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{
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{
"cell_type": "code",
"execution_count": 1,
"id": "cf4ad570-3846-4aac-b5ee-7f9c485a492d",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"\n",
"from datetime import date"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a9720190-d2ef-435a-a471-367dbf840ea8",
"metadata": {},
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{
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" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
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"\n",
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" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
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"\n",
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" <strong>Comm: </strong> tcp://127.0.0.1:41799\n",
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" <strong>Comm: </strong> tcp://127.0.0.1:34997\n",
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" <strong>Nanny: </strong> tcp://127.0.0.1:39369\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:34745\n",
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" <strong>Total threads: </strong> 2\n",
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" <strong>Dashboard: </strong> <a href=\"/proxy/44933/status\" target=\"_blank\">/proxy/44933/status</a>\n",
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" <strong>Nanny: </strong> tcp://127.0.0.1:41889\n",
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" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Comm: </strong> tcp://127.0.0.1:35027\n",
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" <strong>Memory: </strong> 5.62 GiB\n",
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" <strong>Nanny: </strong> tcp://127.0.0.1:43689\n",
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" <strong>Local directory: </strong> /local/v45/aph502/tmp/dask-worker-space/worker-j7jfpjz7\n",
" </td>\n",
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"\n",
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"\n",
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],
"text/plain": [
"<Client: 'tcp://127.0.0.1:41401' processes=4 threads=8, memory=22.46 GiB>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
" \n",
"client = Client()\n",
"client"
]
},
{
"cell_type": "markdown",
"id": "2f67decb-299a-4ac1-9531-773ea29e346c",
"metadata": {},
"source": [
"### 1. get data: apcp, mjo, nino34"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f1241b45-cf26-4c8e-9a62-9e498bb28560",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.75 s, sys: 607 ms, total: 2.36 s\n",
"Wall time: 5.03 s\n"
]
}
],
"source": [
"%%time\n",
"filelist=[]\n",
"for i in range(1981,2014,1):\n",
" filelist.append(f'/g/data/ua8/LE_models/20CRv3/mean_daily/apcp/apcp.{i}.nc')\n",
"filelist\n",
"ds1=xr.open_mfdataset(filelist,combine='by_coords')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0fc3b919-18d8-476b-a2c0-526db3917b19",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;apcp&#x27; (time: 12053, lat: 46, lon: 61)&gt;\n",
"dask.array&lt;getitem, shape=(12053, 46, 61), dtype=float32, chunksize=(366, 46, 61), chunktype=numpy.ndarray&gt;\n",
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" * time (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2013-12-31\n",
"Attributes:\n",
" long_name: daily mean 3-hourly accumulated total precipitation at...\n",
" units: kg/m^2\n",
" GRIB_name: APCP\n",
" var_desc: Precipitation amount\n",
" dataset: NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Dai...\n",
" level_desc: Surface\n",
" statistic: Mean\n",
" parent_stat: Individual Obs\n",
" standard_name: precipitation_amount\n",
" valid_range: [ 0. 100.]\n",
" statistic_method: Ensemble mean is calculated by averaging over all 80 e...\n",
" GridType: Cylindrical Equidistant Projection Grid\n",
" datum: wgs84\n",
" actual_range: [ 0. 35.625]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'apcp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12053</li><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5b00acf7-2002-4ee0-8908-a354c083c5b8' class='xr-array-in' type='checkbox' checked><label for='section-5b00acf7-2002-4ee0-8908-a354c083c5b8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(365, 46, 61), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-a6334523-f9ae-4d2b-900d-500865d43ff2' class='xr-section-summary-in' type='checkbox' checked><label for='section-a6334523-f9ae-4d2b-900d-500865d43ff2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-45.0 -44.0 -43.0 ... -2.0 -1.0 0.0</div><input id='attrs-9d5b86bc-9b09-45f5-a19f-34b317642f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d5b86bc-9b09-45f5-a19f-34b317642f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf145e4a-af42-43cd-99f4-49c292ce434c' class='xr-var-data-in' type='checkbox'><label for='data-bf145e4a-af42-43cd-99f4-49c292ce434c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([-45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34.,\n",
" -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22.,\n",
" -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n",
" -9., -8., -7., -6., -5., -4., -3., -2., -1., 0.],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 101.0 102.0 ... 159.0 160.0</div><input id='attrs-dcab22ce-6428-4da6-87a8-d7217f0981fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcab22ce-6428-4da6-87a8-d7217f0981fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5595beb0-c8cb-47d7-882d-69467aae5005' class='xr-var-data-in' type='checkbox'><label for='data-5595beb0-c8cb-47d7-882d-69467aae5005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[ 0. 359.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111.,\n",
" 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123.,\n",
" 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135.,\n",
" 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147.,\n",
" 148., 149., 150., 151., 152., 153., 154., 155., 156., 157., 158., 159.,\n",
" 160.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-01-01 ... 2013-12-31</div><input id='attrs-c094fed8-083d-44ac-bc02-40f1cd7b02b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c094fed8-083d-44ac-bc02-40f1cd7b02b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10168d4a-4b60-4786-bac7-f01ae1127a18' class='xr-var-data-in' type='checkbox'><label for='data-10168d4a-4b60-4786-bac7-f01ae1127a18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>actual_range :</span></dt><dd>[1586616. 1595352.]</dd><dt><span>coordinate_defines :</span></dt><dd>start</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-01-02T00:00:00.000000000&#x27;,\n",
" &#x27;1981-01-03T00:00:00.000000000&#x27;, ..., &#x27;2013-12-29T00:00:00.000000000&#x27;,\n",
" &#x27;2013-12-30T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cfee481-c531-401f-bd5d-c24c4e3f0d0a' class='xr-section-summary-in' type='checkbox' ><label for='section-8cfee481-c531-401f-bd5d-c24c4e3f0d0a' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily mean 3-hourly accumulated total precipitation at surface</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>GRIB_name :</span></dt><dd>APCP</dd><dt><span>var_desc :</span></dt><dd>Precipitation amount</dd><dt><span>dataset :</span></dt><dd>NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Daily Averages</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>valid_range :</span></dt><dd>[ 0. 100.]</dd><dt><span>statistic_method :</span></dt><dd>Ensemble mean is calculated by averaging over all 80 ensemble members at each time step</dd><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>datum :</span></dt><dd>wgs84</dd><dt><span>actual_range :</span></dt><dd>[ 0. 35.625]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'apcp' (time: 12053, lat: 46, lon: 61)>\n",
"dask.array<getitem, shape=(12053, 46, 61), dtype=float32, chunksize=(366, 46, 61), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 -41.0 ... -3.0 -2.0 -1.0 0.0\n",
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"Attributes:\n",
" long_name: daily mean 3-hourly accumulated total precipitation at...\n",
" units: kg/m^2\n",
" GRIB_name: APCP\n",
" var_desc: Precipitation amount\n",
" dataset: NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Dai...\n",
" level_desc: Surface\n",
" statistic: Mean\n",
" parent_stat: Individual Obs\n",
" standard_name: precipitation_amount\n",
" valid_range: [ 0. 100.]\n",
" statistic_method: Ensemble mean is calculated by averaging over all 80 e...\n",
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]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds=ds1['apcp'].loc[:,-45:0,100:160]\n",
"ds"
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"cell_type": "code",
"execution_count": 5,
"id": "0884d5ac-d0d0-428e-9acc-221039f8eaf3",
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"source": [
"df=pd.read_csv(\"/g/data/w40/dy9345/MJO/MJO_ot12.csv\")\n",
"df['time']=pd.to_datetime(df[['year','month','day']])\n",
"df=df.set_index('time').drop(columns=['year','month','day'])\n",
"df.loc[df['amplitude'].lt(1) ,'phase'] = 0"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "415b9364-8a1e-40b6-9bf0-b59f830bdfa1",
"metadata": {},
"outputs": [
{
"name": "stderr",
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"text": [
"/local/v45/aph502/tmp/ipykernel_2011215/3873111277.py:1: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
" dateparser = lambda d: pd.datetime.strptime(d,'%Y%m%d')\n"
]
}
],
"source": [
"dateparser = lambda d: pd.datetime.strptime(d,'%Y%m%d')\n",
"df2=pd.read_csv('http://climexp.knmi.nl/data/inino34_daily.dat',\n",
" skiprows=12,delim_whitespace=True,\n",
" names=['time','enso'],\n",
" parse_dates=['time'],\n",
" date_parser=dateparser)\n",
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]
},
{
"cell_type": "markdown",
"id": "e2b6e147-3a21-44bd-bfff-66bd65f7afa5",
"metadata": {},
"source": [
"### Create merged dataset"
]
},
{
"cell_type": "markdown",
"id": "caed2e53-9ed8-4a17-b023-be3bdf0c9545",
"metadata": {},
"source": [
"Create MJO and ENSO datasets that span the same time range as the apcp data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1e820aed-6cec-4925-8439-91556d73e5a2",
"metadata": {},
"outputs": [
{
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"text/plain": [
" phase amplitude\n",
"time \n",
"1981-01-01 3 1.330600\n",
"1981-01-02 4 1.497400\n",
"1981-01-03 4 1.589900\n",
"1981-01-04 4 1.587300\n",
"1981-01-05 4 1.508400\n",
"... ... ...\n",
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"2013-12-29 0 0.115490\n",
"2013-12-30 0 0.259610\n",
"2013-12-31 0 0.450530\n",
"\n",
"[12053 rows x 2 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfmjo = df.loc[ (df.index >= ds.time.min().values) & (df.index <= ds.time.max().values)][['phase','amplitude']]\n",
"dfmjo"
]
},
{
"cell_type": "code",
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"id": "d281af88-89cf-4af0-87e3-71131f0c1954",
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"text/plain": [
" enso\n",
"time \n",
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"1981-09-02 -0.045651\n",
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"1981-09-04 -0.278630\n",
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"2013-12-29 -0.500764\n",
"2013-12-30 -0.531343\n",
"2013-12-31 -0.583136\n",
"\n",
"[11810 rows x 1 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
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"source": [
"dfenso = df2.loc[ (df2.index >= ds.time.min().values) & (df2.index <= ds.time.max().values)][['enso']]\n",
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},
{
"cell_type": "markdown",
"id": "fab4a2e6-f13f-4b0c-94e0-0269ac264a7c",
"metadata": {},
"source": [
"Merge the apcp, MJP and ENSO data into a single dataset. They have a common time coordinate, but the outer join is necesary as they don't necessarily have data for every day?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "69ea0cf1-a661-4805-b3bf-2979aeaf39b3",
"metadata": {},
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" * time (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2013-12-31\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
"Data variables:\n",
" apcp (time, lat, lon) float32 dask.array&lt;chunksize=(365, 46, 61), meta=np.ndarray&gt;\n",
" phase (time) int64 3 4 4 4 4 4 5 5 5 5 5 5 ... 0 0 0 0 0 0 0 0 0 0 0 0\n",
" amplitude (time) float64 1.331 1.497 1.59 1.587 ... 0.1155 0.2596 0.4505\n",
" enso (time) float64 nan nan nan nan ... -0.5008 -0.5313 -0.5831\n",
"Attributes: (12/14)\n",
" long_name: daily mean 3-hourly accumulated total precipitation at...\n",
" units: kg/m^2\n",
" GRIB_name: APCP\n",
" var_desc: Precipitation amount\n",
" dataset: NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Dai...\n",
" level_desc: Surface\n",
" ... ...\n",
" standard_name: precipitation_amount\n",
" valid_range: [ 0. 100.]\n",
" statistic_method: Ensemble mean is calculated by averaging over all 80 e...\n",
" GridType: Cylindrical Equidistant Projection Grid\n",
" datum: wgs84\n",
" actual_range: [ 0. 35.625]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4b9ea4d4-c258-46fb-974e-901c1a7fa756' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b9ea4d4-c258-46fb-974e-901c1a7fa756' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12053</li><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a69c782e-025c-4ee6-88dd-a500154ea85d' class='xr-section-summary-in' type='checkbox' checked><label for='section-a69c782e-025c-4ee6-88dd-a500154ea85d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-01-01 ... 2013-12-31</div><input id='attrs-0fde554c-3ed8-45d8-9b26-3397e4214662' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fde554c-3ed8-45d8-9b26-3397e4214662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e7ea28a-cb99-4bfd-8c3f-3eca8b35057e' class='xr-var-data-in' type='checkbox'><label for='data-1e7ea28a-cb99-4bfd-8c3f-3eca8b35057e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>actual_range :</span></dt><dd>[1586616. 1595352.]</dd><dt><span>coordinate_defines :</span></dt><dd>start</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-01-02T00:00:00.000000000&#x27;,\n",
" &#x27;1981-01-03T00:00:00.000000000&#x27;, ..., &#x27;2013-12-29T00:00:00.000000000&#x27;,\n",
" &#x27;2013-12-30T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-45.0 -44.0 -43.0 ... -2.0 -1.0 0.0</div><input id='attrs-d4888284-ce29-42ca-919a-66025e4ed39d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4888284-ce29-42ca-919a-66025e4ed39d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a464f81-bb15-4b67-8508-68d536a85d35' class='xr-var-data-in' type='checkbox'><label for='data-2a464f81-bb15-4b67-8508-68d536a85d35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([-45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34.,\n",
" -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22.,\n",
" -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n",
" -9., -8., -7., -6., -5., -4., -3., -2., -1., 0.],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 101.0 102.0 ... 159.0 160.0</div><input id='attrs-3d438039-fb5f-43b4-8d89-0b20fec9effa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d438039-fb5f-43b4-8d89-0b20fec9effa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f5eddc-b62d-42ae-b915-af1e2537f416' class='xr-var-data-in' type='checkbox'><label for='data-01f5eddc-b62d-42ae-b915-af1e2537f416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[ 0. 359.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111.,\n",
" 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123.,\n",
" 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135.,\n",
" 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147.,\n",
" 148., 149., 150., 151., 152., 153., 154., 155., 156., 157., 158., 159.,\n",
" 160.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17eb13c1-f0bd-4125-8cb3-1efba1908dda' class='xr-section-summary-in' type='checkbox' checked><label for='section-17eb13c1-f0bd-4125-8cb3-1efba1908dda' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>apcp</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365, 46, 61), meta=np.ndarray&gt;</div><input id='attrs-a06e0a2e-05c1-4878-8362-25bf87d9a604' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a06e0a2e-05c1-4878-8362-25bf87d9a604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-053741b7-8ea0-4738-8f66-3076f411d1e9' class='xr-var-data-in' type='checkbox'><label for='data-053741b7-8ea0-4738-8f66-3076f411d1e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily mean 3-hourly accumulated total precipitation at surface</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>GRIB_name :</span></dt><dd>APCP</dd><dt><span>var_desc :</span></dt><dd>Precipitation amount</dd><dt><span>dataset :</span></dt><dd>NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Daily Averages</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>valid_range :</span></dt><dd>[ 0. 100.]</dd><dt><span>statistic_method :</span></dt><dd>Ensemble mean is calculated by averaging over all 80 ensemble members at each time step</dd><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>datum :</span></dt><dd>wgs84</dd><dt><span>actual_range :</span></dt><dd>[ 0. 35.625]</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phase</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 4 4 4 4 4 5 5 ... 0 0 0 0 0 0 0 0</div><input id='attrs-eb0136f5-5022-4488-a186-70d26d7aa1cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb0136f5-5022-4488-a186-70d26d7aa1cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8107797-0e9b-4e26-af2b-d63e9faa5f5b' class='xr-var-data-in' type='checkbox'><label for='data-e8107797-0e9b-4e26-af2b-d63e9faa5f5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3, 4, 4, ..., 0, 0, 0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amplitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.331 1.497 1.59 ... 0.2596 0.4505</div><input id='attrs-94e03879-d0f4-4ef3-816e-abcde8081ced' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94e03879-d0f4-4ef3-816e-abcde8081ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-119e1331-5c1c-4576-acee-0dd0b88d925a' class='xr-var-data-in' type='checkbox'><label for='data-119e1331-5c1c-4576-acee-0dd0b88d925a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.3306 , 1.4974 , 1.5899 , ..., 0.11549, 0.25961, 0.45053])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>enso</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.5313 -0.5831</div><input id='attrs-4306f7b6-b103-4289-8752-dc80f71b9712' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4306f7b6-b103-4289-8752-dc80f71b9712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-828ce081-4512-48d2-8173-eb75d11b53ba' class='xr-var-data-in' type='checkbox'><label for='data-828ce081-4512-48d2-8173-eb75d11b53ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan, nan, nan, ..., -0.5007645, -0.5313434,\n",
" -0.5831357])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20ad75d0-e6a0-43eb-b1ff-8db357411b43' class='xr-section-summary-in' type='checkbox' ><label for='section-20ad75d0-e6a0-43eb-b1ff-8db357411b43' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily mean 3-hourly accumulated total precipitation at surface</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>GRIB_name :</span></dt><dd>APCP</dd><dt><span>var_desc :</span></dt><dd>Precipitation amount</dd><dt><span>dataset :</span></dt><dd>NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Daily Averages</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>valid_range :</span></dt><dd>[ 0. 100.]</dd><dt><span>statistic_method :</span></dt><dd>Ensemble mean is calculated by averaging over all 80 ensemble members at each time step</dd><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>datum :</span></dt><dd>wgs84</dd><dt><span>actual_range :</span></dt><dd>[ 0. 35.625]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 12053, lat: 46, lon: 61)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2013-12-31\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
"Data variables:\n",
" apcp (time, lat, lon) float32 dask.array<chunksize=(365, 46, 61), meta=np.ndarray>\n",
" phase (time) int64 3 4 4 4 4 4 5 5 5 5 5 5 ... 0 0 0 0 0 0 0 0 0 0 0 0\n",
" amplitude (time) float64 1.331 1.497 1.59 1.587 ... 0.1155 0.2596 0.4505\n",
" enso (time) float64 nan nan nan nan ... -0.5008 -0.5313 -0.5831\n",
"Attributes: (12/14)\n",
" long_name: daily mean 3-hourly accumulated total precipitation at...\n",
" units: kg/m^2\n",
" GRIB_name: APCP\n",
" var_desc: Precipitation amount\n",
" dataset: NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Dai...\n",
" level_desc: Surface\n",
" ... ...\n",
" standard_name: precipitation_amount\n",
" valid_range: [ 0. 100.]\n",
" statistic_method: Ensemble mean is calculated by averaging over all 80 e...\n",
" GridType: Cylindrical Equidistant Projection Grid\n",
" datum: wgs84\n",
" actual_range: [ 0. 35.625]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = xr.merge([ds, dfmjo.to_xarray(), dfenso.to_xarray()], join='outer')\n",
"data"
]
},
{
"cell_type": "markdown",
"id": "7a323121-db72-4b83-b52e-a5e03b602b7e",
"metadata": {},
"source": [
"### Calculate apcp threshold"
]
},
{
"cell_type": "markdown",
"id": "c6420093-a5d0-4205-b41c-fdb4f83c8df0",
"metadata": {},
"source": [
"Calculate the threshold value of apcp for the quantile specified by THR for the entire dataset. This is calculated along the `time` dimension so there is threshold value for every spatial location. Note that zero is explicitly excluded from the calculation, and chunking is removed along the time dimension because `quantile` needs all the data at once to calculate the correct value along the `time` dimension"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "43010de4-af90-4345-97c7-44738df450e8",
"metadata": {},
"outputs": [],
"source": [
"THR = 0.67"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0ae853e1-e875-438a-980e-fa2add54ace3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.47 s, sys: 897 ms, total: 4.37 s\n",
"Wall time: 18.2 s\n"
]
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;apcp&#x27; (lat: 46, lon: 61)&gt;\n",
"array([[0.61250001, 0.625 , 0.625 , ..., 0.36250001, 0.375 ,\n",
" 0.38749999],\n",
" [0.58750004, 0.60000002, 0.58750004, ..., 0.37499997, 0.37500003,\n",
" 0.38750002],\n",
" [0.53749996, 0.52499998, 0.51249999, ..., 0.375 , 0.38749999,\n",
" 0.39999998],\n",
" ...,\n",
" [0.75 , 1.08749998, 0.95000005, ..., 0.84999996, 0.83749998,\n",
" 0.82500005],\n",
" [0.96250004, 1.04999995, 0.65775001, ..., 0.73750001, 0.73749997,\n",
" 0.72500002],\n",
" [0.88749999, 0.66250002, 0.52500004, ..., 0.6875 , 0.6875 ,\n",
" 0.67500001]])\n",
"Coordinates:\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 -41.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
" quantile float64 0.67</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'apcp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c632554-a56a-4a89-b802-c0ac955c57a0' class='xr-array-in' type='checkbox' checked><label for='section-9c632554-a56a-4a89-b802-c0ac955c57a0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.6125 0.625 0.625 0.6125 0.6 ... 0.7125 0.7 0.6875 0.6875 0.675</span></div><div class='xr-array-data'><pre>array([[0.61250001, 0.625 , 0.625 , ..., 0.36250001, 0.375 ,\n",
" 0.38749999],\n",
" [0.58750004, 0.60000002, 0.58750004, ..., 0.37499997, 0.37500003,\n",
" 0.38750002],\n",
" [0.53749996, 0.52499998, 0.51249999, ..., 0.375 , 0.38749999,\n",
" 0.39999998],\n",
" ...,\n",
" [0.75 , 1.08749998, 0.95000005, ..., 0.84999996, 0.83749998,\n",
" 0.82500005],\n",
" [0.96250004, 1.04999995, 0.65775001, ..., 0.73750001, 0.73749997,\n",
" 0.72500002],\n",
" [0.88749999, 0.66250002, 0.52500004, ..., 0.6875 , 0.6875 ,\n",
" 0.67500001]])</pre></div></div></li><li class='xr-section-item'><input id='section-fcaf9fbe-bb38-4cc1-a0da-7e6034c7b560' class='xr-section-summary-in' type='checkbox' checked><label for='section-fcaf9fbe-bb38-4cc1-a0da-7e6034c7b560' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-45.0 -44.0 -43.0 ... -2.0 -1.0 0.0</div><input id='attrs-e1bc1105-6f6d-4101-aa04-9f75b017ded2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1bc1105-6f6d-4101-aa04-9f75b017ded2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3470dd8f-c1cd-4b9a-8ab7-7923e772c66a' class='xr-var-data-in' type='checkbox'><label for='data-3470dd8f-c1cd-4b9a-8ab7-7923e772c66a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([-45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34.,\n",
" -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22.,\n",
" -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n",
" -9., -8., -7., -6., -5., -4., -3., -2., -1., 0.],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 101.0 102.0 ... 159.0 160.0</div><input id='attrs-3c636d4e-4236-4988-a40a-58976e36d9b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c636d4e-4236-4988-a40a-58976e36d9b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f77083a7-6936-4c4b-81e9-530cccf90ee3' class='xr-var-data-in' type='checkbox'><label for='data-f77083a7-6936-4c4b-81e9-530cccf90ee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[ 0. 359.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111.,\n",
" 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123.,\n",
" 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135.,\n",
" 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147.,\n",
" 148., 149., 150., 151., 152., 153., 154., 155., 156., 157., 158., 159.,\n",
" 160.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.67</div><input id='attrs-e4741d75-4383-4cf2-9df8-4edcea3b77d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4741d75-4383-4cf2-9df8-4edcea3b77d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5046497e-f94c-4fcb-bc0b-83018da2d54f' class='xr-var-data-in' type='checkbox'><label for='data-5046497e-f94c-4fcb-bc0b-83018da2d54f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.67)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77989dba-6718-40dc-b213-068b87f9f9c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77989dba-6718-40dc-b213-068b87f9f9c8' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'apcp' (lat: 46, lon: 61)>\n",
"array([[0.61250001, 0.625 , 0.625 , ..., 0.36250001, 0.375 ,\n",
" 0.38749999],\n",
" [0.58750004, 0.60000002, 0.58750004, ..., 0.37499997, 0.37500003,\n",
" 0.38750002],\n",
" [0.53749996, 0.52499998, 0.51249999, ..., 0.375 , 0.38749999,\n",
" 0.39999998],\n",
" ...,\n",
" [0.75 , 1.08749998, 0.95000005, ..., 0.84999996, 0.83749998,\n",
" 0.82500005],\n",
" [0.96250004, 1.04999995, 0.65775001, ..., 0.73750001, 0.73749997,\n",
" 0.72500002],\n",
" [0.88749999, 0.66250002, 0.52500004, ..., 0.6875 , 0.6875 ,\n",
" 0.67500001]])\n",
"Coordinates:\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 -41.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
" quantile float64 0.67"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"THR_threshold = data.apcp.where(data.apcp != 0).chunk(dict(time=-1)).quantile(THR, dim='time').compute()\n",
"THR_threshold"
]
},
{
"cell_type": "markdown",
"id": "2b479024-eed9-4108-8a1c-f489016e279e",
"metadata": {},
"source": [
"Plot for a sanity check"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4323aa24-ceab-4770-83df-74f03515a775",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7f6cf27c1a00>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1080x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"THR_threshold.plot(size=10)"
]
},
{
"cell_type": "markdown",
"id": "787d9038-2c68-42c6-9648-7475dfbc4144",
"metadata": {},
"source": [
"### Use groupby to loop over values of phase and calculate your metric"
]
},
{
"cell_type": "markdown",
"id": "782bcf30-b8b5-4188-9049-672281452afb",
"metadata": {},
"source": [
"Using the `xarray.groupby` function it is possible to group the entire dataset by one variable, in this case `phase`. So if you loop over the the `groupby` and print out the index (the value of `phase`) and the subset of data you can see it is only the data matching each value of `phase` (the `break` statement stops this after one loop, but comment it out or remove it to see all values of phase). "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6788cf1a-ab22-497a-b700-92c236f7a181",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"<xarray.Dataset>\n",
"Dimensions: (time: 4778, lat: 46, lon: 61)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1981-01-22 1981-01-23 ... 2013-12-31\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
"Data variables:\n",
" apcp (time, lat, lon) float32 dask.array<chunksize=(139, 46, 61), meta=np.ndarray>\n",
" phase (time) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0\n",
" amplitude (time) float64 0.9787 0.8587 0.6732 0.45 ... 0.1155 0.2596 0.4505\n",
" enso (time) float64 nan nan nan nan ... -0.5008 -0.5313 -0.5831\n",
"Attributes: (12/14)\n",
" long_name: daily mean 3-hourly accumulated total precipitation at...\n",
" units: kg/m^2\n",
" GRIB_name: APCP\n",
" var_desc: Precipitation amount\n",
" dataset: NOAA/CIRES/DOE 20th Century Reanalysis version 3mo Dai...\n",
" level_desc: Surface\n",
" ... ...\n",
" standard_name: precipitation_amount\n",
" valid_range: [ 0. 100.]\n",
" statistic_method: Ensemble mean is calculated by averaging over all 80 e...\n",
" GridType: Cylindrical Equidistant Projection Grid\n",
" datum: wgs84\n",
" actual_range: [ 0. 35.625]\n",
"4778\n"
]
}
],
"source": [
"for i, ds in data.groupby(data.phase):\n",
" print(i)\n",
" print(ds)\n",
" print(len(ds.time))\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "505b3ee1-6053-40bf-b971-8a7736a3afe5",
"metadata": {},
"source": [
"This is very useful, now you've done most of the work, just have to mask the `apcp` variable to values greater than the threshold, count the values and divide them. Create a little function to count the number of non-NAN (not masked) values in `apcp` and divide by the length of the time dimension, which is just a proxy for the total number of points"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e48467f7-c297-4c51-87b2-053fc61e9607",
"metadata": {},
"outputs": [],
"source": [
"def func(ds):\n",
" return ds['apcp'].count('time') / len(ds['time'])"
]
},
{
"cell_type": "markdown",
"id": "939999f0-43ad-4494-8178-df708d4a68dc",
"metadata": {},
"source": [
"Use `where` to mask the data everywhere the apcp is below the apcp threshold, so all that is required is to count the number of valid values of `apcp`"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1d4fc6e1-15c2-4cda-a564-ff73aa81e9f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.13 s, sys: 306 ms, total: 3.43 s\n",
"Wall time: 9.02 s\n"
]
},
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;apcp&#x27; (phase: 9, lat: 46, lon: 61)&gt;\n",
"array([[[0.33842612, 0.34428631, 0.34072834, ..., 0.31331101,\n",
" 0.31247384, 0.30975303],\n",
" [0.34323985, 0.34198409, 0.34051905, ..., 0.31372959,\n",
" 0.31268313, 0.31205525],\n",
" [0.34051905, 0.34344914, 0.34512348, ..., 0.32168271,\n",
" 0.31875262, 0.31624111],\n",
" ...,\n",
" [0.26203432, 0.28045207, 0.2938468 , ..., 0.29740477,\n",
" 0.30200921, 0.29740477],\n",
" [0.26391796, 0.27563834, 0.31059021, ..., 0.30661365,\n",
" 0.3022185 , 0.29866053],\n",
" [0.26161574, 0.2768941 , 0.31896191, ..., 0.31519464,\n",
" 0.31393889, 0.30849728]],\n",
"\n",
" [[0.33471503, 0.33989637, 0.33264249, ..., 0.30051813,\n",
" 0.30051813, 0.30259067],\n",
" [0.33056995, 0.33678756, 0.33678756, ..., 0.28497409,\n",
" 0.29222798, 0.29948187],\n",
" [0.32746114, 0.31917098, 0.32435233, ..., 0.28911917,\n",
" 0.28704663, 0.29222798],\n",
"...\n",
" [0.20936281, 0.20416125, 0.30819246, ..., 0.39401821,\n",
" 0.38621586, 0.39011704],\n",
" [0.21716515, 0.23016905, 0.29518856, ..., 0.37451235,\n",
" 0.37321196, 0.38231469],\n",
" [0.21456437, 0.26527958, 0.26527958, ..., 0.37451235,\n",
" 0.38751625, 0.37841352]],\n",
"\n",
" [[0.29978587, 0.29764454, 0.30299786, ..., 0.30942184,\n",
" 0.30620985, 0.30085653],\n",
" [0.28907923, 0.28907923, 0.2869379 , ..., 0.28372591,\n",
" 0.28158458, 0.28265525],\n",
" [0.28586724, 0.28479657, 0.28265525, ..., 0.28372591,\n",
" 0.29014989, 0.29014989],\n",
" ...,\n",
" [0.3875803 , 0.43361884, 0.44218415, ..., 0.37473233,\n",
" 0.38008565, 0.39079229],\n",
" [0.43683084, 0.47751606, 0.40685225, ..., 0.34154176,\n",
" 0.34582441, 0.3490364 ],\n",
" [0.45824411, 0.44753747, 0.40256959, ..., 0.2987152 ,\n",
" 0.29978587, 0.31049251]]])\n",
"Coordinates:\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 -41.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
" quantile float64 0.67\n",
" * phase (phase) int64 0 1 2 3 4 5 6 7 8</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'apcp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>phase</span>: 9</li><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-536cef48-51ff-4e6c-87cb-e2b3943a7707' class='xr-array-in' type='checkbox' checked><label for='section-536cef48-51ff-4e6c-87cb-e2b3943a7707' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.3384 0.3443 0.3407 0.338 0.3401 ... 0.2966 0.2987 0.2998 0.3105</span></div><div class='xr-array-data'><pre>array([[[0.33842612, 0.34428631, 0.34072834, ..., 0.31331101,\n",
" 0.31247384, 0.30975303],\n",
" [0.34323985, 0.34198409, 0.34051905, ..., 0.31372959,\n",
" 0.31268313, 0.31205525],\n",
" [0.34051905, 0.34344914, 0.34512348, ..., 0.32168271,\n",
" 0.31875262, 0.31624111],\n",
" ...,\n",
" [0.26203432, 0.28045207, 0.2938468 , ..., 0.29740477,\n",
" 0.30200921, 0.29740477],\n",
" [0.26391796, 0.27563834, 0.31059021, ..., 0.30661365,\n",
" 0.3022185 , 0.29866053],\n",
" [0.26161574, 0.2768941 , 0.31896191, ..., 0.31519464,\n",
" 0.31393889, 0.30849728]],\n",
"\n",
" [[0.33471503, 0.33989637, 0.33264249, ..., 0.30051813,\n",
" 0.30051813, 0.30259067],\n",
" [0.33056995, 0.33678756, 0.33678756, ..., 0.28497409,\n",
" 0.29222798, 0.29948187],\n",
" [0.32746114, 0.31917098, 0.32435233, ..., 0.28911917,\n",
" 0.28704663, 0.29222798],\n",
"...\n",
" [0.20936281, 0.20416125, 0.30819246, ..., 0.39401821,\n",
" 0.38621586, 0.39011704],\n",
" [0.21716515, 0.23016905, 0.29518856, ..., 0.37451235,\n",
" 0.37321196, 0.38231469],\n",
" [0.21456437, 0.26527958, 0.26527958, ..., 0.37451235,\n",
" 0.38751625, 0.37841352]],\n",
"\n",
" [[0.29978587, 0.29764454, 0.30299786, ..., 0.30942184,\n",
" 0.30620985, 0.30085653],\n",
" [0.28907923, 0.28907923, 0.2869379 , ..., 0.28372591,\n",
" 0.28158458, 0.28265525],\n",
" [0.28586724, 0.28479657, 0.28265525, ..., 0.28372591,\n",
" 0.29014989, 0.29014989],\n",
" ...,\n",
" [0.3875803 , 0.43361884, 0.44218415, ..., 0.37473233,\n",
" 0.38008565, 0.39079229],\n",
" [0.43683084, 0.47751606, 0.40685225, ..., 0.34154176,\n",
" 0.34582441, 0.3490364 ],\n",
" [0.45824411, 0.44753747, 0.40256959, ..., 0.2987152 ,\n",
" 0.29978587, 0.31049251]]])</pre></div></div></li><li class='xr-section-item'><input id='section-41d7c255-5470-4c22-b2f2-f43f0dd54169' class='xr-section-summary-in' type='checkbox' checked><label for='section-41d7c255-5470-4c22-b2f2-f43f0dd54169' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-45.0 -44.0 -43.0 ... -2.0 -1.0 0.0</div><input id='attrs-6e73d69e-3dd9-4694-92f0-7a8bf4f42609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e73d69e-3dd9-4694-92f0-7a8bf4f42609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2da7c18-38b3-4f36-a0a0-40cefee85884' class='xr-var-data-in' type='checkbox'><label for='data-a2da7c18-38b3-4f36-a0a0-40cefee85884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([-45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34.,\n",
" -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22.,\n",
" -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n",
" -9., -8., -7., -6., -5., -4., -3., -2., -1., 0.],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 101.0 102.0 ... 159.0 160.0</div><input id='attrs-c008014a-48f2-458d-8d79-0ccd5b0a9ef8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c008014a-48f2-458d-8d79-0ccd5b0a9ef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0028600-c1b7-4f64-88c4-a3dbb69053c1' class='xr-var-data-in' type='checkbox'><label for='data-e0028600-c1b7-4f64-88c4-a3dbb69053c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[ 0. 359.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array([100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111.,\n",
" 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123.,\n",
" 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135.,\n",
" 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147.,\n",
" 148., 149., 150., 151., 152., 153., 154., 155., 156., 157., 158., 159.,\n",
" 160.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.67</div><input id='attrs-542f9884-923d-4990-82e6-d88bcbba7f2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-542f9884-923d-4990-82e6-d88bcbba7f2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf9ed1ed-4744-4e22-8928-a4abb0e9bac5' class='xr-var-data-in' type='checkbox'><label for='data-cf9ed1ed-4744-4e22-8928-a4abb0e9bac5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.67)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(phase)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8</div><input id='attrs-e97562df-f8e7-460b-b6b2-20949f3ed707' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e97562df-f8e7-460b-b6b2-20949f3ed707' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95481239-ab66-48eb-acca-0105c3e6569f' class='xr-var-data-in' type='checkbox'><label for='data-95481239-ab66-48eb-acca-0105c3e6569f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50b2d048-e22c-4924-92dd-55ceb64b5584' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-50b2d048-e22c-4924-92dd-55ceb64b5584' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'apcp' (phase: 9, lat: 46, lon: 61)>\n",
"array([[[0.33842612, 0.34428631, 0.34072834, ..., 0.31331101,\n",
" 0.31247384, 0.30975303],\n",
" [0.34323985, 0.34198409, 0.34051905, ..., 0.31372959,\n",
" 0.31268313, 0.31205525],\n",
" [0.34051905, 0.34344914, 0.34512348, ..., 0.32168271,\n",
" 0.31875262, 0.31624111],\n",
" ...,\n",
" [0.26203432, 0.28045207, 0.2938468 , ..., 0.29740477,\n",
" 0.30200921, 0.29740477],\n",
" [0.26391796, 0.27563834, 0.31059021, ..., 0.30661365,\n",
" 0.3022185 , 0.29866053],\n",
" [0.26161574, 0.2768941 , 0.31896191, ..., 0.31519464,\n",
" 0.31393889, 0.30849728]],\n",
"\n",
" [[0.33471503, 0.33989637, 0.33264249, ..., 0.30051813,\n",
" 0.30051813, 0.30259067],\n",
" [0.33056995, 0.33678756, 0.33678756, ..., 0.28497409,\n",
" 0.29222798, 0.29948187],\n",
" [0.32746114, 0.31917098, 0.32435233, ..., 0.28911917,\n",
" 0.28704663, 0.29222798],\n",
"...\n",
" [0.20936281, 0.20416125, 0.30819246, ..., 0.39401821,\n",
" 0.38621586, 0.39011704],\n",
" [0.21716515, 0.23016905, 0.29518856, ..., 0.37451235,\n",
" 0.37321196, 0.38231469],\n",
" [0.21456437, 0.26527958, 0.26527958, ..., 0.37451235,\n",
" 0.38751625, 0.37841352]],\n",
"\n",
" [[0.29978587, 0.29764454, 0.30299786, ..., 0.30942184,\n",
" 0.30620985, 0.30085653],\n",
" [0.28907923, 0.28907923, 0.2869379 , ..., 0.28372591,\n",
" 0.28158458, 0.28265525],\n",
" [0.28586724, 0.28479657, 0.28265525, ..., 0.28372591,\n",
" 0.29014989, 0.29014989],\n",
" ...,\n",
" [0.3875803 , 0.43361884, 0.44218415, ..., 0.37473233,\n",
" 0.38008565, 0.39079229],\n",
" [0.43683084, 0.47751606, 0.40685225, ..., 0.34154176,\n",
" 0.34582441, 0.3490364 ],\n",
" [0.45824411, 0.44753747, 0.40256959, ..., 0.2987152 ,\n",
" 0.29978587, 0.31049251]]])\n",
"Coordinates:\n",
" * lat (lat) float32 -45.0 -44.0 -43.0 -42.0 -41.0 ... -3.0 -2.0 -1.0 0.0\n",
" * lon (lon) float32 100.0 101.0 102.0 103.0 ... 157.0 158.0 159.0 160.0\n",
" quantile float64 0.67\n",
" * phase (phase) int64 0 1 2 3 4 5 6 7 8"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"result = data.where(data.apcp >= THR_threshold).groupby(data.phase).apply(func).compute()\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0b6763ff-467a-4fdc-a85b-969cc2f3c2c8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x7f6ce692bb20>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1368x1296 with 10 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"result.plot(col='phase', col_wrap=3, size=6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba806f25-f6f4-4089-a9a7-744c5292f044",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"language": "python",
"name": "conda-env-analysis3-22.04-py"
},
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