From 6908bda458fd5fb0aeadca73dca0327014c0a96c Mon Sep 17 00:00:00 2001 From: Julius Busecke Date: Mon, 8 Feb 2021 18:07:36 -0500 Subject: [PATCH] Add the bottom removal function (#88) * Add the bottom remove func * added docs for bottom removal --- docs/utils.ipynb | 4992 ++++++++++++++++++++++++++------ xarrayutils/test/test_utils.py | 56 + xarrayutils/utils.py | 20 + 3 files changed, 4152 insertions(+), 916 deletions(-) diff --git a/docs/utils.ipynb b/docs/utils.ipynb index d558806..1a7e95e 100644 --- a/docs/utils.ipynb +++ b/docs/utils.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -30,9 +30,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":219: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 80 from C header, got 88 from PyObject\n" + ] + }, { "data": { "text/html": [ @@ -351,7 +358,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -419,17 +427,17 @@ " Conventions: COARDS\\nGrADS\n", " dataType: Grid\n", " documentation: http://apdrc.soest.hawaii.edu/projects/Argo/index.html\n", - " history: Fri Jun 05 09:22:38 HST 2020 : imported by GrADS Data Ser...
    • mld
      (time, lat, lon)
      float32
      ...
      long_name :
      mixed layer depth (m) 
      [1728000 values with dtype=float32]
    • smld
      (time, lat, lon)
      float32
      ...
      long_name :
      standard mixed layer depth deviation (m) 
      [1728000 values with dtype=float32]
    • nmld
      (time, lat, lon)
      float32
      ...
      long_name :
      number of mixed layer depth profiles 
      [1728000 values with dtype=float32]
    • ild
      (time, lat, lon)
      float32
      ...
      long_name :
      isothermal layer depth (m) 
      [1728000 values with dtype=float32]
    • sild
      (time, lat, lon)
      float32
      ...
      long_name :
      standard isothermal layer depth deviation (m) 
      [1728000 values with dtype=float32]
    • nild
      (time, lat, lon)
      float32
      ...
      long_name :
      number of isothermal layer depth profiles 
      [1728000 values with dtype=float32]
    • ttd
      (time, lat, lon)
      float32
      ...
      long_name :
      top of the thermocline depth (m) 
      [1728000 values with dtype=float32]
    • sttd
      (time, lat, lon)
      float32
      ...
      long_name :
      standard top of the thermocline depth deviation (m) 
      [1728000 values with dtype=float32]
    • nttd
      (time, lat, lon)
      float32
      ...
      long_name :
      number of top of the thermocline depth profiles 
      [1728000 values with dtype=float32]
    • blt
      (time, lat, lon)
      float32
      ...
      long_name :
      (>0) barrier or (<0) compensated layer thickness (ttd - mld) (m) 
      [1728000 values with dtype=float32]
    • sblt
      (time, lat, lon)
      float32
      ...
      long_name :
      standard barrier or compensated layer thickness deviation (m) 
      [1728000 values with dtype=float32]
    • nblt
      (time, lat, lon)
      float32
      ...
      long_name :
      number of barrier or compensated layer-2 thickness profiles 
      [1728000 values with dtype=float32]
    • tid
      (time, lat, lon)
      float32
      ...
      long_name :
      temperature inversion depth (m) 
      [1728000 values with dtype=float32]
    • stid
      (time, lat, lon)
      float32
      ...
      long_name :
      standard temperature inversion depth deviation (m) 
      [1728000 values with dtype=float32]
    • ntid
      (time, lat, lon)
      float32
      ...
      long_name :
      number of temperature inversion depth profiles 
      [1728000 values with dtype=float32]
    • mlt
      (time, lat, lon)
      float32
      ...
      long_name :
      mixed layer temperature (degc) 
      [1728000 values with dtype=float32]
    • smlt
      (time, lat, lon)
      float32
      ...
      long_name :
      standard mixed layer temperature deviation (degc) 
      [1728000 values with dtype=float32]
    • nmlt
      (time, lat, lon)
      float32
      ...
      long_name :
      number of mixed layer temperature profiles 
      [1728000 values with dtype=float32]
    • mls
      (time, lat, lon)
      float32
      ...
      long_name :
      mixed layer salinity (psu) 
      [1728000 values with dtype=float32]
    • smls
      (time, lat, lon)
      float32
      ...
      long_name :
      standard mixed layer salinity deviation (psu) 
      [1728000 values with dtype=float32]
    • nmls
      (time, lat, lon)
      float32
      ...
      long_name :
      number of mixed layer salinity profiles 
      [1728000 values with dtype=float32]
  • title :
    3x3 bin-averaged Mixed Layer Monthly mean (from 2001)
    Conventions :
    COARDS\n", + "GrADS
    dataType :
    Grid
    documentation :
    http://apdrc.soest.hawaii.edu/projects/Argo/index.html
    history :
    Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Server 2.0
    • " ], "text/plain": [ "\n", @@ -477,10 +485,10 @@ " Conventions: COARDS\\nGrADS\n", " dataType: Grid\n", " documentation: http://apdrc.soest.hawaii.edu/projects/Argo/index.html\n", - " history: Fri Jun 05 09:22:38 HST 2020 : imported by GrADS Data Ser..." + " history: Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser..." ] }, - "execution_count": 2, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -491,37 +499,11 @@ "ds" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets find out how much the salinity in each grid point changed over the full period (20 years)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from xarrayutils.utils import linear_trend" - ] - }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n" - ] - }, { "data": { "text/html": [ @@ -840,7 +822,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -876,21 +859,49 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset>\n",
-       "Dimensions:    (lat: 60, lon: 120)\n",
+       "Dimensions:  (lat: 60, lon: 120, time: 240)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n",
-       "  * lon        (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
+       "  * time     (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n",
+       "  * lat      (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n",
+       "  * lon      (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
        "Data variables:\n",
-       "    slope      (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n",
-       "    intercept  (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n",
-       "    r_value    (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n",
-       "    p_value    (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n",
-       "    std_err    (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045
    • mld
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      mixed layer depth (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • smld
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard mixed layer depth deviation (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nmld
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of mixed layer depth profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • ild
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      isothermal layer depth (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • sild
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard isothermal layer depth deviation (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nild
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of isothermal layer depth profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • ttd
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      top of the thermocline depth (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • sttd
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard top of the thermocline depth deviation (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nttd
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of top of the thermocline depth profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • blt
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      (>0) barrier or (<0) compensated layer thickness (ttd - mld) (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • sblt
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard barrier or compensated layer thickness deviation (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nblt
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of barrier or compensated layer-2 thickness profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • tid
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      temperature inversion depth (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • stid
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard temperature inversion depth deviation (m) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • ntid
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of temperature inversion depth profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • mlt
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      mixed layer temperature (degc) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • smlt
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard mixed layer temperature deviation (degc) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nmlt
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of mixed layer temperature profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
    • mls
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      mixed layer salinity (psu) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • smls
      (time, lat, lon)
      float32
      nan nan nan nan ... nan nan nan nan
      long_name :
      standard mixed layer salinity deviation (psu) 
      array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
    • nmls
      (time, lat, lon)
      float32
      0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
      long_name :
      number of mixed layer salinity profiles 
      array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
  • title :
    3x3 bin-averaged Mixed Layer Monthly mean (from 2001)
    Conventions :
    COARDS\n", + "GrADS
    dataType :
    Grid
    documentation :
    http://apdrc.soest.hawaii.edu/projects/Argo/index.html
    history :
    Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Server 2.0
    • " ], "text/plain": [ "\n", - "Dimensions: (lat: 60, lon: 120)\n", + "Dimensions: (lat: 60, lon: 120, time: 240)\n", "Coordinates:\n", - " * lat (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n", - " * lon (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n", + " * time (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n", + " * lat (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n", + " * lon (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n", "Data variables:\n", - " slope (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n", - " intercept (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n", - " r_value (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n", - " p_value (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n", - " std_err (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create an array \n", - "salinity_regressed = linear_trend(ds.mls, 'time')\n", - "salinity_regressed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can plot the slope as a map" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" + " mld (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " smld (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nmld (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " ild (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " sild (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nild (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " ttd (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " sttd (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nttd (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " blt (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " sblt (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nblt (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " tid (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " stid (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " ntid (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " mlt (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " smlt (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nmlt (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " mls (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " smls (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n", + " nmls (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + "Attributes:\n", + " title: 3x3 bin-averaged Mixed Layer Monthly mean (from 2001)\n", + " Conventions: COARDS\\nGrADS\n", + " dataType: Grid\n", + " documentation: http://apdrc.soest.hawaii.edu/projects/Argo/index.html\n", + " history: Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser..." ] }, - "execution_count": 5, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "salinity_regressed.slope.plot(robust=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`linear_trend` converts the dimension over which to integrate into logical indicies, so the units of the plot above are (salinity/timestep of the original product), so here PSS/month." + "ds.load()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Correlation maps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But what about a bit more complex task? Lets find out how mixedlayer salinity and temperature correlate. For this we use `xr_linregress` (for which `linear_trend` is just a thin wrapper):" + "Lets find out how much the salinity in each grid point changed over the full period (20 years)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "from xarrayutils.utils import linear_trend, xr_linregress" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n" - ] - } - ], - "source": [ - "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='time')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "tempxsalt.r_value.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This works in any dimension the dataset has:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1664: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", - " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/xarray/core/computation.py:700: RuntimeWarning: invalid value encountered in sqrt\n", - " result_data = func(*input_data)\n" - ] - } - ], - "source": [ - "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='lon')" + "from xarrayutils.utils import linear_trend" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:    (lat: 60, lon: 120)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n",
+       "  * lon        (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
+       "Data variables:\n",
+       "    slope      (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n",
+       "    intercept  (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n",
+       "    r_value    (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n",
+       "    p_value    (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n",
+       "    std_err    (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 60, lon: 120)\n", + "Coordinates:\n", + " * lat (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n", + " * lon (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n", + "Data variables:\n", + " slope (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n", + " intercept (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n", + " r_value (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n", + " p_value (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n", + " std_err (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create an array \n", + "salinity_regressed = linear_trend(ds.mls, 'time')\n", + "salinity_regressed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the slope as a map" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "salinity_regressed.slope.plot(robust=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`linear_trend` converts the dimension over which to integrate into logical indicies, so the units of the plot above are (salinity/timestep of the original product), so here PSS/month." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correlation maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what about a bit more complex task? Lets find out how mixedlayer salinity and temperature correlate. For this we use `xr_linregress` (for which `linear_trend` is just a thin wrapper):" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from xarrayutils.utils import linear_trend, xr_linregress" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='time')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tempxsalt.r_value.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works in any dimension the dataset has:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/computation.py:724: RuntimeWarning: invalid value encountered in sqrt\n", + " result_data = func(*input_data)\n" + ] + } + ], + "source": [ + "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='lon')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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    " + ] }, "metadata": { "needs_background": "light" @@ -1193,22 +2479,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -1247,22 +2533,22 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -1286,170 +2572,1172 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from xarrayutils.utils import sign_agreement\n", + "\n", + "sign_agreement(da, da.mean('member'), 'member', threshold=1.0).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You could use this information to indicate the areas of the average, where the members do not agree by hatching:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "da.mean('member').plot()\n", + "sign_agreement(\n", + " da, da.mean('member'), 'member'\n", + ").plot.contourf(\n", + " colors='none',\n", + " hatches=['..', None],\n", + " levels=[0,0.5],\n", + " add_colorbar=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Masking values in the mixed layer\n", + "\n", + "Sometimes it is helpful to analyze data by excluding the values in the mixed layer. This can be easily done with `mask_mixedlayer`. Let's see how:\n", + "\n", + "First load a CMIP6 dataset from the cloud" + ] + }, + { + "cell_type": "code", + "execution_count": 37, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--> The keys in the returned dictionary of datasets are constructed as follows:\n", + "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" + ] }, { "data": { - "image/png": "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+ "text/html": [ + "\n", + "
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    " + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from xarrayutils.utils import sign_agreement\n", - "\n", - "sign_agreement(da, da.mean('member'), 'member', threshold=1.0).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You could use this information to indicate the areas of the average, where the members do not agree by hatching:" + "import intake\n", + "url = \"https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json\"\n", + "col = intake.open_esm_datastore(url)\n", + "cat = col.search(\n", + " table_id='Omon',\n", + " grid_label='gn',\n", + " experiment_id='historical',\n", + " member_id='r1i1p1f1',\n", + " variable_id=['thetao','mlotst'],#, \n", + " source_id=[\"ACCESS-ESM1-5\"]\n", + ")\n", + "ddict = cat.to_dataset_dict(\n", + " zarr_kwargs={'consolidated':True, 'decode_times':True},\n", + ")" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
+       "Coordinates:\n",
+       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
+       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
+       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
+       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
+       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
+       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
+       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
+       "Dimensions without coordinates: bnds, vertices\n",
+       "Data variables:\n",
+       "    mlotst              (member_id, time, j, i) float32 dask.array<chunksize=(1, 196, 300, 360), meta=np.ndarray>\n",
+       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(300, 360, 4), meta=np.ndarray>\n",
+       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(300, 360, 4), meta=np.ndarray>\n",
+       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    grid:                    native atmosphere N96 grid (145x192 latxlon)\n",
+       "    forcing_index:           1\n",
+       "    table_id:                Omon\n",
+       "    variant_label:           r1i1p1f1\n",
+       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
+       "    tracking_id:             hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n",
+       "    initialization_index:    1\n",
+       "    physics_index:           1\n",
+       "    run_variant:             forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n",
+       "    branch_time_in_parent:   21915.0\n",
+       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
+       "    cmor_version:            3.4.0\n",
+       "    grid_label:              gn\n",
+       "    nominal_resolution:      250 km\n",
+       "    intake_esm_varname:      mlotst\\nthetao\n",
+       "    experiment_id:           historical\n",
+       "    realization_index:       1\n",
+       "    parent_activity_id:      CMIP\n",
+       "    sub_experiment_id:       none\n",
+       "    source_id:               ACCESS-ESM1-5\n",
+       "    branch_time_in_child:    0.0\n",
+       "    parent_experiment_id:    piControl\n",
+       "    activity_id:             CMIP\n",
+       "    data_specs_version:      01.00.30\n",
+       "    realm:                   ocean\n",
+       "    product:                 model-output\n",
+       "    Conventions:             CF-1.7 CMIP-6.2\n",
+       "    experiment:              all-forcing simulation of the recent past\n",
+       "    mip_era:                 CMIP6\n",
+       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
+       "    branch_method:           standard\n",
+       "    frequency:               mon\n",
+       "    institution_id:          CSIRO\n",
+       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
+       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
+       "    parent_variant_label:    r1i1p1f1\n",
+       "    parent_mip_era:          CMIP6\n",
+       "    parent_time_units:       days since 0101-1-1\n",
+       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
+       "    sub_experiment:          none\n",
+       "    source_type:             AOGCM\n",
+       "    version:                 v20191115\n",
+       "    parent_source_id:        ACCESS-ESM1-5\n",
+       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
+       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn
    " + ], "text/plain": [ - "" + "\n", + "Dimensions: (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n", + "Coordinates:\n", + " * i (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n", + " * j (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n", + " latitude (j, i) float64 dask.array\n", + " longitude (j, i) float64 dask.array\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n", + " time_bnds (time, bnds) datetime64[ns] dask.array\n", + " * member_id (member_id) \n", + "Dimensions without coordinates: bnds, vertices\n", + "Data variables:\n", + " mlotst (member_id, time, j, i) float32 dask.array\n", + " vertices_latitude (j, i, vertices) float64 dask.array\n", + " vertices_longitude (j, i, vertices) float64 dask.array\n", + " thetao (member_id, time, lev, j, i) float32 dask.array\n", + "Attributes:\n", + " grid: native atmosphere N96 grid (145x192 latxlon)\n", + " forcing_index: 1\n", + " table_id: Omon\n", + " variant_label: r1i1p1f1\n", + " source: ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n", + " tracking_id: hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n", + " initialization_index: 1\n", + " physics_index: 1\n", + " run_variant: forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n", + " branch_time_in_parent: 21915.0\n", + " history: 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n", + " cmor_version: 3.4.0\n", + " grid_label: gn\n", + " nominal_resolution: 250 km\n", + " intake_esm_varname: mlotst\\nthetao\n", + " experiment_id: historical\n", + " realization_index: 1\n", + " parent_activity_id: CMIP\n", + " sub_experiment_id: none\n", + " source_id: ACCESS-ESM1-5\n", + " branch_time_in_child: 0.0\n", + " parent_experiment_id: piControl\n", + " activity_id: CMIP\n", + " data_specs_version: 01.00.30\n", + " realm: ocean\n", + " product: model-output\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " experiment: all-forcing simulation of the recent past\n", + " mip_era: CMIP6\n", + " institution: Commonwealth Scientific and Industrial Research ...\n", + " branch_method: standard\n", + " frequency: mon\n", + " institution_id: CSIRO\n", + " title: ACCESS-ESM1-5 output prepared for CMIP6\n", + " table_info: Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n", + " parent_variant_label: r1i1p1f1\n", + " parent_mip_era: CMIP6\n", + " parent_time_units: days since 0101-1-1\n", + " further_info_url: https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n", + " sub_experiment: none\n", + " source_type: AOGCM\n", + " version: v20191115\n", + " parent_source_id: ACCESS-ESM1-5\n", + " license: CMIP6 model data produced by CSIRO is licensed u...\n", + " intake_esm_dataset_key: CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn" ] }, - "execution_count": 34, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pfpj5LgB3edg1nsc3ALjBw94AMD+WQTjU0O4dp4xSGR2Txyk5AQAgMXaExRVyv6RGT1BKxZzoHO3gYidMhbf7cu8EOHMSCbXi0aap7AgyjV6lcba2tMDctc3Bg+ch3bPdzSVOnq3rbEfQAJ0bXr7rLXfuH5n2dfdOkU10jcj6LtZWzvCdfPj+wwIdO05eScOssl6j506tctnfaUVRNv043DuzO4bxLxYsCG13+KOP6Nw79VI2lRS+YWrv2BkEJ4/ARRCb8yfqOEPmoeTJTRZXLp0gx45WdKUpIXTn7I1jR6So6zXkcijF59MfchpiM62lpaVVf5He9KOLiCYDuAnABAAmLP/qlXZOif8GMBXAOgCnMHPgd3KzVEZumz+9rfcrZKq91eL2V8Ws5+zByZOOIuJhNxONTsVfjYvzhnJ7SqeV+s96YpBB7dnoVRonAPRueU98vIJ5MLbtUro5K7etP7Vx2HoVPWsfFDro29KrdHNWlBTIUUI2W/LqbXvLauGdgqke3xke8M2naIdu0vbZ/IYea70LNs94zvIrfNp6dz+iEFCFlzPky7efL9shIA9/9+UnLS75tuDUiKmdoW3iViLZsD6XQNXyqEoAvsjMcwAcCuB8IuoGcDGA+5m5C8D99uPIKpZNHLX2ARy19gG3Q6BYwtJrfoel1/yuumkE8VyhiAWrr8WC1df6HCYyPuv8yzDr/MsGVo0rX8CBp1yIA0+5UJnP+MhnMeMjn1XmQf1HGafseGU8ynwqrZfHASJcd5u/1Kue+76ZtcUsVDd5wNrwv2+sw/eNdS5eYBMX734DF+9+o7rJe7mSw8fmJz7+RHWTB6wN/8THnxDyivOnYKp9+A2miAjJdCL0pxFVs1Ez80Zmfsb+/x5YdqVJsPJO3Gg3uxHAiQN5HbnTJiqP6lSJqZ965d6J7XhlvMb9R1z3fUkvl3txY0F2/49YcRmpoq77cePGI6N25+vgigBKJkJ/GlGD4t4hoqmw7iKbC+BtZh7heG4HM/vSIhLR2QDOBoCJbdnF93x4ha9ff+4dK5+OSninY9KYKg8N70yeKOTe9vnsMIsrhE0o296U4Z2+TVuVwjs9G/qTBoaGdza6o3+B4Z139iiFd3av9yZ3kOu9Xv9NZzINhfCON4xTkZePbbHmLzS8M8ydoycovDNqxgjl8E6+r6hcqCRKeGfer/80YEdN94jhfMvyQ0PbLbrjz9q94xURdQD4HYCLmHm3al1LO3/FGgA4aFSn8F0vdQjIHCBxOXlic/jEy7lYFHJRe2b/XbKx5dKJyMPWyzvSIGeIoXPv+Db7MO7d7MO41OFDCd/GDvg3+4qGdGUqUs+X32iq6aZPRGlYG/4tzHy7jTcR0URm3khEEwFsruUYtLS0tKKK7PBOM6pmR0XWKf0vALzIzJc7nloL4Az7/2fAkWtiIDJKZdeFwCovloQ8cu6dmufqaRAe0/zI1kXKde6dQBXZdF2gVeEFAS9IuDQnj4yXy64Ltw0nQtNeyK3lmf4yAKcD+CcR/d1mXwdwKYDbiOgsAG8D+NjedF4sm0jbn8QV5wYlE3js3I9WwwIVBwgAH1+2+loAwN9Xf8ZVtWeBhM86/zIAwMtXf1XK2+2xqVSPeu22/+3iB55yoc1/7OdEeP32n7j4jI98FgB8PLAfAVepclW5TUtlHv72n2cpzadsXYTcXl8AuPfDK1zVmkR8X1ORTVxTegcA8JXU1GoYp+LYAYAvZ/2c8oRLh0+vhnEqjh0ALu6sonX9xNnV8I6U244dAPjDYYdIwztDWQRqWstmzTZ9Zn4E8ov/H4jUl8ko9LjPFoumCbav+hfKZTADxIxSn4GS/cdfKpUB+0K1lzNbgyv25ZEqWmckxWJJyBMtGTAzCIRSXw6lkl3co1B0cbZTG3C+ALD1FZGN3mr8PJDD6kfMAbN3D8yStSmb+QLADCI3ZyMX0k8/Z6NXeZylvpzweEXzoDKfxWJJui4iXugtWv2Q9f9E0nSvu4MXe/3fKkTqyatnblRNlwCoX5yN2q9TleyYlZh4ibmaoH1HsYw0WY+KbMKENf8inmDG1rybV4wdTp5MMEx7HXfkiq4PCS/veW6LxUvW777zwlbptYGxEzqUj9n7919zNfHNWQ2Re6d7xHC++V8OCWxjlMvoPGCkctWexDjLMKSS+6V9wmglR0p6ZL8JKcxp09Y5UsiF7RNJNaeNWVZ2BHE19054++KOfvdM2DyY23e7uSSnyjY7c6LKeu16awcMO1SQ9Zw1erlqTp0t76n7+Tfk1D8gdhTjz73jFDPDhP8iaMkOsbR55ifIyTMynQx1+ADAAW2pUIdPhWdsB1mBTZDgdZ2Ksul3TlF37yy5874BO2rmjh3Bvz1heWi7Ob+4Q7t36qVssrZVe+rq5BHcsVg3p1DIPHhTk8WVS8e72YfxZhURQXTEqYiOnTQlhM/F5fCR8cYRqVbOajg1zaavpaWlFZesyll60x9ykn7lV0zDW1FYYe52uBVH7p2a8HRSuT3nC+r9K4S2XFyhML1TYevlVVCox2RWujmrkVRmBoN9Z/OVkI6Xq96cpcq9Cgr1pDjRHPNPhESmobdHqRr2o6xomjj2vkdw7H2PVDcBIHqVpVyxhCWX3YQll93kq9pT5Z4cMt1fuhLdX7rSx2d+5tuY+Zlv+3LXyHjUXD1dp30FXad9RZ0H9B9lnLLjFXKV+YxaFcu5vuWyeN1t/kpOPfd9I6jMjOv4HVzP66ubPGBt+Nfzeh8vsok15XewpvyOL/eOjIty8ji5NyfPRTtex0U7XvfxLeVic2z4ACrhnbCfRlTDf5TF9R6LLbdMzatxxdR/rccp7ia23DhNs7eEqN+XE001n3/P4zQIk1LiGgwNKQKoSa8XNbR7x/s1v3OK5YhRCe+0Txpb5WHhiDGTx7u5JKxRarMCQXHn3qlcyI01vBMh987uTdZN0yrhnd73timFd3o3bKnysPXKb3A7goLCOznF3DuN5N6RhXFEPJMg5TDOSPv9EtZ+Rrv7/SAL71ReX1VD2b0zb8IYXnvGsaHtpn3vJu3eGUxJHR0xVe2J7OSpc+4dmGUxF7Svae6dmKti5b08wMmTb8KvADJnzt44duLgje/MURABiZjCN0R0NIArASQB/JyZL/U8vxxWZoI3bXQ7M/+Xyu/ujRpi02cGygpnUOWC+hmZGaWtIC2ASN6EZ4FKqbclxVXiknqfUcaqevyA+rxGWSuVtY/aNkrd2Shto2TErGefAFBU/Jb/nqE+/5Na1beUWqxrnIoj9w4RJQFcDeBIAOsBPElEa5n5BU/TvzDzcXv5u5HUEJu+ioxyGS2lcmyOHR+P6mAJ4W2t7vEHhWKozLVz/sR8XF7trWPHxxVvzmpEMbOv7oE0pAObQ8wzHhd/UGioyDRgh0+zioiQiKdu78EAXmPmN+x+fw2rpojKxj2Q35WqYVew6Ki2Y5TL+PBDj2P5rXf7HCDLb71byA++/BYcfPktPoeJkBeKmHvx1Zh78dU+B4uMzzrvEsw67xI59zpnzvwmZp75TSGP4vAJ6sfHVcapeLxVrjKfxZJ0XYTcXt8PP/S4z7Ej4o2k9ZzDzbxByZlTgolbsAG3YEN1k/dxlX5sfkX+LZ9j54r8W0L+nb438cdCfw2EpheRahGVMUT0lOPnbE9PkwC843i83mZeHUZE/yCiPxHRQRF/N5Ka5kwfiLGqVFxVq+KqolXraly1dhDFNp/R+FCXyYy7ecte+nMGrijz+aHMaByWHlHD0QwxqadW3hpyIVc0nd4lfwbAFGbuIaKVAP4AoEvxdyOrIdw7czqH842HBV8gN8pldE4eoRwuSEbJvTNRLfdOZuQIIRe1b+sc4eZB4Z1UWq3YeqmonnvH9rOrHFdhx07l4zK3uZ02svDO1ii5d97eGX/una05pXZANPfO1gjXKnJlVnLmOGP6YeGdtoR6eGdkRi33TnuE2HaUmP7YMa3hjWxFce8ccs8DA3bUzN9/HN974b+Ftpvw1asCX4uIDgOwmpk/ZD/+GgAw83cDfmcdgCWwNv5Iv6uipjnTH3K5d2LM1UMp/+8MVi6dqFzn3ommqM4c72YfygP6j5J7Z18TUWy5d54E0EVE0wBsAHAqgE94XmsCgE3MzER0MKyw+zYAO8N+d2/UNJu+lpaWVmyKKQ0DM5eI6AIA98CyXV7HzM8T0bn289cAOBnAeURUApADcCpbIRjh7w50TA296Q+53DtRecy5d7Ke/N+BN2cVirE5dnTunWjqv0gaj2PHxyPczOUcjz7LdyuuNAvMfBeAuzzsGsf/rwJwlervDlQNu8pF0xQ7OgIcOyIe5DCp8rgcO04e1WmzF7l6gvqP1bHjyb0TxRmltF4Kjp0Kf8Xow1CSyVwtegL0V7m6pvSOstMm1LEDdceOKFePyLGzr4uIkEgmQ38aUQ1xpk8EJD0pBkyTXM8lk0kkMykkE1R1fyQzKSTts0cvr3x1S5DVCQFIZFJVb66Lpx3cLgVFID+H9RqUSoHS1tkvmRJO6Wo/lEpX4/ZUZj9PJO1UDPYxVB/DxUP78XGFcZoBxyvimVT4fGZSgD3/Sutlr6/F3e8JL0+mEr73ikhRiiJFaTvc8W2lj8v4fWELTsmMd5QqJJD9mZdOENL2QRITULbO/UUcADJESNk8wVT1cTg5EQL7cfKy4wNpBKVBDJQF5pBazZXKOu1N27jUrIXRG8K9o3Pv6Nw7Lj6Ec+/kyu6zekB+o1NPiYXc277PPt64c++02jt0iRlJyK21IyNsuJHcO0M4987CqRP5wW+eFdpuxKe/o3PvDKZ07h0P17l3hlTunfCcNmUJd6vWuXdSQ2jOho505SwtLS2tfUaUaN4iKk1zVEMu905YYfRsu3p71cLog8F17p1AxV21ysvDnDne3Duyfti+BqUllz7TH2IqmibS9qJUqiYlkoSHzziuumFUqi8B8PPLbgIAPPXVVdUNqVLdycftKlEA8MIPPlfd8Ly8sg1WqlABwCvXfrO6cTr5qzdf6uJdqy62+E0iTnj1l99z89O+YrX38sB+/DxsnK9c+83qm0RlHp788unh81ksyddFxO31BYA/fvDw6gYv4/VSpdoUAHw5O9Vx8VbO15St1CpnJydLeWVzrjhwAOBM7F/d+J38bJb3U+GiBG9aHhGBEkPjRCJuNeymH4diq5bV6FWu6lRFK6qijnMwFMd9AYNd5Upv+Ipq0k2/od07ThnlMjoPGKkcLkhEyb0zQS33TnrkyH4eFt7pHCnkAwrvmGX13DtGr9I4W1syKO7YITxeV/tK7p3tarl3tkXJvfPWjvhz78Tg3ikzI+nZQHfb41cJ1+wolpXCO67cOyHhnbakWngH6HfvhGlfdO8smjGZH/ne50LbtZ/8Ze3eqYUoQch0iJ0iFWWQRken/00ke1t1jBA/I3qVVFsrhnkT4NtycnLE6b0xey8nz/NB7SueeafaBc25WAx93b3hqTaj+v+weehz3AgGiOcTgHCtAPF6ZTp6kJH05OXp9uD3SfV1WtTf+rMitN3QU5A8I9s4w7m72Elw+7LnJK5V2l49kVpnWj22HWVeVdcKQOjff+wiNG2N3IbY9LW0tLQGVURASmxhbnQ19KYvdXrYIYWs1zEi4aFpkD2vO1i5dCLn3vEUpQ68OStfqFt1LS8PWy+vgpw/tcy9YzKjBPbViM3bIZQWD5cVEC9I0h3E6fxJgHyhJy11URP79Bv2qIplE0etfQBHrX3AnbOlWMLSa36Hpdf8zrVpyHiuUMSC1ddiweprfbllZHzW+Zdh1vmX+XjU3DgHnnIhDjzlQmU+4yOfxYyPfFaZB/UfZZyy45XxKPOptF6enDzCdbf5S73qsfooKjPjjI0v4ZMbX6pu8oC14Z+58SWc6eEFNnHx7jdw8e43XJu8k3urU33fWIfvG+uUq1nJ+NPl3XrDH6ic6U6CfhpQDX2mD8RXZSkuR0rNq1zFVbUqtuOV8Rr3H3HdB6oSWJiXJkxxOXNU+0mCcGhqRMTetfyiht3Uw9QQ7p2DRnXyrz+0zMe9X/OHHWDl01EJ73RMGlPlYeGI0ZMnCrm3fT47zOIx596pXMiNNbwTIfdO75b3hMcrmoe+TVuVwjs9G/rrrYatV3HjDjcPCO/0Kebe2b1+T2gbr2RhHCd3XsgNC+/sKijm3jHVHUHeC7lBUr2QO7ZFffOb0K4eBx++/zDltiOmqrt35v36TwN21CyeOZUf+8m3Qttlj/yUdu8MpqQ5W2Q5XmpdFavOuXe4WBRyUfua5t6JyMPWyzvSoFw9Rg3DGt7NPox7N3s/V8u9E5VrxSBK6Au5WlpaWvuMtGVz6MsolZEulmJz7NS6KlaU3DtkcuPm3tlLx46PR8zVE5dUQjpOhYV0vDwux45W3CKgSd07DbHpJ1JJtI5237ZTKJvI2DFJo1TGh268E5RI4Omvf9KV42XZJTcAgI8v+J+/AAC89OMvuHLILLjwch/PZ4dh1tni3DVO3t452uZ5dJ16LgDg9T9cg9aWFh9/8083ozVrcyOPA09aZfG7bhLzu3/p4jOO+4yf9+XC+7G52ZJVGufrf7gGLface49XNA8v/ujzofOZKxSx7Ge3C9dFtF49u3rwIUmuHi8v9Ig/fLwa3TXK9binUMTqv/4T/2vpfFf/H7j9PnCZ8dtFi9FiX9jLm2X8+zNPA4CPf+OpFwAA10+cXf1AqDh8vDyZyOOiHW8CAH40ckb1A6HApotvz1tx+iKb+H7fW+hMpPC57AHCaxdRipio3nQ1KqP+gdo6Mqvctm2M1wwd0O9o9bt3Y1HFvRNHV0RHA7gS1h10P2fmSz3P/zuAr9oPewCcx8z/sJ9bB2APrFhgKY7rBzXb9InoOgDHAdjMzHNtthrAZwBUKmh83a4BGdOLRuO1z2kTkYtfNr5+Yus/ntw+NU86oyhmxmfufwLv7KlPqcUo03BAMotTWsYP+TrAja94Eq4RURLA1QCOBLAewJNEtJaZX3A0exPAvzLzDiI6BsAaAM68MyuYeStiUs3cO0R0BKxPrZs8m34PM/8gSl/vGzeKf3/KBwPbGKUyOiaPU06VnBg7wuIK4ZrU6AlKYZCEfaZv8bzNWzztLd7e6T7TzBl2+2yLnycSYu5tb5rB/Ti4Wc29EzzO1pYWmLu2CY/X3d7i6Z7tbi4J72xdt9HiCuu15+1NyuGdXW+5nT4yec9Ig/rf/vr26tl8RXnbUePlW96xcv+ohIO254pK4aD3DHGBeJHqfabfOU6c1kPYNkI+nUpVPBV1Xf2bgbt35hzIf73p8tB2mYNPCHwtIjoMwGpm/pD9+GsAwMzflbQfCeA5Zp5kP14HYEmcm37NzvSZ+WEimlqr/r3KppLCykyRq1/F5sxpicazAVwQWwxsH4VHHWfIPJQ890YNVnWtgSqof+/GDvg3+yqP3eGjVRdRAqTm3hlDRE85Hq9h5jWOx5MAvON4vB7us3ivzgLwJ8djBnAvETGAn3n63ivVI6Z/ARGtAvAUgC8ys/DUjIjOBnA2AOzXoR7709LS0hqwCKoXcreGfKsQffcShleIaAWsTf9wB17GzO8S0TgAfyail5j5YZWByTTYpxM/BTADwAIAGwH8UNaQmdcw8xJmXjKqVXzWaZTKrlvxK8oVS9UwgRIvFIXuEynPF1ypCvp5vhoWUeJGvhp2GXQeZZzS45XwqPMZcb1k626UytVUyyoK7EfA82a5GtZR4my6UjOE8QKb0rw8WoMrAoGSydAfBa0HMNnxeH8A7/pej2gegJ8DOIGZq/FUZn7X/nczgN8DOHgAhwVgkDd9Zt7EzGVmNgFciwEcQKFs4ogb78QRN97p+gPNFUtYfMkNWHzJDa4NQ8oLRcy+8HLMvvByX66YKlfMUTPjxHMx48RzXRtnLp8XcyOPaStXYdrKVa6NOZAffRqmHX2aOg/oX3mc+Xy0nEIq8+nkCuvlzbEjWvcKP/a+R6Qbv+m4flVx5nzg9vt8/Yh43izj5GeexsnPPO3a4KVckpPHyb05eS7a8Tou2vG63viHguLLvfMkgC4imkZEGQCnAljreimiAwDcDuB0Zn7FwduJaFjl/wCOAvDcQA9tUMM7RDSRmTfaD09CDAcgf7FoPL4qV0PNaRNXP7WtxhWXk8fbf8k0kSDCnes34qj9xvuKsEQ9rlorASt/jla9FU/uHWYuEdEFAO6BZdm8jpmfJ6Jz7eevAfCfAEYD+In991exZo4H8HubpQDcysx3D3RMtXTv/ArAcgBjAGwC8C378QJYMa11AM5xfAhIJXPv+HPvjAcgd+w4efuksf085IajYRMnu7nEwZLPdthc7oSpKJFtV3baVGKLoe294w9ob+Z7lcYJAL2bbaeNwg1cpW3vKd3I1rthSz8PWa/Sxm0uHuS02fX2Dt/Gni9bqdKcvHVkVtkRtO1Vy5Ekc+w4uTOnT9iNXb0eV06BTaRAPjumdu8Mvntnyfvm8P/7/fWh7ZJdh+ncOxUx88cF+Bd701cilUTrWP+ie2s4tU2wLJOyy75Onho9ofp/WdqnCk862gIB1biGid+YwvYt7UrjBGDlAQHQJvkbqXJPWCCw/3yvj0uPKyV24Mjah80nALSV+8caNg+7S+7jEtfusvj0DwSX1axo58tvKrWLqlSEcoFvP7fF9TgjqXIVpQRhlE1ftcpVlBuuOvZTv4mqfbz6B4To77/malIHVUPckaulpaU1uCK96Q915YolpAvF2HLj+LhqKEaVt3hy7xiG3T7r55QQc1n7SDze4/I6m2OrxqUQsovSv1dB/RulsnLOn7x9EblFEGIS8RIzUvru2iEpbtJNv2GPquBx7Bx8+S2Y8/krfI6ROZ+/QsgjOVKMPKYffxamH3+Wzwkj41NXnIypK04O4IaDG5i6bCWmLlsp5kuP8fOlx4h5UD8+rjJOteOtckWnk2xdhNxe34Mvv8Xn8BHyQhGzzrsEs867xF/tq8I9/bz/B7/E+3/wSyFXdfgYpTJOfPwJnPj4E9VNHrA2fBE39YY/dEWwzvTDfhpQTXOmDwyGI6VOVa5i66dO/cc2nzVeXyHdm37UuM6fM5RFAVazxlZDVM6av99YvuucjwS2yRVLGD51gnK4oNRpVc5SCe8kR09QCnckHBdyQ9v7cu/EEN5hUz28Y1/IVTkuc88OIRe1z/S6b7CWhXd2r3/L4grrtfvNjcrhnZHdM6T9OHn+zfWB/Tj5rlc3KId3Ntu5f1TCO94LuTKVI/yJNtKF3OH7D1duO2zyeOW2+69eM/DcO/MO4ifuui20XXryXO3eqZda0ylhnpfYcunEletGysV/WK3ZrPBrZGD7SDze4yp7TEGxVeOKmKsnrH/vfcdB/ecFeXlkuXq8m30Y1xqiIn0hV0tLS2vfkt70h5721rkR1cnj/cIan+MlLgeOIR5/UPt8Pj4nkpfXuhpXQCgmG8XBFdERFLV6lxXSIbQoFiDXGkrSZ/p1VSKTrt5tW1GhVMa8L1wBAHjhB59DayaN9Lj9kMsX0L1KXN3JxcdOsriRR9epZwIA3vzj9a5qUxX+1mN/rG6cOcPA9A8dZ/FH7nTxqYcfb/Xz5INobbV5zsC0pct9vK/ImH64dZfxG8885mrv45Sw+LL/z+J/+6u7fYWH9ePkzErjzOUMTD/heOHxiubBKJRC5/PNP16Pyvarsl5vPvOoazzz3y8e5/z3LwfMMt74P79wve6s48+y5sHBWwpFzDvvEgDAyz/9uquql5cnMynkiiX8i6QKm5Nve/VZANZdul996SV848ADMTYjDomNnaAW/y4XI9yRm1YPI6XbxSEwr6JUuIpyw1WUOL33738w1KyWzYbY9IMUm7Oi5g6ZfY0Lcf2qksXk8FHNEZQA4bLZc9DSpHVW9wk16abfEO6dhVP344e+9Wkf935tz0zc3+IK4YWUfaYPhIcv2ice4OHisEkvW+OonH1W2+cMH+dUi5AL29tvvtD22YyYC9pTKa80TgDI79xi9x8ebjJ3bFIKB5W2bOjnIes17P1HKh8Xv/uKUhiquO4l5TDgnjetGhgq4aBXfv8MVFXsVavnq8/0o53pd37q2wN37yyYx3/9852h7TLjpmj3zmCqfs4ciROmtc7ck3snqD2V/Kes0va1dgRFXa+A42LBa0hft8YOIq3Glg7vaGlpae0zItXKWQ2nptn0c4UiyvlCDXPpRHTUhIRissNa1NtTQjkUU3MeMg/ec+rYHD6Rxxm8vt43flCoJ1csKTt8ZOmXtRpMlTQMTaiGPSpX7p1CEd1fulI9l06+gGnHnolpx57pryol5AamHH4cphx+nC+njZDnDEx7/3JMe//y6qYUxqcvWorpi5aK+cJD/XzhoWIe1I+ARxqn0jyozGfEalyRx6mQI0iWk0fAlauwFUvCKlpajSjSuXcaQdqxM1S5ENfPsRPRyTPQql4lZvxz924AwPzhw6s5d0xmPNtrFV2Z1z5MyisymfHPvh4AwPvaOlztK3xBZ6dy/6lSyjeef0jG+cw2K73EotEjlfhT71mFb5ZMGC3l1eMyGX99yyrUc+iUiUgkyMeP3H+ciz/6yluYMX4U9hupnsohqjjRVNtjVQ3h3lk8cwo/+uNvBLbJ5QtIjZusHK4xWkfYXMGR0jZCKbxgZoc7eM7mrZ72Fs8OGyHkwvaUkPbj4myGvm6FUymvNM7W1lYkjN3C43W3t3g7G24uCQe9ktwPAJD1vK5RmR8Hn9ieUj6u1I63lcbZutOTeycgHPTWzbco35C1Y9MuIRe1L/QUq/V8vdW+nNzp3glr3+5xcAW1z7SnlcbZOrpD+ca0SrETFaeT05ET5qTqnDLNzfMFpFNJpATpLVqPOWfg7p1FC/mxhx4IbZcdPlK7d+ql1pYMUlGcG1EdKZEdNeL6ThXu/agNbC/KvRPSf+14yDz0uTd92Xx6N/swHvs4d3p4gONIlGdHlnsnMpfk5BkMnqnhccWdK8nHJc6uWBVTlk0iOhrAlbBq5P6cmS/1PE/28ysB9AH4JDM/o/K7e6PGDEppaWlp1VTxxPSJKAngagDHAOgG8HEi6vY0OwZAl/1zNoCfRvjdyAodtehFiGj5QF84DuXyBdcFvyo38q4LeOHccF2YDOU5w3XhsJ/nquGG5uKy45XwiPNp5HLVsI4KDx5/lHFGe58YpbKraErsvFyuhmMGlcd4XM4L2xXliiUxLxSF+ZWkXPL3XisxJUJ/FHQwgNeY+Q1mLgD4NYATPG1OAHATW/orgBFENFHxdyNLZdS3EdFXyVIrEf1vAN8d6AsPVIViSVrlatrKVZi2cpXfSSLkAVWoKlzRCXNg9zwc2D3PtSHlcjkp75o1C12zZqnzmTPRNXOmOg/oP8o4IzmCVObTwY1cDssWzMGyBXNcG7yTR5mHKA6fKA4uo1TGETfeiSNuvNNXLUvGj1r7AI5a+4Ccl92x+mPvewTH3veIj3/4ocfx4YceV+ayfoRcZZyKx3vEjXeqVzcrltD9pSvR/aUrfY4pIZc4u2oqtTP9MUT0lOPnbE8vkwC843i83mYqbVR+N7JUYvqHALgMwGMAhgG4BcCygb5wXIpeBUnCtZMnZi7EQ3A+ZeMR86iKPg8SLuu/1q8bcR5qX90spoUJEYNgqr3W1pALuaJOvJf0ZG1UfjeyQt07RJQB8B0AR8LKMvwfzPzrgb5wFMncO94beVLjrRw5Kjde0cgJDh5yw9GoCW4ucYb0594Jd9pYuXfUHCn9uXdC2nuPN6C9lXtHwREEIL9js83Db4xK9O1UupHt7Y4ZVS5y7Dj5tDHDXDzouFI73lG7gWvja8o36K27/kZrPAoOlty2HiEXtU8Y7rQZMqdNr1EQclH7ZDqp5AgCgExHWmmcI8d3So/Xy7NjRijfyJaaYFWPU0mFnZm4v/TGPa/icO8sWrSIH37k0dB2w9rbAl+LiA4DsJqZP2Q//hoAMPN3HW1+BuBBZv6V/fhlAMsBTA373b2Rypn+kwDuAPB+AKMB/IyITmbmkwfywnGo5rl0bG56+V46dmrOfbl35O2pFMURFNG5FNEZFaeTJ2X4X0M+zmjvk9gcOzYveN5ZdXPyxHhcIndOQzl2HIrJzP4kgC4imgZgA4BTAXzC02YtgAuI6NewIiu7mHkjEW1R+N3IUtn0z2Lmp+z/vwfgBCI6faAvrKWlpTVUxQDMGHZ9Zi4R0QUA7oFlu7yOmZ8nonPt568BcBcsu+ZrsCybZwb97kDHFLrpOzZ8J7t5oC8cSamWauhGppyRR6ltpHJunJ6ElS5WJcRhto1UCsUYJgm5qH3S84YKal9mtXEmSe1mKwDIJLNK42xtbUW2o0XIRe1v2+QOQxbs+c945v+AHruAeNYTSjJyAp4NDQFV+IRRU5TGmX7+V24ecHNQauJo5ZBFyf79rLfqlt3eyUu9OaWQUblQEnJR+/a2FiEXtU+1ZZXG2Tp6uHKVsTb7TluVtNXpcfv185BcTN6//5yRRzqVQkryrSMOxXXjKjPfBWtjd7JrHP9nAOer/u5A1bA+/ULRcWXfduZMPexov2PksKOFfMaCQzBjwSE+B4iMR3XazJ09C3Nnq/NFB83CooPEfMlcP18yV8yD+hHxqOMMm4eCY54LhoGvnXAYvnbCYT5+6hELceoRC6ubPGBt+CKu4vAxPOOZ2dWFmV1dvnFWuccxMufzV2DO568Q8ii5d5Ze8zssveZ31c0TsDZSId8LR9DyW+/G8lvvVuZB/auOM+h4hTxkPn08IEdWlQuceNf94R7USpUz/bCfRlTT3JELDEUHi+aBfKjl3onJOjPAVD2his1pE5FHnoeavx8k44lDDJQbdFMPU2Pk3umeyU/c8uPANjkjDxo1oXHCOxmvw2SwwzukNM7W1lZkEyzkova3vdbr4tLwTrv1+irhnWkjIoR3bEdKaHjn/1zp5gHhiL5N25XDOzve226NR4d3LB5TeGfYAQe6eUB4J73omAG7dxYsXMT3PviX0HbjR3To3Dv1Umu2BSRwh8hz6dTXaVPwnEYEtRedcdR7/HLu3vS9m31F3s0+jMedq8d7f2iQk4QjOFK8m2goj9kRFIWnBGOSjbNeuXSiOvTiEsPv2msWNc2mr6WlpRWnGiAIsldq6E0/vipXwaGAlraRkdrXi3e0qbcvJ6lm45GFdGRc7NiB42Kuu31QqCeXLKmPX7EwepUrhjiq4xGESpzc+8enmr44dh4yTu93pqB5oEJRfT6jVk+T/L3XSo16oTZMDbvpF4pFTFu5CgDw5l03Vd8IFccOAKx7/O7qBu/lmXarn4pjBwBe//sT1Y3ByV995RUX75o1y+Ivv+zic2fPBgA895KXzxLyRQdZ/Jnn1fiSuRZ/6jkx/5tiPxYn5XG+b474eEXzUHHsAMB373i8usF7OWBdU6k4dgDg1w//rbrxO/lf//FidYOvOHYA4NG/+3mCCK+8+qprnDO7ugDAzW0nCQC8eMXnqxuSlNtOFQB4+uufrG54Xl5RxQkDAI+d+9HqhurkD686troBVxw1APDwGce5+PJb7wYAPPiJo5W4rB8hVxjnM984U3q8Pp4gtfksFNG96mIAwKs3XVrd4CuOHR+3HTuA+++9VmIGyk16qt+wm35F8twjQ8sBonkI106eQNU8R1BEXjfHjmw8NVCT7vm1c+8Q0XUAjgOwmZnn2mwUgP+GlVNiHYBTmHlHWF9L5nXzE2tv9XFvuKbcMcbiCuGISluV9uWkWk6b7buti5gq1aAKZVZ2pFTOOMLat7e1hb5uhWeSpDROADjj+r8CAJIZ9zyUC3kfn7nfcJTy1rqkWtxhGSdfMHlElYeFg+btP9rFg8JBh+18Uql6WmnLBuXwQmnbexZXCAcZ23YJuah9ylERC5CHTXp7DSEXtU9kUsphqHR7VmmcIyeOEXLhjWyjxiuHa4rDrH5V1otGT5KGZ71KTVs4YEfN+xYs4rX3PRTabvrY4dq949ANAK4CcJODXQzgfma+lIguth9/dW9fYLCcOT0FtZw2UR0m9eQVy6ZKe+9mH8a9m30Ylzl89sb5I/raL82xE9UxEtWREsKLnk0/skOmXk6bAJ4SzF2tc2TVSk16ol+7O3KZ+WEA2z34BAA32v+/EcCJtXp9LS0trYGoWe/IHew0DOOZeSMA2P+OkzUkorMrhQm2bNsZ2nHOMFy33Fd5rj7VpqJWg2oUXi7kq2EdFV7KG9WwjlMFw3ClZgjjeSPnSs0QxiNXT5NVYcsXolV3isplVaUGg9fyuILmM6Zqd6WS/7jiFHP4TyNqyObeYeY1zLyEmZeMHT3C93zBmbvDrsoUJZdOaG4WD1/YPQsLu/05Z0TcyOVw2PzZOGz+bF+uGBk/YtEcHLHIn1vmiEVzsGJxt4+vWNwt5EH9iHiUcZYLeTy2+mQ8tvpk1wbv5M4NvpQ38MvzjsYvzzvax2U5eUQ8b+TwsX9ZgI/9ywJfrh4RV6qKJcvxIuCzL7zcl0Nm9oWXC/mC1ddiwepr1XixhCWX3YQll93ky2kj47IqVDIe1L/yOAOOV8hVcul4q92FVjHzV2G77rY/oFZiZpQVfhpRg73pb7JrP8L+d3OcnQ85R0qTOluiq17zIBmN1GlT435iq/IWdd5k/ddpPmvcf1xq1vBOTXPvENFUAHc63DvfB7DNcSF3FDN/JawfmXvHqZxhoNw+WvnGnJ5ku5CL2vcUTCVHUF+x/4JvmNMm4cm9E9S+zKzktEkSKTuCKhdyVdp/6pa/VbnIsePkc6aOdXGZk6d7rPVY5Qau2WPaQ2/gqvBlO58AIL+Rp8LTe7a6eYCTp7R9k/INRzs2bhVyUftir6HktDELJSEXtW9vzwq5qH26rUVpnNnRnco3sqVGW1XmVJxRqbH95V7D1qtt0nQPN+zcO/6L03G4dw6av5BvuzvcvTN3v07t3qmIiH4Fq+TXGCJaD+BbAC6FVWj9LABvA/hYXK/Xms2iLHCf1CsXTZijxpt7J6i96GtkvZw/cTl54nTsiBTmDCnt8fAAJ09J4FaJzfFSL8dOOoV0LY8rplw69XTymE3q36nZps/MH5c89YHIfSVSMNtGhLYzPekSguS1YQZpT6Ec3ghAb1G9TyNC26Kp1jadUI/WffzSB5TbDhsp3lhFekWxXWtavfjFp6ap//ElaLxSu6SptqZR1ZYSb4Iilfr8F6BFMgUXYmVKSDZ5kVJtauua6Bwd3shWcvSE8EaVfkeqrRUAlBX+/uNWg4bsQ9XQd+TKCpTHndMGSffZiWoIJZTboYlsVo2HhTjSbe3K/ZulPBIp91mUWbK+Svt40eZpNR4WAvLysJu5gA4Xl6+7gaRhqOdcUiyMXuVRc8WEhES8Hw+RcwENEne/q4LnIWnk1eczco4s8brXQlYRlebc9YeseydMhUIB0xctxfRFS6tvBgB7VeUqzJnjdbYcvmAODhdUcTpqyUE4aslBatzI4fhD34fjD31fdXMO4nkjh5OWzsdJS+f7HCwVrtJPhb918znVTR6wNvy3bj5HyJ+/6gw8f9UZ1U0esDZ8EVdx+Dh5KW/g2k8fiWs/faTP4VPh7vU1JOtu8SmHH+dzekw5/DgBz2P68Wdh+vFn+RwjQh7VkVIoYtb5l2HW+Zf5HC8y3v2lK9H9pSt9fO7FV2PuxVcrc1k/Mh46zghOJ+X5NPKSdZGsl2TdayVmoFjm0J+BiohGEdGfiehV+19fuIKIJhPRA0T0IhE9T0Sfczy3mog2ENHf7Z+VYa/ZsJt+RUPOadMgvPb1naJpqM1PrXPFRHbUDDUH0VDLiRS7Bs2yWclS0AXgfvuxVyUAX2TmOQAOBXA+EXU7nr+CmRfYP6H1dBujctaCefzXe9f6uPfrXrljrM3Dwzh7SiTkovYlxfDOtgi5d4yiqRzeqcT0w8I7HRHCO2f84CHl8E57h3VuoBLeGTW+Qym8M39K/wlNWHjnh8s9uV+Cwju9W5XCBeVt7yqHd8qV3DsK4R1z17Z+Hhbe8dxcJGvf25sL7MfJE+mUutOmrVXIve3bx7rj9IHhnVHjlcM7hfZRNldYr44xyuGdzPhpA3bUzJ63gK9be39ou2XTxgzotYjoZQDLmXmjbWN/kJlnhfzOHQCuYuY/E9FqAD3M/APV12zomL5s8eN25ngv5MbmkJFViYroVJFWmwrg3o0d8G/2VZ6Oxmudq0e+7lkkyxGqp0V1jMScq8d7R2ncuX1qxgPmIRkl95F0XaKte03EQFnNPzGGiJ5yPF7DzGsivJIrSwERSbMUAFUb/EIATzjwBUS0CsBTsL4RBCaxbOhNX0tLS6sWinAhd2vYmT4R3QdAZGv6RpQxEVEHgN8BuIiZd9v4pwC+DWvI3wbwQwCfCuqnaTb9XM5AOZmrWTWo2Bw7lYu5nrPqoFBMyTSVCoiH9SPiUsfOXjp5vE6bvXXseHlQSEfIQ5wh3nPVoFBPOV+IzbFTc6eN9yasoDTIqdSgV7naWydPxv22Qi5nIJ0W35wVhxhAMaZbbpn5g7LniGgTEU10hHeEWQqIKA1rw7+FmW939L3J0eZaAHeGjadhL+QWCg7ngH1lP07HjjeXjsyxs9dOnghOmyDHTj5CP14e5NgRcoljx8lVHDvlQj7UsePkYY4dH5c6dpxczWEy/fiz1B07+UJsjp3BcPIoj3MvculEckaprJdg3a+/9TbUTAyUTQ79iUFrAZxh//8MAHd4G5B19foXAF5k5ss9z010PDwJwHNhL9gYZ/qUgJkd7kJmogAzY51dmJyG5TNIwDAJZNopBkwCM4GI/BwMIkKuaIJTVvDOKJpC3ls0wWw5B3qLJsrO9g5eqSmaN0rWvXxE2GWUYMDPd+SKaOGUzYvVfkQcBOwySmhx9iPgHz//KuzuyQEgHH/eVaCkdbbG5aKPZzvHgssFsGkCIPRt3QiyL1h7eeVs3yzlwfYL79mZQ8KeBx9P27yYB5sMkJ+3pZMgInxtxTRXacNbBNzs3QHAWkczOwym/W2lsu5OTqW8m7cMg2mfPTp5YthIJOyzzUQ6D1ACRJBwQqJzFBItNs8HcQKBkBg+Cgn77DeRLwh5GgmQ/brpESOQrhRtyRdcPJOw3rflQhGUsPrJjByBjH027uRB/fi52jgT2Q7leUgMG6kwnxY3s8P86yhZL9G6c7rVty/EJQYPlk9fmKWAiPYD8HNmXglgGYDTAfyTiP5u/97XbafO94hoAawvJ+sAnBP2go3h3lm4gB976P8Gtsnlcsglssrhmk071Z02uwtlpTDOzlz/hbkwp03Z4wgKam8yK4V3PnHhT8Bl6wytsuFX5OXZzrE2L9g842nfz9vH7lflYeGg0ZPcd2/KwkFrP7sYgFp4rdU0lMNxyZ4tNg8OB7UVd7t5UHhn52a0tnhv1LLbe3jv5o02Dw+DsNGrFDbhovtsHJCHd9ra3dXTgvqnbLvSOBOdo6XH6+XJkdZ1SJXwDg/vv2YZtl6ZsZM9PId0Oi0M72Q7Rw/YvdM1dz5f8Zt7Q9sd3z1B596pl1pbW4GEwLlRpxw1YU6bPk/VpKD2ojMOWXvvZh/OxU4MGZc6fCI6f2qfEynEAeLZ9IOcPOUW/3Peza+fR3T41Lp6V0D/FKXKlfR4IzqgZDxkvbxJM2TrHpea+Y7cptn0tbS0tGKTHdNvRjX0pj9YzhzV3Duq6X9rxb0KCvVwuRAY0nEqboePl4euVwspt0/mjAgOn4gOE8UQRz8PDt14z21jcwRFddqEcH/uHfk8RMq9E9WRJVn3WihO985QU8Nu+oVCAQd2zwMAvPbCs66LgXNnWze0PffSy1KOhPUGrFSJAoDH//FSdSN38nuefN7Fj1pyEADg3qfc/OTD5wMAfvvIP6obc96Q89OXLwIA3PzgM0r8jBVWHPzGB54Wclp0jvvi7V9+BAAY/i8X+fhuSmDcsf/luni7+Y//CQA+/tbN1rWhKaf/zHVRV8iLeTx7+WkAgHlf+GV1g/fyilTW6/UX/uHisnU/sHsewCbefPLB6oaRyxmY9v7lAODmRh5TV5wMAFj3wG+rG5KU5/OYceK51nj+cE11w/PyyrZWcbYAwKs3XVrdUJ38lTXfdPGZn/m2xa9181nnXQIAePmnX69u8LlCUcwD+pHxsHG+fsca6fH6OCXU5tPIY9pSwbrI1kuy7rWSDu8MYQ213CxRk6RE71+M5Slz4sq9E5HXeB7i40IcwOtU7WvIVbmq03wOVu4dZphNeqbf0O4d79e9XCIr5KL2u/LqVa4KiuGdTTusyhwqYZm+Ylk5jFM54whrf+ZXrnfxoPBOtnOMcnindaTlyFEJ74wc36EU3vnzFw+v8rD1GhUlvNOzVSlcQLs3q+fe2WHdL6MS3nHl3gkL73BRyL3t+3r7LK4Q3qF0WjmMQ9l2pXG2j5vo4QHhnRHjlMM7fWnLbqmyXuWOscrhnTjcO9O65/F//TI0dxlWLZ6s3TuDqUGrcqWYeydqbpxa8yAnj8idU2vHzl47eUxDzAXthbl3ZM6QyLl3ovJgZw4bRSH3ta911arIXD4PkXLvhDmsfLz2sXyndHinnqIEWLLBOFUoqi+St1xhkFSrXHltmHG1/fiZ/1OpXSIl/iMdqJIZ9T+2W1cfqdQuyvxnJR8WIqm8TwCAWryXJ+VKZNXbct4IbxRVqWJ4G1sUoXIXKR5XlONHhHlVXauobeOQlU9fvbpdI6kxNn0F5XI55IrxOXZqXeXK6wgKas9mCZRwLxWb1o1gA+aKN3NVeckOAXk+YCrcK9l8xl3dzM+DnSE+R0pA7heO4kiJ2TkTlXvLNQbenEXpuuXSUa2KVb05y5d7R35zVhxiADHUSBmSapLcO1aOnUUH+XPpLDpIzI9YNAdHLPLnzBHyvahydfryRTh9+SJfzhwZP/vIJTj7yCVCXnj2luqmDVgbeOHZW4TceOZ6GM9cr8YrTp6//Ki6yQfyUgHv3fEVvHfHV1ybvJMrzWfAush4tGpoCrl6PDlepi5bianLVgr5tJWrfDlkpq1cJeQHnnIhDjzlQl+OGhmPlNMmX8DMM7+JmWd+U5kH9a88zoDjFfPg+fRxpdxK/nW/4cYbUUuZzKE/jaimOdMHhp6TZ8g6E/yvHJFH7H2IzWd0B45YUl5zh09M/Q+1eRhCfy+M2CpjDTk1hntn0SJ+9JG/BLbJ5XLYHSG8EyX3zk6jpBTe2Z7rPyuOM/fOGedcohSuSaQyyuGdlmFW5SqV8E772AP6eUh458H/fZqLy8I7GdMOCyis1/C0enhHNfdOu9nn5kHhnZ1blMMaPbXIvVMqCrmofVt7u5APJPdOsnO0cniHRlRy74SHd0y7chagEN6JkHuntb1jwI6aA2a/j7/4C1/CS58uOnyGdu/US62trSgKjia23DsxV7mKknvHu4ED/k19r3nUXD2Si8UyLpvPuufe6XVv+kFVnDiKI6Xmzpn4eKTcO1GdTjFVxapn7h2dhkFLS0trHxEzUChp986Q06Dl3iH3We/eOnYGysNCN956UEHtuVyMzbHj5VGdUWHrNTyt3j5a7p2IDpPIuXpiyr1TZ+4xzgTOAxlGbI6d+ubeia1IypBTQ2z6TAROJF2sUCiga5aVG+eVV19Fa2srylxGLpfDkrlWzpannnPncnHycsJ6gxu5HFYs7gYAPPD0C65cOhV++6PunDknLbVy6fz+MXEuHVluHCc/9YxvovDsLQCAzLx/r27MFWeOk5ulAtgsofS8VSkoddAprvYVnpl/WmA/Tp4nQsdh57ty8vQ8fjUA+Hjvn/9DeLyieejt6wudzweefgEdtgdPZb1et9e3wivr/uorr/g5m3jjb3915XKZvmgpALh5voCpy44FAKx77E/VDclymHh4IlF1qgDAm3f/0p1DxsFb0v05cA78t89Z47/9J66cNhX+6s2XeXLdfM3iv/yeO2fOp75pcU9uHCEvlqX9eHnl7t2wcb55zy3S4/VxSoTPp82nL/ugf11k62XkheteM+ksm0NX8Tk04nE+1NgIUzcNOUdKo+TeiSc1Ue1z78Q2D3H1U1+HWzPH9BvCvbNo8WJ+9NFHfdz7dW+nURZyUftd+f5LQ2HhiD7PZ6Ms/LJ5p3runZNO/5ay08a0wyhh7ZOZNiEXtW/pGKkc3vnDTz6rfFyjWtNK4Z3Olv5vbmHrtd+IdiEfSO6dRO925fAO77Jz7yiEd8yenf08JGzS4qg/HNi+mmJaISyTSCqHcRLtw5TG2T56nJsHhXc6xyqHd3oT1vs17tw7rW1tA3bUTOyay2f9+Deh7b6zslu7dwZTg5V7py/nvg0+ttw4cTlw9oKL3Dkyx07U44rqjIrTyRMt905UHjVXT7CjxvRs+kPR4SPkAfNAgrnbW8eOnw9u7p3BONMnolEA/hvAVFg1bk9h5h2CdusA7IFlZCpVPmhUf9+phr0jV0tLS6tWMpmRL5mhPzHoYgD3M3MXgPvtxzKtYOYFnm8WUX4fQBNt+rlcznWrdhg3cjlXaoAwnjdywgpVcXE2S64UCYPGy0VXqoUwHvW4ZPMZdb2ic8N1S38oNwxXagA3zwt4XszzBVcKg6bhsuOV8qD5jLAuUp5DqeR/P8epssmhPzHoBACVfBI3Ajix1r/fsJu+N/fOzK4uLJnrz82yZO4sIV+xuBsrFnf7csKIeMWpctLS+b7cODJ+xorFOGPFYilXzaVTePYWlJ6/zcdLz98m5EH9+Ljt2Ol5/Gpf7h0RV5kHlfk0AtZFxrtmzkTXzJmK3MD0hYdi+sJD/blcKtybE2bpMZi69Bghn3b0af6cM0efJuQzPvJZzPjIZ305bWS867SvoOu0r6jziLl6gvpXHmfA8Yp58Hz6uMp6Cdb9hhtuQK3ErLzpjyGipxw/Z0d8qfHMvNF6Td4IYJykHQO4l4ie9ryG6u9X1dAxfa+GmsNEO3lsPuQcO1GdJ3Vy7Aw5J89Qc0bV9g9JMffO1rALuUR0H4AJgqe+EWE4y5j5XSIaB+DPRPQSMz8c4ff7x9PI7h2ncrkcdhrlCLl3egCo3UC0PVdUupFql1ESclH7Uz9zqYsHOW3MUqEGuXesvCcquXf+dN0XlY9r4sjhLi7PvWM7RhTWa2RLor65d3ap597p3WY5fVScNmbvHjWnjVkWc1F77/wEtE+0D1MaZ6JjhHrunc4hkHsnBvfO2BndfNJ3bw1td+2/LRzQaxHRywCWM/NGIpoI4EFmnhXyO6sB9DDzD/bm9xviTJ+YQWZw0ZG2lgxKgmBVR5v4in97W5syTycSSLeJi0M4+Scu/EngGJ3yFzyRFUDJSFw4/vZWn/J+nLrtpxcFD9ChdKJ/YsPmIek5+5LNs2xdhJzN/s2G3RfPfNz+tzWbkbS3eW+Pm7dI2rdkwJk0YHp4pWqVh2cT1vFzsRjOzTJa08nq/139O7gz4Vo2afdT8vRv86B+vJwSpDhOU3q8Pl6Z/4D5rHLHc2HrVQ5b95g1iGkY1gI4A8Cl9r++LG9E1A4gwcx77P8fBeC/VH/fq7ps+jL7kZaWltZQkHVz1qBs+pcCuI2IzgLwNoCPAQAR7Qfg58y8EsB4AL+3w1kpALcy891Bvx+kep7pr2DmrQPpYKjl3olchSqu6ld7kXsnb+SUc/5ErRqmc+/YXOfesXnj5d4BD07uHWbeBuADAv4ugJX2/98AMD/K7wepod07smpKUaoyBVV3qnKFalmGkYtWhSpqlauozpyQ/qNU9YpSNUxpPgewXmFVtIKrL6lXy/LziNWjAqplRa2iFWe1LBEPHadi1bBpK1dFmE+VallOLl73WqmShmEQLJuDrnpt+jL7UVVEdHbFBrVlq/wLwVBzgDSKZWeozVvDcCEN4HWrclWnKloNXC3LKWagZHLoTyOqLu4dItrPaT8C8D+C7Eeyylner3u7i2Iuar8r3x+vCwtHGIrhnSPP/L51fArhnfyeHXXNvfPfa76qHN5po5LweEXzMCKbUsy903++EbZe44dHCO/UIvfOTvXcO+Vd2/p5WHiHi0Lubd/X2xvYj5NTKq0cxqFsu9I4O8ZNlB6vl9OIIZB7J4bKWSOmzuHl/3F9aLs7PnOYzr2jIjteBWbeTES/B3AwgMie08HKvWMY7jv/ZNWyIlehqmPuHVHeHGkunahc596xeEhOGzaKQh61n0Hn+0DuHWZu2iIqgx7eIaJ2IhpW+T8s+9Fzgz0OLS0trSA1a0y/Hmf6QfajvVYul0MuQmH0qA6TqA6WMCePV4FVrsxSbA4fL49avStsHpAd5uZ76dgZOA+5OQtuBd6cZeRjc+zUmrel0srtidLq/Ud1LkV1RoXdnOWxEAXdnBWHKmkYmlGDvukH2Y/kv2SCPGloC4UCMhm7yk8uh67ueWAQnnvJXX1p0UFWtR0v/9dFcwAAj//jJVd1JxH/t/91N966+RwAwJTTf4ZEynqDm6W8i2c7x1rDLRew+Y//CQAYd+x/gZIZHx9++IWu6lS7//Iji//LRUKuUuXq5ivOqVbvuvnBZ4RVvSq8LZ1E3shh1QqL//YRd1UsJ8+mW6rz8+FD3wcAuPep513zVuF/fTZ8Po1cDovmzxGui2i9jD070NU9DwDw2gvPuqtleTiV8pYD5P3LAQBvPvmguyqTzdf939+4qj5NXXGyxR/4rZ+zidf/cA1aW2yez2PGiecCgI8feMr/AOCvctW16mIf79u1AzM/820AwCvXftNdFcvBKzdM5QpFzDrvEgDAyz/9evXGKCd/5dr/lPbj46Q2zl6Q9Hi9PJnvDZ9Pm2db2n3rIluv1158zrfuq7/5DXzmU59ErcR60x/6GnrOnPo4fKLnCIrGh5yjZqjl3ql1bpy4+q95dTPJ6zZA7h1mwGzSTb8hcu8sXriAH3vo/wa2yeVyyCWyEXLvWI4IlfDOB755b7XgReUsvyIn793ybpVz2S4anvTcMGXzfM8uD5eHg8ySWhHz2356kXK4ps2+PV+l/YjW/nODsLDYuM4OIffn3rHDAgrr1WoasefeaSvudvOA8EV55+bq2W2V5+32Ht67eaPNw8MmbPQqhXGcKRFyBev/1fQHHt7W7nZwBYZ3su1quXc6R0uP18uTIyu5d8LDQTy8PyFknLl3sp2jB+yoGTZ5Fi/+wprQdg99Ybl279RLra2tQELk3IjHYeLd7MO4d7MP53LnDwk+l+OqclXrqliD4dgR8xBniGfTD3KklFv8z3k3v35eY+dMRrzuUh7QPwmek7eXHW9ER5OMh6yXN/PWoBRGb1L3TtNs+lpaWlpxiVGzXG51V0Nv+oOWe8cjWagnLKTj5xFz9UR0BAWFbpLlpLpjJ6rTKebcO60tpNw+Wu6dqLl01EIc/Tym3DshIR0fj9kR5HM6BcxDMorTaSjn3oHl1W9GNeymXygUcKDE0TF39iwAfmeIkyPR70g5bL7lGPE6TCp8xEevkjp2nDzMsePm4Y4dLxc5dpw8b1wQ6NhxciK/Y+fkwy1TlZd/2OZex85RSw4S8rD5fPwfL6Gz6twIX6/XX/iHi8vW/cDueQCbPgfINJEzRNFhUuUBjh0nr2xrMieMk7+yRu7YcfIwx46Lhzl2BDxsnK/fsUbJuTTjxHMBSqjNp5HHtKWCdZGtl2Tda6YmvpDbsJt+RY2TY2doOXm0Y6fChXjoOXmGnCOo+Rw7bnHTWjYb2r3j/bqXS2SFXNQ+Su6dYy9x5/2RhXd63lsHQC28Y+zaohzGMe2CGWHtf7Pmyy4eFN5pS6uHd7Kw+lcJ1wzPJGPPvTMqSnhHMfcO7d6sHN4p77Bz7yiEd8ya5N6xqnyphHcoHX/unXZv7p2g8M6Iccrhnb60VWUt7tw7cbh32vebyd3nXBXa7qnVH9LunZqITSSM3T7cXtkL7Bwm2Q7rTZVtr7xZ3B9oTv7RW/6m/PLDRnrfZLI33VTlPpMZ9a+nt33nRKV2zgpXAIBWsaMDrWlk04IMHK3D/AxAe1rs6GhP+6totaUTaBNw67l+nk30r03YeiVsG2b1dT3r7uRJKgq5t315z45qKMYsuksnenllI69yo0fc3uhxbfr9HML2xT73NaNUlfe6eLJo3U1dcGPYNbFcPJFOSfvx8lSboTROc1dGzAXzkCx2hM5nRe2j7b8BhfXiVIt03WulZj3Tb4xNX0tLS2uQ1aybfsMWUfEql+u/gcfNc0JeLuRRLuSVuVnMwyxG4KV8NQykwrlUAJcKQu4salJR3sgJuWHkXEVNQnkuJ3QpxcVl8x+dG66iGqHcMFxFO8J53lUUxMXz/nXJ5QtiXihWwy5NxWXHK+NB8ynkEdcrZ6BUKvl4XGJmmGb4TyOqYTf9QsFR5ceutiOrpiTij60+GY+tPtm1wZcLeSE3i3k8f9UZeP6qM1wbvJTbDp+3bj7HtcE7uXOD51IB793xFbx3x1eE/KSl833VrE5aOt/Hg6p6yapcHbXkIBy15CBflSsZP3zBHBy+wF8Vq8K987ywexYWdkerfuXnEassGQamHH4cphx+nK9aUz/PO3ge048/C9OPP0vIo1ShmnX+ZZh1/mWujTNXKEp595euRPeXrlTmcy++GnMvvlqZB/WvPM6o1bhC5tPPFdZLsO7X33obailmDv0ZqIhoFBH9mYhetf8dKWgzi4j+7vjZTUQX2c+tJqINjudWhr1mw276ItX/ir/vlSNySesh53jZx3hUZ4uQBvF6OXNk7aP2U691qa3YDP+JQRcDuJ+ZuwDcbz92j4P5ZWZewMwLACwG0Afg944mV1SeZ+a7wl6wMdw7C+bxX+9dG9gmlzNQ7hijfEPW0T95HACQzLgdBZUzfCffvqmneiafSLvbO/meHf1np2G5egq97otalTN8SmV8/Jf/83glp006kVBOAV25kKvitGl3XPQNaz/Wk3tHNv8p+6Keynole7Yo38iT7LFKa4al9s30bvfwgNw72zcp39DUu+U9iys4bUp9OaUbr8xiSchF7dvbW4Vc1D7V1qo0Tiv3jpojKDl6gsUVnFHJ0fs5eMh6jdnfzXMG0umUMPdOZvy0ATtqWid08YzTrght9/wPjx/QaxHRywCWM/NGIpoI4EFmnhXQ/igA32LmZfbj1QB6mPkHqq/ZNBdyW1uzKAusXDJ7l3ezD+PezT6Uh+bqcW/63s3eyYdSlav65diJWGUppCpWudfL5bliylFy1ETNjVNHnhI8tzc5fIQ8au6dsPXycsm6xyYetAu545l5IwDYG/+4kPanAviVh11ARKsAPAXgi8y8I6iDpgrvaGlpacUjhsnhPwDGENFTjp+zvT0R0X1E9Jzg54QoIyKiDIAPA/iNA/8UwAwACwBsBPDDsH4a4kyfzBISfTt93PuV8Lo3rShfofKV0HP24ORzJnVWeSlv8VSLu32Fv+p5XVEICAASdgHxoBBQRbeuPlI5R03ZDsGFtW/Ptgm5qH2mTMq5cSoFbFRCMR2ZhNLNcQk7DCPrx8k7PKflgVWutr2rdHNQedt7yiGL0jYrZKMSijG27RJyUftU0X3+mrPDOK1p959lb68h5KL2iUxK2o+Xp+37IMLGObJUFHJhyChfUA6FGQV7PArrlUikpOteC1kJ15TO9LeGhXeY+YOy54hoExFNdIR3Ngd0dQyAZ5h5k6Pv6v+J6FoAd4YNuGHP9AuFIqYuPQZTlx7juuJfMAx87YTD8LUTDqtu8kG8lDdw47kfwo3nfqi6yXu5isOnXMjj2ctPw7OXn+Zz+Ii4kcvhiEVzcMQivxNGxlcs7saKxd3KPKj/w+bP9vHD5s8WcpkDR8ZndnVhZleXMp+x4BDMWHCInHscHaJ1r/Bpx57pc4xMO/ZMP4/qSCkUMfvCyzH7wst9jhcZX7D6WixYfa2cO2P1xRKWXHYTllx2k48ffPktOPjyW5S5rB8hVxmn4vHOvvDySE4n4brI1kuy7jWTHd4J+4lBawGcYf//DAB3BLT9ODyhHfuDoqKToFBvvCHO9IMUV06PyJaI6AOKNJ74crk0Kxfi+lWhiqt/cTexOVtq7iCq9XwOohFvkHz4lwK4jYjOAvA2gI8BABHtB+DnzLzSftwG4EgA53h+/3tEtADWl5N1gud9agj3zpJ53fzE2lt93Pt17+ebLIurSnjnn+v7v4aHhne2um8mkYV3dm7eCUAxvHPeYfGHd9oihHeS9Q3vJOsc3ilt2aDDO6rhnYljpMfr5alR45XDO8VhVr8q60WjJymHd1LTFg7YvdMydgbv/9HvhbZ742cn69w7gynZ4ns3+zDu3ez93L3px+XwqbVzJohnkv5TpvpVuQrhPe5NP8jpwQJ3iNQxUuuqVSG86Nn0vZv0oPE4HUFRnE4xOXxqpUa94zZMDb3pa2lpadVKbHqNos2hptn0c4aBgmEEhnScCgvpeLkspCPjYTdzeRUUiikz16xqVdxVrjoy7ZHaK/OQm3f8PPjmIO+5alCop1QoqletisoVQzGqvD2j3r6mxxW1SlfIerkDl9a6p1Pim7NiEXPTbvoN7d6pqHJlX9WxUzDCHTtOHuTYkeXqCXPyRHHaxOXYEXGZY0fEgxw7UZ08oY4dJw9x7Pi5zAHi4IoOk65VF8fr2HHyqE6bmJ08cTl2fHwvcvWEO3n8637dbX9ArcSwNv2wn0ZU05zpA3V07MgHFIlrx04YF+LaO3nq5diR8dicPEPMuRRT/7GIGWbRnz20GdQQ7p33LVjEa+97KLCNkcth3S5DuRrU8xus3Csq4aCX39ujFA76zw9Mr/Kw8EUy480hI29fZrUwSJLUwyaVC7kq7Z0FT8Lat7Mh5N72hW0bLK4QruHtG5WrXJU2vW3xkPBCatdWNw8IX/Rt2q4cZtnxnvW+ynq4Ybd38lJvDkbJOlvMppLu9g5eLpSEXNS+va1FyEXtU21ZpXG2jh6uHG5qmzDa4grhoPQ4R+6dkPUadsCBbm7k7fCO+7gAIL3omAE7ajKjpvDYD30jtN27vz5Hu3fqpWxrK1ry/k9+WY6a+B0+boU5UgplFnJR+7Lgc7nujhoZ7zPE3Ns+JNeKn0d0eoQ4c7wZ44McKSxwvcicMN5NNJQLNq3B4inBmGTjrLkjKKbcPnGqUcM3YWqaTV9LS0srLlVi+s2oht70ZQ6ToILgIh52M5dXslBPbE6VveQdberty0mq2zijOnO85/5BoZ5ShNwvtXbaiEIlTu7941MJ9dSEh4zT+10taB4oiiMoJodPTcT6TH/IqVAoYNmCOQCAR//+YnXjzxs5nHrEQgDArx/+W3WD9/JqP7bDBwC+e8fj1Y3fyT+15t7qBl/KG7j200cCAD7z8z+7+NzZswAAz730cnVjy+VyUr7oIIs/87waXzLX4k89J+Z/U+zH4qQ8zvfNsfirL7t51ywxn7HgEADA639/QsorWfdzhoGphx0NAFj3+N3Vjd/J3/zjDdU/9JyRx7SVqyx+101+bpbx6k2XVjeMimMEgJsXipjzeStf+otXfL66IUl5sYTFl9wAAHj665+sbnheXpFRLGHpNb8DADx27kerG6qTP7zq2OoGbJTKOOJGK1fWw2cc5+LLb70bAPDgJ45W4rJ+hFxhnM9840zp8fp4gtTms1BEt2hdZOslWffaiWHqTX9oSjte9lEupLVf96hWm6jOHJlqbjSLyKXHW6eqZHGLmWEKalY3gxraveMN77y50woHqIR3Xt3WX8QkLLzz5k73ZT9ZeOfLS62EdyphjUKZlcMglQu5cYZ3MhHCO5xXr3KV6Nuhlnund1s/Dwvv9O3w8IDwzuZ3lMIFxc3vKod3+t6zxqoS3slt213loeEdTwFyWfilty8v5KL2yUxKObyTsqtshY1z5IRR0uP18rbxo5TDO6VOO/eOwnqlxh+gHN6Jw72T6pzEncs+G9pu+5/+Q7t3BlOyXDEyx87eO3ncf5x769gZSlyUe6du4wlx8nCfl8udPKVaVrmK6GAJc/KUPJt+3Zw8EccfNA+iuWskx45TOqavpaWlta+oidMw1GXTJ6KjAVwJIAkrZ/SlQe3TScKEDvHZQkW5XA4TW9Rz1Jh22gQVh8+nprFSYW7TdNwqHhLiGD5shJCL2qcSSaXQCpll5ZARlQylcba2tiLRt9vBg+fBd3OWJHTTt/EtmyukQd70trLTo7BxvcVDQjel9zyF0QNCFsbWncpOmB2bdgm5qH2hpwijbPOkp72Dlx3ZOMPat2czQi5qn2lPK41T9SYyACjlrPVQCYUNK5v9PGS9OuFWLl9AOpVEKin+9jJwNe+mP+i5d4goCeBqWKW/ugF8nIi6o/ZTKDhyd9g5XpYt8OeWWbZgjpCffPh8nHz4/OomD1gbvojncgamL1qK6YuWVje3YJ7Dgd3zcGD3PF9uGRnvmjULXbP8uWu6Zs2KVIUqqB8RjzZOhXlQrHI1beUqTFu5yp9rRcT3ospV95euRPeXrvTliqnyCDltjrjxzurmBvQ7YUT8qLUP4Ki1D6jxchnH3vcIjr3vkermHMY//NDj+PBDjyvzoP6VxxlwvCIeOUeQynoJ1v2Gex5FrWSVSzRDfxpR9TjTPxjAa8z8BgAQ0a8BnADghYF2XOvcO0POwdIwXIhrX90pJidPVNU+R1BEXrfUUw3s5NHunRhfkOhkAEcz86ftx6cDOISZL/C0OxvA2QAwefLkxS+/8kpgv6VSCe/1FH2pVksl2ynh4eu29wm5qP2hHXuk/Ti5mR0e+roVnsy2C7moPSeSSuMks6w0TgDValgq7RPGbiEXtc8UepSOq7hlg82TnvZlHy9tehsl+0zV+3XeyyvhnZIdOkglE572Fs9t2CLkova5rTuQSni4fZbn5Xu27BZyUftCT1Haj5M7wzth7Vta0kIuap/pSCuNs3V0h9I4AaB17EiLh8x/KpnAsAPGC7mofdv+B3h4GQkiJBL+YEXrMQPPh5PsGM/ZBZ8Ibdf36I+0e0dBog9o3ycPM68BsAYAFi1eHPrJlEqlkEr5m8nybdebe0ca1J4T/rhlvccv5QUJ97UXx2KlXBK7lXNx5HJvuHejA/ybXyPymh5XjPMv5rWK5VtiAFzWMf24tB7AZMfj/QG8W4dxaGlpaYnFg5NPn4g+RkTPE5FJRNJvDER0NBG9TESvEdHFDj6KiP5MRK/a/44Me816bPpPAugiomlElAFwKoC1dRiHlpaWlkSDVkTlOQAfAfCwrEGI+eViAPczcxeA++3HgRr08A4zl4joAgD3wLJsXsfMzw/2OLS0tLSkGqQLucz8IhBqIggyv5wAYLnd7kYADwL4alBndfHpM/NdAO5Sbf+3Z57Z2tba+pYHjwGwVdS+wdWMx9WMxwTo4xqqmjLQDji37Z7i368fo9A0S0RPOR6vsa9HxqlJAN5xPF4P4BD7/+OZeSMAMPNGIhoX1llD3JHLzGO9jIiearSr5ipqxuNqxmMC9HE1s5j56Lj6IqL7AEwQPPUNZr5DpQsB22vbZUNs+lpaWlqNKmb+4AC7CDK/bCKiifZZ/kQAm8M6q8eFXC0tLS0tdQWZX9YCOMP+/xkAQr85NPKmH3fcbKioGY+rGY8J0MelNUAR0UlEtB7AYQD+SET32Hw/IroLsMwvACrmlxcB3OYwv1wK4EgiehXAkfbj4NdshHz6WlpaWlrxqJHP9LW0tLS0Ikpv+lpaWlr7kBpy05fdktzIIqJ1RPRPIvq7x/fbUCKi64hoMxE952CRbxUfapIc12oi2mCv2d+JaGU9xxhVRDSZiB4gohftVACfs3nDr5eWXA236ceVj3+IagUzL2hwj/QNALwe58i3ig9B3QD/cQHAFfaaLbBvOmwklQB8kZnnADgUwPn231IzrJeWRA236cNxSzIzFwBUbknWGgJi5ocBbPfgE2DdIg773xMHc0xxSHJcDS1m3sjMz9j/3wPLGTIJTbBeWnI14qYvuiV5Up3GEqcYwL1E9LRdS6CZ5LpVHEDoreINpAuI6Fk7/NOwYRAimgpgIYAn0Nzrtc+rETf9WG9JHkJaxsyLYIWtzieiI+o9IK1Q/RTADAALAGwE8MO6jmYvRUQdAH4H4CJm3h3WXqux1YibflPm42fmd+1/NwP4PawwVrNok32LOFRvFW8EMfMmZi4zswngWjTgmhFRGtaGfwsz327jplwvLUuNuOk3XT5+ImonomGV/wM4Clae7WZR5FvFG0GVjdHWSWiwNSMrn+8vALzIzJc7nmrK9dKy1JB35NrWuB+hPx//d+o7ooGJiKbDOrsHrCR4tzbqMRHRr2Dl9x4DYBOAbwH4A4DbABwA4G0AH2PmhrooKjmu5bBCOwxgHYBzKrHwRhARHQ7gLwD+CcC08ddhxfUber205GrITV9LS0tLa+/UiOEdLS0tLa29lN70tbS0tPYh6U1fS0tLax+S3vS1tLS09iHpTV9LS0trH5Le9LW0tLT2IelNX0tLS2sfkt70tRpKRPTtSt53+/F3iOjCeo5JS6uRpG/O0moo2dkgb2fmRUSUAPAqgIOZeVt9R6al1RhK1XsAWlpRxMzriGgbES0EMB7A3/SGr6WlLr3pazWifg7gkwAmALiuvkPR0mos6fCOVsPJzq76TwBpAF3MXK7zkLS0Gkb6TF+r4cTMBSJ6AMBOveFraUWT3vS1Gk72BdxDAXys3mPR0mo0acumVkOJiLoBvAbgfmZ+td7j0dJqNOmYvpaWltY+JH2mr6WlpbUPSW/6WlpaWvuQ9KavpaWltQ9Jb/paWlpa+5D0pq+lpaW1D+n/B5NsmdpduQsvAAAAAElFTkSuQmCC\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "da.mean('member').plot()\n", - "sign_agreement(\n", - " da, da.mean('member'), 'member'\n", - ").plot.contourf(\n", - " colors='none',\n", - " hatches=['..', None],\n", - " levels=[0,0.5],\n", - " add_colorbar=False\n", - ")" + "ds = ddict['CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn']\n", + "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Masking values in the mixed layer\n", - "\n", - "Sometimes it is helpful to analyze data by excluding the values in the mixed layer. This can be easily done with `mask_mixedlayer`. Let's see how:\n", - "\n", - "First load a CMIP6 dataset from the cloud" + "We can remove the values in the mixed layer" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/juliusbusecke/miniconda/envs/xarrayutils-docs/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3418: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--> The keys in the returned dictionary of datasets are constructed as follows:\n", - "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" + "/home/jovyan/xarrayutils/xarrayutils/utils.py:805: UserWarning: Cell bounds [{z_bounds}] not found in input. Masking is performed with cell centers, which might be less accurate\n", + " warnings.warn(\n" ] }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - " \n", - " 100.00% [1/1 00:00<00:00]\n", - "
    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import intake\n", - "url = \"https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json\"\n", - "col = intake.open_esm_datastore(url)\n", - "cat = col.search(\n", - " table_id='Omon',\n", - " grid_label='gn',\n", - " experiment_id='historical',\n", - " member_id='r1i1p1f1',\n", - " variable_id=['thetao','mlotst'],#, \n", - " source_id=[\"ACCESS-ESM1-5\"]\n", - ")\n", - "ddict = cat.to_dataset_dict(\n", - " zarr_kwargs={'consolidated':True, 'decode_times':True},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ { "data": { "text/html": [ @@ -1768,7 +4056,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -1804,64 +4093,105 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset>\n",
-       "Dimensions:    (i: 360, j: 300, lev: 50, member_id: 1, time: 1980)\n",
+       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
        "Coordinates:\n",
-       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 ... 352 353 354 355 356 357 358 359\n",
-       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 ... 292 293 294 295 296 297 298 299\n",
-       "    latitude   (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
-       "    longitude  (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
-       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
-       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
-       "  * lev        (lev) float64 5.0 15.0 25.0 ... 5.166e+03 5.499e+03 5.831e+03\n",
+       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
+       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
+       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
+       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
+       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
+       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
+       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
+       "Dimensions without coordinates: bnds, vertices\n",
        "Data variables:\n",
-       "    mlotst     (member_id, time, j, i) float32 dask.array<chunksize=(1, 196, 300, 360), meta=np.ndarray>\n",
-       "    thetao     (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
+       "    mlotst              (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
+       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
+       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
+       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    branch_time_in_parent:   21915.0\n",
-       "    nominal_resolution:      250 km\n",
-       "    physics_index:           1\n",
-       "    table_id:                Omon\n",
-       "    institution_id:          CSIRO\n",
-       "    parent_mip_era:          CMIP6\n",
-       "    sub_experiment_id:       none\n",
-       "    grid:                    native atmosphere N96 grid (145x192 latxlon)\n",
-       "    mip_era:                 CMIP6\n",
-       "    intake_esm_varname:      mlotst\\nthetao\n",
-       "    activity_id:             CMIP\n",
-       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
-       "    parent_source_id:        ACCESS-ESM1-5\n",
-       "    variant_label:           r1i1p1f1\n",
-       "    branch_method:           standard\n",
-       "    frequency:               mon\n",
-       "    run_variant:             forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n",
-       "    tracking_id:             hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n",
-       "    realm:                   ocean\n",
-       "    source_id:               ACCESS-ESM1-5\n",
-       "    forcing_index:           1\n",
-       "    parent_variant_label:    r1i1p1f1\n",
-       "    cmor_version:            3.4.0\n",
-       "    sub_experiment:          none\n",
-       "    parent_experiment_id:    piControl\n",
-       "    product:                 model-output\n",
-       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
-       "    branch_time_in_child:    0.0\n",
-       "    initialization_index:    1\n",
-       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
-       "    version:                 v20191115\n",
-       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
-       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
-       "    Conventions:             CF-1.7 CMIP-6.2\n",
-       "    realization_index:       1\n",
-       "    source_type:             AOGCM\n",
-       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
-       "    experiment:              all-forcing simulation of the recent past\n",
-       "    parent_time_units:       days since 0101-1-1\n",
-       "    grid_label:              gn\n",
-       "    experiment_id:           historical\n",
-       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
-       "    parent_activity_id:      CMIP\n",
-       "    data_specs_version:      01.00.30\n",
-       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn
    " + "hdl:21.14100/c26507e7-0f70-402c-9f87-741919c26350
    initialization_index :
    1
    physics_index :
    1
    run_variant :
    forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)
    branch_time_in_parent :
    21915.0
    history :
    2019-11-15T15:38:06Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.\n", + "2019-11-15T15:08:04Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.
    cmor_version :
    3.4.0
    grid_label :
    gn
    nominal_resolution :
    250 km
    intake_esm_varname :
    mlotst\n", + "thetao
    experiment_id :
    historical
    realization_index :
    1
    parent_activity_id :
    CMIP
    sub_experiment_id :
    none
    source_id :
    ACCESS-ESM1-5
    branch_time_in_child :
    0.0
    parent_experiment_id :
    piControl
    activity_id :
    CMIP
    data_specs_version :
    01.00.30
    realm :
    ocean
    product :
    model-output
    Conventions :
    CF-1.7 CMIP-6.2
    experiment :
    all-forcing simulation of the recent past
    mip_era :
    CMIP6
    institution :
    Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia
    branch_method :
    standard
    frequency :
    mon
    institution_id :
    CSIRO
    title :
    ACCESS-ESM1-5 output prepared for CMIP6
    table_info :
    Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371
    parent_variant_label :
    r1i1p1f1
    parent_mip_era :
    CMIP6
    parent_time_units :
    days since 0101-1-1
    further_info_url :
    https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r1i1p1f1
    sub_experiment :
    none
    source_type :
    AOGCM
    version :
    v20191115
    parent_source_id :
    ACCESS-ESM1-5
    license :
    CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
    intake_esm_dataset_key :
    CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn
    mixed_layer_values_removed_based_on :
    lev
    " ], "text/plain": [ "\n", - "Dimensions: (i: 360, j: 300, lev: 50, member_id: 1, time: 1980)\n", + "Dimensions: (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n", "Coordinates:\n", - " * i (i) int32 0 1 2 3 4 5 6 7 8 ... 352 353 354 355 356 357 358 359\n", - " * j (j) int32 0 1 2 3 4 5 6 7 8 ... 292 293 294 295 296 297 298 299\n", - " latitude (j, i) float64 dask.array\n", - " longitude (j, i) float64 dask.array\n", - " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", - " * member_id (member_id) \n", + " longitude (j, i) float64 dask.array\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n", + " time_bnds (time, bnds) datetime64[ns] dask.array\n", + " * member_id (member_id) \n", + "Dimensions without coordinates: bnds, vertices\n", "Data variables:\n", - " mlotst (member_id, time, j, i) float32 dask.array\n", - " thetao (member_id, time, lev, j, i) float32 dask.array\n", + " mlotst (member_id, time, j, i, lev) float32 dask.array\n", + " vertices_latitude (j, i, vertices, lev, member_id, time) float64 dask.array\n", + " vertices_longitude (j, i, vertices, lev, member_id, time) float64 dask.array\n", + " thetao (member_id, time, lev, j, i) float32 dask.array\n", "Attributes:\n", - " branch_time_in_parent: 21915.0\n", - " nominal_resolution: 250 km\n", - " physics_index: 1\n", - " table_id: Omon\n", - " institution_id: CSIRO\n", - " parent_mip_era: CMIP6\n", - " sub_experiment_id: none\n", - " grid: native atmosphere N96 grid (145x192 latxlon)\n", - " mip_era: CMIP6\n", - " intake_esm_varname: mlotst\\nthetao\n", - " activity_id: CMIP\n", - " title: ACCESS-ESM1-5 output prepared for CMIP6\n", - " parent_source_id: ACCESS-ESM1-5\n", - " variant_label: r1i1p1f1\n", - " branch_method: standard\n", - " frequency: mon\n", - " run_variant: forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n", - " tracking_id: hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n", - " realm: ocean\n", - " source_id: ACCESS-ESM1-5\n", - " forcing_index: 1\n", - " parent_variant_label: r1i1p1f1\n", - " cmor_version: 3.4.0\n", - " sub_experiment: none\n", - " parent_experiment_id: piControl\n", - " product: model-output\n", - " license: CMIP6 model data produced by CSIRO is licensed u...\n", - " branch_time_in_child: 0.0\n", - " initialization_index: 1\n", - " source: ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n", - " version: v20191115\n", - " table_info: Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n", - " history: 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " realization_index: 1\n", - " source_type: AOGCM\n", - " institution: Commonwealth Scientific and Industrial Research ...\n", - " experiment: all-forcing simulation of the recent past\n", - " parent_time_units: days since 0101-1-1\n", - " grid_label: gn\n", - " experiment_id: historical\n", - " further_info_url: https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n", - " parent_activity_id: CMIP\n", - " data_specs_version: 01.00.30\n", - " intake_esm_dataset_key: CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn" + " grid: native atmosphere N96 grid (145x192...\n", + " forcing_index: 1\n", + " table_id: Omon\n", + " variant_label: r1i1p1f1\n", + " source: ACCESS-ESM1.5 (2019): \\naerosol: CL...\n", + " tracking_id: hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n", + " initialization_index: 1\n", + " physics_index: 1\n", + " run_variant: forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n", + " branch_time_in_parent: 21915.0\n", + " history: 2019-11-15T15:38:06Z ; CMOR rewrote...\n", + " cmor_version: 3.4.0\n", + " grid_label: gn\n", + " nominal_resolution: 250 km\n", + " intake_esm_varname: mlotst\\nthetao\n", + " experiment_id: historical\n", + " realization_index: 1\n", + " parent_activity_id: CMIP\n", + " sub_experiment_id: none\n", + " source_id: ACCESS-ESM1-5\n", + " branch_time_in_child: 0.0\n", + " parent_experiment_id: piControl\n", + " activity_id: CMIP\n", + " data_specs_version: 01.00.30\n", + " realm: ocean\n", + " product: model-output\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " experiment: all-forcing simulation of the recen...\n", + " mip_era: CMIP6\n", + " institution: Commonwealth Scientific and Industr...\n", + " branch_method: standard\n", + " frequency: mon\n", + " institution_id: CSIRO\n", + " title: ACCESS-ESM1-5 output prepared for C...\n", + " table_info: Creation Date:(30 April 2019) MD5:e...\n", + " parent_variant_label: r1i1p1f1\n", + " parent_mip_era: CMIP6\n", + " parent_time_units: days since 0101-1-1\n", + " further_info_url: https://furtherinfo.es-doc.org/CMIP...\n", + " sub_experiment: none\n", + " source_type: AOGCM\n", + " version: v20191115\n", + " parent_source_id: ACCESS-ESM1-5\n", + " license: CMIP6 model data produced by CSIRO ...\n", + " intake_esm_dataset_key: CMIP.CSIRO.ACCESS-ESM1-5.historical...\n", + " mixed_layer_values_removed_based_on: lev" ] }, - "execution_count": 15, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ds = ddict['CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn']\n", - "ds" + "from xarrayutils.utils import mask_mixedlayer\n", + "ds_wo_ml = mask_mixedlayer(ds, ds.mlotst)\n", + "ds_wo_ml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can remove the values in the mixed layer" + "Or to have the mixed layer values only" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/juliusbusecke/code/xarrayutils/xarrayutils/utils.py:805: UserWarning: Cell bounds [{z_bounds}] not found in input. Masking is performed with cell centers, which might be less accurate\n", + "/home/jovyan/xarrayutils/xarrayutils/utils.py:805: UserWarning: Cell bounds [{z_bounds}] not found in input. Masking is performed with cell centers, which might be less accurate\n", " warnings.warn(\n" ] }, @@ -2581,7 +5177,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -2617,65 +5214,70 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset>\n",
-       "Dimensions:    (i: 360, j: 300, lev: 50, member_id: 1, time: 1980)\n",
+       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
        "Coordinates:\n",
-       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 ... 352 353 354 355 356 357 358 359\n",
-       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 ... 292 293 294 295 296 297 298 299\n",
-       "    latitude   (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
-       "    longitude  (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
-       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
-       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
-       "  * lev        (lev) float64 5.0 15.0 25.0 ... 5.166e+03 5.499e+03 5.831e+03\n",
+       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
+       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
+       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
+       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
+       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
+       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
+       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
+       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
+       "Dimensions without coordinates: bnds, vertices\n",
        "Data variables:\n",
-       "    mlotst     (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
-       "    thetao     (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
+       "    mlotst              (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
+       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
+       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
+       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    branch_time_in_parent:                21915.0\n",
-       "    nominal_resolution:                   250 km\n",
-       "    physics_index:                        1\n",
-       "    table_id:                             Omon\n",
-       "    institution_id:                       CSIRO\n",
-       "    parent_mip_era:                       CMIP6\n",
-       "    sub_experiment_id:                    none\n",
        "    grid:                                 native atmosphere N96 grid (145x192...\n",
-       "    mip_era:                              CMIP6\n",
-       "    intake_esm_varname:                   mlotst\\nthetao\n",
-       "    activity_id:                          CMIP\n",
-       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
-       "    parent_source_id:                     ACCESS-ESM1-5\n",
+       "    forcing_index:                        1\n",
+       "    table_id:                             Omon\n",
        "    variant_label:                        r1i1p1f1\n",
-       "    branch_method:                        standard\n",
-       "    frequency:                            mon\n",
-       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
+       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
        "    tracking_id:                          hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n",
-       "    realm:                                ocean\n",
-       "    source_id:                            ACCESS-ESM1-5\n",
-       "    forcing_index:                        1\n",
-       "    parent_variant_label:                 r1i1p1f1\n",
+       "    initialization_index:                 1\n",
+       "    physics_index:                        1\n",
+       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
+       "    branch_time_in_parent:                21915.0\n",
+       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
        "    cmor_version:                         3.4.0\n",
-       "    sub_experiment:                       none\n",
+       "    grid_label:                           gn\n",
+       "    nominal_resolution:                   250 km\n",
+       "    intake_esm_varname:                   mlotst\\nthetao\n",
+       "    experiment_id:                        historical\n",
+       "    realization_index:                    1\n",
+       "    parent_activity_id:                   CMIP\n",
+       "    sub_experiment_id:                    none\n",
+       "    source_id:                            ACCESS-ESM1-5\n",
+       "    branch_time_in_child:                 0.0\n",
        "    parent_experiment_id:                 piControl\n",
+       "    activity_id:                          CMIP\n",
+       "    data_specs_version:                   01.00.30\n",
+       "    realm:                                ocean\n",
        "    product:                              model-output\n",
-       "    license:                              CMIP6 model data produced by CSIRO ...\n",
-       "    branch_time_in_child:                 0.0\n",
-       "    initialization_index:                 1\n",
-       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
-       "    version:                              v20191115\n",
-       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
-       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
        "    Conventions:                          CF-1.7 CMIP-6.2\n",
-       "    realization_index:                    1\n",
-       "    source_type:                          AOGCM\n",
-       "    institution:                          Commonwealth Scientific and Industr...\n",
        "    experiment:                           all-forcing simulation of the recen...\n",
+       "    mip_era:                              CMIP6\n",
+       "    institution:                          Commonwealth Scientific and Industr...\n",
+       "    branch_method:                        standard\n",
+       "    frequency:                            mon\n",
+       "    institution_id:                       CSIRO\n",
+       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
+       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
+       "    parent_variant_label:                 r1i1p1f1\n",
+       "    parent_mip_era:                       CMIP6\n",
        "    parent_time_units:                    days since 0101-1-1\n",
-       "    grid_label:                           gn\n",
-       "    experiment_id:                        historical\n",
        "    further_info_url:                     https://furtherinfo.es-doc.org/CMIP...\n",
-       "    parent_activity_id:                   CMIP\n",
-       "    data_specs_version:                   01.00.30\n",
+       "    sub_experiment:                       none\n",
+       "    source_type:                          AOGCM\n",
+       "    version:                              v20191115\n",
+       "    parent_source_id:                     ACCESS-ESM1-5\n",
+       "    license:                              CMIP6 model data produced by CSIRO ...\n",
        "    intake_esm_dataset_key:               CMIP.CSIRO.ACCESS-ESM1-5.historical...\n",
-       "    mixed_layer_values_removed_based_on:  lev