWwHUBjKAPJWmqz2gAkCbsQFFXiYnBF54YB2728IAP/WrHHAEXO5OWFOzS1e3NMqr5CdOCdP/t/6DhwKt8gJcZjiPkmohQA3QCsATARwEIiehWKUerr1rQFEW0EkAPgSWb+xQh9duXAqfyA6S1qVwu8qGYVZt3XV3OgVBnT5m/HXQNa6qTI2ew5nqep3ehUczOihkKgGVU7OzLobhSIqCaAWQAmMnMOET0P4EFmnkVENwL4EMBQAFkAmjHzSSLqAeA7IurEzDke97sbwN0A0KxZdEx9mEFhcSn6v7w0YLsezQPXK7AKPZpHv5eU0/jlkcB1tq2J/SOXfaHrCiMRxUMxCJ8x82z18O0Ayra/BtALAJi5gJlPqtvrAewGUGlFkZlnMHNPZu6ZnJysp/yoJiv7nNkSdOdft/U0W4Jj0bqI37ROdKYoaVg7cB1nuxZ30tP7iKCMArYz8+tupw4DKMtwNgTATrV9MhHFqtstAbQBIBPCIWJjN2oXWkt1BpPrX9DGzDWBE8N9eXcfA5ToQ9emSQHbdJyyUH8hJqDn9FE/AGMBpBNRmnrscQB3AXiTiOIAnIc6FQRgAIBniagYQAmAe5n5lI76bM0rCzPMlqA7Wgu1ZGWfQ+t6khcpUjBzwMJGGc9frikLabRz8mwBLqxZ1WwZEUU3o8DMK+F7Yq6Hl/azoEw1CRFgnob6CUM7aHvSjnb2nsgXoxAh5m3OwspdxwO2s4NBSEyIQ+55/6PMQNkCohHrRy0JulErijyPfHFDjyYB29z1yToDlDiDcTM3+E0I17VpEtKn2iN30KOXtzdbgimIUXAwF1SvYraEsHnlBvumMI5GJl7WBokJ0f+wAQC39A7s3ai1uFA0IUbBhuSe999RayXE4bnRnTBpRDuDFOmHlgyqQmTQknl2sMbF/2iAiDDrvvIwqiFe/m/RWIY0EPaKPRcAAJ2n/uT3/OapIwxSItgJOwds+aJuzfLR9H/uuBhbDmXjyrdXuo4dz7VfDiQZKQiCIPigmRpnUaeGYhwuamztglSRQEYKDuNuB6R8uKB6PE7bcK7XbNbu9e8hvnzSIGOEGAgRIXP6KL9tSkoZsRrdo6MBGSk4jEdssI7giXuthWZ1qmPu+P5RVekrWvjfav8Ba80vrGGQEmvR/6UlZkuIKGIUHEZcFNROCJbObkP6Ie3roXFSNdzap7mJigQncTj7vNkSIor9fiEEx1FXQ0SpncsnGoGkCinn0tZ1zZagK2IUbMaWQ9k+z9k5y+iqx4YgqXp8haJBo1Mbubb/+1umCarsQcaRXAx6dZnZMizDc16mJo/YaLQgRsFmHD7jOzvq34e0NlCJsTSsXQ1pU4ZXSGfRr1X5E936fb5Lkgr+GfHPFQFdL52SMgUAkrxkAujz4mKs3HnCS+voQ4yCg3DaBMp13cuLu6zafdJEJfbn37dfHLiRTbighvdMALd+uMYW05RiFGxE5ok8PPhlms/zdWvYK5tjINwX1U/mFZqoRHAK36UdMltC2IhRsBGDXl2GvELfWRs7N7F/4I0QWeZo+JHzlv7B7sy8q7fX40eyoz/CWYyC4Bim/6it0LxQzoQv0gK2aZxUTX8hFqNvK+8eSFryQ1kdMQo24cRZ/08o1eKjP799uLy/fLfZEmxJYoIkRihj9kaZPhIswhdr9/s9v2Bif4OUWAsnPsUazfjL2pgtQYggYhRswiE/rqiAc1MQ2KGQkFkUl5QGbJOcWBUJDh2FNrnAng8cYhRsgr9qWFuecW6qbM+sHnZwGTSKtxbvDNjmv3c6xxXVkzo+XFOjHTEKDqBmVefO+cZ4FOEpFZugGS31E1rWrWmAEmtin7yoFRGjYHNmjO1htgRT8azMViJWQTNaitpVq+LMqSMAeHNMN6/HC4sDT7tZGTEKNsBXJ0xtmoThnRoYrMZaeKa5F6OgHQrwLPzJX3oZpMSapNStgY+9vAcPfZVmvJgIEtAokEJTI8QIoeFrkVnKFwN39E2psL8045g5QqIQf/2nyQXVMKBtsnFiLEqplzWquZuzTFASOQIaBVZW5r7TX4oQKvLb75uruzaqsP/MD1tNUhJ9FPsZVd0zsJWBSiyMDQeeWqePVhORc90MLMzOo7k4V+Q9tUX3ZvZNla0VzzWFozkFeG/Zbuw4kmOSouhh0bajXo/fN6gVbu3dzOs5p1Elzn4z8FrdUgYDuIeI9gHIg/JwyszcRTdlQkCuensl0v3UT5h8RXsD1UQPLy3YgZcW7AhYe9eJZBzJxYh/rvDbZnC7epWMrVPp2+pCr8dX7zmJPi29n7M6Wo3CFbqqEELCn0EAgHgblt4U9EVLls/4WDEIZfgyjmNmrI7ahw6/vxpEVEvdzPXxJwiW556BLc2WEDUcOu0/Mh5QvNqEcto3SLRVOpVAj5Iz1X/XA1jn9le2L5iEROZq57ErOpgtIWr4ftPhgG1k6qgiCyYOwK+Th1Q6fiwnOkt0+p0+YuYr1X9bEFEdAG0AJBghTPBPIH97O5feFMzjmtRGgRsJAIA/jp5FvVrR93OpadKZiP4GYDmABQCmqv9O0U+WEIgZv+zxe75nSh2DlAh2QUtgXzT+yBlFj+YVvf3u/O9ak5SEh9aVyAkALgawj5kHA+gGwB5VqqOUlxdk+D3fvVmSMUIE27A9K7Cb7gRJk+2Tz/5WsRpbUUl0TvFqNQrnmfk8ABBRVWbeAaCdfrKEcElMkJTR7tzaR/zqI0ENBydXDIRdUohr/YQPElESlMjmRUR0GkDgFSlBF3YdO2u2hKhD1uUFQRuaRgrMfC0zn2HmqQCeAvAhgGt01CX4YX56dOdWMYMJQytPeyz/47gJSqzLhv2nzZYgWICgo5uYeTkzf8/MhXoIEgIjDoHBUy+x8gJpoBKmTmPKHP95oZITqxqkRDATCXmNMvILi/Haoj/8thlzsSS19Ubm9FF4+YbyzCySRlsb48W9WTPe4hWiDd2MAhE1JaKlRLSdiLYS0QT1eCoRrSaiNCJaR0S93K55jIh2EVEGETm3hqQfDgeoxQwA13ZrbICS6OTGnuUGc/NB/2lCBIW7BigR4QPaSKrsQHhGNs/RkDbEaujpSlAM4GFm3kBEiQDWE9EiAC8DeIaZfySiker+ICLqCGAMgE4AGgH4mYjaMrP3FKAO5cAp/0Zh/vj+6Niolt82gsKRKI041QN/EfKJCfH45ZHBqC8xCkEz4Ys0jE6Nroc03UYKzJzFzBvU7VwA2wE0hpKBvOxXqzbKvZhGA/iCmQuYeS+AXQCcXdrJC3f+93e/5+1aTDySdG1S27Wdc77IRCXWYWaA9ZWmdarbMk20UBlDPmUiSoES8LYGwEQArxDRAQCvAnhMbdYYwAG3yw6qxzzvdbc67bTu+HHxHvGkelV7+ErrSYxbjc7lGdKHAGCZvA8R4/sH+lXY7/3Czzjvo+aJFdHdKBBRTQCzAExk5hwA9wF4kJmbAngQinsr4N2pptKYlplnMHNPZu6ZnCxznO4M7VAftSRoLSCxbgndJLebEGm6NEmqsH80pwBZ2dEzVamrUSCieCgG4TNmnq0evh1A2fbXKJ8iOgjA3W2mCRweILd+32nsOnYWk2dtxtmC4oDt+7WOzqIeRuM+UtCS2sEJ5BcG7l+CM9DT+4igjAK2M/PrbqcOAxiobg8BsFPd/h7AGCKqSkQtoGRkjc6MUhFg+o87cP17v2Ho68vxxe8HMG3edrMl2YarujR0bb+zdLeJSqzDr7tOmi3B1kRTqns9Rwr9AIwFMER1P01TvY3uAvAaEW0C8AKAuwGAmbcC+ArANihZWMc52fPo/eUVf6w+X7sf2ef8L4pGUb8zlVv7NK90bNb6g9h/Mt8ENeaydMcxbAlQwU8In6fmbMFvu07g4Gnr9zHdXFKZeSV8B9/28HHNNADT9NIULfgKqpo2b5vf65Kqy3qCFjyLxLyx6A+8uVgZsEZrCcVQCeTNJoTGDT2a4Jv1B137v+46iV93nUSV2Bj8Mc3a1Y3Fx8yCLNp2xOvx/EL/A6droswf2iqUGQSnkavBHfe5ay4yQIn98PU0XFhSaqiOUBCjYEF85WHPPe9/MdB9AVUQAvHnf60J2MZ9/UXQjrcEjGVYfX1BjIJFYGa8+ON2HDiVX9kPV+VIFLm1Cdbm4Ol8pGtYS0iqLsGQodDkguo+zxVbPOeWGAWL8PnaA/hg+R7c87/1GP/5Rq9tMo7mVjrWul5NvaXZkidGdjBbgqm8+OMOsyXYHs9KbGUUWXwKSYyCRXj823QAQHFpcB2me7MkXNetMZY8PDBwY8HFmF7eM8lafWgfKeZt9l+TY+ZdvbHjucsNUmNPWibX8Hp8/ylreyCJUbAYwf4mXd+9CV6/KRUtk2XEEAy+ypWu2CmlxwGgb6u6tikvaRZ1a3qvP3H/pxsMVhIcYhQsxs4gS232bilRzJFkhVRjEyJEfGwMFnsZwedZPHpcjIIguJGnIZ2IIGillZcRfIzFE26JURAENw5pKGIkCOFgbZMgRsESLMs4ZrYER1K3ZmV3y192nkDGkcpeXnZCS3JFQT+a1vHtrmoFxCiYzN4Tebj30/Vmy3AkV3Vt5PW4N9dfOzH1+61mS3AUrTy8kNbsPYVNB86YI0YDYhRMZvCry3C+KDS/5a/uuSTCapyFL0+vWIvP+YbLr7v8e1itfeIyg5Q4g1gvmQb+9P4qE5RoQ4xCFNOrRR2zJdgCz/cx1sbfiryC4oAFX+olSi3mSOJtYdnKOZBs3P2tj69sqIIx1EpQkgTf0KMJNj41zHXcM4uqHSgpZTz0VRo6Pb3QbCmOo2PDWl6Pv7N0l8FKtCFGwUTCCXe/f1CrCCpxJvcPbo2nruyI67s3qZB2fO+JPBNV6cPHv2Vi9oZDZstwJM/6yDT7ysIMg5VoQ4yCibw4P/RqakM71o+gEmeSEB+Lv17aArExVGF0MN2GeYGO5EgyRbOoUSW6IsPFKJjIT9uOhnxt92YXRFCJYHe0Toi1rOs9X48QOtE2HSlGwURKHZJ8LVpo3yDRbAm6oTUJ25J/DNJXiIO5Z2BLsyVoQoyCiYhNsBav3NDVbAm68eMW79X8BGPInD4Kj11ROV376bxCE9T4R4yCiYjzkbUoCjJtuSAEyy29m1XYf26u/7rrZiBGwURO5RWYLUFwo5qkihZ0Ztq1nSvsnyvyX3fdDMQomMDWw9k4cbYg5JHCjT2bRFaQAADo4OZPXmzh4CLBPlhxWi/ObAFOZNRbK30W4NDCM1d793sWIkdeYQkyT+Sga9Mks6WETWGxGDhBOzJSMIkTZwNPHa17cqjX49WizO85Gnni23SMfudXHDxt7dKJWth6OFtTu+YXWjt7p10Y2blBwDYz1+zX9BuhB2IUDOKad37FlDlbUBrEnFHdmlXx04MDdFQl+GKuWsM4+1yRyUqM4c+9m2HxQ1Ln2wh6Nvefs2zviTw8/m06bv33GoMUVUSmjwwi7cAZpB04E3Qu+7b1E3HfoFZ4b9lunZQJgfgxPQtvL9mFuX+/FDFeMl5anUDBU1ueGYGaVeWnwChu7tUMGw+cwQ+bDns9X7aetcOkuh4yUjCYUPLPTBreDvdJriNTeGfpLkz4Mg3bsnJQEKVz89FnxuxNtSqxeO1PvmNi3G34nDTj81WJUbAog9slu7ZjYgiPXt7eRDXOZX76EddCbYcpC0xWow1mRt8XFyNl8jy8/lMGTgUIkIq23Dx2ID7Wt6l+YX557q0JX6Rh30ljEzTKmNEAQolabH6h9xw0/dvUDVeOYFPSD2bjqv9bWeHYW0v8p2ce2qFe1OXmsQP+3vMlOyqW5y0qMTbKVYyCAcz4ZU/Q17CXHBgbnhqGGlXlqU7wzssLg8vuOqhdMv59+8U6qRGCobikFEdzC9A4qVqlc7ExhKKSUmSfKwrLlV0rMn1kAKGsTY7oVNltrU6NKqgaJ0ZBT27q2dRsCYZhxA+M4JteKYoXEjNj+o870G/6EvyeeapSu1JmTPp6E3o+/zNyzhfhvM5R0GIUDIBCWOrr21qmicxA7xiQtXtPWSaY7JmrO5ktwdGsVQ3AgVPnsCRDmTLyVrv5972nMEf1VOoy9Sdc+tJSXXWJUbAQ3ZolAZAcPGYSqO71sTCK1Ww5lI0bP1iFlxboU8TnSIDay57UEDdUS5BXWIxiP+sGk2enV3is1DuoTYyCAWiZPurcuDbeGtMNAGTdwERGdm7o9/ynq/eFfO8M1e9857GzId/DH3rdV9AX5sC5tozMqCxGwQg0eHc8OaqDq06wVFWzLlXDGMU9/PUmAMCKP45HSo5gA4pLS1FkoTz6YhR05mxBMd5avDNguxbJNZCYEI954y/Fm+qIQbAe/vzLw2XXsVzdFxEF6zBusBKQuvdEHkqcYBSIqCkRLSWi7US0lYgmqMe/JKI09S+TiNLU4ylEdM7t3Pt6aTOSi55eGLDNHX1TUC8xAQDQqVFtSXhnEZrWqewe6B5YFA6ZJ/JQUFxuAHLPF2Ho6yvQ/qnQAuQOnzkXVPvbLmke0usIkaNvK8WZZMIXaQEDDI1Ez5FCMYCHmbkDgD4AxhFRR2a+iZlTmTkVwCwAs92u2V12jpnv1VGbIfyyU9s0wf2SwsJSrH3iMsy67xIk6Oj+O+jVZXhsVjqKS0qRX1iM80XheSQtD3JK6tnRkn7dbGIDLDZ+fe8lPs8F+xAQDLq5HzBzFoAsdTuXiLYDaAxgGwCQEtJ3I4Ahemkwm7EfrtXUrl6tBJ2VCMFQLzEB9RIT4GtAfyT7PBrUDv8zm73xEM4Xl2B++hFc7iUuJRiCmdT6x/C2Yb2WEBkCTUVenOLbE07PPFyGrCkQUQqAbgDcc8H2B3CUmd0n3FsQ0UYiWk5E/Y3QFmmyzxUh9dmf8PW6A2ZLEcKkXf1E17Z7pGleYXCZbv0xP12pvLVga3gVuE5qnH64Z0BL3D1ARqZWwF+qi0DVFWN1TE2iu1EgoppQpokmMnOO26mbAXzutp8FoBkzdwPwEICZRFQLHhDR3US0jojWHT9urhfHusxTSJk8D99uPOg69v7y3TiTX4RJ32w2UZkQCaZfX15P96M7y9NBVInV92vjrebGusxTGDdzg88FyVcWZmi692MjO6BKnPiXWAF/tVUeGtbO77XFpVE6UiCieCgG4TNmnu12PA7AdQC+LDvGzAXMfFLdXg9gN4BK41xmnsHMPZm5Z3JysufpkNh2OAdZ2cHP0d2gRh8++OUmV66iYOf6ojA9v2NITIjHt/f3xe9PDEVbt1GDl7RUEeXjVZmVjt3w/irM25yF1xdV/vG3SoS0EBz+PI7Kpifd64a7U6yjt5Ke3kcE4EMA25n5dY/TQwHsYOaDbu2TiShW3W4JoA2A4DPJhcDIt37BJS8uCeoazw/0kGoM5qR5L5zhixjJUGlpujW7AMmJFXMEbT+S46N1ZNh/yncJ0N/3nq50bO7m4PqcYA1KvDxduKfMB3yPJoa/sUIXTYC+I4V+AMYCGOLmZjpSPTcGFaeOAGAAgM1EtAnANwDuZebK2aF0xFtmUl+kH6pY9zbU9MNiFKKPe/63PuhrgvFDj/MzfCwoKUVxSSmYGWfylXWEh77apOm+13VrrFmDoD/e0uN/MLYn0qYMc+1fY8Jnpqf30Ur4cIpg5ju8HJsFZarJNI6fLXDFCwSiyCMsPdSfdrEJ9ufOj9ZiaYb29S9/JT83HTiDLs/8hEkj2uGZH7ZhxaTBmu/bT5IsWgpvabKrxMWgSlwV1/69A1tiaId6KCwpxbHcAmzYdxpvB6iRES6y4uRGMNlMH5i5oeK1If64TxjaJrQLBcOZfl3nwI28EIxBAJQ1Ln/kF5Zg8XYlq6a/qSbB+lxxUbkrcmrTpErniQht6ieiU6PaGNyuHlrXq+k6F8zMRjA4Mk3imfxCrNp9Eh0b1cK89CzX8dIg3uSjORUzFe4/mY+GtStb/kDc0ksiS6MFb3PAevDLzhMB26zcpbQJxj3WOokUhDLeu7UHAGB+elbADL1AxWnIwpJSXeqrONIoZJ7Mx32fbah0fMmOY7i5V7OQ7nnTjNXInD5Kc/vEhDjkno+cv7ugPznnrPd5BbO+kdq0to5KhHAIlJ23DHevo6IShh7Zzx05feTLz/yx2emubWbG64v+wM6jubpoqKd6tJAjP4HopF2Dmn7P5xUENhr1ErVVO/tDh37Xul5i4EaCpakwUtDJFdmRP0lV4nwvAJS96ZsPZuOtxTtx879W66Lh07/1xss3dEGthHhd7i9EniHt67u2d3nULpi3OQudnl6IrYezPS+rQMvkyh4n3nBPkLbnuNRJEBQGtC13WV20LbwoeF840ijE+4lIHfLaMgDAbf9R8hadOFtYaUHH15c0mIWfhrWr4UYH1QO2G9PmbUPmiTxc9toyvPZTBpaq5RS3HvK/SOyv77nzz5//cG1PnpXup2Vl7urfIqj2QvTQOKkaruuuuKmWORtEGjEKHuw7qXhzuE8FzFy737V9+Mw5DHltuddrMwIM+RdMjMp0ToIXSlnJdLr7eB7eXrLL5bfGAZZzk6pX8Xnuqq6NXNur95SH6AS6pyeTRrSv4KUi2Iu/9FOM/rCO9QO0DA1HGoVAuV/u/GhthQWd9IPlUwL+0mH7q7MKAO0bKCHrjSKQYVMwF89U1cvU/f+szPR7XTcvbodlvH5jV6/Hg3V6qhIXg3njL8X3D/QL7kIhKqhdTZly1subzJFGIdAQ3tOv3N1ABBPL4I2Zd/XGd+Pky2o3jucqLsoZR3Oxes9J13HPKcXb+6Z4vX7yFe199st1+yqntvBFWY7+qnGx6NxYvI3sSNlnvHCLrClEjGCzXH6z/mDgRgCufHtlwDZ9W9WV+glRTG8NvuRjZpQ7J2Rln69wLjaGsOThgfj+gX64s18KAODmXs1w78DK6az7vLA4aH2f/rW3a9s99YpWryfB+pSlQVm8Q9YUIkaoqYPfWrwTj8yqmBL7b5fKop6TSIgPLljIPbfVogcHAABaJtdElyZJrmmAlnW9eyQdyTmPlRoC2dypVa2y43qLujWw9omhQd1HsC7+0qBEAkcGrwUqg+eL1xf9UelYXXkCcxQvXd8FfV4M/ASffa4I5wpLcOhMeRqKNvUrxgnc1b8lzheVYqyfesmZJ/OC0ue5/hBMQKUQHehZYAdwqFEIhce/9e4WWPa0JzgDrWU431u2G+8v3+23TY2qcZh8RfsKxyaNaFehYE6wWXSb1qkeVHsh+igbKeg1YHDk9FEozFyz3+txf7EGd/ZLwZ4XRvo8L9gXzy/syM7aajDfP6ji2sK2LP/BcJ7IQ4r9Sawah9GpjfD1vZfocn8ZKYSJv6mo1KZJiIkhfDeuX6VCLYK92Xui4rRP/zbaqgR61uX4dLX3hxHBucTEEN4c002/++t25yjjosbey96FysPD2mJ0qhJ5mNo0yWvudMG+/OjhLhjqOpYgGI1jRwr/uq0n1uw5icSEeHRrloRuzZLQeepPQd2jLNzckzdu6opruzWJhEwhSki5sDoyT/qubSAV9oRowbEjhWEd6+PJKztiwtA2GNA2GYkJ8UF7arzoo+iKQWn3BZO4ycs60rzx/lOYRGKgsPuFkZg0op3P80+O6hD+iwiOx7FGIRL4KnAhRsHe3NRLMQrJiVWROX0UMqePQo2qcZj790t9XhOJgUJsDPlMupg+dTj+1r9l+C8iOB4xCh64z/2Gug4gNsHelPWQRh794yI/aSWCmT5KiA/+axkXI19lITJIT/Lgqi7lFZCGtK/ns9288eVPhW08MlLqVTtVsAahfLqJCdqX7/z1O19dS2yCECmkK3ng/p17+qqOPtu5p0D2fAq8QmNpPcE5aHVJDRVZyBYihRgFD9yfxOL8JM5zn1p65PKKi3819SicKliGjg1roWPDWphyZeWF3XVPes8xpLW4TiDKumf7BokVFp31Tn0gOAcxCh54js63PjMi4NqCv+G+YD8S4mMxf0J/9GheOWNq3ZqVgxRv6BF59+RhHetj3ODWrn29k6QJzkGMgge3qcnJZoztAUDJT/Pr5CEV2ky79qIK++5RqIPb6TtNIEQXe18ciVf/5L14ji+8GZHFDw8EAFyprnmNTi2v0ta/Td0wFApCRWSew4OLU+p4jVcYndoIc9IOY+HEAWjXINHLlQp1akg6C0Hhy7v7VEpboYUh7etj4cQBGPHPFa5jZc4LLZNrVuifO5673JVfXxAigRgFjbxyQ1c8MbKDzwI5e18ciU/X7Me13bxHOQvOIX3qcJSUst96zIHwtCU1q3pPdBdsfQdBCIQYBY1UiYvxWzGNiDC2j++8+IJzSEwIP1Opp+up1pTdghAusqYgCIIguBCjIAgWpFWy9xKdgqA3YhQEwYLExcbgoWFtzZYhOBAxCoJgUcrWFbRWbROESCBGQRAsTuvkmoEbCUKEEKMgCBaFJd+uYAJiFARBEAQXYhQEwaIMaqfk1BokubUEA5HgNUGwKKlNk4IuESsI4aLbSIGImhLRUiLaTkRbiWiCevxLIkpT/zKJKM3tmseIaBcRZRDRCL20CYIgCN7Rc6RQDOBhZt5ARIkA1hPRIma+qawBEb0GIFvd7ghgDIBOABoB+JmI2jJziY4aBUEQBDd0GykwcxYzb1C3cwFsB+DKFkdK+sgbAXyuHhoN4AtmLmDmvQB2Aeillz5BEAShMoYsNBNRCoBuANa4He4P4Cgz71T3GwM44Hb+INyMiNu97iaidUS07vjx4zopFgRBcCa6GwUiqglgFoCJzJzjdupmlI8SAMBbUvhKjtrMPIOZezJzz+RkKWgjCIIQSXT1PiKieCgG4TNmnu12PA7AdQB6uDU/CKCp234TAIf11CcIgiBURE/vIwLwIYDtzPy6x+mhAHYw80G3Y98DGENEVYmoBYA2ANbqpU8QBEGojJ4jhX4AxgJId3M7fZyZ50PxMnKfOgIzbyWirwBsg+K5NE48jwRBEIyF2LPEUxRBRMcB7AvjFnUBnIiQnEhhRU2A6AoFK2qzoibAuroA62oLR1dzZva6KBvVRiFciGgdM/c0W4c7VtQEiK5QsKI2K2oCrKsLsK42vXRJ7iNBEATBhRgFQRAEwYXTjcIMswV4wYqaANEVClbUZkVNgHV1AdbVposuR68pCIIgCBVx+khBEARBcEOMgiAIguBCjIIgCILgwtZGQU21YTmIqIPZGrxBRA8T0XB12zLvHRHVdtu2ki7LaHFH+ldwSP+qiC2NAhGNJqKPAXQ1W4snRPQ2gPlqOnFLQETDiWghgEcB3AYAbAEPBCIaoqZIeY+IHgcso0v6VxBI/woOs/uXbWo0ExExMxPRYADPASgCcAkR7WPm02brcjtUB8BpAEOJ6H/MXGCWLgDxAKYAGAjgRQBVAFysZrctNvMLoqZcfxzKZ7kWwMdEVJ2ZnzRJj/SvIHVB+lcweizTv2wxUvD4YuwFMALAJAC9AXSxgi4iilUPrwbwHoBboGSCNU0XMxcCmMPM/dVEhacBjGHmIpO/sDEAakIpurSRmQ8A+BuAm4iovQl6pH+FoEv6l2Y9lupfUW8UiOgBALOJ6EEiasDMmWop0CUAjgIYSESVKrgZqGsiETVi5hIiqgLgcgDfAlgKJVX4dURkWLUgj/erITP/rh6PZ+blAPYQ0RVG6XHTdT8RXQ8AzFwKpcBSMpQvL5h5D5T37Vm1vSHzrdK/QtYl/UubLsv1r6g2CkR0LYDbAbwFxaI+SUSpbk0+A9AWisV1v07XD9xDV1cAjxNRD/XJaR0znwCwE8B4ANPgveqc3rq6AHiCiMrmLYuJqA6UrLOGpSwnokQieh/KNMPHpBRgAjMfhZJGfaJb88kAehNRJyOeNKV/haVL+ldgbZbsX1FtFKC8We8x81IAU6EMvcaXnWTmzQB+B3ARKYtKj6rH9f7Avem6Tz03ioh+gbLo9h2U4X6Ol3sYpWsCoLwnzHwKQDUAgwHXMFtXmDkXwHJmbgBgLoB33E4/CyCViEYSUVX1CW8ulLlqI5D+Fb4u6V++sWT/igqj4GkZ3fb3APgzADDzPgDzANQgoqvdmn8OZb7wSyj5xyNmaYPUlURElwB4E8BvzJzKzLcBaAAgoi6EYb5fnwLoRUQJ6pfECF3fq/9OBHAzEbVRNZ4F8DKUokyPE9GzAPoDyIqkLj86Te1fQeoyrH8Fqcuw/uVHl/SvIIgKowAPy+1mKb8BkE9Eo9X9LADLAHQkhZpQviTpALow8ySP643UtQTAACj1qh91u+xaZt4YIT2h6FoG9f1Sj1UD8AX0GeJ71cXMeUQUw8xHALwL4N9ubb4A8AKUKZBkAFeoQ/+IQURlX7ZYd10wuX8Fqcuw/hXq+6Ue061/+dJlgf6V4r5vlf7lE2a27B+AngC+Vt+YSwHEqsdj1H8JwJ0AFqA8ud8kAFPV7TgA9Syk62l1O7asrZV0lWkzUpebthi39vsBXALlKbd3mfYIayIA1aE8ia30PGdW/wpTl279KxK69OhfgXSZ1b/Ue3YH8DOAT9z/32b2Ly1/lhwpqFZyOoD3oczxHQXwAIBmgMt7AFCeOhZCsbAziKgRgG5QfHzBzMXMfMxCuorVdiUcwaFzpHSVaTNSFzOXqk9Etd0ufQnArwBWAEhQ20b06YgV8tXdZCK6T9Uc6/ZahvavCOjSpX9FSleZNiN1mdG/1H7/BBRD9QUz31b2/1ZHLKb1L00YbYWCsLJXALhA3W4I5Q2u6Xb+WfUN7QYlYOd5KEOvd6HD067o0lXXAgD93drvAPAqgHgddZGq559QRi+bASRZ4P0SXZHV9YxJ/esZAB+67XeD8uRfNiJ4zoz3S5N2M1/c400cCHUo53G8PxQ3tlXqBz8Yim/xTACtPdpWF13RrQtARwBN9dSFilMJ3wFoAuBtANMBNAdQQ9XVysj3S3Tpr8uI/qXu14BijF6D4kE0F4qL6TVGfh9D+r+YLgBIBDAbwCkA/0H5U2XZXGAnAIPV7TuhzM+1cLs+4nPzossUXbo8HfnSpZ5rC+B1dfsqKK6bmzyuN/T9El266TKjf90CZU1hgLp/D4CPADTX+/0K588KawqFUDwnbgVwGMCfgPJ5cGbeyoofLwAsh/IhFAGu+Tm93NpEl7G69Apo8qpL5TCAtkT0PZTphOVQfMXhpsvQ90t06abL8P7FzJ8BuJGZV6iHfoYyVWTE9zFkTDEKRHQbEQ0koiRWEnb9G8ob9geAnkTUVm3n6Y87HIrmXKDCAqroEl1B64JimA5D8RfvwcxXAWhCRD1El+gKVxcRESsBe2UMg5Je46weuiKFYTWa1R+GBlDm0koB7IYy7zaBlbB8kBJUcjuA88z8vHqsKpT56JcAHALwCDPvEF2iKwxdBcz8nHqsNjNnu92nwr7oEl0h6HLv9zFQFsDfhOIK+2gk+70eGDJScHNbSwRwiJkvA3A/lHm4D8raMfNOAOsBNCKi1uoPSSkUV8anmfnqCP+QiC5n6mqo6qoG4Lx6jxi1TSR/SESXM3WV9fsEKCODQ1D6/WirGwRA53oKpCSfehZALBHNB1ALaiQjMxcT0XgAh4loICsZFMHM35JSOWoBlFX6wcycDiWqT3SJrojrArCdIxs3IrpEV5muIcy8DcrIIjpgnVawobhobYKS2/0uKIEil0MZQvVya3cfgKVu+38CkAfgX9AnGll0iS7RJbqiUpcRf/rdWJk/Huu2/676Bt4BYL16LAbKPN1XUN0T1ev6iy7RJbpEl+gy/k+/Gyv5SKqiPM/NLQBeVLfTAPxd3e4J4HPD/sOiS3SJLtEVpbqM+NNtoZmZ85m5gMv9g4cBOK5u3wmgAxHNhZL2YINeOkSX6BJdossuuoxA14VmwJXGlgHUR3le81woRbMvArCXmQ/prUN0iS7RJbrsoktPjHBJLYWSR/8EgC6qdX0KQCkzrzTxDRVdokt0ia5o1aUfRsxRAegD5c1dCeCvZs+ZiS7RJbpEV7Tr0uvPkIhmImoCYCyUpFUFur+gRkRXcIiu4BBdwSG6rIFhaS4EQRAE62OFLKmCIAiCRRCjIAiCILgQoyAIgiC4EKMgCIIguBCjIAgaIKILiShN/TtCRIfU7bNE9K7Z+gQhUoj3kSAECRFNBXCWmV81W4sgRBoZKQhCGBDRIDXKFUQ0lYg+JqKfiCiTiK4jopeJKJ2IFhBRvNquBxEtJ6L1RLSQiBqa+78QhHLEKAhCZGkFYBSA0QA+hZJrvzOAcwBGqYbhbQA3MHMPAP8BMM0ssYLgie4J8QTBYfzIzEVElA4gFkoFLkCpOJcCoB2URGqLiAhqmywTdAqCV8QoCEJkKQAAZi4loiIuX7QrhfJ9IwBbmfkSswQKgj9k+kgQjCUDQDIRXQIARBRPRJ1M1iQILsQoCIKBMHMhgBsAvEREm6BU8eprqihBcENcUgVBEAQXMlIQBEEQXIhREARBEFyIURAEQRBciFEQBEEQXIhREARBEFyIURAEQRBciFEQBEEQXIhREARBEFz8Pwcqugl+QhvfAAAAAElFTkSuQmCC\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"air_temp.air.mean(dim=['lat','lon']).plot() "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"air_temp.air.max(dim='time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"anomaly = air_temp.air - air_temp.air.mean(dim='time')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"anomaly.sel(time='2014-07-01T00:00:00').plot()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"anomaly.sel(time='2014-12-01T00:00:00').plot()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"array([[[-1.91756439e+01, -1.76825867e+01, -1.63859253e+01, ...,\n",
" -1.80151215e+01, -1.64373322e+01, -1.48374176e+01],\n",
" [-1.89337158e+01, -1.82936096e+01, -1.80489044e+01, ...,\n",
" -1.69549713e+01, -1.62852173e+01, -1.50585022e+01],\n",
" [-1.47680969e+01, -1.45270996e+01, -1.51714020e+01, ...,\n",
" -1.74070740e+01, -1.71924744e+01, -1.60147552e+01],\n",
" ...,\n",
" [-1.04931641e+00, -7.52960205e-01, -2.29125977e-01, ...,\n",
" -1.41033936e+00, -1.18792725e+00, -1.11624146e+00],\n",
" [-2.22869873e+00, -1.73648071e+00, -6.80053711e-01, ...,\n",
" -9.59106445e-01, -8.76861572e-01, -1.24349976e+00],\n",
" [-2.07592773e+00, -1.59591675e+00, -1.01385498e+00, ...,\n",
" -4.37774658e-01, -4.91027832e-01, -7.05017090e-01]],\n",
"\n",
" [[-1.82756500e+01, -1.74825897e+01, -1.67859344e+01, ...,\n",
" -1.88151093e+01, -1.83373413e+01, -1.76374207e+01],\n",
" [-1.91337128e+01, -1.86936188e+01, -1.85489044e+01, ...,\n",
" -1.87549591e+01, -1.90852051e+01, -1.86584930e+01],\n",
" [-1.15681000e+01, -1.14370880e+01, -1.19614105e+01, ...,\n",
" -1.98070831e+01, -2.01924744e+01, -1.92147522e+01],\n",
"...\n",
" -1.72033691e+00, -1.59793091e+00, -1.52621460e+00],\n",
" [-1.83868408e+00, -7.46459961e-01, 1.19934082e-01, ...,\n",
" -1.56909180e+00, -1.68685913e+00, -2.05349731e+00],\n",
" [-5.75927734e-01, 4.05883789e-03, 3.76129150e-01, ...,\n",
" -1.64776611e+00, -1.79104614e+00, -2.11502075e+00]],\n",
"\n",
" [[-1.52856445e+01, -1.58925934e+01, -1.65959320e+01, ...,\n",
" -9.12512207e+00, -1.04473419e+01, -1.16474152e+01],\n",
" [-1.28437042e+01, -1.35036163e+01, -1.43589020e+01, ...,\n",
" -1.01649628e+01, -1.12952118e+01, -1.26685028e+01],\n",
" [-1.77810669e+00, -2.13708496e+00, -2.67141724e+00, ...,\n",
" -1.07170715e+01, -1.09924774e+01, -1.14247589e+01],\n",
" ...,\n",
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" dtype=float32)\n",
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" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anomaly"
]
}
],
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"text/plain": [
"<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
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" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00xarray.DataArray
'air'
- time: 2920
- lat: 25
- lon: 53
- -19.18 -17.68 -16.39 -15.48 -14.92 ... -0.6273 -0.8478 -1.091 -1.615
array([[[-1.91756439e+01, -1.76825867e+01, -1.63859253e+01, ...,\n", " -1.80151215e+01, -1.64373322e+01, -1.48374176e+01],\n", " [-1.89337158e+01, -1.82936096e+01, -1.80489044e+01, ...,\n", " -1.69549713e+01, -1.62852173e+01, -1.50585022e+01],\n", " [-1.47680969e+01, -1.45270996e+01, -1.51714020e+01, ...,\n", " -1.74070740e+01, -1.71924744e+01, -1.60147552e+01],\n", " ...,\n", " [-1.04931641e+00, -7.52960205e-01, -2.29125977e-01, ...,\n", " -1.41033936e+00, -1.18792725e+00, -1.11624146e+00],\n", " [-2.22869873e+00, -1.73648071e+00, -6.80053711e-01, ...,\n", " -9.59106445e-01, -8.76861572e-01, -1.24349976e+00],\n", " [-2.07592773e+00, -1.59591675e+00, -1.01385498e+00, ...,\n", " -4.37774658e-01, -4.91027832e-01, -7.05017090e-01]],\n", "\n", " [[-1.82756500e+01, -1.74825897e+01, -1.67859344e+01, ...,\n", " -1.88151093e+01, -1.83373413e+01, -1.76374207e+01],\n", " [-1.91337128e+01, -1.86936188e+01, -1.85489044e+01, ...,\n", " -1.87549591e+01, -1.90852051e+01, -1.86584930e+01],\n", " [-1.15681000e+01, -1.14370880e+01, -1.19614105e+01, ...,\n", " -1.98070831e+01, -2.01924744e+01, -1.92147522e+01],\n", "...\n", " -1.72033691e+00, -1.59793091e+00, -1.52621460e+00],\n", " [-1.83868408e+00, -7.46459961e-01, 1.19934082e-01, ...,\n", " -1.56909180e+00, -1.68685913e+00, -2.05349731e+00],\n", " [-5.75927734e-01, 4.05883789e-03, 3.76129150e-01, ...,\n", " -1.64776611e+00, -1.79104614e+00, -2.11502075e+00]],\n", "\n", " [[-1.52856445e+01, -1.58925934e+01, -1.65959320e+01, ...,\n", " -9.12512207e+00, -1.04473419e+01, -1.16474152e+01],\n", " [-1.28437042e+01, -1.35036163e+01, -1.43589020e+01, ...,\n", " -1.01649628e+01, -1.12952118e+01, -1.26685028e+01],\n", " [-1.77810669e+00, -2.13708496e+00, -2.67141724e+00, ...,\n", " -1.07170715e+01, -1.09924774e+01, -1.14247589e+01],\n", " ...,\n", " [-3.85931396e+00, -3.26293945e+00, -1.53912354e+00, ...,\n", " -1.52032471e+00, -1.19793701e+00, -1.12622070e+00],\n", " [-2.03869629e+00, -1.04647827e+00, -2.80059814e-01, ...,\n", " -1.16909790e+00, -1.08685303e+00, -1.25347900e+00],\n", " [-6.75933838e-01, -2.95928955e-01, -2.38647461e-02, ...,\n", " -8.47778320e-01, -1.09103394e+00, -1.61502075e+00]]],\n", " dtype=float32) - lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0
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array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n", " 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n", " 15. ], dtype=float32) - lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0
- standard_name :
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- units :
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array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n", " 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n", " 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n", " 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n", " 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n", " 325. , 327.5, 330. ], dtype=float32) - time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00
- standard_name :
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- long_name :
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array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')
 |