preprocess_target.ipynb

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    "import os, sys\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "sys.path.append('./s2spy')\n",
    "import preprocessing\n",
    "from s2spy import RGDR, time\n",
    "import func_SPI, utils, h5py\n",
    "path_obs_data = '/data/volume_2/observational/'\n",
    "filename = 'chrips_precip_1981-2021.nc'\n",
    "path_tp = os.path.join(path_obs_data, 'raw', filename)"
   ]
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      "text/plain": [
       "           aggregation_start_inclusive aggregation_end_inclusive\n",
       "init_date                                                       \n",
       "2006-10-22                  2006-11-06                2006-12-03\n",
       "2006-10-29                  2006-11-13                2006-12-10\n",
       "2006-11-05                  2006-11-20                2006-12-17\n",
       "2006-11-12                  2006-11-27                2006-12-24\n",
       "2006-11-19                  2006-12-04                2006-12-31\n",
       "2006-11-26                  2006-12-11                2007-01-07\n",
       "2006-12-03                  2006-12-18                2007-01-14\n",
       "2006-12-10                  2006-12-25                2007-01-21\n",
       "2006-12-17                  2007-01-01                2007-01-28\n",
       "2006-12-24                  2007-01-08                2007-02-04\n",
       "2006-12-31                  2007-01-15                2007-02-11\n",
       "2007-01-02                  2007-01-17                2007-02-13\n",
       "2007-01-09                  2007-01-24                2007-02-20\n",
       "2007-01-16                  2007-01-31                2007-02-27\n",
       "2007-01-23                  2007-02-07                2007-03-06\n",
       "2007-01-30                  2007-02-14                2007-03-13\n",
       "2007-02-06                  2007-02-21                2007-03-20\n",
       "2007-02-13                  2007-02-28                2007-03-27\n",
       "2007-02-20                  2007-03-07                2007-04-03\n",
       "2007-02-27                  2007-03-14                2007-04-10"
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   "source": [
    "# load intervals and target periods from EC46 initialization dates\n",
    "dfs = pd.read_hdf('/data/volume_2/subseasonal/ecmwf/aggregated/aggregation_timestamps.h5').sort_index()\n",
    "df = dfs[['aggregation_start_inclusive', 'aggregation_end_inclusive']].copy()\n",
    "sel_months = [10, 11, 12]\n",
    "mon_start_mask = [True if m in sel_months else False for m in df.set_index('aggregation_start_inclusive').index.month]\n",
    "mon_end_mask = [True if m in sel_months else False for m in df.set_index('aggregation_end_inclusive').index.month]\n",
    "mon_mask = np.logical_and(mon_start_mask, mon_end_mask)\n",
    "df.iloc[360:380]"
   ]
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;precip&#x27; (time: 14975)&gt;\n",
       "array([0.0000000e+00, 4.8481959e-01, 2.6115529e-02, ..., 4.2302564e-01,\n",
       "       4.3223627e-02, 4.4919315e-04], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2021-12-31</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'>'precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14975</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e39670f2-5b5d-4240-801f-ba1cf8555606' class='xr-array-in' type='checkbox' checked><label for='section-e39670f2-5b5d-4240-801f-ba1cf8555606' 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 0.4848 0.02612 0.2982 0.1057 ... 2.488 0.423 0.04322 0.0004492</span></div><div class='xr-array-data'><pre>array([0.0000000e+00, 4.8481959e-01, 2.6115529e-02, ..., 4.2302564e-01,\n",
       "       4.3223627e-02, 4.4919315e-04], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-960d5148-b0d8-4c6f-8cb8-a9fabd2e40e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-960d5148-b0d8-4c6f-8cb8-a9fabd2e40e8' 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'>1981-01-01 ... 2021-12-31</div><input id='attrs-6d2204c0-5fa3-46ea-be0b-063f2cec2f75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d2204c0-5fa3-46ea-be0b-063f2cec2f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ec2652-94f5-440a-a367-b89c129cc520' class='xr-var-data-in' type='checkbox'><label for='data-19ec2652-94f5-440a-a367-b89c129cc520' 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>axis :</span></dt><dd>T</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;2021-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-30T00:00:00.000000000&#x27;, &#x27;2021-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ceb6421b-22b3-4743-8424-1f153a866755' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ceb6421b-22b3-4743-8424-1f153a866755' 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 'precip' (time: 14975)>\n",
       "array([0.0000000e+00, 4.8481959e-01, 2.6115529e-02, ..., 4.2302564e-01,\n",
       "       4.3223627e-02, 4.4919315e-04], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2021-12-31"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp = xr.open_dataarray(path_tp)\n",
    "# select region\n",
    "tp = tp.sel(latitude=slice(-5,8), longitude=slice(38,53))\n",
    "# spatial mean\n",
    "tp_sm = tp.mean(dim=('latitude', 'longitude'))\n",
    "tp_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bc1d6af0-c635-46b5-97f1-105c1902b789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 28-day rolling mean\n",
    "tp_rm = tp_sm.rolling({'time':28}, min_periods=1, center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ab881beb-54c4-46d4-bf9a-9e05c3698a59",
   "metadata": {},
   "outputs": [
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       "       1.10407178, 1.07486989, 1.07822711, 1.15091264, 1.16999695,\n",
       "       1.23134155, 1.23602402, 1.25558565, 1.23115544, 1.2977155 ,\n",
       "       1.36725662, 1.40466826, 1.45097582, 1.57584016, 1.54432416,\n",
       "       1.55185077, 1.64442992, 1.66610076, 1.6806844 , 1.72784944,\n",
       "       1.75894299, 1.73547573, 1.6383554 , 1.62475219, 1.66412065,\n",
       "       1.69092238, 1.697082  , 1.68257935, 1.69508479, 1.58915248,\n",
       "       1.47411525, 1.55390751, 1.59497371, 1.57130144, 1.58784883,\n",
       "       1.6409445 , 1.65256181, 1.69594367, 1.61138163, 1.66021631,\n",
       "       1.59894221, 1.49985571, 1.43151619, 1.48564122, 1.42446039,\n",
       "       1.36931753, 1.32733228, 1.30657423, 1.23376882, 1.21855569,\n",
       "       1.18436162, 1.15656106, 1.10580482, 1.09070883, 1.08825564,\n",
       "       1.10621576, 1.17243562, 1.11237993, 0.93646269, 0.92088175,\n",
       "       0.82374479, 0.88861455, 0.87208858, 0.8623396 , 0.83695356,\n",
       "       0.82476575, 0.8120901 , 0.74863809, 0.7269014 , 0.68181416,\n",
       "       0.66108757, 0.63343939, 0.63153273, 0.6315021 , 0.59748528,\n",
       "       0.52909535, 0.50944179, 0.48686999, 0.46819514, 0.45725304,\n",
       "       0.40976018, 0.31608163, 0.27174468, 0.22954546, 0.21373285,\n",
       "       0.2206379 , 0.21358332, 0.20853708, 0.20278758, 0.18775887,\n",
       "       0.15705588])\n",
       "Coordinates:\n",
       "    quantile   float64 0.33\n",
       "  * dayofyear  (dayofyear) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366</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'>'0.33'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dayofyear</span>: 366</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c6fb90d7-810f-417e-9427-ff26e973640d' class='xr-array-in' type='checkbox' checked><label for='section-c6fb90d7-810f-417e-9427-ff26e973640d' 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>0.1587 0.1613 0.1602 0.1666 0.1631 ... 0.2085 0.2028 0.1878 0.1571</span></div><div class='xr-array-data'><pre>array([0.15870125, 0.16130118, 0.1602353 , 0.16664413, 0.163122  ,\n",
       "       0.16224493, 0.15494163, 0.15962094, 0.14233505, 0.12464583,\n",
       "       0.10831681, 0.11645305, 0.11834736, 0.10933021, 0.10373813,\n",
       "       0.11316288, 0.10858094, 0.10321271, 0.0965468 , 0.09643591,\n",
       "       0.09469874, 0.09488363, 0.09539632, 0.08522961, 0.08229516,\n",
       "       0.08540759, 0.08443118, 0.09080722, 0.0910161 , 0.09264203,\n",
       "       0.09127538, 0.10215352, 0.12199633, 0.12825343, 0.12834638,\n",
       "       0.12286096, 0.12878813, 0.13081736, 0.13849336, 0.14181847,\n",
       "       0.13599763, 0.13321972, 0.15216017, 0.16327419, 0.16763127,\n",
       "       0.16837562, 0.18364023, 0.18025673, 0.19211178, 0.1749154 ,\n",
       "       0.1952204 , 0.18759964, 0.19418497, 0.1846394 , 0.1771765 ,\n",
       "       0.18591073, 0.1683515 , 0.17942898, 0.2063292 , 0.2088068 ,\n",
       "       0.19854017, 0.19461265, 0.19733666, 0.19420467, 0.21126394,\n",
       "       0.22931904, 0.24707928, 0.27681366, 0.31145852, 0.34722347,\n",
       "       0.32579687, 0.33327683, 0.40678015, 0.44525903, 0.47318448,\n",
       "       0.53771191, 0.59840527, 0.59384928, 0.61831367, 0.67309701,\n",
       "       0.68897771, 0.76185713, 0.90110389, 0.90284725, 0.95237861,\n",
       "       1.04204922, 1.14272294, 1.33152194, 1.34221053, 1.50426028,\n",
       "       1.5352211 , 1.74471116, 1.771931  , 1.96447091, 2.09520025,\n",
       "       2.22356415, 2.20007319, 2.33333511, 2.36356516, 2.34478159,\n",
       "...\n",
       "       0.80767161, 0.87712631, 0.90396674, 0.92433883, 1.00371456,\n",
       "       1.10407178, 1.07486989, 1.07822711, 1.15091264, 1.16999695,\n",
       "       1.23134155, 1.23602402, 1.25558565, 1.23115544, 1.2977155 ,\n",
       "       1.36725662, 1.40466826, 1.45097582, 1.57584016, 1.54432416,\n",
       "       1.55185077, 1.64442992, 1.66610076, 1.6806844 , 1.72784944,\n",
       "       1.75894299, 1.73547573, 1.6383554 , 1.62475219, 1.66412065,\n",
       "       1.69092238, 1.697082  , 1.68257935, 1.69508479, 1.58915248,\n",
       "       1.47411525, 1.55390751, 1.59497371, 1.57130144, 1.58784883,\n",
       "       1.6409445 , 1.65256181, 1.69594367, 1.61138163, 1.66021631,\n",
       "       1.59894221, 1.49985571, 1.43151619, 1.48564122, 1.42446039,\n",
       "       1.36931753, 1.32733228, 1.30657423, 1.23376882, 1.21855569,\n",
       "       1.18436162, 1.15656106, 1.10580482, 1.09070883, 1.08825564,\n",
       "       1.10621576, 1.17243562, 1.11237993, 0.93646269, 0.92088175,\n",
       "       0.82374479, 0.88861455, 0.87208858, 0.8623396 , 0.83695356,\n",
       "       0.82476575, 0.8120901 , 0.74863809, 0.7269014 , 0.68181416,\n",
       "       0.66108757, 0.63343939, 0.63153273, 0.6315021 , 0.59748528,\n",
       "       0.52909535, 0.50944179, 0.48686999, 0.46819514, 0.45725304,\n",
       "       0.40976018, 0.31608163, 0.27174468, 0.22954546, 0.21373285,\n",
       "       0.2206379 , 0.21358332, 0.20853708, 0.20278758, 0.18775887,\n",
       "       0.15705588])</pre></div></div></li><li class='xr-section-item'><input id='section-b813053d-78f4-4d21-b7e5-038c38ee85cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b813053d-78f4-4d21-b7e5-038c38ee85cd' class='xr-section-summary' >Coordinates: <span>(2)</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>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.33</div><input id='attrs-b69ccb74-36f0-4532-96f5-1d3c5572c742' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b69ccb74-36f0-4532-96f5-1d3c5572c742' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b261915-3e9a-4a22-a15a-47c6a7e02c77' class='xr-var-data-in' type='checkbox'><label for='data-4b261915-3e9a-4a22-a15a-47c6a7e02c77' 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(0.33)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dayofyear</span></div><div class='xr-var-dims'>(dayofyear)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 362 363 364 365 366</div><input id='attrs-29fe3fa5-762a-483c-8da4-47d972563243' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29fe3fa5-762a-483c-8da4-47d972563243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93076d7f-29f3-4b1b-84ab-8f0992d1b320' class='xr-var-data-in' type='checkbox'><label for='data-93076d7f-29f3-4b1b-84ab-8f0992d1b320' 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,   2,   3, ..., 364, 365, 366])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b113197-6cc0-40c1-9caa-a1bc0357aeb6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b113197-6cc0-40c1-9caa-a1bc0357aeb6' 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 '0.33' (dayofyear: 366)>\n",
       "array([0.15870125, 0.16130118, 0.1602353 , 0.16664413, 0.163122  ,\n",
       "       0.16224493, 0.15494163, 0.15962094, 0.14233505, 0.12464583,\n",
       "       0.10831681, 0.11645305, 0.11834736, 0.10933021, 0.10373813,\n",
       "       0.11316288, 0.10858094, 0.10321271, 0.0965468 , 0.09643591,\n",
       "       0.09469874, 0.09488363, 0.09539632, 0.08522961, 0.08229516,\n",
       "       0.08540759, 0.08443118, 0.09080722, 0.0910161 , 0.09264203,\n",
       "       0.09127538, 0.10215352, 0.12199633, 0.12825343, 0.12834638,\n",
       "       0.12286096, 0.12878813, 0.13081736, 0.13849336, 0.14181847,\n",
       "       0.13599763, 0.13321972, 0.15216017, 0.16327419, 0.16763127,\n",
       "       0.16837562, 0.18364023, 0.18025673, 0.19211178, 0.1749154 ,\n",
       "       0.1952204 , 0.18759964, 0.19418497, 0.1846394 , 0.1771765 ,\n",
       "       0.18591073, 0.1683515 , 0.17942898, 0.2063292 , 0.2088068 ,\n",
       "       0.19854017, 0.19461265, 0.19733666, 0.19420467, 0.21126394,\n",
       "       0.22931904, 0.24707928, 0.27681366, 0.31145852, 0.34722347,\n",
       "       0.32579687, 0.33327683, 0.40678015, 0.44525903, 0.47318448,\n",
       "       0.53771191, 0.59840527, 0.59384928, 0.61831367, 0.67309701,\n",
       "       0.68897771, 0.76185713, 0.90110389, 0.90284725, 0.95237861,\n",
       "       1.04204922, 1.14272294, 1.33152194, 1.34221053, 1.50426028,\n",
       "       1.5352211 , 1.74471116, 1.771931  , 1.96447091, 2.09520025,\n",
       "       2.22356415, 2.20007319, 2.33333511, 2.36356516, 2.34478159,\n",
       "...\n",
       "       0.80767161, 0.87712631, 0.90396674, 0.92433883, 1.00371456,\n",
       "       1.10407178, 1.07486989, 1.07822711, 1.15091264, 1.16999695,\n",
       "       1.23134155, 1.23602402, 1.25558565, 1.23115544, 1.2977155 ,\n",
       "       1.36725662, 1.40466826, 1.45097582, 1.57584016, 1.54432416,\n",
       "       1.55185077, 1.64442992, 1.66610076, 1.6806844 , 1.72784944,\n",
       "       1.75894299, 1.73547573, 1.6383554 , 1.62475219, 1.66412065,\n",
       "       1.69092238, 1.697082  , 1.68257935, 1.69508479, 1.58915248,\n",
       "       1.47411525, 1.55390751, 1.59497371, 1.57130144, 1.58784883,\n",
       "       1.6409445 , 1.65256181, 1.69594367, 1.61138163, 1.66021631,\n",
       "       1.59894221, 1.49985571, 1.43151619, 1.48564122, 1.42446039,\n",
       "       1.36931753, 1.32733228, 1.30657423, 1.23376882, 1.21855569,\n",
       "       1.18436162, 1.15656106, 1.10580482, 1.09070883, 1.08825564,\n",
       "       1.10621576, 1.17243562, 1.11237993, 0.93646269, 0.92088175,\n",
       "       0.82374479, 0.88861455, 0.87208858, 0.8623396 , 0.83695356,\n",
       "       0.82476575, 0.8120901 , 0.74863809, 0.7269014 , 0.68181416,\n",
       "       0.66108757, 0.63343939, 0.63153273, 0.6315021 , 0.59748528,\n",
       "       0.52909535, 0.50944179, 0.48686999, 0.46819514, 0.45725304,\n",
       "       0.40976018, 0.31608163, 0.27174468, 0.22954546, 0.21373285,\n",
       "       0.2206379 , 0.21358332, 0.20853708, 0.20278758, 0.18775887,\n",
       "       0.15705588])\n",
       "Coordinates:\n",
       "    quantile   float64 0.33\n",
       "  * dayofyear  (dayofyear) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = tp_rm.groupby(tp_rm.time.dt.dayofyear).quantile(q=.33, dim='time', skipna=True)\n",
    "quantiles.name = '0.33'\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f5709474-abda-4d76-9e9d-3a7dc2407554",
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_tercile = (tp_rm.groupby(tp_rm.time.dt.dayofyear) < quantiles).astype(int).drop('dayofyear').drop('quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0c27c65b-f338-4cbc-9952-b8541ec5af36",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = tp_tercile.to_dataset(name='binary')\n",
    "ds['tp_28d_rm'] = tp_rm\n",
    "ds['quantile'] = quantiles\n",
    "ds['spatial_mean_raw'] = tp_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "287a9130-294b-4455-a7a1-053079088481",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_netcdf(os.path.join(path_obs_data, 'chrips_1981-2021_target.nc'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c197b64-4b60-42ed-aefa-0df2d3078021",
   "metadata": {},
   "source": [
    "Note that the rolling mean is centered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e1c4a9a3-9810-4c2c-b667-fe12ebdecad9",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "\n",
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       "  color: var(--xr-disabled-color);\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;precip&#x27; (time: 1113)&gt;\n",
       "array([0.10109671, 0.06325012, 0.06289413, ..., 0.15035173, 0.08285904,\n",
       "       0.08602343], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-17 2000-01-24 ... 2021-01-15</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'>'precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1113</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9af525fd-0e21-489e-aaf2-070428da1d16' class='xr-array-in' type='checkbox' checked><label for='section-9af525fd-0e21-489e-aaf2-070428da1d16' 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>0.1011 0.06325 0.06289 0.109 0.1106 ... 0.3698 0.1504 0.08286 0.08602</span></div><div class='xr-array-data'><pre>array([0.10109671, 0.06325012, 0.06289413, ..., 0.15035173, 0.08285904,\n",
       "       0.08602343], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-7b963edc-71c6-46c3-bc3d-0bf893694a12' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b963edc-71c6-46c3-bc3d-0bf893694a12' 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'>2000-01-17 ... 2021-01-15</div><input id='attrs-bf4184d0-4ecd-4467-a679-cbeba2ff3755' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf4184d0-4ecd-4467-a679-cbeba2ff3755' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03cff886-c788-4044-b374-abfe79187343' class='xr-var-data-in' type='checkbox'><label for='data-03cff886-c788-4044-b374-abfe79187343' 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;2000-01-17T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2000-01-24T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2000-01-31T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2021-01-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-01-08T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-01-15T00:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a5b7893-d5f0-4403-aba6-15916e681cc4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a5b7893-d5f0-4403-aba6-15916e681cc4' 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 'precip' (time: 1113)>\n",
       "array([0.10109671, 0.06325012, 0.06289413, ..., 0.15035173, 0.08285904,\n",
       "       0.08602343], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-17 2000-01-24 ... 2021-01-15"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select center of 28-day rolling mean\n",
    "df['center'] = df[['aggregation_start_inclusive']] + pd.Timedelta('14d')\n",
    "tp_rm_s = tp_rm.sel(time=pd.to_datetime(df['center'].values)) \n",
    "# label alignment left\n",
    "tp_rm_s['time'] = pd.to_datetime(df[['aggregation_start_inclusive']].values)\n",
    "tp_rm_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ab84f442-c12d-4e96-81cf-144154fee51b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5601ce67-5fae-4d8a-a4e4-479b31586267",
   "metadata": {},
   "outputs": [],
   "source": [
    "# later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cfdecfb-6515-41e0-941c-66c5cb1eb225",
   "metadata": {},
   "outputs": [],
   "source": [
    "aggr = 1\n",
    "path_spi = filename.split('_')[0] + f'_SPI_{aggr}_' + '_'.join(filename.split('_')[2:])\n",
    "output = func_SPI.calc_SPI_from_daily(tp.to_array(), aggr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed4d3e90-29e1-4f60-aecb-4cfe7d950db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_netcdf(os.path.join(path_obs_data, 'preprocessed', path_spi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1226df7-5a9e-469c-a783-b9e342110899",
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = 0 ; longitude=30\n",
    "ts_raw = tp.to_array().sel(latitude=latitude, longitude=longitude, method='nearest')\n",
    "ts_stn = output.sel(latitude=latitude, longitude=longitude, method='nearest')\n",
    "# ts_pack = SMI_package.sel(latitude=40, longitude=240)\n",
    "\n",
    "fig = plt.figure(figsize=(20,10) )\n",
    "\n",
    "# plot observed versus corresponding Gamma probability\n",
    "ax1 = plt.subplot(1, 2, 1)\n",
    "vals = ax1.plot(ts_raw, '.k');\n",
    "# ax1.set_ylabel('Cumulative probability (from Gamma distribution)')\n",
    "# ax1.set_xlabel('Aggregated Precipitation [mm] - Original values')\n",
    "# plot transformed standard normal from Gamma probability\n",
    "ax1 = plt.subplot(1, 2, 2)\n",
    "vals = ax1.plot(ts_stn, '.k');\n",
    "# ax1.plot(ts_pack, '.r')\n",
    "# ax1.set_ylabel('Cumulative probability (from Gamma distribution)')\n",
    "ax1.set_xlabel('SPI - Gamma prob. transformed to standard normal ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49b01336-eb43-4726-b385-4fec6f87178b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a566f87c-40f2-410c-a6df-849ae401153c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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