From 7be4bac7fa7afa16c6a17b763424fbdc3e22864d Mon Sep 17 00:00:00 2001 From: max Date: Tue, 28 Nov 2023 11:35:47 +1100 Subject: [PATCH 1/3] converting from cc to intake --- DocumentedExamples/True_Zonal_Mean.ipynb | 853 +++++++++++++---------- 1 file changed, 501 insertions(+), 352 deletions(-) diff --git a/DocumentedExamples/True_Zonal_Mean.ipynb b/DocumentedExamples/True_Zonal_Mean.ipynb index 4d4eccd5..431aa962 100644 --- a/DocumentedExamples/True_Zonal_Mean.ipynb +++ b/DocumentedExamples/True_Zonal_Mean.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "e3c7ab35", "metadata": {}, "source": [ "# True Zonal Mean\n", @@ -18,13 +19,16 @@ { "cell_type": "code", "execution_count": 1, + "id": "b8cf50e7", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format='retina'\n", "\n", - "import cosima_cookbook as cc\n", + "import pandas as pd\n", + "import intake\n", + "catalog = intake.cat.access_nri\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", @@ -35,6 +39,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "44ded25b", "metadata": {}, "outputs": [ { @@ -44,20 +49,20 @@ "
\n", "
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Client

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Client-7841c4ca-63af-11ed-b67c-00000745fe80

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Client-9cc9d3f1-881e-11ee-8ca4-00000188fe80

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Connection method: Cluster objectCluster type: distributed.LocalClusterConnection method: Direct
\n", - " Dashboard: /proxy/45875/status\n", + " Dashboard: /proxy/8787/status\n", "
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Cluster Info

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LocalCluster

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36ee4029

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Status: runningUsing processes: True
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Scheduler Info

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Scheduler Info

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Scheduler

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Scheduler-bcca7116-ba86-403c-b231-2a7eaf5a438f

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Scheduler-7c846815-9f3c-4666-a6ac-f4150b5ef1f2

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:38367\n", + " Comm: tcp://10.6.48.33:8786\n", " \n", - " Workers: 4\n", + " Workers: 1\n", "
\n", - " Dashboard: /proxy/45875/status\n", + " Dashboard: /proxy/8787/status\n", " \n", " Total threads: 12\n", @@ -130,10 +104,10 @@ "
\n", - " Started: Just now\n", + " Started: 3 hours ago\n", " \n", - " Total memory: 48.00 GiB\n", + " Total memory: 46.00 GiB\n", "
\n", @@ -151,174 +125,80 @@ "
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Worker: 0

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Worker: tcp://10.6.48.33:39653

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\n", - " Comm: tcp://127.0.0.1:34211\n", + " Comm: tcp://10.6.48.33:39653\n", " \n", - " Total threads: 3\n", + " Total threads: 12\n", "
\n", - " Dashboard: /proxy/34243/status\n", + " Dashboard: /proxy/45111/status\n", " \n", - " Memory: 12.00 GiB\n", + " Memory: 46.00 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:36995\n", + " Nanny: tcp://10.6.48.33:36117\n", "
\n", - " Local directory: /jobfs/63557182.gadi-pbs/dask-worker-space/worker-m8jj48ym\n", + " Local directory: /scratch/iq82/mp7041/dasktmp/dask-scratch-space/worker-3h8pqbrb\n", "
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Worker: 1

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\n", - " Comm: tcp://127.0.0.1:35937\n", - " \n", - " Total threads: 3\n", - "
\n", - " Dashboard: /proxy/37687/status\n", + " Tasks executing: \n", " \n", - " Memory: 12.00 GiB\n", + " Tasks in memory: \n", "
\n", - " Nanny: tcp://127.0.0.1:36083\n", - "
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Worker: 2

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\n", - " Comm: tcp://127.0.0.1:43161\n", + " Tasks ready: \n", " \n", - " Total threads: 3\n", + " Tasks in flight: \n", "
\n", - " Dashboard: /proxy/35393/status\n", - " \n", - " Memory: 12.00 GiB\n", + " CPU usage: 2.0%\n", "
\n", - " Nanny: tcp://127.0.0.1:41997\n", - "
\n", - " Local directory: /jobfs/63557182.gadi-pbs/dask-worker-space/worker-dxgdqph2\n", + " Last seen: Just now\n", "
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Worker: 3

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\n", - " Comm: tcp://127.0.0.1:33151\n", + " Memory usage: 9.15 GiB\n", " \n", - " Total threads: 3\n", + " Spilled bytes: 0 B\n", "
\n", - " Dashboard: /proxy/46755/status\n", + " Read bytes: 5.49 kiB\n", " \n", - " Memory: 12.00 GiB\n", - "
\n", - " Nanny: tcp://127.0.0.1:33291\n", + " Write bytes: 5.19 kiB\n", "
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" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -349,31 +225,167 @@ } ], "source": [ - "client = Client()\n", + "client = Client(\"tcp://10.6.48.33:8786\")\n", "client" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "id": "f073f448", "metadata": {}, "outputs": [], - "source": [ - "session = cc.database.create_session()" - ] + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "436211de", + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", + "id": "00372e86", "metadata": {}, "source": [ "Choose the run and the variable we want to average. I am using temperature but you can choose any scalar." ] }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9088c3ef-3e46-401b-a3e5-5cfddebbc7b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Intake dataframe catalog with 7 source(s) across 39 rows:

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modeldescriptionrealmfrequencyvariable
name
025deg_jra55_iaf_omip2_cycle1{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1yr, 1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_iaf_omip2_cycle2{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1yr, 1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_iaf_omip2_cycle3{ACCESS-OM2}{Cycle 3/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1yr, 1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_iaf_omip2_cycle4{ACCESS-OM2}{Cycle 4/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1yr, 1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_iaf_omip2_cycle5{ACCESS-OM2}{Cycle 5/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1yr, 1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_iaf_omip2_cycle6{ACCESS-OM2}{Cycle 6/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean, seaIce}{1mon, fx, 1day}{aiso_bih, mld_min, trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, sfc_salt_flux_runoff, flwup_ai_m, total_ocean_runoff_heat, salt_tendency, bv_freq, psiv, pe_tot, salt_surface_ave, total_ocean_...
025deg_jra55_ryf9091_gadi{ACCESS-OM2}{0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991)}{ocean, seaIce}{1yr, 1mon, fx}{trsig_m, kmt, total_ocean_sfc_salt_flux_coupler, flwup_ai_m, total_ocean_runoff_heat, sea_levelsq, pe_tot, salt_surface_ave, total_ocean_calving, total_ocean_runoff, strocny_m, average_T2, fswfac...
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "catalog.search(name='.*025deg_jra55.*')" + ] + }, { "cell_type": "code", "execution_count": 4, + "id": "9484c76c", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n" + ] + }, { "data": { "text/html": [ @@ -408,6 +420,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -637,6 +650,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -658,14 +676,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -675,13 +695,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -719,7 +742,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -728,26 +752,24 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.DataArray 'temp' (time: 300, st_ocean: 50, yt_ocean: 1080,\n",
+       "
<xarray.DataArray 'temp' (time: 732, st_ocean: 50, yt_ocean: 1080,\n",
        "                          xt_ocean: 1440)>\n",
-       "dask.array<concatenate, shape=(300, 50, 1080, 1440), dtype=float32, chunksize=(1, 10, 216, 288), chunktype=numpy.ndarray>\n",
+       "dask.array<concatenate, shape=(732, 50, 1080, 1440), dtype=float32, chunksize=(1, 25, 108, 120), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
        "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
        "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 2018-12-14T12:00:00\n",
        "  * st_ocean  (st_ocean) float64 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n",
-       "  * time      (time) datetime64[ns] 1958-06-30T12:00:00 ... 2257-06-30T12:00:00\n",
        "Attributes:\n",
        "    long_name:      Conservative temperature\n",
-       "    units:          deg_C\n",
+       "    units:          K\n",
        "    valid_range:    [-10. 500.]\n",
        "    cell_methods:   time: mean\n",
        "    time_avg_info:  average_T1,average_T2,average_DT\n",
-       "    coordinates:    geolon_t geolat_t\n",
-       "    time_bounds:    <xarray.DataArray 'time_bounds' (time: 300, nv: 2)>\\ndask...\n",
-       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...
" + " 3.290172e+03, 3.505757e+03, 3.722423e+03, 3.939909e+03, 4.158017e+03,\n", + " 4.376593e+03, 4.595523e+03, 4.814719e+03, 5.034116e+03, 5.253663e+03])
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
      +       "       -278.125, -277.875, -277.625,\n",
      +       "       ...\n",
      +       "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
      +       "         79.375,   79.625,   79.875],\n",
      +       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
      +       "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
      +       "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
      +       "       -80.12661353099249,\n",
      +       "       ...\n",
      +       "         88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
      +       "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
      +       "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
      +       "        89.94728232704986],\n",
      +       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
      +       "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
      +       "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
      +       "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
      +       "               '1958-09-14 00:00:00', '1958-10-14 12:00:00',\n",
      +       "               ...\n",
      +       "               '2018-03-14 12:00:00', '2018-04-14 00:00:00',\n",
      +       "               '2018-05-14 12:00:00', '2018-06-14 00:00:00',\n",
      +       "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
      +       "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
      +       "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
      +       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
    • st_ocean
      PandasIndex
      PandasIndex(Index([ 1.151749849319458, 3.6486740112304688,  6.564918518066406,\n",
      +       "        9.970869064331055, 13.948708534240723, 18.594379425048828,\n",
      +       "       24.019872665405273, 30.355884552001953,  37.75489044189453,\n",
      +       "        46.39470291137695,  56.48257064819336,   68.2598648071289,\n",
      +       "        82.00740814208984,  98.05146789550781, 116.77034759521484,\n",
      +       "        138.6016082763672,  164.0494384765625,  193.6920623779297,\n",
      +       "       228.18814086914062,  268.2812194824219,  314.8001708984375,\n",
      +       "        368.6535339355469,  430.8139343261719,   502.289306640625,\n",
      +       "        584.0770874023438,  677.1002807617188,   782.126708984375,\n",
      +       "        899.6817626953125, 1029.9681396484375, 1172.8133544921875,\n",
      +       "       1327.6619873046875,  1493.618408203125,  1669.533447265625,\n",
      +       "       1854.1141357421875,  2046.034423828125,  2244.025146484375,\n",
      +       "          2446.9365234375,   2653.77001953125,   2863.68798828125,\n",
      +       "          3076.0048828125,   3290.17236328125,   3505.75732421875,\n",
      +       "        3722.423095703125,    3939.9091796875,    4158.0166015625,\n",
      +       "          4376.5927734375,   4595.52294921875,   4814.71923828125,\n",
      +       "         5034.11572265625,    5253.6630859375],\n",
      +       "      dtype='float64', name='st_ocean'))
  • long_name :
    Conservative temperature
    units :
    K
    valid_range :
    [-10. 500.]
    cell_methods :
    time: mean
    time_avg_info :
    average_T1,average_T2,average_DT
    standard_name :
    sea_water_conservative_temperature
  • " ], "text/plain": [ - "\n", - "dask.array\n", + "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", + " * time (time) datetime64[ns] 1958-01-14T12:00:00 ... 2018-12-14T12:00:00\n", " * st_ocean (st_ocean) float64 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n", - " * time (time) datetime64[ns] 1958-06-30T12:00:00 ... 2257-06-30T12:00:00\n", "Attributes:\n", " long_name: Conservative temperature\n", - " units: deg_C\n", + " units: K\n", " valid_range: [-10. 500.]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT\n", - " coordinates: geolon_t geolat_t\n", - " time_bounds: \\ndask...\n", - " ncfiles: ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..." + " standard_name: sea_water_conservative_temperature" ] }, "execution_count": 4, @@ -943,14 +1014,19 @@ } ], "source": [ - "experiment = '025deg_jra55v13_iaf_gmredi6' #Choose any run\n", + "experiment = '025deg_jra55_iaf_omip2_cycle2' #Choose any run\n", "variable = 'temp' #any scalar variable where volume weighted averaging makes sense\n", - "variable_to_average = cc.querying.getvar(experiment, variable, session, ncfile='ocean.nc')\n", + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable=variable)\n", + "darray = var_search.to_dask()\n", + "darray = darray[variable]\n", + "variable_to_average = darray\n", "variable_to_average" ] }, { "cell_type": "markdown", + "id": "643ddcbe", "metadata": {}, "source": [ "First we show the standard approach, which is to take the arithmetic mean of all grid cells along the quasi-longitudinal coordinate. For MOM5's tri-polar grid this approach is in principle \"okay\" for the southern hemisphere, where grid cell areas are constant at fixed latitude. It doesn't though, take into account partial cells.\n", @@ -967,21 +1043,21 @@ { "cell_type": "code", "execution_count": 5, + "id": "bb47754a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ - "
    " + "
    " ] }, "metadata": { "image/png": { - "height": 332, - "width": 586 - }, - "needs_background": "light" + "height": 470, + "width": 818 + } }, "output_type": "display_data" } @@ -996,6 +1072,7 @@ }, { "cell_type": "markdown", + "id": "1e55737f", "metadata": {}, "source": [ "The main issue with this average is that the 'latitude' coordinate may be meaningless near the north pole, particulalrly when comparing to observational analyses or other models which can have either a regular grid or a different curvilinear grid. Even different versions of MOM might have different grids! \n", @@ -1022,14 +1099,118 @@ { "cell_type": "code", "execution_count": 6, + "id": "c477fcf7-47c7-400d-9b63-84645f3ce2ad", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['time_bounds',\n", + " 'TLON',\n", + " 'TLAT',\n", + " 'ULON',\n", + " 'ULAT',\n", + " 'NCAT',\n", + " 'tmask',\n", + " 'blkmask',\n", + " 'tarea',\n", + " 'uarea',\n", + " 'dxt',\n", + " 'dyt',\n", + " 'dxu',\n", + " 'dyu',\n", + " 'HTN',\n", + " 'HTE',\n", + " 'ANGLE',\n", + " 'ANGLET',\n", + " 'hi',\n", + " 'hs',\n", + " 'aice',\n", + " 'uvel',\n", + " 'vvel',\n", + " 'frzmlt',\n", + " 'congel',\n", + " 'frazil',\n", + " 'snoice',\n", + " 'meltt',\n", + " 'melts',\n", + " 'meltb',\n", + " 'dvidtt',\n", + " 'dvidtd',\n", + " 'daidtt',\n", + " 'daidtd',\n", + " 'fsurf_ai',\n", + " 'fcondtop_ai']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_subset.df.iloc[0]['variable']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0106047d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " .applymap(type)\n" + ] + }, + { + "ename": "KeyError", + "evalue": "0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:414\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_range\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "\u001b[0;31mValueError\u001b[0m: 0 is not in range", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m cat_subset \u001b[38;5;241m=\u001b[39m catalog[experiment]\n\u001b[1;32m 11\u001b[0m var_search \u001b[38;5;241m=\u001b[39m cat_subset\u001b[38;5;241m.\u001b[39msearch(variable\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m var_search \u001b[38;5;241m=\u001b[39m var_search\u001b[38;5;241m.\u001b[39msearch(path\u001b[38;5;241m=\u001b[39m\u001b[43mvar_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpath\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 13\u001b[0m darray \u001b[38;5;241m=\u001b[39m var_search\u001b[38;5;241m.\u001b[39mto_dask()\n\u001b[1;32m 14\u001b[0m darray \u001b[38;5;241m=\u001b[39m darray[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", + "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", + "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:416\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 416\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 0" + ] + } + ], "source": [ "coord = 'geolat_t' #can be any scalar (2D, 3D, eulerian, lagrangian etc)\n", - "variable_as_coord = cc.querying.getvar(experiment, coord, session, n=1) #might need ncfile='ocean.nc' if a tracer\n", + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable=coord)\n", + "var_search = var_search.search(path=var_search.df['path'][0])\n", + "darray = var_search.to_dask()\n", + "darray = darray[coord]\n", + "variable_as_coord = darray #might need ncfile='ocean.nc' if a tracer\n", "\n", "#Define the coordinate bins as the latitude edges of the T-cells\n", - "yu_ocean = cc.querying.getvar(experiment, 'yu_ocean', session, n=1)\n", + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable='yu_ocean')\n", + "var_search = var_search.search(path=var_search.df['path'][0])\n", + "darray = var_search.to_dask()\n", + "darray = darray['yu_ocean']\n", + "yu_ocean = darray\n", "#make numpy array (using .values) and add 1st edge at -90\n", "bins = np.insert(yu_ocean.values, 0, np.array(-90), axis=0) \n", "\n", @@ -1037,14 +1218,24 @@ "#coordbins = np.linspace(-80, 90, 50)\n", "#or use a grid from a different (coarser) model.\n", "\n", - "dzt = cc.querying.getvar(experiment, 'dzt', session, ncfile='ocean.nc') #thickness of cells\n", - "area_t = cc.querying.getvar(experiment, 'area_t', session, n=1) #area of cells\n", + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable='dzt')\n", + "darray = var_search.to_dask()\n", + "darray = darray['dzt']\n", + "dzt = darray #thickness of cells\n", + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable='area_t')\n", + "var_search = var_search.search(path=var_search.df['path'][0])\n", + "darray = var_search.to_dask()\n", + "darray = darray['area_t']\n", + "area_t = darray #area of cells\n", "\n", "dVol = dzt * area_t #Volume of cells" ] }, { "cell_type": "markdown", + "id": "511fec74", "metadata": {}, "source": [ "Now let's compute the numerator and denominator of the equation above using `xhistogram`, then the time mean and then the zonal mean." @@ -1052,7 +1243,8 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, + "id": "fa09dd3a", "metadata": {}, "outputs": [], "source": [ @@ -1063,6 +1255,7 @@ }, { "cell_type": "markdown", + "id": "164c8deb", "metadata": {}, "source": [ "Now we can plot the results which thankfully retain all the data-array info on variables and axis etc." @@ -1070,26 +1263,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "id": "0a28d65a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 332, - "width": 587 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(10, 5))\n", "coord_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", @@ -1098,6 +1275,7 @@ }, { "cell_type": "markdown", + "id": "f995e265", "metadata": {}, "source": [ "Since we used the same bin edges as the standard `yt_ocean` coordinate we can take a difference between the arithmetic mean along the model's x-axis and our mean along grid cells within latitude bands. The main differences are near the North Pole where the grid is furthest for being regular. There are also differences near the Antacrtic Shelf suggesting partial cells also matter." @@ -1105,26 +1283,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "id": "c47e9e8c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 332, - "width": 592 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "zonal_minus_x_mean = coord_mean.sel(year=2000) - x_arith_mean.sel(year=2000).values\n", "\n", @@ -1135,6 +1297,7 @@ }, { "cell_type": "markdown", + "id": "0a2e3978", "metadata": {}, "source": [ "`xarray` has a new `weighted` functionality which allows it to do weighted means instead of arithmetic mean.\n", @@ -1144,7 +1307,8 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "id": "bf7b4fbe", "metadata": {}, "outputs": [], "source": [ @@ -1155,26 +1319,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, + "id": "5b427451", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 333, - "width": 593 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "zonal_minus_x_mean = coord_mean.sel(year=2000) - meanweighted_y2000.values\n", "\n", @@ -1185,6 +1333,7 @@ }, { "cell_type": "markdown", + "id": "6b2ab410", "metadata": {}, "source": [ "South of 65N, where complications of the tripolar grid don't matter, `xarray`'s weighted mean does the job! But in the region of the tripolar we need to be more careful." @@ -1212,9 +1361,9 @@ } }, "kernelspec": { - "display_name": "Python [conda env:miniconda3-analysis3-22.07] *", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-miniconda3-analysis3-22.07-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1226,10 +1375,10 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.13" }, "thumbnail_figure": 1 }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } From 6e266c59ab7bea3c669887f740882cdeec1ac23e Mon Sep 17 00:00:00 2001 From: julia-neme Date: Mon, 5 Aug 2024 14:31:14 +1000 Subject: [PATCH 2/3] failed to run --- DocumentedExamples/True_Zonal_Mean.ipynb | 2223 ++++++++++++++-------- 1 file changed, 1392 insertions(+), 831 deletions(-) diff --git a/DocumentedExamples/True_Zonal_Mean.ipynb b/DocumentedExamples/True_Zonal_Mean.ipynb index 431aa962..27e7bb43 100644 --- a/DocumentedExamples/True_Zonal_Mean.ipynb +++ b/DocumentedExamples/True_Zonal_Mean.ipynb @@ -23,13 +23,9 @@ "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format='retina'\n", - "\n", - "import pandas as pd\n", "import intake\n", - "catalog = intake.cat.access_nri\n", "import matplotlib.pyplot as plt\n", + "import cmocean as cm\n", "import xarray as xr\n", "import numpy as np\n", "from dask.distributed import Client\n", @@ -49,13 +45,13 @@ "
    \n", "
    \n", "

    Client

    \n", - "

    Client-9cc9d3f1-881e-11ee-8ca4-00000188fe80

    \n", + "

    Client-b75669cc-52e2-11ef-8907-000007dcfe80

    \n", " \n", "\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", @@ -78,20 +74,57 @@ "\n", " \n", "
    \n", - "

    Scheduler Info

    \n", + "

    Cluster Info

    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    LocalCluster

    \n", + "

    36b153c4

    \n", + "
    Connection method: DirectConnection method: Cluster objectCluster type: distributed.LocalCluster
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + "
    \n", + " Dashboard: /proxy/8787/status\n", + " \n", + " Workers: 28\n", + "
    \n", + " Total threads: 28\n", + " \n", + " Total memory: 125.19 GiB\n", + "
    Status: runningUsing processes: True
    \n", + "\n", + "
    \n", + " \n", + "

    Scheduler Info

    \n", + "
    \n", + "\n", "
    \n", "
    \n", "
    \n", "
    \n", "

    Scheduler

    \n", - "

    Scheduler-7c846815-9f3c-4666-a6ac-f4150b5ef1f2

    \n", + "

    Scheduler-ae782047-44ce-483e-a584-e4afd2459e6e

    \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -99,106 +132,1280 @@ " Dashboard:/proxy/8787/status\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    \n", - " Comm: tcp://10.6.48.33:8786\n", + " Comm: tcp://127.0.0.1:37279\n", " \n", - " Workers: 1\n", + " Workers: 28\n", "
    \n", - " Total threads: 12\n", + " Total threads: 28\n", "
    \n", - " Started: 3 hours ago\n", + " Started: Just now\n", " \n", - " Total memory: 46.00 GiB\n", + " Total memory: 125.19 GiB\n", "
    \n", "
    \n", "
    \n", "\n", - "
    \n", - " \n", - "

    Workers

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    Workers

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    Worker: 0

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    \n", + " Comm: tcp://127.0.0.1:43853\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/38613/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:44461\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rnsg58uc\n", + "
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    Worker: 1

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    \n", + " Comm: tcp://127.0.0.1:33151\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/41151/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:34631\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-puwzbq7w\n", + "
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    Worker: 2

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    \n", + " Comm: tcp://127.0.0.1:40883\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/38233/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46769\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-_0zaynvr\n", + "
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    Worker: 3

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    \n", + " Comm: tcp://127.0.0.1:45191\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/37385/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:35969\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qjv0ljft\n", + "
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    Worker: 4

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:34473\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/42649/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38377\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rj9sotgo\n", + "
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    Worker: 5

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:43851\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/34387/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:36481\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-5yohu427\n", + "
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    Worker: 6

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:44345\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/43799/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46839\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ghn4gxpw\n", + "
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    Worker: 7

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:39589\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36237/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46801\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-71908290\n", + "
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    Worker: 8

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    \n", + " Comm: tcp://127.0.0.1:35015\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/43575/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:42401\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-tqw40eyj\n", + "
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    Worker: 9

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    \n", + " Comm: tcp://127.0.0.1:38511\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/37037/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:41881\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qrzwmezz\n", + "
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    Worker: 10

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    \n", + " Comm: tcp://127.0.0.1:36295\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45885/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:43135\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ayethmrn\n", + "
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    Worker: 11

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:43667\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36501/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:43053\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qzoait51\n", + "
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    Worker: 12

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    \n", + " Comm: tcp://127.0.0.1:39877\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/34161/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:33613\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-w4x9trv7\n", + "
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    Worker: 13

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    \n", + " Comm: tcp://127.0.0.1:34433\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36827/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:33553\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-pa92_86v\n", + "
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    Worker: 14

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:40799\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/40495/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38493\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-698_cg4r\n", + "
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    Worker: 15

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:34305\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/41975/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:41567\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-us2bps4h\n", + "
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    Worker: 16

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:35205\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/33287/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:45605\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-v9ekzu1c\n", + "
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    Worker: 17

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:41925\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/39699/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46183\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rn36xx5m\n", + "
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    Worker: 18

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:34085\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/37211/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:36707\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-sfumz_8t\n", + "
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    Worker: 19

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:44683\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45631/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:42873\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-z7_6lhff\n", + "
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    Worker: 20

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:38655\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/41549/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38201\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-m9ialdtr\n", + "
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    Worker: 21

    \n", + "
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
    \n", + " Comm: tcp://127.0.0.1:44395\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/42625/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46445\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ajh0kfuq\n", + "
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    \n", + "
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    \n", + " \n", + "

    Worker: 22

    \n", + "
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
    \n", + " Comm: tcp://127.0.0.1:39755\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/42869/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:34911\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ueaaq1fm\n", + "
    \n", + "
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    Worker: 23

    \n", + "
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", "\n", + "
    \n", + " Comm: tcp://127.0.0.1:42765\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/38195/status\n", + " \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:40213\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-b3dgnjam\n", + "
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    \n", " \n", - "

    Worker: tcp://10.6.48.33:39653

    \n", + "

    Worker: 24

    \n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", + "\n", + "
    \n", - " Comm: tcp://10.6.48.33:39653\n", + " Comm: tcp://127.0.0.1:33245\n", " \n", - " Total threads: 12\n", + " Total threads: 1\n", "
    \n", - " Dashboard: /proxy/45111/status\n", + " Dashboard: /proxy/40507/status\n", " \n", - " Memory: 46.00 GiB\n", + " Memory: 4.47 GiB\n", "
    \n", - " Nanny: tcp://10.6.48.33:36117\n", + " Nanny: tcp://127.0.0.1:36287\n", "
    \n", - " Local directory: /scratch/iq82/mp7041/dasktmp/dask-scratch-space/worker-3h8pqbrb\n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-6egnw_ed\n", "
    \n", + "
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    Worker: 25

    \n", + "
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    \n", + " Comm: tcp://127.0.0.1:36869\n", + " \n", + " Total threads: 1\n", + "
    \n", - " Tasks executing: \n", + " Dashboard: /proxy/33207/status\n", " \n", - " Tasks in memory: \n", + " Memory: 4.47 GiB\n", + "
    \n", + " Nanny: tcp://127.0.0.1:35257\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-lv961ahr\n", "
    \n", + "
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    Worker: 26

    \n", + "
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    \n", - " Tasks ready: \n", + " Comm: tcp://127.0.0.1:41149\n", " \n", - " Tasks in flight: \n", + " Total threads: 1\n", "
    \n", - " CPU usage: 2.0%\n", + " Dashboard: /proxy/39819/status\n", + " \n", + " Memory: 4.47 GiB\n", "
    \n", - " Last seen: Just now\n", + " Nanny: tcp://127.0.0.1:46463\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-8wb1bq5d\n", "
    \n", + "
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    Worker: 27

    \n", + "
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    \n", - " Memory usage: 9.15 GiB\n", + " Comm: tcp://127.0.0.1:33863\n", " \n", - " Spilled bytes: 0 B\n", + " Total threads: 1\n", "
    \n", - " Read bytes: 5.49 kiB\n", + " Dashboard: /proxy/38259/status\n", " \n", - " Write bytes: 5.19 kiB\n", + " Memory: 4.47 GiB\n", "
    \n", + " Nanny: tcp://127.0.0.1:44249\n", + "
    \n", + " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-i700zukt\n", + "
    \n", @@ -209,6 +1416,10 @@ "\n", "
    \n", "
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    \n", " \n", " \n", "\n", @@ -216,7 +1427,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -225,44 +1436,46 @@ } ], "source": [ - "client = Client(\"tcp://10.6.48.33:8786\")\n", + "client = Client(threads_per_worker = 1)\n", "client" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "f073f448", + "cell_type": "markdown", + "id": "196737d8-0467-4078-8878-bcf3840564e5", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Open ACCESS-NRI's default catalog:" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "436211de", + "execution_count": 3, + "id": "f073f448", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "catalog = intake.cat.access_nri" + ] }, { "cell_type": "markdown", "id": "00372e86", "metadata": {}, "source": [ - "Choose the run and the variable we want to average. I am using temperature but you can choose any scalar." + "Choose the experiment and the variable we want to average. This example uses temperature but you can choose any scalar, 3D variable:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "9088c3ef-3e46-401b-a3e5-5cfddebbc7b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

    Intake dataframe catalog with 7 source(s) across 39 rows:

    \n", + "

    Intake dataframe catalog with 9 source(s) across 49 rows:

    \n", "
    <xarray.DataArray 'temp' (time: 732, st_ocean: 50, yt_ocean: 1080,\n",
    -       "                          xt_ocean: 1440)>\n",
    -       "dask.array<concatenate, shape=(732, 50, 1080, 1440), dtype=float32, chunksize=(1, 25, 108, 120), chunktype=numpy.ndarray>\n",
    -       "Coordinates:\n",
    -       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
    -       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
    -       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 2018-12-14T12:00:00\n",
    -       "  * st_ocean  (st_ocean) float64 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n",
    -       "Attributes:\n",
    -       "    long_name:      Conservative temperature\n",
    -       "    units:          K\n",
    -       "    valid_range:    [-10. 500.]\n",
    -       "    cell_methods:   time: mean\n",
    -       "    time_avg_info:  average_T1,average_T2,average_DT\n",
    -       "    standard_name:  sea_water_conservative_temperature
    " - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * xt_ocean (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", - " * yt_ocean (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", - " * time (time) datetime64[ns] 1958-01-14T12:00:00 ... 2018-12-14T12:00:00\n", - " * st_ocean (st_ocean) float64 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n", - "Attributes:\n", - " long_name: Conservative temperature\n", - " units: K\n", - " valid_range: [-10. 500.]\n", - " cell_methods: time: mean\n", - " time_avg_info: average_T1,average_T2,average_DT\n", - " standard_name: sea_water_conservative_temperature" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "experiment = '025deg_jra55_iaf_omip2_cycle2' #Choose any run\n", - "variable = 'temp' #any scalar variable where volume weighted averaging makes sense\n", + "variable = 'temp' #any scalar variable where volume weighted averaging makes sense" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9eded697-29d4-4101-ba05-fe3d0509fd67", + "metadata": {}, + "outputs": [], + "source": [ "cat_subset = catalog[experiment]\n", "var_search = cat_subset.search(variable=variable)\n", "darray = var_search.to_dask()\n", - "darray = darray[variable]\n", - "variable_to_average = darray\n", - "variable_to_average" + "variable_to_average = darray[variable].mean('time')" ] }, { @@ -1042,32 +1639,65 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "bb47754a", + "execution_count": 7, + "id": "ccab5ab3-54ca-47ec-9a07-3e70abdc7b2f", + "metadata": {}, + "outputs": [], + "source": [ + "zonal_arith_mean = variable_to_average.mean(dim='xt_ocean')" + ] + }, + { + "cell_type": "markdown", + "id": "764f7f1e-012c-4ae0-a640-704026aa4c56", + "metadata": {}, + "source": [ + "Load to speed up the plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": 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- "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 470, - "width": 818 - } - }, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/distributed/client.py:3161: UserWarning: Sending large graph of size 40.92 MiB.\n", + "This may cause some slowdown.\n", + "Consider scattering data ahead of time and using futures.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "%%time\n", + "zonal_arith_mean = zonal_arith_mean.load()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ce7d6db-1a62-48bf-a08a-9aecf434ebac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/distributed/client.py:3161: UserWarning: Sending large graph of size 40.92 MiB.\n", + "This may cause some slowdown.\n", + "Consider scattering data ahead of time and using futures.\n", + " warnings.warn(\n" + ] } ], "source": [ - "x_arith_mean = variable_to_average.groupby('time.year').mean(dim='time').mean(dim='xt_ocean')\n", - "\n", "plt.figure(figsize=(10, 5))\n", - "x_arith_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", - "plt.title('x-coordinate arithmetic mean');" + "zonal_arith_mean.plot(yincrease=False, vmin=273, vmax=300, cmap=cm.cm.thermal)\n", + "plt.title('Zonal arithmetic mean');" ] }, { @@ -1075,7 +1705,7 @@ "id": "1e55737f", "metadata": {}, "source": [ - "The main issue with this average is that the 'latitude' coordinate may be meaningless near the north pole, particulalrly when comparing to observational analyses or other models which can have either a regular grid or a different curvilinear grid. Even different versions of MOM might have different grids! \n", + "The main issue with this average is that the 'latitude' coordinate may be meaningless near the north pole, particularly when comparing to observational analyses or other models which can have either a regular grid or a different curvilinear grid. Even different versions of MOM might have different grids! \n", "\n", "Let us consider what the true zonal average looks like. That is consider a set of latitude 'edges' $\\{\\phi'_{1/2},\\phi'_{1+1/2},...,\\phi'_{\\ell-1/2},\\phi'_{\\ell+1/2},...,\\phi'_{L+1/2}\\}$ between which we want to compute an average of $T$ at $\\{\\phi'_{1},\\phi'_{2},...,\\phi'_{\\ell},...,\\phi'_{L}\\}$ such that\n", "\n", @@ -1098,103 +1728,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "c477fcf7-47c7-400d-9b63-84645f3ce2ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['time_bounds',\n", - " 'TLON',\n", - " 'TLAT',\n", - " 'ULON',\n", - " 'ULAT',\n", - " 'NCAT',\n", - " 'tmask',\n", - " 'blkmask',\n", - " 'tarea',\n", - " 'uarea',\n", - " 'dxt',\n", - " 'dyt',\n", - " 'dxu',\n", - " 'dyu',\n", - " 'HTN',\n", - " 'HTE',\n", - " 'ANGLE',\n", - " 'ANGLET',\n", - " 'hi',\n", - " 'hs',\n", - " 'aice',\n", - " 'uvel',\n", - " 'vvel',\n", - " 'frzmlt',\n", - " 'congel',\n", - " 'frazil',\n", - " 'snoice',\n", - " 'meltt',\n", - " 'melts',\n", - " 'meltb',\n", - " 'dvidtt',\n", - " 'dvidtd',\n", - " 'daidtt',\n", - " 'daidtd',\n", - " 'fsurf_ai',\n", - " 'fcondtop_ai']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat_subset.df.iloc[0]['variable']" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0106047d", + "execution_count": null, + "id": "4a23fc10-5c4e-4929-b355-18fe7616dce9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "ename": "KeyError", - "evalue": "0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:414\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_range\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "\u001b[0;31mValueError\u001b[0m: 0 is not in range", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m cat_subset \u001b[38;5;241m=\u001b[39m catalog[experiment]\n\u001b[1;32m 11\u001b[0m var_search \u001b[38;5;241m=\u001b[39m cat_subset\u001b[38;5;241m.\u001b[39msearch(variable\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m var_search \u001b[38;5;241m=\u001b[39m var_search\u001b[38;5;241m.\u001b[39msearch(path\u001b[38;5;241m=\u001b[39m\u001b[43mvar_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpath\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 13\u001b[0m darray \u001b[38;5;241m=\u001b[39m var_search\u001b[38;5;241m.\u001b[39mto_dask()\n\u001b[1;32m 14\u001b[0m darray \u001b[38;5;241m=\u001b[39m darray[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", - "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", - "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:416\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 416\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 0" - ] - } - ], + "outputs": [], "source": [ "coord = 'geolat_t' #can be any scalar (2D, 3D, eulerian, lagrangian etc)\n", "cat_subset = catalog[experiment]\n", @@ -1202,8 +1739,24 @@ "var_search = var_search.search(path=var_search.df['path'][0])\n", "darray = var_search.to_dask()\n", "darray = darray[coord]\n", - "variable_as_coord = darray #might need ncfile='ocean.nc' if a tracer\n", - "\n", + "variable_as_coord = darray #might need ncfile='ocean.nc' if a tracer" + ] + }, + { + "cell_type": "markdown", + "id": "267ebeed-af2d-4c33-b2a1-31f7085f2c37", + "metadata": {}, + "source": [ + "Now we want to define the coordinate bins as the latitude edges of the t-cells, adding the first edge (0) at latitude -90:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "338680da-7b90-41eb-9e3d-a2e19d939e4d", + "metadata": {}, + "outputs": [], + "source": [ "#Define the coordinate bins as the latitude edges of the T-cells\n", "cat_subset = catalog[experiment]\n", "var_search = cat_subset.search(variable='yu_ocean')\n", @@ -1212,8 +1765,16 @@ "darray = darray['yu_ocean']\n", "yu_ocean = darray\n", "#make numpy array (using .values) and add 1st edge at -90\n", - "bins = np.insert(yu_ocean.values, 0, np.array(-90), axis=0) \n", - "\n", + "bins = np.insert(yu_ocean.values, 0, np.array(-90), axis=0) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ce8c76f-02b7-4c6e-869e-7accafe4e1b8", + "metadata": {}, + "outputs": [], + "source": [ "#Alternatively we could just use some regular grid like this \n", "#coordbins = np.linspace(-80, 90, 50)\n", "#or use a grid from a different (coarser) model.\n", @@ -1361,9 +1922,9 @@ } }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:analysis3-24.01] *", "language": "python", - "name": "python3" + "name": "conda-env-analysis3-24.01-py" }, "language_info": { "codemirror_mode": { From eda62ea18ae5ae00ceec989a7ba54f4b46612424 Mon Sep 17 00:00:00 2001 From: dougiesquire Date: Tue, 6 Aug 2024 13:58:44 +1000 Subject: [PATCH 3/3] Changes to align with original cosima-cookbook example --- DocumentedExamples/True_Zonal_Mean.ipynb | 1532 +++++++++++++++++----- 1 file changed, 1224 insertions(+), 308 deletions(-) diff --git a/DocumentedExamples/True_Zonal_Mean.ipynb b/DocumentedExamples/True_Zonal_Mean.ipynb index 27e7bb43..c1e585a1 100644 --- a/DocumentedExamples/True_Zonal_Mean.ipynb +++ b/DocumentedExamples/True_Zonal_Mean.ipynb @@ -45,7 +45,7 @@ "
    \n", "
    \n", "

    Client

    \n", - "

    Client-b75669cc-52e2-11ef-8907-000007dcfe80

    \n", + "

    Client-dd4c2afe-53a7-11ef-9bc8-0000018bfe80

    \n", " \n", "\n", " \n", @@ -80,22 +80,22 @@ " \n", "
    \n", "

    LocalCluster

    \n", - "

    36b153c4

    \n", + "

    b14ac344

    \n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -117,14 +117,14 @@ "
    \n", "
    \n", "

    Scheduler

    \n", - "

    Scheduler-ae782047-44ce-483e-a584-e4afd2459e6e

    \n", + "

    Scheduler-2b681948-7bb8-4ce7-a2df-bb72d43443a8

    \n", "
    \n", " Dashboard: /proxy/8787/status\n", " \n", - " Workers: 28\n", + " Workers: 48\n", "
    \n", - " Total threads: 28\n", + " Total threads: 48\n", " \n", - " Total memory: 125.19 GiB\n", + " Total memory: 188.56 GiB\n", "
    \n", " \n", " \n", " \n", " \n", " \n", @@ -132,7 +132,7 @@ " Dashboard:/proxy/8787/status\n", " \n", " \n", " \n", " \n", @@ -140,7 +140,7 @@ " Started: Just now\n", " \n", " \n", " \n", "
    \n", - " Comm: tcp://127.0.0.1:37279\n", + " Comm: tcp://127.0.0.1:40671\n", " \n", - " Workers: 28\n", + " Workers: 48\n", "
    \n", - " Total threads: 28\n", + " Total threads: 48\n", "
    \n", - " Total memory: 125.19 GiB\n", + " Total memory: 188.56 GiB\n", "
    \n", @@ -163,7 +163,7 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -208,7 +208,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43853\n", + " Comm: tcp://127.0.0.1:46097\n", " \n", " Total threads: 1\n", @@ -171,21 +171,21 @@ "
    \n", - " Dashboard: /proxy/38613/status\n", + " Dashboard: /proxy/35493/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:44461\n", + " Nanny: tcp://127.0.0.1:44571\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rnsg58uc\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-98h2ycbl\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -253,7 +253,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33151\n", + " Comm: tcp://127.0.0.1:34957\n", " \n", " Total threads: 1\n", @@ -216,21 +216,21 @@ "
    \n", - " Dashboard: /proxy/41151/status\n", + " Dashboard: /proxy/42827/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:34631\n", + " Nanny: tcp://127.0.0.1:39183\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-puwzbq7w\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-hz_x596l\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -298,7 +298,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:40883\n", + " Comm: tcp://127.0.0.1:37669\n", " \n", " Total threads: 1\n", @@ -261,21 +261,21 @@ "
    \n", - " Dashboard: /proxy/38233/status\n", + " Dashboard: /proxy/35125/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46769\n", + " Nanny: tcp://127.0.0.1:35267\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-_0zaynvr\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-xfpo68mw\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -343,7 +343,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:45191\n", + " Comm: tcp://127.0.0.1:40359\n", " \n", " Total threads: 1\n", @@ -306,21 +306,21 @@ "
    \n", - " Dashboard: /proxy/37385/status\n", + " Dashboard: /proxy/42371/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:35969\n", + " Nanny: tcp://127.0.0.1:45455\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qjv0ljft\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-3y4aklun\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -388,7 +388,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34473\n", + " Comm: tcp://127.0.0.1:35987\n", " \n", " Total threads: 1\n", @@ -351,21 +351,21 @@ "
    \n", - " Dashboard: /proxy/42649/status\n", + " Dashboard: /proxy/37549/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:38377\n", + " Nanny: tcp://127.0.0.1:35259\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rj9sotgo\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-q9c74pex\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -433,7 +433,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43851\n", + " Comm: tcp://127.0.0.1:33583\n", " \n", " Total threads: 1\n", @@ -396,21 +396,21 @@ "
    \n", - " Dashboard: /proxy/34387/status\n", + " Dashboard: /proxy/37777/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:36481\n", + " Nanny: tcp://127.0.0.1:34397\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-5yohu427\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-f6x2h466\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -478,7 +478,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44345\n", + " Comm: tcp://127.0.0.1:33505\n", " \n", " Total threads: 1\n", @@ -441,21 +441,21 @@ "
    \n", - " Dashboard: /proxy/43799/status\n", + " Dashboard: /proxy/35793/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46839\n", + " Nanny: tcp://127.0.0.1:32815\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ghn4gxpw\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-v3bmqaa9\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -523,7 +523,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:39589\n", + " Comm: tcp://127.0.0.1:36299\n", " \n", " Total threads: 1\n", @@ -486,21 +486,21 @@ "
    \n", - " Dashboard: /proxy/36237/status\n", + " Dashboard: /proxy/36193/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46801\n", + " Nanny: tcp://127.0.0.1:39543\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-71908290\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-i89na82z\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -568,7 +568,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:35015\n", + " Comm: tcp://127.0.0.1:35065\n", " \n", " Total threads: 1\n", @@ -531,21 +531,21 @@ "
    \n", - " Dashboard: /proxy/43575/status\n", + " Dashboard: /proxy/42169/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:42401\n", + " Nanny: tcp://127.0.0.1:38305\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-tqw40eyj\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-dq00xqai\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -613,7 +613,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38511\n", + " Comm: tcp://127.0.0.1:42797\n", " \n", " Total threads: 1\n", @@ -576,21 +576,21 @@ "
    \n", - " Dashboard: /proxy/37037/status\n", + " Dashboard: /proxy/36515/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:41881\n", + " Nanny: tcp://127.0.0.1:38799\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qrzwmezz\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-pa7rayqm\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -658,7 +658,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:36295\n", + " Comm: tcp://127.0.0.1:35653\n", " \n", " Total threads: 1\n", @@ -621,21 +621,21 @@ "
    \n", - " Dashboard: /proxy/45885/status\n", + " Dashboard: /proxy/37545/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:43135\n", + " Nanny: tcp://127.0.0.1:37879\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ayethmrn\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-qsxcpcns\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -703,7 +703,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43667\n", + " Comm: tcp://127.0.0.1:35755\n", " \n", " Total threads: 1\n", @@ -666,21 +666,21 @@ "
    \n", - " Dashboard: /proxy/36501/status\n", + " Dashboard: /proxy/39951/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:43053\n", + " Nanny: tcp://127.0.0.1:35903\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-qzoait51\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-xacp44kl\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -748,7 +748,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:39877\n", + " Comm: tcp://127.0.0.1:43095\n", " \n", " Total threads: 1\n", @@ -711,21 +711,21 @@ "
    \n", - " Dashboard: /proxy/34161/status\n", + " Dashboard: /proxy/38671/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:33613\n", + " Nanny: tcp://127.0.0.1:43509\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-w4x9trv7\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-u53a69jl\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -793,7 +793,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34433\n", + " Comm: tcp://127.0.0.1:34585\n", " \n", " Total threads: 1\n", @@ -756,21 +756,21 @@ "
    \n", - " Dashboard: /proxy/36827/status\n", + " Dashboard: /proxy/42807/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:33553\n", + " Nanny: tcp://127.0.0.1:34747\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-pa92_86v\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-m5ss5prv\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -838,7 +838,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:40799\n", + " Comm: tcp://127.0.0.1:46543\n", " \n", " Total threads: 1\n", @@ -801,21 +801,21 @@ "
    \n", - " Dashboard: /proxy/40495/status\n", + " Dashboard: /proxy/38137/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:38493\n", + " Nanny: tcp://127.0.0.1:35861\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-698_cg4r\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-5bc9riei\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -883,7 +883,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34305\n", + " Comm: tcp://127.0.0.1:40403\n", " \n", " Total threads: 1\n", @@ -846,21 +846,21 @@ "
    \n", - " Dashboard: /proxy/41975/status\n", + " Dashboard: /proxy/44507/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:41567\n", + " Nanny: tcp://127.0.0.1:35453\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-us2bps4h\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-nir1p194\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -928,7 +928,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:35205\n", + " Comm: tcp://127.0.0.1:44435\n", " \n", " Total threads: 1\n", @@ -891,21 +891,21 @@ "
    \n", - " Dashboard: /proxy/33287/status\n", + " Dashboard: /proxy/42503/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:45605\n", + " Nanny: tcp://127.0.0.1:35149\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-v9ekzu1c\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-55qmlkxp\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -973,7 +973,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:41925\n", + " Comm: tcp://127.0.0.1:33439\n", " \n", " Total threads: 1\n", @@ -936,21 +936,21 @@ "
    \n", - " Dashboard: /proxy/39699/status\n", + " Dashboard: /proxy/36007/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46183\n", + " Nanny: tcp://127.0.0.1:34445\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-rn36xx5m\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-19d4rmry\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1018,7 +1018,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34085\n", + " Comm: tcp://127.0.0.1:33147\n", " \n", " Total threads: 1\n", @@ -981,21 +981,21 @@ "
    \n", - " Dashboard: /proxy/37211/status\n", + " Dashboard: /proxy/42825/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:36707\n", + " Nanny: tcp://127.0.0.1:43703\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-sfumz_8t\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-arc2upek\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1063,7 +1063,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44683\n", + " Comm: tcp://127.0.0.1:44635\n", " \n", " Total threads: 1\n", @@ -1026,21 +1026,21 @@ "
    \n", - " Dashboard: /proxy/45631/status\n", + " Dashboard: /proxy/39717/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:42873\n", + " Nanny: tcp://127.0.0.1:40419\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-z7_6lhff\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-vpo7shxq\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1108,7 +1108,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38655\n", + " Comm: tcp://127.0.0.1:45825\n", " \n", " Total threads: 1\n", @@ -1071,21 +1071,21 @@ "
    \n", - " Dashboard: /proxy/41549/status\n", + " Dashboard: /proxy/40037/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:38201\n", + " Nanny: tcp://127.0.0.1:43171\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-m9ialdtr\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-r4ckcqtw\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1153,7 +1153,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44395\n", + " Comm: tcp://127.0.0.1:44703\n", " \n", " Total threads: 1\n", @@ -1116,21 +1116,21 @@ "
    \n", - " Dashboard: /proxy/42625/status\n", + " Dashboard: /proxy/38159/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46445\n", + " Nanny: tcp://127.0.0.1:38197\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ajh0kfuq\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-gt5yq6rc\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1198,7 +1198,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:39755\n", + " Comm: tcp://127.0.0.1:39819\n", " \n", " Total threads: 1\n", @@ -1161,21 +1161,21 @@ "
    \n", - " Dashboard: /proxy/42869/status\n", + " Dashboard: /proxy/39715/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:34911\n", + " Nanny: tcp://127.0.0.1:43367\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-ueaaq1fm\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-kmll47qd\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1243,7 +1243,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:42765\n", + " Comm: tcp://127.0.0.1:44663\n", " \n", " Total threads: 1\n", @@ -1206,21 +1206,21 @@ "
    \n", - " Dashboard: /proxy/38195/status\n", + " Dashboard: /proxy/35999/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:40213\n", + " Nanny: tcp://127.0.0.1:34199\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-b3dgnjam\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-757va698\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1288,7 +1288,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33245\n", + " Comm: tcp://127.0.0.1:35043\n", " \n", " Total threads: 1\n", @@ -1251,21 +1251,21 @@ "
    \n", - " Dashboard: /proxy/40507/status\n", + " Dashboard: /proxy/39273/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:36287\n", + " Nanny: tcp://127.0.0.1:45945\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-6egnw_ed\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-eyp4kkqj\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1333,7 +1333,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:36869\n", + " Comm: tcp://127.0.0.1:36277\n", " \n", " Total threads: 1\n", @@ -1296,21 +1296,21 @@ "
    \n", - " Dashboard: /proxy/33207/status\n", + " Dashboard: /proxy/45803/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:35257\n", + " Nanny: tcp://127.0.0.1:41647\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-lv961ahr\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-6rm06dt5\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1378,7 +1378,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:41149\n", + " Comm: tcp://127.0.0.1:41827\n", " \n", " Total threads: 1\n", @@ -1341,21 +1341,21 @@ "
    \n", - " Dashboard: /proxy/39819/status\n", + " Dashboard: /proxy/35435/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:46463\n", + " Nanny: tcp://127.0.0.1:43487\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-8wb1bq5d\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-se6tlqg4\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
    \n", - " Comm: tcp://127.0.0.1:33863\n", + " Comm: tcp://127.0.0.1:38317\n", " \n", " Total threads: 1\n", @@ -1386,21 +1386,921 @@ "
    \n", - " Dashboard: /proxy/38259/status\n", + " Dashboard: /proxy/33693/status\n", " \n", - " Memory: 4.47 GiB\n", + " Memory: 3.93 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:44249\n", + " Nanny: tcp://127.0.0.1:33649\n", "
    \n", - " Local directory: /jobfs/122299192.gadi-pbs/dask-scratch-space/worker-i700zukt\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-hbhvpfu2\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " \n", + "

    Worker: 28

    \n", + "
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
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    Worker: 29

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    Worker: 30

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    Worker: 31

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    Worker: 32

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    Worker: 33

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    Worker: 34

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    Worker: 35

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    Worker: 36

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    Worker: 37

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    Worker: 38

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    Worker: 39

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    Worker: 40

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    Worker: 41

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    Worker: 42

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    Worker: 43

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    Worker: 44

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    Worker: 45

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    Worker: 46

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    Worker: 47

    \n", + "
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    Intake dataframe catalog with 9 source(s) across 49 rows:

    \n", + "

    Intake dataframe catalog with 2 source(s) across 5 rows:

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