{ "cells": [ { "cell_type": "markdown", "id": "723a3b22-c5f9-4a34-b278-d36e29e0c296", "metadata": {}, "source": [ "# Along-isobath averaging\n", "\n", "This recipe shows how to perform an along-isobath averaging for scalar quantities (and vector quantities that are not rotated to along- and across-contour) in ACCESS-OM2-01.\n", "\n", "Such along-isobath averaging is relevant for Antarctic shelf analyses, where a zonal average over a section of cross-shelf regions is needed.\n", "\n", "\n", "### Some caveats: \n", "\n", "1) If there are deep valleys or troughs around the coastline, those will be averaged with deeper isobaths further offshore. Similarly, if there are seamounts off-shore, these will be averaged with shallower regions on the continental slope. Careful adjustment of the mask is necessary to ensure your results are interpretable. \n", "\n", "2) The bottom depths in ht and hu reflect the adaptive vertical grid in ACCESS, but the variables have NaNs at some of these depths. So, we end up with uneven binning if we bin along isobaths which do not exist in the variable field. As a simple fix, we mask our region of interest by `ht` and `hu`, but we bin by the bottom depth derived from the 3D variables (e.g., temperature) -- these are the `hu_coarse` and `ht_coarse` variables. This workaround can be further inspected in the future. \n", "\n", "#### Resources\n", "\n", "This recipe was generated on a Large ARE instance, and may not work on smaller sizes.\n", "___\n", "\n", "This notebook was run with `PYTHONWARNINGS=\"ignore\"` set in the environment variables. We do not recommend doing so as it may hide useful information. However, for the purposes of this recipe, it was necessary to hide these warnings. If you run this recipe without `PYTHONWARNINGS=\"ignore\"` set, you may see a number of additional warnings. " ] }, { "cell_type": "markdown", "id": "f6c3c196-fa07-4433-87ea-8e9106f09655", "metadata": {}, "source": [ "Load the necessary modules." ] }, { "cell_type": "code", "execution_count": 1, "id": "a476d924-8b6d-4c03-801b-386e07944e2f", "metadata": {}, "outputs": [], "source": [ "import intake\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from xhistogram.xarray import histogram\n", "from dask.distributed import Client\n", "\n", "import warnings, os\n", "# Set warnings to only show once - we are going to get a lot of repetitive warnings later on\n", "warnings.filterwarnings(action='once',category=RuntimeWarning)\n", "warnings.filterwarnings(action='once',category=FutureWarning)\n", "os.environ[\"PYTHONWARNINGS\"] = \"once\" # This gets subprocesses - like dask workers - to follow this too\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "e3cc23c9-8622-4a9a-9b5f-2313a8553955", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n", "Perhaps you already have a cluster running?\n", "Hosting the HTTP server on port 36729 instead\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-44593102-78c2-11f0-984b-00000190fe80

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Cluster objectCluster type: distributed.LocalCluster
\n", " Dashboard: /proxy/36729/status\n", "
\n", "\n", " \n", " \n", " \n", "\n", " \n", "
\n", "

Cluster Info

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

LocalCluster

\n", "

f342d737

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
\n", " Dashboard: /proxy/36729/status\n", " \n", " Workers: 48\n", "
\n", " Total threads: 48\n", " \n", " Total memory: 188.56 GiB\n", "
Status: runningUsing processes: True
\n", "\n", "
\n", " \n", "

Scheduler Info

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

Scheduler

\n", "

Scheduler-06cd113b-669c-4dc6-9a44-9f77e6cfa118

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:39791\n", " \n", " Workers: 0 \n", "
\n", " Dashboard: /proxy/36729/status\n", " \n", " Total threads: 0\n", "
\n", " Started: Just now\n", " \n", " Total memory: 0 B\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

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

Worker: 0

\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://127.0.0.1:43723\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34009/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36841\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-bckyp8ii\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 1

\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://127.0.0.1:39035\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33463/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36727\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-q779j7wn\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 2

\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://127.0.0.1:33475\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38421/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36911\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-46_qtn1h\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 3

\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://127.0.0.1:39659\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36581/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36205\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-r2pzth3p\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 4

\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://127.0.0.1:40511\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41913/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34843\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-w_xn1_vw\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 5

\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://127.0.0.1:39907\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41383/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41417\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-gl2mxq5u\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 6

\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://127.0.0.1:37661\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40807/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46369\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-p711qi_z\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 7

\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://127.0.0.1:36855\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40885/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46081\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-a3peklw9\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 8

\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://127.0.0.1:35203\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44537/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44739\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-edn4ux93\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 9

\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://127.0.0.1:39529\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46543/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40869\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-2l8riyor\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 10

\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://127.0.0.1:45723\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36953/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40269\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-oufyv18i\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 11

\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://127.0.0.1:40437\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40185/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44513\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-awjjtv_z\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 12

\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://127.0.0.1:41425\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43853/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38365\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-v6oeka2r\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 13

\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://127.0.0.1:33101\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42087/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36063\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ovdzvtzl\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 14

\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://127.0.0.1:45899\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/32983/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42531\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-y98pzxp3\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 15

\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://127.0.0.1:44351\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44843/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44619\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-hhdf_i95\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 16

\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://127.0.0.1:34679\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46387/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33287\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-txhsbo8l\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 17

\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://127.0.0.1:44787\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41181/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46035\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-2_2x470d\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 18

\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://127.0.0.1:42099\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37179/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45793\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-uuhkal4h\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 19

\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://127.0.0.1:39465\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44633/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44029\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-h75pxn2_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 20

\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://127.0.0.1:42901\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45619/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36905\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-7xe3ajtx\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:33237\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38477/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41445\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-q54htnae\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\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://127.0.0.1:43067\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43441/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35101\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-60ke1whj\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:42749\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37881/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41781\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ux8ciyeh\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \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://127.0.0.1:43561\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40947/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45517\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-i91pzo7c\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 25

\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://127.0.0.1:46493\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43743/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43783\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-5qvan0he\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 26

\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://127.0.0.1:42171\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37839/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34077\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-efrkg_xg\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 27

\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://127.0.0.1:34801\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41209/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35169\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ytheas3j\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", "
\n", " Comm: tcp://127.0.0.1:45641\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37391/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36833\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-w95363zk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 29

\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://127.0.0.1:44669\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33709/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42131\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-his5byfz\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 30

\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://127.0.0.1:37817\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39577/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34979\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-svdqvvt9\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 31

\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://127.0.0.1:39405\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40377/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45171\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-j0wbod6p\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 32

\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://127.0.0.1:42075\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34811/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36587\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-doxf6b2c\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 33

\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://127.0.0.1:38343\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39961/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41063\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-434fsbsw\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 34

\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://127.0.0.1:39367\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36945/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34779\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-6d9kgu3o\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 35

\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://127.0.0.1:44661\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46007/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45401\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-q7mf1mpa\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 36

\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://127.0.0.1:46051\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45911/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41741\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-4_3gpbas\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 37

\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://127.0.0.1:40863\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35087/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35587\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-58h4w8g0\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 38

\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://127.0.0.1:33679\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33599/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38763\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-jt6gweqk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 39

\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://127.0.0.1:35339\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43983/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43717\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-0b2muu7a\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 40

\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://127.0.0.1:45247\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39197/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46143\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-czob480_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 41

\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://127.0.0.1:44343\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37403/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37665\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-texjvoav\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 42

\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://127.0.0.1:43099\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43459/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38567\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-rtnnjg9c\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 43

\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://127.0.0.1:43625\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41949/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33591\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-er4eao9s\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 44

\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://127.0.0.1:46463\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38185/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42477\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-k0pi8cq_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 45

\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://127.0.0.1:43501\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45993/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38751\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-grthfmds\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 46

\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://127.0.0.1:45653\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37605/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34431\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-l1l8c_3l\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 47

\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://127.0.0.1:33561\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37689/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45209\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-_pfo_m29\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ ":241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n" ] } ], "source": [ "client = Client(threads_per_worker = 1)\n", "client" ] }, { "cell_type": "code", "execution_count": 3, "id": "665b7147-a240-47f2-b0ad-2b45b7d8dc96", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

01deg_jra55v13_ryf9091 catalog with 22 dataset(s) from 11947 asset(s):

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
unique
filename3469
path11947
file_id22
frequency5
start_date3361
end_date3360
variable205
variable_long_name197
variable_standard_name36
variable_cell_methods3
variable_units52
realm2
derived_variable0
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "catalog = intake.cat.access_nri\n", "experiment = '01deg_jra55v13_ryf9091' # the RYF90-91 experiment\n", "\n", "esm_ds = catalog[experiment]\n", "esm_ds" ] }, { "cell_type": "markdown", "id": "d19bc01e-d8c0-45e7-a34f-c9e489d052d1", "metadata": {}, "source": [ "Note that the bathymetry provided by `ht` and `hu` is not discretised into the same bins as `st_ocean(_edges)`. The histogram function in this code thus yields uneven bins if we bin `ht` by `st_ocean_edges` bins. As a result, we use `ht` and `hu` for our masking, but we obtain bottom bathymetry from the variable itself, which _is_ discretised into `st_ocean bins`. " ] }, { "cell_type": "markdown", "id": "1433b8c5-baa4-422f-820a-8403403d145a", "metadata": {}, "source": [ "### 1. Define your region of interest. \n", "Bathymetry variables, `ht` and `hu`, are used for masking unwanted deep regions on the continental shelf and shallow regions offshore." ] }, { "cell_type": "code", "execution_count": 4, "id": "e68ae50e-9095-4d19-a5dc-f06acdda656f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

01deg_jra55v13_ryf9091 catalog with 1 dataset(s) from 1116 asset(s):

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
unique
filename1
path1116
file_id1
frequency1
start_date1116
end_date1116
variable20
variable_long_name20
variable_standard_name2
variable_cell_methods2
variable_units6
realm1
derived_variable0
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Search for bathymetry - on u and t grid. \n", "esm_ds.search(variable=['hu','ht'])" ] }, { "cell_type": "markdown", "id": "1d077f45-3666-4edd-be67-c48153d30531", "metadata": {}, "source": [ "They're both in a single dataset, so we can load them together using `.to_dask()`. We use `xarray_open_kwargs = {'chunks' : \"auto\"}` to let dask determine the chunks. We also set `\"decode_timedelta\" : False` to disable a bunch of useless warnings related to a library change.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "2a3ad8b4-c98f-4512-9382-ba5ba21331aa", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", " records = grouped.get_group(internal_key).to_dict(orient='records')\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.py:25: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " VAR_ATTRIBUTES = yaml.load(\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/argopy/__init__.py:32: DeprecationWarning: The 'argopy.utilities' has moved to 'argopy.utils'. After 0.1.15, importing 'utilities' will raise an error. Please update your script.\n", " from . import utilities # noqa: E402 # being deprecated until 0.1.15, then remove\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/core/npstats.py:238: DeprecationWarning: invalid escape sequence '\\s'\n", " \"\"\"Gaussian frequency spectrum (Bunney et al., 2014).\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/tornado/ioloop.py:945: ResourceWarning: unclosed file <_io.TextIOWrapper name='/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/wavespectra/output/ww3.yml' mode='r' encoding='UTF-8'>\n", " val = self.callback()\n", "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'hu' (yu_ocean: 2700, xu_ocean: 3600)> Size: 39MB\n",
       "dask.array<open_dataset-hu, shape=(2700, 3600), dtype=float32, chunksize=(2700, 3600), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xu_ocean  (xu_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.8 79.9 80.0\n",
       "  * yu_ocean  (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n",
       "    geolon_c  (yu_ocean, xu_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    geolat_c  (yu_ocean, xu_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "Attributes:\n",
       "    long_name:     ocean depth on u-cells\n",
       "    units:         m\n",
       "    valid_range:   [-1.e+09  1.e+09]\n",
       "    cell_methods:  time: point
" ], "text/plain": [ " Size: 39MB\n", "dask.array\n", "Coordinates:\n", " * xu_ocean (xu_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.8 79.9 80.0\n", " * yu_ocean (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n", " geolon_c (yu_ocean, xu_ocean) float32 39MB dask.array\n", " geolat_c (yu_ocean, xu_ocean) float32 39MB dask.array\n", "Attributes:\n", " long_name: ocean depth on u-cells\n", " units: m\n", " valid_range: [-1.e+09 1.e+09]\n", " cell_methods: time: point" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bathy = esm_ds.search(variable=['hu','ht']).to_dask(\n", " xarray_open_kwargs={\n", " \"chunks\" : \"auto\",\n", " }\n", ")\n", "\n", "hu = bathy['hu']\n", "ht = bathy['ht']\n", "hu" ] }, { "cell_type": "markdown", "id": "672738a2-fc0f-4660-bfc3-462681e1bd9b", "metadata": {}, "source": [ "As a simple example, we mask out everything but the Ross Sea shallower than 2000 meters.\n", "Alternatively, users can define any polygon as their mask." ] }, { "cell_type": "code", "execution_count": 6, "id": "33c677b7-ac59-4709-a46a-96189d4838db", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define the region of interest\n", "lon_slice = slice(-200, -150)\n", "lat_slice = slice(-80, -68)\n", "mask_depth = 2000\n", "\n", "# select correct extent of ht, hu for use later:\n", "ht = ht.sel(xt_ocean=lon_slice).sel(yt_ocean=lat_slice)\n", "hu = hu.sel(xu_ocean=lon_slice).sel(yu_ocean=lat_slice)\n", "\n", "# create masks:\n", "mask_t = ht.sel(xt_ocean=lon_slice).sel(yt_ocean=lat_slice) < mask_depth\n", "mask_u = hu.sel(xu_ocean=lon_slice).sel(yu_ocean=lat_slice) < mask_depth\n", "mask_t = mask_t.where(mask_t != 0)\n", "mask_u = mask_u.where(mask_u != 0)\n", "\n", "# mask bathymetry:\n", "hu_masked = hu * mask_u\n", "ht_masked = ht * mask_t\n", "\n", "# Plot the bathymetry in the mask region:\n", "ht_masked.plot()\n", "plt.title('Bathymetry in region of interest')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1f6fcf85-03a2-47bc-98fd-753e75a68354", "metadata": {}, "source": [ "### 2. Import variables for averaging, set up thickness and area for weighting, and isobath bins." ] }, { "cell_type": "code", "execution_count": 7, "id": "30f78ece-a74a-4a48-bc21-b979427bd71d", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "

01deg_jra55v13_ryf9091 catalog with 1 dataset(s) from 8 asset(s):

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
unique
filename1
path8
file_id1
frequency1
start_date8
end_date8
variable46
variable_long_name42
variable_standard_name11
variable_cell_methods2
variable_units18
realm1
derived_variable0
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import properties to average along isobaths.\n", "\n", "# Define the start and end times #\n", "start_time, end_time = '2170-01-01', '2179-12-16'\n", "# For searching with the intake catalog, we can use wildcard searches: so start_date = '217[0,9].*' means any date starting with 2170,2171..2179\n", "# See https://access-nri-intake-catalog.readthedocs.io/en/latest/usage/quickstart.html#data-discovery for more info\n", "\n", "# Define the variables we want to load\n", "variables =['salt','temp','pot_rho_0','u','v']\n", "\n", "esm_ds.search(variable=variables,frequency='1mon', start_date='217[0,9].*')" ] }, { "cell_type": "markdown", "id": "f5d87724-18f9-4eb9-b033-bf7d24bb7371", "metadata": {}, "source": [ "All our variables are in the one dataset - so we can load them all at once - this is more efficient than doing things separately" ] }, { "cell_type": "code", "execution_count": 8, "id": "94b4d4bf-d9dd-4ec8-8c6d-c7dedec5d5ad", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", " records = grouped.get_group(internal_key).to_dict(orient='records')\n" ] } ], "source": [ "# Load the dataset\n", "var_dataset = esm_ds.search(\n", " variable=variables,frequency='1mon', start_date='217[0,9].*'\n", ").to_dask(\n", " xarray_open_kwargs={\n", " 'chunks' : \"auto\",\n", " }\n", ")\n", "# Select out slice\n", "var_dataset = var_dataset.sel(time=slice(start_time, end_time)).sel(yt_ocean=lat_slice)\n", "\n", "# Separate our variables into data arrays\n", "temp = var_dataset['temp']\n", "salt = var_dataset['salt']\n", "density = var_dataset['pot_rho_0']\n", "u = var_dataset['u']\n", "v = var_dataset['v']\n" ] }, { "cell_type": "markdown", "id": "667a875c-5ad5-4580-89fe-c2c491b22f87", "metadata": {}, "source": [ "Compute volume and area for weighted averaging using `xhistogram` package.\n", "\n", "Note that the thickness here is **not** time-varying. Use `dzt` for time-varying thickness if necessary." ] }, { "cell_type": "code", "execution_count": 9, "id": "64a2f85c-6981-4324-87c3-214369da103b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ocean.1mon.grid_xt_ocean:3600.grid_xu_ocean:3600.grid_yt_ocean:2700.grid_yu_ocean:2700.neutral:80.neutralrho_edges:81.nv:2.potrho:80.potrho_edges:81.st_edges_ocean:76.st_ocean:75.sw_edges_ocean:76.sw_ocean:75.xt_ocean:3600.xu_ocean:3600.yt_ocean:2700.yu_ocean:2700',\n", " 'ocean.1mon.nv:2.st_edges_ocean:76.st_ocean:75.xt_ocean:3600.yt_ocean:2700']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "esm_ds.search(variable='st_ocean',frequency='1mon').unique().file_id" ] }, { "cell_type": "code", "execution_count": 10, "id": "3d2666d9-03b3-4f4f-91ce-b5ed9e008f19", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", " records = grouped.get_group(internal_key).to_dict(orient='records')\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", " records = grouped.get_group(internal_key).to_dict(orient='records')\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 11.3 s, sys: 1.66 s, total: 12.9 s\n", "Wall time: 11.5 s\n" ] } ], "source": [ "%%time\n", "grid_vars = ['xt_ocean','yt_ocean','st_edges_ocean','st_ocean']\n", "coord_ds = esm_ds.search(\n", " variable=grid_vars, \n", " file_id='ocean.1mon.grid_xt_ocean:3600.grid_xu_ocean:3600.grid_yt_ocean:2700.grid_yu_ocean:2700.neutral:80.neutralrho_edges:81.nv:2.potrho:80.potrho_edges:81.st_edges_ocean:76.st_ocean:75.sw_edges_ocean:76.sw_ocean:75.xt_ocean:3600.xu_ocean:3600.yt_ocean:2700.yu_ocean:2700',\n", " frequency='1mon'\n", ").to_dask(\n", " xarray_open_kwargs={\n", " 'chunks' : \"auto\",\n", " }\n", ")\n", "xt_ocean = coord_ds['xt_ocean'].sel(xt_ocean=lon_slice)\n", "yt_ocean = coord_ds['yt_ocean'].sel(yt_ocean=lat_slice)\n", "st_ocean = coord_ds['st_ocean']\n", "\n", "st_edges_ocean = coord_ds['st_edges_ocean']\n", "st_edges_array = st_edges_ocean.expand_dims({'yt_ocean': yt_ocean, 'xt_ocean': xt_ocean}, axis=[1, 2])\n", "\n", "# adjust for partial bottom cells:\n", "st_edges_with_partial = st_edges_array.where(st_edges_array" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pcolor(T_mean.ht_coarse_bin, st_ocean, T_mean.mean('time') - 273.15)\n", "plt.gca().invert_yaxis()\n", "plt.ylim(2000,0)\n", "plt.xlabel('Isobath [m]')\n", "plt.ylabel('Depth [m]')\n", "plt.colorbar(label = 'Temperature [$^\\circ$C]')\n", "plt.title('Temperature averaged along isobath');\n", "\n", "## Each variable is averaged along isobaths, so the bathymetry looks diagonal in the plot #" ] }, { "cell_type": "markdown", "id": "c0730328-7f61-409d-8c3f-0de16a51259b", "metadata": {}, "source": [ "We can rescale the isobath (x-axis) to be scaled by the cumulative surface area covered by each isobath, from shallowest to deepest. This produces a \"normalised area\" or pseduo-latitude $x$-axis." ] }, { "cell_type": "code", "execution_count": 16, "id": "4d16836d-bfe0-4824-8e23-e2fe2ef63619", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 81.39 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 81.39 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 81.65 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 81.66 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n" ] } ], "source": [ "normalised_area_cumsum = (A_sum.cumsum('ht_coarse_bin') / np.nansum(A_sum)).values\n", "normalised_area_cumsum_u = (Au_sum.cumsum('hu_coarse_bin') / np.nansum(Au_sum)).values\n", "\n", "lat_max, lat_min = abs(mask_t.yt_ocean.max().values), abs(mask_t.yt_ocean.min().values)\n", "\n", "## Here, we plot the averaged quantities as a function of normalised area (between 0 and 1)\n", "## and pseudo-latitude\n", "def normalised_to_pseudo_lat(normalised_area):\n", " return -1 * (lat_min - (lat_min - lat_max) * normalised_area)\n", "\n", "def pseudo_lat_to_normalised(pseudo_lat):\n", " return (lat_min + pseudo_lat) / (lat_min - lat_max)\n", "\n", "pseudo_lat = normalised_to_pseudo_lat(normalised_area_cumsum)\n", "pseudo_lat_u = normalised_to_pseudo_lat(normalised_area_cumsum_u)" ] }, { "cell_type": "code", "execution_count": 17, "id": "2ba60ef3-4f7f-4c7e-93f6-fab544d0be64", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 38.5 s, sys: 8.9 s, total: 47.4 s\n", "Wall time: 57.8 s\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "## Finally, plot the isobath-averaged properties. This should take about 3 minutes on a Large ARE Instance\n", "\n", "\n", "fig, axs = plt.subplots(2, 2, figsize=(12, 10), facecolor='w', edgecolor='k')\n", "fig.subplots_adjust(hspace = 0.35, wspace=0.2)\n", "axs = axs.ravel()\n", "\n", "fontsize = 13\n", "\n", "im1 = axs[0].contourf(normalised_area_cumsum, st_ocean, T_mean.mean('time') - 273.15, levels=500,\n", " vmin=-2, vmax=2, cmap=plt.cm.bwr)\n", "im2 = axs[1].contourf(normalised_area_cumsum, st_ocean, S_mean.mean('time'), levels=500, vmin=34.1,\n", " vmax = 34.7, cmap=plt.cm.viridis)\n", "im3 = axs[2].contourf(normalised_area_cumsum, st_ocean, rho_mean.mean('time') - 1027, levels=500,\n", " vmin=0.2, vmax=0.87, cmap=plt.cm.plasma)\n", "im4 = axs[3].contourf(normalised_area_cumsum_u, st_ocean, u_mean.mean('time'), levels=500,\n", " vmin=-0.06, vmax=0.06, cmap=plt.cm.bwr)\n", "\n", "density_contours = np.linspace(1027.7, 1028, 5)\n", "\n", "for i, ax in enumerate(axs):\n", " cont = ax.contour(normalised_area_cumsum_u, st_ocean, rho_mean.mean('time'), density_contours,\n", " colors='gray', zorder=2, fontsize=fontsize)\n", " ax.invert_yaxis()\n", " ax.set_xlabel('Normalised distance from Antarctica')\n", " ax.set_ylim(mask_depth, 0)\n", "\n", " # Secondary x-axis: pseudo_lat\n", " twin_ax = ax.twiny()\n", " twin_ax.set_xlim(ax.get_xlim()) # Align the limits to match the shared x-axis\n", " twin_ax.set_xticks(pseudo_lat_to_normalised(np.arange(-80, -68, 2))) # Match tick positions\n", " twin_ax.set_xticklabels(np.arange(-80, -68, 2)) # Replace tick labels with pseudo_lat values\n", " twin_ax.set_xlabel('Pseudo-Latitude (°)')\n", "\n", "axs[0].set_ylabel('Depth [m]')\n", "\n", "cb1 = plt.colorbar(im1, ax=axs[0], fraction=0.03, pad=0.2, orientation='horizontal')\n", "cb2 = plt.colorbar(im2, ax=axs[1], fraction=0.03, pad=0.2, orientation='horizontal')\n", "cb3 = plt.colorbar(im3, ax=axs[2], fraction=0.03, pad=0.2, orientation='horizontal')\n", "cb4 = plt.colorbar(im4, ax=axs[3], fraction=0.03, pad=0.2, orientation='horizontal')\n", "\n", "cb1.set_label('Time-mean Temp ($^\\circ$C)')\n", "cb2.set_label('Time-mean Salinity (g/kg)')\n", "cb3.set_label('Time-mean Density (+1027 kg/m$^3$)')\n", "cb4.set_label('Time-mean Zonal Velocity (m/s)')\n", "\n", "cb1.set_ticks(np.linspace(np.min(T_mean.mean('time') - 273.15), np.max(T_mean.mean('time') - 273.15), 5))\n", "cb2.set_ticks(np.linspace(np.min(S_mean.mean('time')), np.max(S_mean.mean('time')), 5))\n", "cb3.set_ticks(np.linspace(np.min(rho_mean.mean('time') - 1027), np.max(rho_mean.mean('time') - 1027), 5))\n", "cb4.set_ticks(np.linspace(np.min(u_mean.mean('time')), np.max(u_mean.mean('time')), 4))\n", "\n", "plt.suptitle('Isobath-averaged properties', y=0.95);\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }