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larsbuntemeyer/cmip6-resampling.ipynb

Created April 9, 2022 17:06
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cmip6-resampling.ipynb
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"cell_type": "markdown",
"id": "fd99d13f-dd30-45bb-bb8c-dfc1c2dadb31",
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"# CMIP6 time averaging"
]
},
{
"cell_type": "markdown",
"id": "6c84d28b-bae6-4b0f-a54b-6e32ab802eb7",
"metadata": {},
"source": [
"If I have hourly model output of instantaneous values, how is it correctly resampled to subdaily mean frequencies? Common tools seem to use an `close='left'` approach by default, i'll demonstrate with the default `xarray` example. For example, let's assume `1hr` point value output like this:"
]
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;data&#x27; (time: 49)&gt;\n",
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
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" * time (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ... 2000-01-03</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'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 49</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3b70f876-9c96-4589-aa7c-f7eb07ea1805' class='xr-array-in' type='checkbox' checked><label for='section-3b70f876-9c96-4589-aa7c-f7eb07ea1805' 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 1 2 3 4 5 6 7 8 9 10 11 12 ... 37 38 39 40 41 42 43 44 45 46 47 48</span></div><div class='xr-array-data'><pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48])</pre></div></div></li><li class='xr-section-item'><input id='section-51d43974-df49-4779-802a-d3f14dd99960' class='xr-section-summary-in' type='checkbox' checked><label for='section-51d43974-df49-4779-802a-d3f14dd99960' class='xr-section-summary' >Coordinates: <span>(1)</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'>2000-01-01 ... 2000-01-03</div><input id='attrs-9eb2e989-4bbb-4cae-b183-6aead8bbabe0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9eb2e989-4bbb-4cae-b183-6aead8bbabe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe82a7c1-71f4-4403-b747-bed19964cec5' class='xr-var-data-in' type='checkbox'><label for='data-fe82a7c1-71f4-4403-b747-bed19964cec5' 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([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T01:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T02:00:00.000000000&#x27;, &#x27;2000-01-01T03:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T04:00:00.000000000&#x27;, &#x27;2000-01-01T05:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T06:00:00.000000000&#x27;, &#x27;2000-01-01T07:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T08:00:00.000000000&#x27;, &#x27;2000-01-01T09:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T10:00:00.000000000&#x27;, &#x27;2000-01-01T11:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T13:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T14:00:00.000000000&#x27;, &#x27;2000-01-01T15:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T16:00:00.000000000&#x27;, &#x27;2000-01-01T17:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T18:00:00.000000000&#x27;, &#x27;2000-01-01T19:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T20:00:00.000000000&#x27;, &#x27;2000-01-01T21:00:00.000000000&#x27;,\n",
" &#x27;2000-01-01T22:00:00.000000000&#x27;, &#x27;2000-01-01T23:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T00:00:00.000000000&#x27;, &#x27;2000-01-02T01:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T02:00:00.000000000&#x27;, &#x27;2000-01-02T03:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T04:00:00.000000000&#x27;, &#x27;2000-01-02T05:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T06:00:00.000000000&#x27;, &#x27;2000-01-02T07:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T08:00:00.000000000&#x27;, &#x27;2000-01-02T09:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T10:00:00.000000000&#x27;, &#x27;2000-01-02T11:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T12:00:00.000000000&#x27;, &#x27;2000-01-02T13:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T14:00:00.000000000&#x27;, &#x27;2000-01-02T15:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T16:00:00.000000000&#x27;, &#x27;2000-01-02T17:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T18:00:00.000000000&#x27;, &#x27;2000-01-02T19:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T20:00:00.000000000&#x27;, &#x27;2000-01-02T21:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T22:00:00.000000000&#x27;, &#x27;2000-01-02T23:00:00.000000000&#x27;,\n",
" &#x27;2000-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1583fa32-48a0-45df-aff9-734d8a01ae5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1583fa32-48a0-45df-aff9-734d8a01ae5f' 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 'data' (time: 49)>\n",
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ... 2000-01-03"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import pandas as pd\n",
"from cdo import Cdo\n",
"cdo = Cdo()\n",
"\n",
"da = xr.DataArray(\n",
" range(49),\n",
" coords=[\n",
" pd.date_range(\n",
" \"2000-01-01T00:00:00\",\n",
" periods=49,\n",
" freq=pd.DateOffset(hours=1),\n",
" )\n",
" ],\n",
" dims=\"time\",\n",
" name=\"data\",\n",
")\n",
"da"
]
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"That's worth of two days of model output. Now, the questions is, how do i correctly group the data for resampling? Let's assume, we want to resample to daily mean output, how do i treat timestep `2000-01-02T00:00:00`? Should it contribute to \n",
"\n",
"* 1. daily mean of `2000-01-02`? (closed='left')\n",
"* 2. daily mean of `2000-01-01`? (closed='right')\n",
"* 3. both? (closed='both')"
]
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" * time (time) datetime64[ns] 2000-01-01T12:00:00 ... 2000-01-03T12:00:00</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'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6b639825-555b-4566-b298-00fdbff7319a' class='xr-array-in' type='checkbox' checked><label for='section-6b639825-555b-4566-b298-00fdbff7319a' 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>11.5 35.5 48.0</span></div><div class='xr-array-data'><pre>array([11.5, 35.5, 48. ])</pre></div></div></li><li class='xr-section-item'><input id='section-70c5c779-a963-4960-849f-3aabe66507c8' class='xr-section-summary-in' type='checkbox' checked><label for='section-70c5c779-a963-4960-849f-3aabe66507c8' class='xr-section-summary' >Coordinates: <span>(1)</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'>2000-01-01T12:00:00 ... 2000-01-...</div><input id='attrs-cace0e79-40c0-4d3a-80d2-737442ee7323' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cace0e79-40c0-4d3a-80d2-737442ee7323' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f044de62-8acd-47ac-9c7e-1d27fe630029' class='xr-var-data-in' type='checkbox'><label for='data-f044de62-8acd-47ac-9c7e-1d27fe630029' 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([&#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-02T12:00:00.000000000&#x27;,\n",
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"source": [
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"da.resample(time='D', closed='left', loffset=pd.Timedelta(12, \"H\")).mean()"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;data&#x27; (time: 3)&gt;\n",
"array([11.5, 35.5, 48. ])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2000-01-01T11:30:00 ... 2000-01-03\n",
"Attributes:\n",
" cell_methods: time: mean</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'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eeb2413b-b0bd-4c01-ae50-f6ce976ec6e1' class='xr-array-in' type='checkbox' checked><label for='section-eeb2413b-b0bd-4c01-ae50-f6ce976ec6e1' 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>11.5 35.5 48.0</span></div><div class='xr-array-data'><pre>array([11.5, 35.5, 48. ])</pre></div></div></li><li class='xr-section-item'><input id='section-c0584da5-9c9c-4767-b7b2-a6adfd2083ca' class='xr-section-summary-in' type='checkbox' checked><label for='section-c0584da5-9c9c-4767-b7b2-a6adfd2083ca' class='xr-section-summary' >Coordinates: <span>(1)</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'>2000-01-01T11:30:00 ... 2000-01-03</div><input id='attrs-efa1a0c9-87d3-4f1f-af95-702f0c10a4f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efa1a0c9-87d3-4f1f-af95-702f0c10a4f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb0b7a6d-ee26-4758-881e-853ed52c1bc4' class='xr-var-data-in' type='checkbox'><label for='data-eb0b7a6d-ee26-4758-881e-853ed52c1bc4' 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>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T11:30:00.000000000&#x27;, &#x27;2000-01-02T11:30:00.000000000&#x27;,\n",
" &#x27;2000-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8351cf45-8828-4e04-9a07-3af8a194f5c6' class='xr-section-summary-in' type='checkbox' checked><label for='section-8351cf45-8828-4e04-9a07-3af8a194f5c6' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'data' (time: 3)>\n",
"array([11.5, 35.5, 48. ])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2000-01-01T11:30:00 ... 2000-01-03\n",
"Attributes:\n",
" cell_methods: time: mean"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. option cdo\n",
"cdo.daymean(option='--timestat_date middle', input=da.to_dataset(), returnXArray='data')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "32897b23-2a40-41f4-8235-77d71cd87224",
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;data&#x27; (time: 3)&gt;\n",
"array([ 0. , 12.5, 36.5])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1999-12-31T06:00:00 ... 2000-01-02T06:00:00</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'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5ab93fb6-e452-46e5-91b0-39e798668b56' class='xr-array-in' type='checkbox' checked><label for='section-5ab93fb6-e452-46e5-91b0-39e798668b56' 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>0.0 12.5 36.5</span></div><div class='xr-array-data'><pre>array([ 0. , 12.5, 36.5])</pre></div></div></li><li class='xr-section-item'><input id='section-9eabd013-105c-4499-94d8-66c0fc5a0c4b' class='xr-section-summary-in' type='checkbox' checked><label for='section-9eabd013-105c-4499-94d8-66c0fc5a0c4b' class='xr-section-summary' >Coordinates: <span>(1)</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'>1999-12-31T06:00:00 ... 2000-01-...</div><input id='attrs-fe28ff41-ff52-4400-aa21-9aaa7adce4c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe28ff41-ff52-4400-aa21-9aaa7adce4c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ce29c96-f330-42a9-8260-0c9f4e93c056' class='xr-var-data-in' type='checkbox'><label for='data-6ce29c96-f330-42a9-8260-0c9f4e93c056' 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([&#x27;1999-12-31T06:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
" &#x27;2000-01-02T06:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aae9426b-0294-42db-8622-306b0e6f61a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aae9426b-0294-42db-8622-306b0e6f61a1' 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 'data' (time: 3)>\n",
"array([ 0. , 12.5, 36.5])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1999-12-31T06:00:00 ... 2000-01-02T06:00:00"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. option xarray/pandas\n",
"da.resample(time='D', closed='right', loffset=0.5*pd.Timedelta(12, \"H\")).mean()"
]
},
{
"cell_type": "markdown",
"id": "758d3b33-9d29-469d-99d3-bd816fa50486",
"metadata": {},
"source": [
"The option `closed='both'` is not implemented in `xarray`/`pandas` since it would probably mean overlapping groups. I can emulate this behaviour, e.g., using `rolling` windows:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2c82da6c-afe4-4b29-8664-89944216740f",
"metadata": {},
"outputs": [],
"source": [
"def resample_closed_both(ds, hfreq, op, **kwargs):\n",
" rolling = getattr(ds.rolling(time=hfreq+1, center=True), op)()\n",
" freq = \"{}H\".format(hfreq)\n",
" return rolling.resample(time=freq, loffset=0.5*pd.Timedelta(hfreq, \"H\")).nearest()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9759291c-d0e2-45b9-bec7-66d6250d8742",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;data&#x27; (time: 3)&gt;\n",
"array([12., 36., nan])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2000-01-01T12:00:00 ... 2000-01-03T12:00:00</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'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6ffa78ee-b09b-45a6-af82-188b0ed3d5e5' class='xr-array-in' type='checkbox' checked><label for='section-6ffa78ee-b09b-45a6-af82-188b0ed3d5e5' 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>12.0 36.0 nan</span></div><div class='xr-array-data'><pre>array([12., 36., nan])</pre></div></div></li><li class='xr-section-item'><input id='section-c635a294-5c81-4e8d-bbfe-006ec191f4b2' class='xr-section-summary-in' type='checkbox' checked><label for='section-c635a294-5c81-4e8d-bbfe-006ec191f4b2' class='xr-section-summary' >Coordinates: <span>(1)</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'>2000-01-01T12:00:00 ... 2000-01-...</div><input id='attrs-a80f3764-c69f-4c9d-a44a-dedfef32ff8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a80f3764-c69f-4c9d-a44a-dedfef32ff8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac50964c-b42a-4a6e-9878-4d8648a5fc78' class='xr-var-data-in' type='checkbox'><label for='data-ac50964c-b42a-4a6e-9878-4d8648a5fc78' 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([&#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-02T12:00:00.000000000&#x27;,\n",
" &#x27;2000-01-03T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96a4346e-caa7-426b-99cf-c008c9061bfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96a4346e-caa7-426b-99cf-c008c9061bfe' 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 'data' (time: 3)>\n",
"array([12., 36., nan])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2000-01-01T12:00:00 ... 2000-01-03T12:00:00"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resample_closed_both(da, 24, 'mean')"
]
},
{
"cell_type": "markdown",
"id": "a4e92b81-99b3-4b71-a38a-6ba31f4e227a",
"metadata": {},
"source": [
"The differences are not big, but it probably also depends on the input and output, e.g., if i want daily sums instead of means, the grouping should be done with `resampling='right'` if my model output provides hourly accumulated output, e.g., for precipitation, date `2000-01-02T00:00:00` would provided the accumulated precipitation from `2000-01-01T23:00:00` to `2000-01-02T00:00:00` so it should be included in the summation for `2000-01-01` but not for the following day."
]
},
{
"cell_type": "markdown",
"id": "bb66c7f3-7c79-4807-8323-b912f82f6aac",
"metadata": {},
"source": [
"I am a little lost here and couldn't really find a proper guidance on this!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:pyremo-test]",
"language": "python",
"name": "conda-env-pyremo-test-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
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"nbformat": 4,
"nbformat_minor": 5
}
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