From 701b3cfdc4dca1e4abaf5ca17e799c60fdc253bc Mon Sep 17 00:00:00 2001 From: Marc White Date: Fri, 27 Sep 2024 16:08:00 +1000 Subject: [PATCH 1/8] Convert to intake, xarray updates --- .../Apply_function_to_every_gridpoint.ipynb | 3832 ++++++++++------- 1 file changed, 2191 insertions(+), 1641 deletions(-) diff --git a/Recipes/Apply_function_to_every_gridpoint.ipynb b/Recipes/Apply_function_to_every_gridpoint.ipynb index c5ddacc0..9b89a002 100644 --- a/Recipes/Apply_function_to_every_gridpoint.ipynb +++ b/Recipes/Apply_function_to_every_gridpoint.ipynb @@ -37,12 +37,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 62, "id": "dd347ec4-a296-416b-a8fe-3c9b979d1dea", "metadata": {}, "outputs": [], "source": [ - "import cosima_cookbook as cc\n", + "import intake\n", + "cat = intake.cat.access_nri\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", @@ -51,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 63, "id": "a7e4909a-3364-4209-9e89-c51b7766c1a8", "metadata": {}, "outputs": [ @@ -62,7 +63,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-125d3056-5db3-11ef-88cb-00000189fe80

\n", + "

Client-0bd7a589-7c95-11ef-939a-000007c4fe80

\n", " \n", "\n", " \n", @@ -75,7 +76,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -84,7 +85,7 @@ "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", "
\n", "\n", " \n", - " \n", " \n", @@ -97,19 +98,19 @@ "
\n", "
\n", "

LocalCluster

\n", - "

ba24ef27

\n", + "

ca275d4a

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", " \n", - " Workers: 48\n", + " Workers: 14\n", "
\n", - " Total threads: 48\n", + " Total threads: 14\n", " \n", " Total memory: 0 B\n", @@ -134,22 +135,22 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-e6a2b992-5e09-4d3b-b879-690f4785f8e8

\n", + "

Scheduler-01e2b333-988a-47b1-9ba1-2c0de5b5a0a7

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -180,1537 +181,7 @@ "
\n", - " Comm: tcp://127.0.0.1:46407\n", + " Comm: tcp://127.0.0.1:44089\n", " \n", - " Workers: 48\n", + " Workers: 14\n", "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", " \n", - " Total threads: 48\n", + " Total threads: 14\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://127.0.0.1:45601\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40469/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42417\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-3syvdgo3\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://127.0.0.1:35763\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/33163/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:37841\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-9jmkqz6b\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://127.0.0.1:39201\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/43003/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:34551\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-kxp47mlo\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://127.0.0.1:42029\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/39921/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:32775\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-kqqnff8q\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://127.0.0.1:35765\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/38747/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:39941\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-dkefo58_\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://127.0.0.1:33179\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40095/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:38141\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-_q0pyvko\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://127.0.0.1:36859\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/34753/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:43539\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-fl329pxj\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://127.0.0.1:44173\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/39005/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:45701\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-oddph78x\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://127.0.0.1:46589\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/33295/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42009\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-1w4230an\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://127.0.0.1:37407\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/35099/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:34333\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-n5nueeqt\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://127.0.0.1:43823\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/45797/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42065\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-vorojrrc\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://127.0.0.1:42451\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/37957/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:37785\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-gdz4ajpn\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://127.0.0.1:35529\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/46465/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40993\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-7wr4f2jb\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://127.0.0.1:34125\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/38437/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40595\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-24g6_72p\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://127.0.0.1:39915\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40143/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40821\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-aq36xv9v\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://127.0.0.1:45047\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/44849/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:32843\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-drgr3pfy\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://127.0.0.1:38025\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/45123/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40581\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-hyh1u_ic\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://127.0.0.1:46473\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/34251/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40479\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-hide9xgi\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://127.0.0.1:39273\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/38261/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42303\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-pxrl7y9g\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://127.0.0.1:40123\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/36363/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:36861\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-0pjc0zk6\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://127.0.0.1:45159\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/34267/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:37727\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-tpm7_imk\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://127.0.0.1:43189\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40093/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42685\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-5ob1vn4j\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://127.0.0.1:45137\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40589/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40771\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-8psps1vb\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://127.0.0.1:38981\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/46211/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:32777\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-gqi6vsms\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://127.0.0.1:43875\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/43369/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42535\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-jyvzcr7r\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://127.0.0.1:44897\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/44811/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40709\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-u8d1lbcb\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://127.0.0.1:43837\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/46777/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:37033\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-1qiury10\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://127.0.0.1:34181\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/40329/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:37729\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-vk72cmp3\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://127.0.0.1:41067\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/42345/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:39643\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-uqjncy2k\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://127.0.0.1:46213\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/35999/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:40179\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-zi4de9c8\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://127.0.0.1:46457\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/33019/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:35583\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-kypw7rfz\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://127.0.0.1:43201\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/35477/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:33323\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-u56tka6c\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://127.0.0.1:46401\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/32845/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:39689\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-eywdr26z\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://127.0.0.1:38737\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/36937/status\n", - " \n", - " Memory: 0 B\n", - "
\n", - " Nanny: tcp://127.0.0.1:42713\n", - "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-hptdjxq7\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", @@ -1750,12 +221,12 @@ "
\n", "
\n", " \n", - "

Worker: 35

\n", + "

Worker: 1

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:40029\n", + " Comm: tcp://127.0.0.1:38673\n", " \n", " Total threads: 1\n", @@ -1718,7 +189,7 @@ "
\n", - " Dashboard: /proxy/36535/status\n", + " Dashboard: http://127.0.0.1:37869/status\n", " \n", " Memory: 0 B\n", @@ -1726,13 +197,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:44913\n", + " Nanny: tcp://127.0.0.1:42893\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-n7kplyv0\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-jv8blnmi\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1795,12 +266,12 @@ "
\n", "
\n", " \n", - "

Worker: 36

\n", + "

Worker: 2

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:45183\n", + " Comm: tcp://127.0.0.1:43571\n", " \n", " Total threads: 1\n", @@ -1763,7 +234,7 @@ "
\n", - " Dashboard: /proxy/35665/status\n", + " Dashboard: http://127.0.0.1:45605/status\n", " \n", " Memory: 0 B\n", @@ -1771,13 +242,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:43605\n", + " Nanny: tcp://127.0.0.1:33049\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-_z9p1zh0\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-npqxkz5f\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1840,12 +311,12 @@ "
\n", "
\n", " \n", - "

Worker: 37

\n", + "

Worker: 3

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:33549\n", + " Comm: tcp://127.0.0.1:43763\n", " \n", " Total threads: 1\n", @@ -1808,7 +279,7 @@ "
\n", - " Dashboard: /proxy/38171/status\n", + " Dashboard: http://127.0.0.1:38003/status\n", " \n", " Memory: 0 B\n", @@ -1816,13 +287,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:33303\n", + " Nanny: tcp://127.0.0.1:42963\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-5eryz58z\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-5t4zg22e\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1885,12 +356,12 @@ "
\n", "
\n", " \n", - "

Worker: 38

\n", + "

Worker: 4

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:36955\n", + " Comm: tcp://127.0.0.1:32841\n", " \n", " Total threads: 1\n", @@ -1853,7 +324,7 @@ "
\n", - " Dashboard: /proxy/38619/status\n", + " Dashboard: http://127.0.0.1:33001/status\n", " \n", " Memory: 0 B\n", @@ -1861,13 +332,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:37291\n", + " Nanny: tcp://127.0.0.1:35279\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-bcj9hpuk\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-zom5tbqz\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1930,12 +401,12 @@ "
\n", "
\n", " \n", - "

Worker: 39

\n", + "

Worker: 5

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:43593\n", + " Comm: tcp://127.0.0.1:43861\n", " \n", " Total threads: 1\n", @@ -1898,7 +369,7 @@ "
\n", - " Dashboard: /proxy/43245/status\n", + " Dashboard: http://127.0.0.1:42755/status\n", " \n", " Memory: 0 B\n", @@ -1906,13 +377,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:39693\n", + " Nanny: tcp://127.0.0.1:33835\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-z37p4b9l\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-9_yv3mcr\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1975,12 +446,12 @@ "
\n", "
\n", " \n", - "

Worker: 40

\n", + "

Worker: 6

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:34783\n", + " Comm: tcp://127.0.0.1:42367\n", " \n", " Total threads: 1\n", @@ -1943,7 +414,7 @@ "
\n", - " Dashboard: /proxy/36467/status\n", + " Dashboard: http://127.0.0.1:46587/status\n", " \n", " Memory: 0 B\n", @@ -1951,13 +422,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:38501\n", + " Nanny: tcp://127.0.0.1:41571\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-kop9fkkh\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-kkx07xqu\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2020,12 +491,12 @@ "
\n", "
\n", " \n", - "

Worker: 41

\n", + "

Worker: 7

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:34443\n", + " Comm: tcp://127.0.0.1:38889\n", " \n", " Total threads: 1\n", @@ -1988,7 +459,7 @@ "
\n", - " Dashboard: /proxy/34113/status\n", + " Dashboard: http://127.0.0.1:46355/status\n", " \n", " Memory: 0 B\n", @@ -1996,13 +467,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:33433\n", + " Nanny: tcp://127.0.0.1:32803\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-w6bxnvf8\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-b159vcuy\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2065,12 +536,12 @@ "
\n", "
\n", " \n", - "

Worker: 42

\n", + "

Worker: 8

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:37441\n", + " Comm: tcp://127.0.0.1:45855\n", " \n", " Total threads: 1\n", @@ -2033,7 +504,7 @@ "
\n", - " Dashboard: /proxy/41969/status\n", + " Dashboard: http://127.0.0.1:33975/status\n", " \n", " Memory: 0 B\n", @@ -2041,13 +512,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:36291\n", + " Nanny: tcp://127.0.0.1:34217\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-ez3hjap0\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-w_rwqpvj\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2110,12 +581,12 @@ "
\n", "
\n", " \n", - "

Worker: 43

\n", + "

Worker: 9

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:38673\n", + " Comm: tcp://127.0.0.1:37359\n", " \n", " Total threads: 1\n", @@ -2078,7 +549,7 @@ "
\n", - " Dashboard: /proxy/45459/status\n", + " Dashboard: http://127.0.0.1:38755/status\n", " \n", " Memory: 0 B\n", @@ -2086,13 +557,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:42745\n", + " Nanny: tcp://127.0.0.1:33463\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-13f0qkxm\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-awmw6r9c\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2155,12 +626,12 @@ "
\n", "
\n", " \n", - "

Worker: 44

\n", + "

Worker: 10

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:45831\n", + " Comm: tcp://127.0.0.1:41579\n", " \n", " Total threads: 1\n", @@ -2123,7 +594,7 @@ "
\n", - " Dashboard: /proxy/40627/status\n", + " Dashboard: http://127.0.0.1:35203/status\n", " \n", " Memory: 0 B\n", @@ -2131,13 +602,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:41765\n", + " Nanny: tcp://127.0.0.1:46679\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-utwf9_u3\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-261sx3mk\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2200,12 +671,12 @@ "
\n", "
\n", " \n", - "

Worker: 45

\n", + "

Worker: 11

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:40061\n", + " Comm: tcp://127.0.0.1:43531\n", " \n", " Total threads: 1\n", @@ -2168,7 +639,7 @@ "
\n", - " Dashboard: /proxy/38113/status\n", + " Dashboard: http://127.0.0.1:42935/status\n", " \n", " Memory: 0 B\n", @@ -2176,13 +647,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:43421\n", + " Nanny: tcp://127.0.0.1:39125\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-x_ecid47\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-1pnmv0or\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2245,12 +716,12 @@ "
\n", "
\n", " \n", - "

Worker: 46

\n", + "

Worker: 12

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:41337\n", + " Comm: tcp://127.0.0.1:44679\n", " \n", " Total threads: 1\n", @@ -2213,7 +684,7 @@ "
\n", - " Dashboard: /proxy/44485/status\n", + " Dashboard: http://127.0.0.1:39581/status\n", " \n", " Memory: 0 B\n", @@ -2221,13 +692,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:45303\n", + " Nanny: tcp://127.0.0.1:35357\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-uhex_yom\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-fpreryjx\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2290,12 +761,12 @@ "
\n", "
\n", " \n", - "

Worker: 47

\n", + "

Worker: 13

\n", " \n", "
\n", - " Comm: tcp://127.0.0.1:46669\n", + " Comm: tcp://127.0.0.1:42365\n", " \n", " Total threads: 1\n", @@ -2258,7 +729,7 @@ "
\n", - " Dashboard: /proxy/36845/status\n", + " Dashboard: http://127.0.0.1:44023/status\n", " \n", " Memory: 0 B\n", @@ -2266,13 +737,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:44697\n", + " Nanny: tcp://127.0.0.1:35221\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-nsdc8bwe\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-vw8es1nq\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2344,10 +815,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2360,31 +831,1160 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 64, "id": "f7120133-0beb-441f-9c9f-caa81649227d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 5GB\n",
+       "Dimensions:   (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Coordinates:\n",
+       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "  * time      (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n",
+       "Data variables:\n",
+       "    sst       (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(1, 216, 240), meta=np.ndarray>\n",
+       "Attributes: (12/16)\n",
+       "    filename:                                 ocean_month.nc\n",
+       "    title:                                    ACCESS-OM2\n",
+       "    grid_type:                                mosaic\n",
+       "    grid_tile:                                1\n",
+       "    intake_esm_vars:                          ['sst']\n",
+       "    intake_esm_attrs:realm:                   ocean\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_cell_methods:   time: mean,time: mean,time: mea...\n",
+       "    intake_esm_attrs:variable_units:          dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n",
+       "    intake_esm_attrs:filename:                ocean_month.nc\n",
+       "    intake_esm_attrs:file_id:                 ocean_month\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   ocean_month.1mon
\n", - " Comm: tcp://127.0.0.1:33445\n", + " Comm: tcp://127.0.0.1:34975\n", " \n", " Total threads: 1\n", @@ -2303,7 +774,7 @@ "
\n", - " Dashboard: /proxy/36531/status\n", + " Dashboard: http://127.0.0.1:44123/status\n", " \n", " Memory: 0 B\n", @@ -2311,13 +782,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:40059\n", + " Nanny: tcp://127.0.0.1:36075\n", "
\n", - " Local directory: /jobfs/123164893.gadi-pbs/dask-scratch-space/worker-qlc2hlae\n", + " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-r9sijbi_\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", + "
Array Chunk
Bytes 4.24 GiB 202.50 kiB
Shape (732, 1080, 1440) (1, 216, 240)
Dask graph 21960 chunks in 123 graph layers
Data type float32 numpy.ndarray
\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", + " \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", + " 1440\n", + " 1080\n", + " 732\n", + "\n", + "
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
+       "       -278.125, -277.875, -277.625,\n",
+       "       ...\n",
+       "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
+       "         79.375,   79.625,   79.875],\n",
+       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
+       "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
+       "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
+       "       -80.12661353099249,\n",
+       "       ...\n",
+       "         88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
+       "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
+       "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
+       "        89.94728232704986],\n",
+       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
+       "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
+       "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
+       "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
+       "               '1958-09-14 00:00:00', '1958-10-14 12:00:00',\n",
+       "               ...\n",
+       "               '2018-03-14 12:00:00', '2018-04-14 00:00:00',\n",
+       "               '2018-05-14 12:00:00', '2018-06-14 00:00:00',\n",
+       "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
+       "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
+       "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
+       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " + ], + "text/plain": [ + " Size: 5GB\n", + "Dimensions: (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n", + "Coordinates:\n", + " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", + " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", + "Data variables:\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", + "Attributes: (12/16)\n", + " filename: ocean_month.nc\n", + " title: ACCESS-OM2\n", + " grid_type: mosaic\n", + " grid_tile: 1\n", + " intake_esm_vars: ['sst']\n", + " intake_esm_attrs:realm: ocean\n", + " ... ...\n", + " intake_esm_attrs:variable_cell_methods: time: mean,time: mean,time: mea...\n", + " intake_esm_attrs:variable_units: dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n", + " intake_esm_attrs:filename: ocean_month.nc\n", + " intake_esm_attrs:file_id: ocean_month\n", + " intake_esm_attrs:_data_format_: netcdf\n", + " intake_esm_dataset_key: ocean_month.1mon" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get some data\n", - "session = cc.database.create_session()\n", + "# sst = cc.querying.getvar(experiment, 'sst', session, frequency='1 monthly', chunks={})\n", + "\n", "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", - "sst = cc.querying.getvar(experiment, 'sst', session, frequency='1 monthly', chunks={})" + "sst = cat[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", + "sst" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 65, "id": "92072502-a7c6-4d54-9778-768c531df475", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 5GB\n",
+       "Dimensions:   (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Coordinates:\n",
+       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "  * time      (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n",
+       "Data variables:\n",
+       "    sst       (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(732, 216, 240), meta=np.ndarray>\n",
+       "Attributes: (12/16)\n",
+       "    filename:                                 ocean_month.nc\n",
+       "    title:                                    ACCESS-OM2\n",
+       "    grid_type:                                mosaic\n",
+       "    grid_tile:                                1\n",
+       "    intake_esm_vars:                          ['sst']\n",
+       "    intake_esm_attrs:realm:                   ocean\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_cell_methods:   time: mean,time: mean,time: mea...\n",
+       "    intake_esm_attrs:variable_units:          dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n",
+       "    intake_esm_attrs:filename:                ocean_month.nc\n",
+       "    intake_esm_attrs:file_id:                 ocean_month\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   ocean_month.1mon
    " + ], + "text/plain": [ + " Size: 5GB\n", + "Dimensions: (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n", + "Coordinates:\n", + " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", + " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", + "Data variables:\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", + "Attributes: (12/16)\n", + " filename: ocean_month.nc\n", + " title: ACCESS-OM2\n", + " grid_type: mosaic\n", + " grid_tile: 1\n", + " intake_esm_vars: ['sst']\n", + " intake_esm_attrs:realm: ocean\n", + " ... ...\n", + " intake_esm_attrs:variable_cell_methods: time: mean,time: mean,time: mea...\n", + " intake_esm_attrs:variable_units: dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n", + " intake_esm_attrs:filename: ocean_month.nc\n", + " intake_esm_attrs:file_id: ocean_month\n", + " intake_esm_attrs:_data_format_: netcdf\n", + " intake_esm_dataset_key: ocean_month.1mon" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Rechunk so that there is only one chunk in time dimension, used by the linear regression\n", - "sst = sst.chunk({'time': -1}) " + "sst = sst.chunk({'time': -1})\n", + "sst" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 66, "id": "a094eebf-28f6-4226-8a40-29b2964d7cd7", "metadata": {}, "outputs": [], @@ -2416,10 +2016,509 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 69, "id": "e1c5f18b-8439-4135-bc7b-663121e65663", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440, stat_type: 2)> Size: 25MB\n",
+       "dask.array<transpose, shape=(1080, 1440, 2), dtype=float64, chunksize=(216, 240, 2), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "Dimensions without coordinates: stat_type
    " + ], + "text/plain": [ + " Size: 25MB\n", + "dask.array\n", + "Coordinates:\n", + " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", + "Dimensions without coordinates: stat_type" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define a timeseries for the linear regression to work (because scipy doesn't like datetimes)\n", "# (This line is specific to the function being applied - in this instance, we want to apply \n", @@ -2429,16 +2528,19 @@ " dims=('time',),\n", " coords={'time': sst.time})\n", "\n", - "#Pass data through to the `xarray.apply_ufunc`\n", + "# Pass data through to the `xarray.apply_ufunc`\n", "stats = xr.apply_ufunc(get_trend, # function being used\n", " years_since_start, # Argument 1 for function\n", - " sst, # Argument 2 for function\n", + " sst[\"sst\"], # Argument 2 for function\n", " input_core_dims=(('time',), ('time',)), # Dimensions the function needs for each argument\n", " output_core_dims=(('stat_type',),), # Dimensions of each output from the function\n", - " output_sizes={'stat_type': 2}, # The new dimension will have size 2\n", + " dask_gufunc_kwargs = {\n", + " \"output_sizes\": {'stat_type': 2}, # The new dimension will have size 2\n", + " },\n", " vectorize=True, # The function needs to only have one lat and lon at a time\n", " dask = 'parallelized', # Dask is fine, but the function can't handle it so apply_ufunc needs to\n", - " )" + " )\n", + "stats" ] }, { @@ -2462,7 +2564,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 70, "id": "58dd2ab9-b505-4885-bed5-37567a403fc4", "metadata": {}, "outputs": [ @@ -2470,27 +2572,457 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 25.2 s, sys: 1.7 s, total: 26.9 s\n", - "Wall time: 32.1 s\n" + "CPU times: user 38.5 s, sys: 2.3 s, total: 40.8 s\n", + "Wall time: 2min 55s\n" ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440)> Size: 12MB\n",
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
+       "Coordinates:\n",
+       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95
    " + ], + "text/plain": [ + " Size: 12MB\n", + "array([[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]])\n", + "Coordinates:\n", + " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "%time stats.load()\n", + "%%time \n", + "stats.load()\n", "# Put data back into some more useful variable names\n", "sst_trend = stats.sel(stat_type=0)\n", - "p_value = stats.sel(stat_type=1)" + "p_value = stats.sel(stat_type=1)\n", + "sst_trend" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 71, "id": "539a6348-94b0-491c-94bf-a55ca72a25bd", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "text/plain": [ + "Text(0.5, 1.0, 'ACCESS-OM2-025 SST trend')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", "text/plain": [ "
    " ] @@ -2504,15 +3036,33 @@ "sst_trend.plot(cbar_kwargs={'label': '°C/yr'})\n", "plt.contourf(p_value.xt_ocean, p_value.yt_ocean, p_value,\n", " levels=(0, 0.05), colors='None', hatches=('...',))\n", - "plt.title('ACCESS-OM2-025 SST trend');" + "plt.title('ACCESS-OM2-025 SST trend')" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "f27843df", + "metadata": {}, + "outputs": [], + "source": [ + "client.close()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58fd01b1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:analysis3]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-analysis3-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2524,7 +3074,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, From f8d42258454017a0bd61b85103b78c4d9e70e3d2 Mon Sep 17 00:00:00 2001 From: Marc White Date: Fri, 27 Sep 2024 16:14:34 +1000 Subject: [PATCH 2/8] Remove stray comment --- Recipes/Apply_function_to_every_gridpoint.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/Recipes/Apply_function_to_every_gridpoint.ipynb b/Recipes/Apply_function_to_every_gridpoint.ipynb index 9b89a002..4be7d281 100644 --- a/Recipes/Apply_function_to_every_gridpoint.ipynb +++ b/Recipes/Apply_function_to_every_gridpoint.ipynb @@ -1418,7 +1418,6 @@ ], "source": [ "# Get some data\n", - "# sst = cc.querying.getvar(experiment, 'sst', session, frequency='1 monthly', chunks={})\n", "\n", "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", "sst = cat[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", From dd7d8fe9b0a86d97ed4762d0fcbf59c137c66b51 Mon Sep 17 00:00:00 2001 From: "Navid C. Constantinou" Date: Fri, 4 Oct 2024 13:54:40 +1000 Subject: [PATCH 3/8] some tweaks --- .../Apply_function_to_every_gridpoint.ipynb | 2745 ++++++++++++----- 1 file changed, 1927 insertions(+), 818 deletions(-) diff --git a/Recipes/Apply_function_to_every_gridpoint.ipynb b/Recipes/Apply_function_to_every_gridpoint.ipynb index 4be7d281..27cabac8 100644 --- a/Recipes/Apply_function_to_every_gridpoint.ipynb +++ b/Recipes/Apply_function_to_every_gridpoint.ipynb @@ -5,7 +5,9 @@ "id": "76a0672f-e42b-4907-928b-c74f6cbfec54", "metadata": {}, "source": [ - "## Using `xarray.apply_ufunc` to apply a function to every gridpoint\n", + "## Apply a function to every gridpoint using `xarray.apply_ufunc`\n", + "\n", + "This tutorial demonstrates best practice to vectorise functions that want to be applied across all grid points.\n", "\n", "When you need to compute something separately at many gridpoints, especially if it is fast at a single gridpoint, putting this computation into a for loop can be very slow. Instead, it is prefereable to vectorise a function, so that the numpy and/or dask backend can distribute the work across multiple cores.\n", "\n", @@ -26,33 +28,31 @@ "\n", "Some functions, such as ```scipy.stats.linregress```, do not have in-build vectorisation, but you might want to apply a function like this to every gridpoint, and for loops would be slow. \n", "\n", - "This notebook **provides a few examples of how to apply functions which do not natively vectorise many times to an xarray dataset, vectorised so that a dask client can speed up the calculation**. We answer here a dummy question \"What is sea-surface temperature trend at each gridpoint of an ocean model, and is it significant?\". Scientifically, this question mostly applies to the forcing dataset and not the ocean model, but it's as good an example as any.**\n", + "This tutorial **provides a few examples of how to apply functions which do not natively vectorise many times to an xarray dataset, vectorised so that a dask client can speed up the calculation**. We answer here a dummy question \"What is sea-surface temperature trend at each gridpoint of an ocean model, and is it significant?\". Scientifically, this question mostly applies to the forcing dataset and not the ocean model, but it's as good an example as any.**\n", "\n", "To achieve this goal, we use ```xarray.apply_ufunc```, which is very versatile, but therefore takes many arguments that can be difficult to interpret at first glance. The aim of the example below is to give something that will work on a problem similar to what COSIMA users may encounter.\n", "\n", - "For full documentation of ```xarray.apply_ufunc``` refer to: https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html\n", - "\n", - "Code takes about 5 minutes total on 4 cores" + "For full documentation of ```xarray.apply_ufunc``` refer to: https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 1, "id": "dd347ec4-a296-416b-a8fe-3c9b979d1dea", "metadata": {}, "outputs": [], "source": [ - "import intake\n", - "cat = intake.cat.access_nri\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", - "import scipy.stats" + "import scipy.stats\n", + "import intake\n", + "cat = intake.cat.access_nri" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 2, "id": "a7e4909a-3364-4209-9e89-c51b7766c1a8", "metadata": {}, "outputs": [ @@ -63,7 +63,7 @@ "
    \n", "
    \n", "

    Client

    \n", - "

    Client-0bd7a589-7c95-11ef-939a-000007c4fe80

    \n", + "

    Client-99754ea3-8203-11ef-90ec-000001adfe80

    \n", " \n", "\n", " \n", @@ -76,7 +76,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -85,7 +85,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: /proxy/8787/status\n", "
    \n", "\n", " \n", - " \n", " \n", @@ -98,19 +98,19 @@ "
    \n", "
    \n", "

    LocalCluster

    \n", - "

    ca275d4a

    \n", + "

    02966292

    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    \n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: /proxy/8787/status\n", " \n", - " Workers: 14\n", + " Workers: 48\n", "
    \n", - " Total threads: 14\n", + " Total threads: 48\n", " \n", " Total memory: 0 B\n", @@ -135,22 +135,22 @@ "
    \n", "
    \n", "

    Scheduler

    \n", - "

    Scheduler-01e2b333-988a-47b1-9ba1-2c0de5b5a0a7

    \n", + "

    Scheduler-7be50f08-de22-4731-85fa-38250fb5d679

    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -181,7 +181,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44089\n", + " Comm: tcp://127.0.0.1:43997\n", " \n", - " Workers: 14\n", + " Workers: 48\n", "
    \n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: /proxy/8787/status\n", " \n", - " Total threads: 14\n", + " Total threads: 48\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -226,7 +226,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38673\n", + " Comm: tcp://127.0.0.1:35103\n", " \n", " Total threads: 1\n", @@ -189,7 +189,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:37869/status\n", + " Dashboard: /proxy/38311/status\n", " \n", " Memory: 0 B\n", @@ -197,13 +197,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42893\n", + " Nanny: tcp://127.0.0.1:42615\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-jv8blnmi\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-p9_htlzn\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -271,7 +271,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43571\n", + " Comm: tcp://127.0.0.1:43811\n", " \n", " Total threads: 1\n", @@ -234,7 +234,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:45605/status\n", + " Dashboard: /proxy/38427/status\n", " \n", " Memory: 0 B\n", @@ -242,13 +242,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33049\n", + " Nanny: tcp://127.0.0.1:33837\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-npqxkz5f\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-3xvzvj8q\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -316,7 +316,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43763\n", + " Comm: tcp://127.0.0.1:46305\n", " \n", " Total threads: 1\n", @@ -279,7 +279,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:38003/status\n", + " Dashboard: /proxy/36681/status\n", " \n", " Memory: 0 B\n", @@ -287,13 +287,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42963\n", + " Nanny: tcp://127.0.0.1:36727\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-5t4zg22e\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-7zmenml_\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -361,7 +361,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:32841\n", + " Comm: tcp://127.0.0.1:44279\n", " \n", " Total threads: 1\n", @@ -324,7 +324,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:33001/status\n", + " Dashboard: /proxy/37695/status\n", " \n", " Memory: 0 B\n", @@ -332,13 +332,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35279\n", + " Nanny: tcp://127.0.0.1:45305\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-zom5tbqz\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-yyu0ctvo\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -406,7 +406,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43861\n", + " Comm: tcp://127.0.0.1:40177\n", " \n", " Total threads: 1\n", @@ -369,7 +369,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:42755/status\n", + " Dashboard: /proxy/40043/status\n", " \n", " Memory: 0 B\n", @@ -377,13 +377,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33835\n", + " Nanny: tcp://127.0.0.1:41541\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-9_yv3mcr\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9bvarjq8\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -451,7 +451,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:42367\n", + " Comm: tcp://127.0.0.1:33725\n", " \n", " Total threads: 1\n", @@ -414,7 +414,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:46587/status\n", + " Dashboard: /proxy/38195/status\n", " \n", " Memory: 0 B\n", @@ -422,13 +422,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:41571\n", + " Nanny: tcp://127.0.0.1:37363\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-kkx07xqu\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-flh_8is1\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -496,7 +496,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38889\n", + " Comm: tcp://127.0.0.1:33807\n", " \n", " Total threads: 1\n", @@ -459,7 +459,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:46355/status\n", + " Dashboard: /proxy/46243/status\n", " \n", " Memory: 0 B\n", @@ -467,13 +467,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:32803\n", + " Nanny: tcp://127.0.0.1:41537\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-b159vcuy\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-lca4r0q7\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -541,7 +541,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:45855\n", + " Comm: tcp://127.0.0.1:33471\n", " \n", " Total threads: 1\n", @@ -504,7 +504,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:33975/status\n", + " Dashboard: /proxy/37735/status\n", " \n", " Memory: 0 B\n", @@ -512,13 +512,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:34217\n", + " Nanny: tcp://127.0.0.1:34171\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-w_rwqpvj\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-rklup1gt\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -586,7 +586,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37359\n", + " Comm: tcp://127.0.0.1:40939\n", " \n", " Total threads: 1\n", @@ -549,7 +549,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:38755/status\n", + " Dashboard: /proxy/38073/status\n", " \n", " Memory: 0 B\n", @@ -557,13 +557,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33463\n", + " Nanny: tcp://127.0.0.1:33569\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-awmw6r9c\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bf_vgp4k\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -631,7 +631,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:41579\n", + " Comm: tcp://127.0.0.1:35795\n", " \n", " Total threads: 1\n", @@ -594,7 +594,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:35203/status\n", + " Dashboard: /proxy/36181/status\n", " \n", " Memory: 0 B\n", @@ -602,13 +602,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:46679\n", + " Nanny: tcp://127.0.0.1:35543\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-261sx3mk\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bt8crafk\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -676,7 +676,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43531\n", + " Comm: tcp://127.0.0.1:36029\n", " \n", " Total threads: 1\n", @@ -639,7 +639,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:42935/status\n", + " Dashboard: /proxy/37333/status\n", " \n", " Memory: 0 B\n", @@ -647,13 +647,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:39125\n", + " Nanny: tcp://127.0.0.1:42905\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-1pnmv0or\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0f5_3lqo\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -721,7 +721,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44679\n", + " Comm: tcp://127.0.0.1:37131\n", " \n", " Total threads: 1\n", @@ -684,7 +684,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:39581/status\n", + " Dashboard: /proxy/41095/status\n", " \n", " Memory: 0 B\n", @@ -692,13 +692,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35357\n", + " Nanny: tcp://127.0.0.1:46255\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-fpreryjx\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-n2vtyaj2\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -766,7 +766,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:42365\n", + " Comm: tcp://127.0.0.1:40829\n", " \n", " Total threads: 1\n", @@ -729,7 +729,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:44023/status\n", + " Dashboard: /proxy/39091/status\n", " \n", " Memory: 0 B\n", @@ -737,13 +737,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35221\n", + " Nanny: tcp://127.0.0.1:35323\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-vw8es1nq\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-vcaqwy2a\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -801,633 +801,1576 @@ " \n", " \n", " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " \n", + "

    Worker: 14

    \n", + "     \n", + "
    \n", - " Comm: tcp://127.0.0.1:34975\n", + " Comm: tcp://127.0.0.1:46131\n", " \n", " Total threads: 1\n", @@ -774,7 +774,7 @@ "
    \n", - " Dashboard: http://127.0.0.1:44123/status\n", + " Dashboard: /proxy/38685/status\n", " \n", " Memory: 0 B\n", @@ -782,13 +782,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:36075\n", + " Nanny: tcp://127.0.0.1:33915\n", "
    \n", - " Local directory: /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-r9sijbi_\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-j01emtrx\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", + " Comm: tcp://127.0.0.1:43159\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/40425/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:44219\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bas8smc9\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", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dask.distributed import Client\n", - "client = Client(threads_per_worker=1, memory_limit=0)\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "f7120133-0beb-441f-9c9f-caa81649227d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset> Size: 5GB\n",
-       "Dimensions:   (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n",
-       "Coordinates:\n",
-       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
-       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
-       "  * time      (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n",
-       "Data variables:\n",
-       "    sst       (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(1, 216, 240), meta=np.ndarray>\n",
-       "Attributes: (12/16)\n",
-       "    filename:                                 ocean_month.nc\n",
-       "    title:                                    ACCESS-OM2\n",
-       "    grid_type:                                mosaic\n",
-       "    grid_tile:                                1\n",
-       "    intake_esm_vars:                          ['sst']\n",
-       "    intake_esm_attrs:realm:                   ocean\n",
-       "    ...                                       ...\n",
-       "    intake_esm_attrs:variable_cell_methods:   time: mean,time: mean,time: mea...\n",
-       "    intake_esm_attrs:variable_units:          dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n",
-       "    intake_esm_attrs:filename:                ocean_month.nc\n",
-       "    intake_esm_attrs:file_id:                 ocean_month\n",
-       "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   ocean_month.1mon
    \n", + " Comm: tcp://127.0.0.1:35083\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45101/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:34271\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mbslqki3\n", + "
    \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", + " \n", + "\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://127.0.0.1:41495\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45403/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:36083\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-u5nk0i6v\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://127.0.0.1:38491\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/41895/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:43913\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-k_u74xdn\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://127.0.0.1:37289\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45073/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38297\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-cza1drap\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://127.0.0.1:38181\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/43175/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:33995\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_2y8cpin\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://127.0.0.1:37841\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/38615/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:36617\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ck_dn52_\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://127.0.0.1:37175\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/45069/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:46225\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-l2l7emzc\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://127.0.0.1:32801\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/38841/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38869\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xufmaoop\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://127.0.0.1:34295\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36097/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:45359\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-75q7srmw\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://127.0.0.1:42393\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/37311/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:36507\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-c78thjkw\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://127.0.0.1:39679\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/39787/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38385\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hr4wredq\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://127.0.0.1:43847\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36153/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:37713\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-25clcjhx\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", + " \n", + " \n", " \n", + "\n", " \n", + "\n", + " \n", + "\n", + "
    \n", + " Comm: tcp://127.0.0.1:45295\n", + " \n", + " Total threads: 1\n", + "
    Array Chunk \n", + " Dashboard: /proxy/38853/status\n", + " \n", + " Memory: 0 B\n", + "
    Bytes 4.24 GiB 202.50 kiB \n", + " Nanny: tcp://127.0.0.1:44225\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-33k_1z6x\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", " \n", - " \n", - "
    Shape (732, 1080, 1440) (1, 216, 240) \n", + " Comm: tcp://127.0.0.1:46191\n", + " \n", + " Total threads: 1\n", + "
    Dask graph 21960 chunks in 123 graph layers \n", + " Dashboard: /proxy/36859/status\n", + " \n", + " Memory: 0 B\n", + "
    Data type float32 numpy.ndarray \n", + " Nanny: tcp://127.0.0.1:43475\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gd0tuixy\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", + "
    \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", - " \n", - " \n", - " \n", + " \n", "\n", - " \n", - " \n", + " \n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "
    \n", + " Comm: tcp://127.0.0.1:44223\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/35053/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:38039\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-exb9jp4z\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", - " \n", - " \n", - " \n", + " \n", "\n", - " \n", - " \n", + " \n", "\n", - " \n", - " 1440\n", - " 1080\n", - " 732\n", - "\n", - " \n", - " \n", - "
    \n", + " Comm: tcp://127.0.0.1:32967\n", + " \n", + " Total threads: 1\n", + "
    \n", + " Dashboard: /proxy/36879/status\n", + " \n", + " Memory: 0 B\n", + "
    \n", + " Nanny: tcp://127.0.0.1:40347\n", + "
    \n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-c59faq4c\n", + "
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
-       "       -278.125, -277.875, -277.625,\n",
-       "       ...\n",
-       "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
-       "         79.375,   79.625,   79.875],\n",
-       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
-       "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
-       "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
-       "       -80.12661353099249,\n",
-       "       ...\n",
-       "         88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
-       "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
-       "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
-       "        89.94728232704986],\n",
-       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
-       "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
-       "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
-       "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
-       "               '1958-09-14 00:00:00', '1958-10-14 12:00:00',\n",
-       "               ...\n",
-       "               '2018-03-14 12:00:00', '2018-04-14 00:00:00',\n",
-       "               '2018-05-14 12:00:00', '2018-06-14 00:00:00',\n",
-       "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
-       "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
-       "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
-       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " + "
    \n", + " \n", + "
    \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + "" ], "text/plain": [ - " Size: 5GB\n", - "Dimensions: (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n", - "Coordinates:\n", - " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", - " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", - " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", - "Data variables:\n", - " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", - "Attributes: (12/16)\n", - " filename: ocean_month.nc\n", - " title: ACCESS-OM2\n", - " grid_type: mosaic\n", - " grid_tile: 1\n", - " intake_esm_vars: ['sst']\n", - " intake_esm_attrs:realm: ocean\n", - " ... ...\n", - " intake_esm_attrs:variable_cell_methods: time: mean,time: mean,time: mea...\n", - " intake_esm_attrs:variable_units: dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n", - " intake_esm_attrs:filename: ocean_month.nc\n", - " intake_esm_attrs:file_id: ocean_month\n", - " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon" + "" ] }, - "execution_count": 64, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Get some data\n", - "\n", - "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", - "sst = cat[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", - "sst" + "from dask.distributed import Client\n", + "client = Client(threads_per_worker=1, memory_limit=0)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "0b27537e-7766-48cb-a8cc-db6bf3e9fb79", + "metadata": {}, + "source": [ + "Get some data" ] }, { "cell_type": "code", - "execution_count": 65, - "id": "92072502-a7c6-4d54-9778-768c531df475", + "execution_count": 3, + "id": "f7120133-0beb-441f-9c9f-caa81649227d", "metadata": {}, "outputs": [ { @@ -1804,7 +2747,7 @@ " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", "Data variables:\n", - " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(732, 216, 240), meta=np.ndarray>\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(1, 216, 240), meta=np.ndarray>\n", "Attributes: (12/16)\n", " filename: ocean_month.nc\n", " title: ACCESS-OM2\n", @@ -1818,11 +2761,11 @@ " intake_esm_attrs:filename: ocean_month.nc\n", " intake_esm_attrs:file_id: ocean_month\n", " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ " Size: 5GB\n", @@ -1953,7 +2932,7 @@ " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", "Data variables:\n", - " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", "Attributes: (12/16)\n", " filename: ocean_month.nc\n", " title: ACCESS-OM2\n", @@ -1970,53 +2949,29 @@ " intake_esm_dataset_key: ocean_month.1mon" ] }, - "execution_count": 65, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Rechunk so that there is only one chunk in time dimension, used by the linear regression\n", - "sst = sst.chunk({'time': -1})\n", + "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", + "sst = cat[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", "sst" ] }, { - "cell_type": "code", - "execution_count": 66, - "id": "a094eebf-28f6-4226-8a40-29b2964d7cd7", + "cell_type": "markdown", + "id": "2a578cd9-ae55-4d7b-8477-c2d463a7973a", "metadata": {}, - "outputs": [], "source": [ - "# Make a function that takes the data we have and returns what we want\n", - "def get_trend(time, timeseries):\n", - " '''Calculate the trend through a timeseries using scipy.stats.linregress, \n", - " and return just the slope and p value as an array, for the purposes of \n", - " demonstrating xarray.apply_ufunc\n", - "\n", - " Inputs:\n", - " time: np.ndarray\n", - " the times or x values of whatever the slope will go through\n", - " timeseries: np.ndarray\n", - " the data to calculate the slope of\n", - "\n", - " Outputs:\n", - " stats: np.ndarray\n", - " 1st element is the trend in timeseries\n", - " 2nd element is the p_value of this trend, indicating the significance\n", - " They're lumped together into one variable, a) so .load() can be called \n", - " on both at once, and b) to demonstrate some of the nuance in xr.apply_ufunc\n", - " when using it for more complicated applications\n", - " '''\n", - "\n", - " slope, intercept, r, p, se = scipy.stats.linregress(time, timeseries)\n", - " return np.array((slope, p)) # Combine into one array because it's easier to load in one go" + "Rechunk so that there is only one chunk in time dimension, used by the linear regression." ] }, { "cell_type": "code", - "execution_count": 69, - "id": "e1c5f18b-8439-4135-bc7b-663121e65663", + "execution_count": 4, + "id": "92072502-a7c6-4d54-9778-768c531df475", "metadata": {}, "outputs": [ { @@ -2386,12 +3341,32 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440, stat_type: 2)> Size: 25MB\n",
-       "dask.array<transpose, shape=(1080, 1440, 2), dtype=float64, chunksize=(216, 240, 2), chunktype=numpy.ndarray>\n",
+       "
    <xarray.Dataset> Size: 5GB\n",
+       "Dimensions:   (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n",
        "Coordinates:\n",
        "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
        "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
-       "Dimensions without coordinates: stat_type
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
        "       -278.125, -277.875, -277.625,\n",
        "       ...\n",
        "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
        "         79.375,   79.625,   79.875],\n",
-       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
+       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
        "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
        "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
        "       -80.12661353099249,\n",
@@ -2502,79 +3476,110 @@
        "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
        "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
        "        89.94728232704986],\n",
-       "      dtype='float64', name='yt_ocean', length=1080))
    • " + " dtype='float64', name='yt_ocean', length=1080))
  • time
    PandasIndex
    PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
+       "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
+       "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
+       "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
+       "               '1958-09-14 00:00:00', '1958-10-14 12:00:00',\n",
+       "               ...\n",
+       "               '2018-03-14 12:00:00', '2018-04-14 00:00:00',\n",
+       "               '2018-05-14 12:00:00', '2018-06-14 00:00:00',\n",
+       "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
+       "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
+       "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
+       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ - " Size: 25MB\n", - "dask.array\n", + " Size: 5GB\n", + "Dimensions: (time: 732, yt_ocean: 1080, xt_ocean: 1440)\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", - "Dimensions without coordinates: stat_type" + " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", + "Data variables:\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", + "Attributes: (12/16)\n", + " filename: ocean_month.nc\n", + " title: ACCESS-OM2\n", + " grid_type: mosaic\n", + " grid_tile: 1\n", + " intake_esm_vars: ['sst']\n", + " intake_esm_attrs:realm: ocean\n", + " ... ...\n", + " intake_esm_attrs:variable_cell_methods: time: mean,time: mean,time: mea...\n", + " intake_esm_attrs:variable_units: dbar,Pa,(kg/m^3)*m,m,meter,m^2,...\n", + " intake_esm_attrs:filename: ocean_month.nc\n", + " intake_esm_attrs:file_id: ocean_month\n", + " intake_esm_attrs:_data_format_: netcdf\n", + " intake_esm_dataset_key: ocean_month.1mon" ] }, - "execution_count": 69, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Define a timeseries for the linear regression to work (because scipy doesn't like datetimes)\n", - "# (This line is specific to the function being applied - in this instance, we want to apply \n", - "# scipy.stats.linregress, and it needs a timeseries of x values so we make it one)\n", + "sst = sst.chunk({'time': -1})\n", + "sst" + ] + }, + { + "cell_type": "markdown", + "id": "45e1532f-f4a0-4a53-b21b-394b62c80e00", + "metadata": {}, + "source": [ + "Define a function that takes the data we have and returns what we want." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a094eebf-28f6-4226-8a40-29b2964d7cd7", + "metadata": {}, + "outputs": [], + "source": [ + "def get_trend(time, timeseries):\n", + " '''Calculate the trend through a timeseries using scipy.stats.linregress, \n", + " and return just the slope and the p value as an array, for the purposes of \n", + " demonstrating xarray.apply_ufunc\n", "\n", - "years_since_start = xr.DataArray(np.arange(sst.time.shape[0])/12,\n", - " dims=('time',),\n", - " coords={'time': sst.time})\n", + " Inputs:\n", + " time: np.ndarray\n", + " the times or x values of whatever the slope will go through\n", + " timeseries: np.ndarray\n", + " the data to calculate the slope of\n", "\n", - "# Pass data through to the `xarray.apply_ufunc`\n", - "stats = xr.apply_ufunc(get_trend, # function being used\n", - " years_since_start, # Argument 1 for function\n", - " sst[\"sst\"], # Argument 2 for function\n", - " input_core_dims=(('time',), ('time',)), # Dimensions the function needs for each argument\n", - " output_core_dims=(('stat_type',),), # Dimensions of each output from the function\n", - " dask_gufunc_kwargs = {\n", - " \"output_sizes\": {'stat_type': 2}, # The new dimension will have size 2\n", - " },\n", - " vectorize=True, # The function needs to only have one lat and lon at a time\n", - " dask = 'parallelized', # Dask is fine, but the function can't handle it so apply_ufunc needs to\n", - " )\n", - "stats" + " Outputs:\n", + " stats: np.ndarray\n", + " 1st element is the trend in timeseries\n", + " 2nd element is the p value of this trend, indicating the significance\n", + " They're lumped together into one variable, a) so .load() can be called \n", + " on both at once, and b) to demonstrate some of the nuance in xr.apply_ufunc\n", + " when using it for more complicated applications\n", + " '''\n", + "\n", + " slope, intercept, r, p, se = scipy.stats.linregress(time, timeseries)\n", + " return np.array((slope, p)) # Combine into one array because it's easier to load in one go" ] }, { "cell_type": "markdown", - "id": "b91a7eb9-3bcc-438c-8168-5b9a57c366e5", + "id": "ceae099b-ee5d-4dc6-8cbb-ee0c9c58b6d2", "metadata": {}, "source": [ - "This last function call is roughly equivalent to\n", + "Define a timeseries for the linear regression to work (because scipy doesn't like datetimes).\n", "\n", - "```python\n", - "stats = np.zeros(2, len(sst.xt_ocean), len(sst.yt_ocean))\n", "\n", - "for i in range(len(sst.xt_ocean)):\n", - " for j in range(len(sst.yt_ocean)):\n", - " slope,intercept = get_trend(years_since_start, sst.isel(xt_ocean=i, yt_ocean=j)\n", - " stats[0, i, j] = slope\n", - " stats[1, i, j] = intercept\n", - "```\n", - "But the loading step (next cell) should be faster than this computation in a for loop" + "**Note**: This line is specific to the function being applied - in this instance, we want to apply `scipy.stats.linregress`, and it needs a timeseries of x values so we make it one." ] }, { "cell_type": "code", - "execution_count": 70, - "id": "58dd2ab9-b505-4885-bed5-37567a403fc4", + "execution_count": 6, + "id": "e1c5f18b-8439-4135-bc7b-663121e65663", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 38.5 s, sys: 2.3 s, total: 40.8 s\n", - "Wall time: 2min 55s\n" - ] - }, { "data": { "text/html": [ @@ -2942,29 +3947,114 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440)> Size: 12MB\n",
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
+       "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440, stat_type: 2)> Size: 25MB\n",
+       "dask.array<transpose, shape=(1080, 1440, 2), dtype=float64, chunksize=(216, 240, 2), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
        "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
-       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95
    • " ], "text/plain": [ - " Size: 12MB\n", - "array([[nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " ...,\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan]])\n", + " Size: 25MB\n", + "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", - " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95" + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", + "Dimensions without coordinates: stat_type" ] }, - "execution_count": 70, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "years_since_start = xr.DataArray(np.arange(sst.time.shape[0])/12,\n", + " dims=('time',),\n", + " coords={'time': sst.time})\n", + "\n", + "# Pass data through to the `xarray.apply_ufunc`\n", + "stats = xr.apply_ufunc(get_trend, # function being used\n", + " years_since_start, # Argument 1 for function\n", + " sst[\"sst\"], # Argument 2 for function\n", + " input_core_dims=(('time',), ('time',)), # Dimensions the function needs for each argument\n", + " output_core_dims=(('stat_type',),), # Dimensions of each output from the function\n", + " dask_gufunc_kwargs = {\n", + " \"output_sizes\": {'stat_type': 2}, # The new dimension will have size 2\n", + " },\n", + " vectorize=True, # The function needs to only have one lat and lon at a time\n", + " dask = 'parallelized', # Dask is fine, but the function can't handle it so apply_ufunc needs to\n", + " )\n", + "stats" + ] + }, + { + "cell_type": "markdown", + "id": "b91a7eb9-3bcc-438c-8168-5b9a57c366e5", + "metadata": {}, + "source": [ + "This last function call is roughly equivalent to\n", + "\n", + "```python\n", + "stats = np.zeros(2, len(sst.xt_ocean), len(sst.yt_ocean))\n", + "\n", + "for i in range(len(sst.xt_ocean)):\n", + " for j in range(len(sst.yt_ocean)):\n", + " slope,intercept = get_trend(years_since_start, sst.isel(xt_ocean=i, yt_ocean=j)\n", + " stats[0, i, j] = slope\n", + " stats[1, i, j] = intercept\n", + "```\n", + "But the loading step (next cell) should be faster than this computation in a for loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58dd2ab9-b505-4885-bed5-37567a403fc4", + "metadata": {}, + "outputs": [], "source": [ "%%time \n", "stats.load()\n", + "\n", "# Put data back into some more useful variable names\n", "sst_trend = stats.sel(stat_type=0)\n", "p_value = stats.sel(stat_type=1)\n", "sst_trend" ] }, + { + "cell_type": "markdown", + "id": "437d4d93-c95a-4606-a483-c5409ac37619", + "metadata": {}, + "source": [ + "Plot the calculated slope, stippling all regions that are significant at $p<0.05$." + ] + }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "539a6348-94b0-491c-94bf-a55ca72a25bd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'ACCESS-OM2-025 SST trend')" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Plot the calculated slope, stippling all regions that are significant at p<0.05\n", "sst_trend.plot(cbar_kwargs={'label': '°C/yr'})\n", "plt.contourf(p_value.xt_ocean, p_value.yt_ocean, p_value,\n", " levels=(0, 0.05), colors='None', hatches=('...',))\n", @@ -3040,28 +4157,20 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "f27843df", "metadata": {}, "outputs": [], "source": [ "client.close()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58fd01b1", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:analysis3-24.07] *", "language": "python", - "name": "python3" + "name": "conda-env-analysis3-24.07-py" }, "language_info": { "codemirror_mode": { From 4c89ed27841eb71c369602453d4165adc83764bb Mon Sep 17 00:00:00 2001 From: "Navid C. Constantinou" Date: Fri, 4 Oct 2024 14:29:22 +1000 Subject: [PATCH 4/8] fix tripole --- .../Apply_function_to_every_gridpoint.ipynb | 929 +++++++++++++----- 1 file changed, 700 insertions(+), 229 deletions(-) diff --git a/Recipes/Apply_function_to_every_gridpoint.ipynb b/Recipes/Apply_function_to_every_gridpoint.ipynb index 27cabac8..4c5eda7f 100644 --- a/Recipes/Apply_function_to_every_gridpoint.ipynb +++ b/Recipes/Apply_function_to_every_gridpoint.ipynb @@ -46,8 +46,9 @@ "import xarray as xr\n", "import numpy as np\n", "import scipy.stats\n", + "import cartopy.crs as ccrs\n", "import intake\n", - "cat = intake.cat.access_nri" + "catalog = intake.cat.access_nri" ] }, { @@ -63,7 +64,7 @@ "
    \n", "
    \n", "

    Client

    \n", - "

    Client-99754ea3-8203-11ef-90ec-000001adfe80

    \n", + "

    Client-c07d9f81-8207-11ef-99aa-000001adfe80

    \n", " \n", "\n", " \n", @@ -98,7 +99,7 @@ " \n", "
    \n", "

    LocalCluster

    \n", - "

    02966292

    \n", + "

    079f76c6

    \n", "
    \n", " \n", "
    \n", @@ -135,11 +136,11 @@ "
    \n", "
    \n", "

    Scheduler

    \n", - "

    Scheduler-7be50f08-de22-4731-85fa-38250fb5d679

    \n", + "

    Scheduler-17648e8b-c006-4d22-83f9-d8440e29bd2b

    \n", " \n", " \n", " \n", "
    \n", - " Comm: tcp://127.0.0.1:43997\n", + " Comm: tcp://127.0.0.1:35663\n", " \n", " Workers: 48\n", @@ -181,7 +182,7 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -226,7 +227,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:35103\n", + " Comm: tcp://127.0.0.1:37715\n", " \n", " Total threads: 1\n", @@ -189,7 +190,7 @@ "
    \n", - " Dashboard: /proxy/38311/status\n", + " Dashboard: /proxy/44669/status\n", " \n", " Memory: 0 B\n", @@ -197,13 +198,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42615\n", + " Nanny: tcp://127.0.0.1:35785\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-p9_htlzn\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gt4j48wq\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -271,7 +272,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43811\n", + " Comm: tcp://127.0.0.1:32881\n", " \n", " Total threads: 1\n", @@ -234,7 +235,7 @@ "
    \n", - " Dashboard: /proxy/38427/status\n", + " Dashboard: /proxy/41371/status\n", " \n", " Memory: 0 B\n", @@ -242,13 +243,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33837\n", + " Nanny: tcp://127.0.0.1:39537\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-3xvzvj8q\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2msnrca0\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -316,7 +317,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:46305\n", + " Comm: tcp://127.0.0.1:37867\n", " \n", " Total threads: 1\n", @@ -279,7 +280,7 @@ "
    \n", - " Dashboard: /proxy/36681/status\n", + " Dashboard: /proxy/41029/status\n", " \n", " Memory: 0 B\n", @@ -287,13 +288,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:36727\n", + " Nanny: tcp://127.0.0.1:41519\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-7zmenml_\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2r4b2mvb\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -361,7 +362,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44279\n", + " Comm: tcp://127.0.0.1:39653\n", " \n", " Total threads: 1\n", @@ -324,7 +325,7 @@ "
    \n", - " Dashboard: /proxy/37695/status\n", + " Dashboard: /proxy/45289/status\n", " \n", " Memory: 0 B\n", @@ -332,13 +333,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:45305\n", + " Nanny: tcp://127.0.0.1:40867\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-yyu0ctvo\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bg2ksfi4\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -406,7 +407,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:40177\n", + " Comm: tcp://127.0.0.1:45411\n", " \n", " Total threads: 1\n", @@ -369,7 +370,7 @@ "
    \n", - " Dashboard: /proxy/40043/status\n", + " Dashboard: /proxy/41823/status\n", " \n", " Memory: 0 B\n", @@ -377,13 +378,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:41541\n", + " Nanny: tcp://127.0.0.1:34091\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9bvarjq8\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-46008wu1\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -451,7 +452,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33725\n", + " Comm: tcp://127.0.0.1:44789\n", " \n", " Total threads: 1\n", @@ -414,7 +415,7 @@ "
    \n", - " Dashboard: /proxy/38195/status\n", + " Dashboard: /proxy/40261/status\n", " \n", " Memory: 0 B\n", @@ -422,13 +423,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:37363\n", + " Nanny: tcp://127.0.0.1:34987\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-flh_8is1\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-guwrezef\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -496,7 +497,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33807\n", + " Comm: tcp://127.0.0.1:37825\n", " \n", " Total threads: 1\n", @@ -459,7 +460,7 @@ "
    \n", - " Dashboard: /proxy/46243/status\n", + " Dashboard: /proxy/34001/status\n", " \n", " Memory: 0 B\n", @@ -467,13 +468,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:41537\n", + " Nanny: tcp://127.0.0.1:34281\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-lca4r0q7\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-15opc7ob\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -541,7 +542,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33471\n", + " Comm: tcp://127.0.0.1:34959\n", " \n", " Total threads: 1\n", @@ -504,7 +505,7 @@ "
    \n", - " Dashboard: /proxy/37735/status\n", + " Dashboard: /proxy/36273/status\n", " \n", " Memory: 0 B\n", @@ -512,13 +513,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:34171\n", + " Nanny: tcp://127.0.0.1:37891\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-rklup1gt\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-iechw5o3\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -586,7 +587,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:40939\n", + " Comm: tcp://127.0.0.1:41343\n", " \n", " Total threads: 1\n", @@ -549,7 +550,7 @@ "
    \n", - " Dashboard: /proxy/38073/status\n", + " Dashboard: /proxy/43897/status\n", " \n", " Memory: 0 B\n", @@ -557,13 +558,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33569\n", + " Nanny: tcp://127.0.0.1:41113\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bf_vgp4k\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-nfec_bh8\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -631,7 +632,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:35795\n", + " Comm: tcp://127.0.0.1:32835\n", " \n", " Total threads: 1\n", @@ -594,7 +595,7 @@ "
    \n", - " Dashboard: /proxy/36181/status\n", + " Dashboard: /proxy/38471/status\n", " \n", " Memory: 0 B\n", @@ -602,13 +603,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35543\n", + " Nanny: tcp://127.0.0.1:41115\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bt8crafk\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-qmxevv3r\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -676,7 +677,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:36029\n", + " Comm: tcp://127.0.0.1:43481\n", " \n", " Total threads: 1\n", @@ -639,7 +640,7 @@ "
    \n", - " Dashboard: /proxy/37333/status\n", + " Dashboard: /proxy/45391/status\n", " \n", " Memory: 0 B\n", @@ -647,13 +648,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42905\n", + " Nanny: tcp://127.0.0.1:34843\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0f5_3lqo\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-or3t7xxb\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -721,7 +722,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37131\n", + " Comm: tcp://127.0.0.1:43313\n", " \n", " Total threads: 1\n", @@ -684,7 +685,7 @@ "
    \n", - " Dashboard: /proxy/41095/status\n", + " Dashboard: /proxy/40727/status\n", " \n", " Memory: 0 B\n", @@ -692,13 +693,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:46255\n", + " Nanny: tcp://127.0.0.1:39047\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-n2vtyaj2\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mvm6y070\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -766,7 +767,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:40829\n", + " Comm: tcp://127.0.0.1:39739\n", " \n", " Total threads: 1\n", @@ -729,7 +730,7 @@ "
    \n", - " Dashboard: /proxy/39091/status\n", + " Dashboard: /proxy/35913/status\n", " \n", " Memory: 0 B\n", @@ -737,13 +738,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35323\n", + " Nanny: tcp://127.0.0.1:37225\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-vcaqwy2a\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-j7qd0kpr\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -811,7 +812,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:46131\n", + " Comm: tcp://127.0.0.1:40891\n", " \n", " Total threads: 1\n", @@ -774,7 +775,7 @@ "
    \n", - " Dashboard: /proxy/38685/status\n", + " Dashboard: /proxy/40431/status\n", " \n", " Memory: 0 B\n", @@ -782,13 +783,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33915\n", + " Nanny: tcp://127.0.0.1:40985\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-j01emtrx\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-z7y8fovn\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -856,7 +857,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43159\n", + " Comm: tcp://127.0.0.1:43749\n", " \n", " Total threads: 1\n", @@ -819,7 +820,7 @@ "
    \n", - " Dashboard: /proxy/40425/status\n", + " Dashboard: /proxy/42187/status\n", " \n", " Memory: 0 B\n", @@ -827,13 +828,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44219\n", + " Nanny: tcp://127.0.0.1:38917\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bas8smc9\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hs25rcxb\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -901,7 +902,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:35083\n", + " Comm: tcp://127.0.0.1:35245\n", " \n", " Total threads: 1\n", @@ -864,7 +865,7 @@ "
    \n", - " Dashboard: /proxy/45101/status\n", + " Dashboard: /proxy/43609/status\n", " \n", " Memory: 0 B\n", @@ -872,13 +873,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:34271\n", + " Nanny: tcp://127.0.0.1:36331\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mbslqki3\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-m982hhmu\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -946,7 +947,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37407\n", + " Comm: tcp://127.0.0.1:41155\n", " \n", " Total threads: 1\n", @@ -909,7 +910,7 @@ "
    \n", - " Dashboard: /proxy/45141/status\n", + " Dashboard: /proxy/42205/status\n", " \n", " Memory: 0 B\n", @@ -917,13 +918,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:45335\n", + " Nanny: tcp://127.0.0.1:36329\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-yp2wc3lp\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-llzw63ag\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -991,7 +992,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:36793\n", + " Comm: tcp://127.0.0.1:39049\n", " \n", " Total threads: 1\n", @@ -954,7 +955,7 @@ "
    \n", - " Dashboard: /proxy/40441/status\n", + " Dashboard: /proxy/40893/status\n", " \n", " Memory: 0 B\n", @@ -962,13 +963,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:34733\n", + " Nanny: tcp://127.0.0.1:40521\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-01rqb3wg\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ht__ndt1\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1036,7 +1037,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33389\n", + " Comm: tcp://127.0.0.1:44663\n", " \n", " Total threads: 1\n", @@ -999,7 +1000,7 @@ "
    \n", - " Dashboard: /proxy/36105/status\n", + " Dashboard: /proxy/32799/status\n", " \n", " Memory: 0 B\n", @@ -1007,13 +1008,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:43009\n", + " Nanny: tcp://127.0.0.1:46077\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-499khvzy\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9v9qs7hf\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1081,7 +1082,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:32811\n", + " Comm: tcp://127.0.0.1:45639\n", " \n", " Total threads: 1\n", @@ -1044,7 +1045,7 @@ "
    \n", - " Dashboard: /proxy/39777/status\n", + " Dashboard: /proxy/39191/status\n", " \n", " Memory: 0 B\n", @@ -1052,13 +1053,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:37425\n", + " Nanny: tcp://127.0.0.1:44051\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-fwfr27a7\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-evn_hg_d\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1126,7 +1127,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34293\n", + " Comm: tcp://127.0.0.1:33631\n", " \n", " Total threads: 1\n", @@ -1089,7 +1090,7 @@ "
    \n", - " Dashboard: /proxy/33357/status\n", + " Dashboard: /proxy/44117/status\n", " \n", " Memory: 0 B\n", @@ -1097,13 +1098,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44321\n", + " Nanny: tcp://127.0.0.1:41425\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-t_st9bhk\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xh7jep_f\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1171,7 +1172,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44227\n", + " Comm: tcp://127.0.0.1:45915\n", " \n", " Total threads: 1\n", @@ -1134,7 +1135,7 @@ "
    \n", - " Dashboard: /proxy/34505/status\n", + " Dashboard: /proxy/37301/status\n", " \n", " Memory: 0 B\n", @@ -1142,13 +1143,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44655\n", + " Nanny: tcp://127.0.0.1:35953\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-7wp958gv\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-97xqxpp2\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1216,7 +1217,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34895\n", + " Comm: tcp://127.0.0.1:42037\n", " \n", " Total threads: 1\n", @@ -1179,7 +1180,7 @@ "
    \n", - " Dashboard: /proxy/37595/status\n", + " Dashboard: /proxy/36571/status\n", " \n", " Memory: 0 B\n", @@ -1187,13 +1188,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35421\n", + " Nanny: tcp://127.0.0.1:46039\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-e3g8s5v9\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0pju21oy\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1261,7 +1262,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:42133\n", + " Comm: tcp://127.0.0.1:42687\n", " \n", " Total threads: 1\n", @@ -1224,7 +1225,7 @@ "
    \n", - " Dashboard: /proxy/43929/status\n", + " Dashboard: /proxy/35489/status\n", " \n", " Memory: 0 B\n", @@ -1232,13 +1233,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:43113\n", + " Nanny: tcp://127.0.0.1:43559\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ruiu8dmf\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_pz0cw0g\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1306,7 +1307,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43613\n", + " Comm: tcp://127.0.0.1:44457\n", " \n", " Total threads: 1\n", @@ -1269,7 +1270,7 @@ "
    \n", - " Dashboard: /proxy/34839/status\n", + " Dashboard: /proxy/32831/status\n", " \n", " Memory: 0 B\n", @@ -1277,13 +1278,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:39337\n", + " Nanny: tcp://127.0.0.1:36785\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-l8kgpqje\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-q0kcg5tm\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1351,7 +1352,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44715\n", + " Comm: tcp://127.0.0.1:44037\n", " \n", " Total threads: 1\n", @@ -1314,7 +1315,7 @@ "
    \n", - " Dashboard: /proxy/46159/status\n", + " Dashboard: /proxy/36473/status\n", " \n", " Memory: 0 B\n", @@ -1322,13 +1323,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:45007\n", + " Nanny: tcp://127.0.0.1:40497\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-rnhhxrsp\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-g1tj8325\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1396,7 +1397,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:36953\n", + " Comm: tcp://127.0.0.1:32787\n", " \n", " Total threads: 1\n", @@ -1359,7 +1360,7 @@ "
    \n", - " Dashboard: /proxy/39165/status\n", + " Dashboard: /proxy/40355/status\n", " \n", " Memory: 0 B\n", @@ -1367,13 +1368,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:37381\n", + " Nanny: tcp://127.0.0.1:41453\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-aiyzznsa\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-zel265fq\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1441,7 +1442,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37791\n", + " Comm: tcp://127.0.0.1:43611\n", " \n", " Total threads: 1\n", @@ -1404,7 +1405,7 @@ "
    \n", - " Dashboard: /proxy/34631/status\n", + " Dashboard: /proxy/40989/status\n", " \n", " Memory: 0 B\n", @@ -1412,13 +1413,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42877\n", + " Nanny: tcp://127.0.0.1:36305\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8pasuemn\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2vc82qu4\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1486,7 +1487,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34095\n", + " Comm: tcp://127.0.0.1:35155\n", " \n", " Total threads: 1\n", @@ -1449,7 +1450,7 @@ "
    \n", - " Dashboard: /proxy/44331/status\n", + " Dashboard: /proxy/43237/status\n", " \n", " Memory: 0 B\n", @@ -1457,13 +1458,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:40919\n", + " Nanny: tcp://127.0.0.1:32901\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-p7rdwugq\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hkgdw9y9\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1531,7 +1532,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:41287\n", + " Comm: tcp://127.0.0.1:34867\n", " \n", " Total threads: 1\n", @@ -1494,7 +1495,7 @@ "
    \n", - " Dashboard: /proxy/44461/status\n", + " Dashboard: /proxy/35095/status\n", " \n", " Memory: 0 B\n", @@ -1502,13 +1503,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:40835\n", + " Nanny: tcp://127.0.0.1:46721\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mquzw4pc\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8ux3c6kk\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1576,7 +1577,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:33963\n", + " Comm: tcp://127.0.0.1:35947\n", " \n", " Total threads: 1\n", @@ -1539,7 +1540,7 @@ "
    \n", - " Dashboard: /proxy/38061/status\n", + " Dashboard: /proxy/46305/status\n", " \n", " Memory: 0 B\n", @@ -1547,13 +1548,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42291\n", + " Nanny: tcp://127.0.0.1:46547\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8x6scvui\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-07etaj27\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1621,7 +1622,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38741\n", + " Comm: tcp://127.0.0.1:42651\n", " \n", " Total threads: 1\n", @@ -1584,7 +1585,7 @@ "
    \n", - " Dashboard: /proxy/43031/status\n", + " Dashboard: /proxy/45251/status\n", " \n", " Memory: 0 B\n", @@ -1592,13 +1593,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:45537\n", + " Nanny: tcp://127.0.0.1:42409\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-qgl1yqa4\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hvu4d16y\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1666,7 +1667,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34023\n", + " Comm: tcp://127.0.0.1:33015\n", " \n", " Total threads: 1\n", @@ -1629,7 +1630,7 @@ "
    \n", - " Dashboard: /proxy/38391/status\n", + " Dashboard: /proxy/37769/status\n", " \n", " Memory: 0 B\n", @@ -1637,13 +1638,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:41055\n", + " Nanny: tcp://127.0.0.1:46435\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-oppnp1h9\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-euwaaplp\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1711,7 +1712,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:41495\n", + " Comm: tcp://127.0.0.1:46581\n", " \n", " Total threads: 1\n", @@ -1674,7 +1675,7 @@ "
    \n", - " Dashboard: /proxy/45403/status\n", + " Dashboard: /proxy/46669/status\n", " \n", " Memory: 0 B\n", @@ -1682,13 +1683,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:36083\n", + " Nanny: tcp://127.0.0.1:34623\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-u5nk0i6v\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-fd5x6q8b\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1756,7 +1757,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38491\n", + " Comm: tcp://127.0.0.1:34861\n", " \n", " Total threads: 1\n", @@ -1719,7 +1720,7 @@ "
    \n", - " Dashboard: /proxy/41895/status\n", + " Dashboard: /proxy/44459/status\n", " \n", " Memory: 0 B\n", @@ -1727,13 +1728,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:43913\n", + " Nanny: tcp://127.0.0.1:38641\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-k_u74xdn\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gl37dal0\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1801,7 +1802,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37289\n", + " Comm: tcp://127.0.0.1:43107\n", " \n", " Total threads: 1\n", @@ -1764,7 +1765,7 @@ "
    \n", - " Dashboard: /proxy/45073/status\n", + " Dashboard: /proxy/36175/status\n", " \n", " Memory: 0 B\n", @@ -1772,13 +1773,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:38297\n", + " Nanny: tcp://127.0.0.1:46843\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-cza1drap\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8waprgwb\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1846,7 +1847,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:38181\n", + " Comm: tcp://127.0.0.1:42559\n", " \n", " Total threads: 1\n", @@ -1809,7 +1810,7 @@ "
    \n", - " Dashboard: /proxy/43175/status\n", + " Dashboard: /proxy/37495/status\n", " \n", " Memory: 0 B\n", @@ -1817,13 +1818,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33995\n", + " Nanny: tcp://127.0.0.1:33841\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_2y8cpin\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ci4arahq\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1891,7 +1892,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37841\n", + " Comm: tcp://127.0.0.1:44337\n", " \n", " Total threads: 1\n", @@ -1854,7 +1855,7 @@ "
    \n", - " Dashboard: /proxy/38615/status\n", + " Dashboard: /proxy/37197/status\n", " \n", " Memory: 0 B\n", @@ -1862,13 +1863,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:36617\n", + " Nanny: tcp://127.0.0.1:44949\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ck_dn52_\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-oaqn_sqn\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1936,7 +1937,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:37175\n", + " Comm: tcp://127.0.0.1:44023\n", " \n", " Total threads: 1\n", @@ -1899,7 +1900,7 @@ "
    \n", - " Dashboard: /proxy/45069/status\n", + " Dashboard: /proxy/42029/status\n", " \n", " Memory: 0 B\n", @@ -1907,13 +1908,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:46225\n", + " Nanny: tcp://127.0.0.1:38731\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-l2l7emzc\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_lej3rzu\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1981,7 +1982,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:32801\n", + " Comm: tcp://127.0.0.1:41611\n", " \n", " Total threads: 1\n", @@ -1944,7 +1945,7 @@ "
    \n", - " Dashboard: /proxy/38841/status\n", + " Dashboard: /proxy/43283/status\n", " \n", " Memory: 0 B\n", @@ -1952,13 +1953,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:38869\n", + " Nanny: tcp://127.0.0.1:35387\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xufmaoop\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-kqqf83i6\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2026,7 +2027,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:34295\n", + " Comm: tcp://127.0.0.1:45835\n", " \n", " Total threads: 1\n", @@ -1989,7 +1990,7 @@ "
    \n", - " Dashboard: /proxy/36097/status\n", + " Dashboard: /proxy/32959/status\n", " \n", " Memory: 0 B\n", @@ -1997,13 +1998,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:45359\n", + " Nanny: tcp://127.0.0.1:34925\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-75q7srmw\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xx2cisgd\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2071,7 +2072,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:42393\n", + " Comm: tcp://127.0.0.1:41785\n", " \n", " Total threads: 1\n", @@ -2034,7 +2035,7 @@ "
    \n", - " Dashboard: /proxy/37311/status\n", + " Dashboard: /proxy/44317/status\n", " \n", " Memory: 0 B\n", @@ -2042,13 +2043,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:36507\n", + " Nanny: tcp://127.0.0.1:40949\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-c78thjkw\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-sy8iipu1\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2116,7 +2117,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:39679\n", + " Comm: tcp://127.0.0.1:34363\n", " \n", " Total threads: 1\n", @@ -2079,7 +2080,7 @@ "
    \n", - " Dashboard: /proxy/39787/status\n", + " Dashboard: /proxy/45161/status\n", " \n", " Memory: 0 B\n", @@ -2087,13 +2088,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:38385\n", + " Nanny: tcp://127.0.0.1:39795\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hr4wredq\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-3dkpfhsc\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2161,7 +2162,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:43847\n", + " Comm: tcp://127.0.0.1:46215\n", " \n", " Total threads: 1\n", @@ -2124,7 +2125,7 @@ "
    \n", - " Dashboard: /proxy/36153/status\n", + " Dashboard: /proxy/40665/status\n", " \n", " Memory: 0 B\n", @@ -2132,13 +2133,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:37713\n", + " Nanny: tcp://127.0.0.1:43837\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-25clcjhx\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0wcv4hfc\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2206,7 +2207,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:45295\n", + " Comm: tcp://127.0.0.1:41671\n", " \n", " Total threads: 1\n", @@ -2169,7 +2170,7 @@ "
    \n", - " Dashboard: /proxy/38853/status\n", + " Dashboard: /proxy/42979/status\n", " \n", " Memory: 0 B\n", @@ -2177,13 +2178,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44225\n", + " Nanny: tcp://127.0.0.1:35759\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-33k_1z6x\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-x_o60uqr\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2251,7 +2252,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:46191\n", + " Comm: tcp://127.0.0.1:34885\n", " \n", " Total threads: 1\n", @@ -2214,7 +2215,7 @@ "
    \n", - " Dashboard: /proxy/36859/status\n", + " Dashboard: /proxy/38773/status\n", " \n", " Memory: 0 B\n", @@ -2222,13 +2223,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:43475\n", + " Nanny: tcp://127.0.0.1:42795\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gd0tuixy\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mc87qnke\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2296,7 +2297,7 @@ "
    \n", - " Comm: tcp://127.0.0.1:44223\n", + " Comm: tcp://127.0.0.1:44357\n", " \n", " Total threads: 1\n", @@ -2259,7 +2260,7 @@ "
    \n", - " Dashboard: /proxy/35053/status\n", + " Dashboard: /proxy/36337/status\n", " \n", " Memory: 0 B\n", @@ -2267,13 +2268,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:38039\n", + " Nanny: tcp://127.0.0.1:44301\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-exb9jp4z\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gd2dowuz\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2345,7 +2346,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -2761,11 +2762,11 @@ " intake_esm_attrs:filename: ocean_month.nc\n", " intake_esm_attrs:file_id: ocean_month\n", " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon
    \n", - " Comm: tcp://127.0.0.1:32967\n", + " Comm: tcp://127.0.0.1:45973\n", " \n", " Total threads: 1\n", @@ -2304,7 +2305,7 @@ "
    \n", - " Dashboard: /proxy/36879/status\n", + " Dashboard: /proxy/36235/status\n", " \n", " Memory: 0 B\n", @@ -2312,13 +2313,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:40347\n", + " Nanny: tcp://127.0.0.1:46107\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-c59faq4c\n", + " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9vhw_awp\n", "
    \n", + " dtype='datetime64[ns]')
    • sst
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 216, 240), meta=np.ndarray>
      long_name :
      Potential temperature
      units :
      K
      valid_range :
      [-10. 500.]
      cell_methods :
      time: mean
      time_avg_info :
      average_T1,average_T2,average_DT
      standard_name :
      sea_surface_temperature
    • \n", " \n", "
    \n", " \n", @@ -2897,12 +2898,12 @@ "\n", " \n", " \n", - "
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
+       "
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
        "       -278.125, -277.875, -277.625,\n",
        "       ...\n",
        "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
        "         79.375,   79.625,   79.875],\n",
-       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
+       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
        "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
        "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
        "       -80.12661353099249,\n",
@@ -2911,7 +2912,7 @@
        "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
        "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
        "        89.94728232704986],\n",
-       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
+       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
        "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
        "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
        "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
@@ -2922,7 +2923,7 @@
        "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
        "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
        "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
-       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " + " dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ " Size: 5GB\n", @@ -2956,7 +2957,7 @@ ], "source": [ "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", - "sst = cat[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", + "sst = catalog[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", "sst" ] }, @@ -2965,7 +2966,7 @@ "id": "2a578cd9-ae55-4d7b-8477-c2d463a7973a", "metadata": {}, "source": [ - "Rechunk so that there is only one chunk in time dimension, used by the linear regression." + "Rechunk so that there is only one chunk in the time dimension that will be used the linear regression." ] }, { @@ -3362,11 +3363,11 @@ " intake_esm_attrs:filename: ocean_month.nc\n", " intake_esm_attrs:file_id: ocean_month\n", " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ " Size: 5GB\n", @@ -3952,7 +3953,7 @@ "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", - "Dimensions without coordinates: stat_type" + " dtype='float64', name='yt_ocean', length=1080))
    • " ], "text/plain": [ " Size: 25MB\n", @@ -4120,10 +4121,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "58dd2ab9-b505-4885-bed5-37567a403fc4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 31.5 s, sys: 3.98 s, total: 35.5 s\n", + "Wall time: 1min 6s\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (yt_ocean: 1080, xt_ocean: 1440)> Size: 12MB\n",
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
+       "Coordinates:\n",
+       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95
    " + ], + "text/plain": [ + " Size: 12MB\n", + "array([[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]])\n", + "Coordinates:\n", + " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", + " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%%time \n", "stats.load()\n", @@ -4131,6 +4559,7 @@ "# Put data back into some more useful variable names\n", "sst_trend = stats.sel(stat_type=0)\n", "p_value = stats.sel(stat_type=1)\n", + "\n", "sst_trend" ] }, @@ -4139,25 +4568,67 @@ "id": "437d4d93-c95a-4606-a483-c5409ac37619", "metadata": {}, "source": [ - "Plot the calculated slope, stippling all regions that are significant at $p<0.05$." + "Plot the calculated slope, stippling all regions that are significant at $p<0.05$. Before we plot we need to load the unmasked coordinates and attach them to the dataarray otherwise regions neal the poles will be distorted (see )." ] }, { "cell_type": "code", - "execution_count": null, - "id": "539a6348-94b0-491c-94bf-a55ca72a25bd", + "execution_count": 10, + "id": "836b90e1-6089-4b74-b73a-ad98f8eac1cf", "metadata": {}, "outputs": [], "source": [ - "sst_trend.plot(cbar_kwargs={'label': '°C/yr'})\n", - "plt.contourf(p_value.xt_ocean, p_value.yt_ocean, p_value,\n", - " levels=(0, 0.05), colors='None', hatches=('...',))\n", - "plt.title('ACCESS-OM2-025 SST trend')" + "geolon_t = xr.open_dataset(\"/g/data/ik11/grids/ocean_grid_025.nc\").geolon_t\n", + "geolat_t = xr.open_dataset(\"/g/data/ik11/grids/ocean_grid_025.nc\").geolat_t\n", + "\n", + "sst_trend = sst_trend.assign_coords({\"geolon_t\": geolon_t, \"geolat_t\": geolat_t})\n", + "p_value = p_value.assign_coords({\"geolon_t\": geolon_t, \"geolat_t\": geolat_t})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "539a6348-94b0-491c-94bf-a55ca72a25bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "\n", + "ax = plt.axes(projection=ccrs.Robinson())\n", + "\n", + "ax.coastlines(resolution=\"50m\")\n", + "ax.gridlines(draw_labels=False)\n", + "\n", + "sst_trend.plot(ax=ax,\n", + " x=\"geolon_t\",\n", + " y=\"geolat_t\",\n", + " transform=ccrs.PlateCarree(),\n", + " cbar_kwargs={'label': '°C/yr',\n", + " 'extend': 'both'})\n", + "\n", + "plt.contourf(p_value.geolon_t, p_value.geolat_t, p_value,\n", + " levels=(0, 0.05),\n", + " colors='None',\n", + " hatches=('...',),\n", + " transform=ccrs.PlateCarree())\n", + "\n", + "plt.title('ACCESS-OM2-025 SST trend');" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f27843df", "metadata": {}, "outputs": [], From d2978172fe04f9000ac9890771599b30703a6795 Mon Sep 17 00:00:00 2001 From: "Navid C. Constantinou" Date: Fri, 4 Oct 2024 14:30:10 +1000 Subject: [PATCH 5/8] move to tutorial --- {Recipes => Tutorials}/Apply_function_to_every_gridpoint.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {Recipes => Tutorials}/Apply_function_to_every_gridpoint.ipynb (100%) diff --git a/Recipes/Apply_function_to_every_gridpoint.ipynb b/Tutorials/Apply_function_to_every_gridpoint.ipynb similarity index 100% rename from Recipes/Apply_function_to_every_gridpoint.ipynb rename to Tutorials/Apply_function_to_every_gridpoint.ipynb From d11a373f267c18d6c90c131465155b414b38cdc7 Mon Sep 17 00:00:00 2001 From: "Navid C. Constantinou" Date: Fri, 4 Oct 2024 14:34:37 +1000 Subject: [PATCH 6/8] fix ref to cartopy tutorial --- Tutorials/Apply_function_to_every_gridpoint.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tutorials/Apply_function_to_every_gridpoint.ipynb b/Tutorials/Apply_function_to_every_gridpoint.ipynb index 4c5eda7f..255e0268 100644 --- a/Tutorials/Apply_function_to_every_gridpoint.ipynb +++ b/Tutorials/Apply_function_to_every_gridpoint.ipynb @@ -4568,7 +4568,7 @@ "id": "437d4d93-c95a-4606-a483-c5409ac37619", "metadata": {}, "source": [ - "Plot the calculated slope, stippling all regions that are significant at $p<0.05$. Before we plot we need to load the unmasked coordinates and attach them to the dataarray otherwise regions neal the poles will be distorted (see )." + "Plot the calculated slope, stippling all regions that are significant at $p<0.05$. Before we plot we need to load the unmasked coordinates and attach them to the dataarray otherwise regions near the poles are distorted (see the [Making_Maps_with_Cartopy](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Making_Maps_with_Cartopy.html#Fixing-the-tripole) tutorial)." ] }, { From 3512608259842990cab629a96b8f79cb629ec5e8 Mon Sep 17 00:00:00 2001 From: "Navid C. Constantinou" Date: Fri, 4 Oct 2024 14:38:48 +1000 Subject: [PATCH 7/8] simpler language --- Tutorials/Apply_function_to_every_gridpoint.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tutorials/Apply_function_to_every_gridpoint.ipynb b/Tutorials/Apply_function_to_every_gridpoint.ipynb index 255e0268..17ea905b 100644 --- a/Tutorials/Apply_function_to_every_gridpoint.ipynb +++ b/Tutorials/Apply_function_to_every_gridpoint.ipynb @@ -24,7 +24,7 @@ "```python\n", "out = data.mean(dim=('time'))\n", "```\n", - "which would tell xarray to take the mean in the time dimension for every one of the gridpoints.\n", + "which xarray inteprets as to take the mean in the time dimension for every gridpoint.\n", "\n", "Some functions, such as ```scipy.stats.linregress```, do not have in-build vectorisation, but you might want to apply a function like this to every gridpoint, and for loops would be slow. \n", "\n", From c83b7f65aa1215216921626855a990cdd0dcdc80 Mon Sep 17 00:00:00 2001 From: anton-seaice Date: Tue, 8 Oct 2024 09:26:13 +1100 Subject: [PATCH 8/8] chunksize and notes --- .../Apply_function_to_every_gridpoint.ipynb | 2166 +++-------------- 1 file changed, 312 insertions(+), 1854 deletions(-) diff --git a/Tutorials/Apply_function_to_every_gridpoint.ipynb b/Tutorials/Apply_function_to_every_gridpoint.ipynb index 17ea905b..0544581a 100644 --- a/Tutorials/Apply_function_to_every_gridpoint.ipynb +++ b/Tutorials/Apply_function_to_every_gridpoint.ipynb @@ -5,7 +5,7 @@ "id": "76a0672f-e42b-4907-928b-c74f6cbfec54", "metadata": {}, "source": [ - "## Apply a function to every gridpoint using `xarray.apply_ufunc`\n", + "## Apply a function to every gridpoint\n", "\n", "This tutorial demonstrates best practice to vectorise functions that want to be applied across all grid points.\n", "\n", @@ -28,7 +28,7 @@ "\n", "Some functions, such as ```scipy.stats.linregress```, do not have in-build vectorisation, but you might want to apply a function like this to every gridpoint, and for loops would be slow. \n", "\n", - "This tutorial **provides a few examples of how to apply functions which do not natively vectorise many times to an xarray dataset, vectorised so that a dask client can speed up the calculation**. We answer here a dummy question \"What is sea-surface temperature trend at each gridpoint of an ocean model, and is it significant?\". Scientifically, this question mostly applies to the forcing dataset and not the ocean model, but it's as good an example as any.**\n", + "This tutorial **provides a few examples of how to apply functions which do not natively vectorise many times to an xarray dataset, vectorised so that a dask client can speed up the calculation**. We answer here a dummy question \"What is sea-surface temperature trend at each gridpoint of an ocean model, and is it significant?\". Scientifically, this question mostly applies to the forcing dataset and not the ocean model, but it's as good an example as any.\n", "\n", "To achieve this goal, we use ```xarray.apply_ufunc```, which is very versatile, but therefore takes many arguments that can be difficult to interpret at first glance. The aim of the example below is to give something that will work on a problem similar to what COSIMA users may encounter.\n", "\n", @@ -39,1770 +39,166 @@ "cell_type": "code", "execution_count": 1, "id": "dd347ec4-a296-416b-a8fe-3c9b979d1dea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:19.317170Z", + "iopub.status.busy": "2024-10-07T22:21:19.316960Z", + "iopub.status.idle": "2024-10-07T22:21:22.347852Z", + "shell.execute_reply": "2024-10-07T22:21:22.346956Z", + "shell.execute_reply.started": "2024-10-07T22:21:19.317149Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", - "import scipy.stats\n", - "import cartopy.crs as ccrs\n", - "import intake\n", - "catalog = intake.cat.access_nri" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a7e4909a-3364-4209-9e89-c51b7766c1a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "
    \n", - "
    \n", - "

    Client

    \n", - "

    Client-c07d9f81-8207-11ef-99aa-000001adfe80

    \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/8787/status\n", - "
    \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
    \n", - "

    Cluster Info

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

    LocalCluster

    \n", - "

    079f76c6

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - " \n", - "
    \n", - " Dashboard: /proxy/8787/status\n", - " \n", - " Workers: 48\n", - "
    \n", - " Total threads: 48\n", - " \n", - " Total memory: 0 B\n", - "
    Status: runningUsing processes: True
    \n", - "\n", - "
    \n", - " \n", - "

    Scheduler Info

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

    Scheduler

    \n", - "

    Scheduler-17648e8b-c006-4d22-83f9-d8440e29bd2b

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " Comm: tcp://127.0.0.1:35663\n", - " \n", - " Workers: 48\n", - "
    \n", - " Dashboard: /proxy/8787/status\n", - " \n", - " Total threads: 48\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://127.0.0.1:37715\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/44669/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:35785\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gt4j48wq\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://127.0.0.1:32881\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/41371/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:39537\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2msnrca0\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://127.0.0.1:37867\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/41029/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:41519\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2r4b2mvb\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://127.0.0.1:39653\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/45289/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:40867\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-bg2ksfi4\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://127.0.0.1:45411\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/41823/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:34091\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-46008wu1\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://127.0.0.1:44789\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40261/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:34987\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-guwrezef\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://127.0.0.1:37825\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/34001/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:34281\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-15opc7ob\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://127.0.0.1:34959\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/36273/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:37891\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-iechw5o3\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://127.0.0.1:41343\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/43897/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:41113\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-nfec_bh8\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://127.0.0.1:32835\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/38471/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:41115\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-qmxevv3r\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://127.0.0.1:43481\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/45391/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:34843\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-or3t7xxb\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://127.0.0.1:43313\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40727/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:39047\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mvm6y070\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://127.0.0.1:39739\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/35913/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:37225\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-j7qd0kpr\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://127.0.0.1:40891\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40431/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:40985\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-z7y8fovn\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://127.0.0.1:43749\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/42187/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:38917\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hs25rcxb\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://127.0.0.1:35245\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/43609/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:36331\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-m982hhmu\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://127.0.0.1:41155\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/42205/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:36329\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-llzw63ag\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://127.0.0.1:39049\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40893/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:40521\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ht__ndt1\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://127.0.0.1:44663\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/32799/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46077\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9v9qs7hf\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://127.0.0.1:45639\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/39191/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:44051\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-evn_hg_d\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://127.0.0.1:33631\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/44117/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:41425\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xh7jep_f\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://127.0.0.1:45915\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/37301/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:35953\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-97xqxpp2\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://127.0.0.1:42037\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/36571/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46039\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0pju21oy\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://127.0.0.1:42687\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/35489/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:43559\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_pz0cw0g\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://127.0.0.1:44457\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/32831/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:36785\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-q0kcg5tm\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://127.0.0.1:44037\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/36473/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:40497\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-g1tj8325\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://127.0.0.1:32787\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40355/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:41453\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-zel265fq\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://127.0.0.1:43611\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/40989/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:36305\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-2vc82qu4\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://127.0.0.1:35155\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/43237/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:32901\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hkgdw9y9\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://127.0.0.1:34867\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/35095/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46721\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8ux3c6kk\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://127.0.0.1:35947\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/46305/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46547\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-07etaj27\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://127.0.0.1:42651\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/45251/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:42409\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-hvu4d16y\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", + "import scipy.stats\n", + "import cartopy.crs as ccrs\n", + "import intake\n", + "catalog = intake.cat.access_nri" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a7e4909a-3364-4209-9e89-c51b7766c1a8", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:22.349577Z", + "iopub.status.busy": "2024-10-07T22:21:22.349386Z", + "iopub.status.idle": "2024-10-07T22:21:25.475658Z", + "shell.execute_reply": "2024-10-07T22:21:25.474583Z", + "shell.execute_reply.started": "2024-10-07T22:21:22.349558Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "
    \n", + "
    \n", + "

    Client

    \n", + "

    Client-7bfba035-84fa-11ef-8a80-00000089fe80

    \n", + "
    \n", - " Comm: tcp://127.0.0.1:33015\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/37769/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46435\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-euwaaplp\n", - "
    \n", "\n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n", - "
    Connection method: Cluster objectCluster type: distributed.LocalCluster
    \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", - " \n", + "
    \n", - " Comm: tcp://127.0.0.1:46581\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/46669/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:34623\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-fd5x6q8b\n", - "
    \n", + " Dashboard: /proxy/8787/status\n", + "
    \n", "\n", - " \n", + " \n", + " \n", + " \n", "\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", + "

    Cluster Info

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

    LocalCluster

    \n", + "

    fd012bc5

    \n", + "
    \n", - " Comm: tcp://127.0.0.1:34861\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/44459/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:38641\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gl37dal0\n", - "
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", "\n", - " \n", + " \n", + "
    \n", + " Dashboard: /proxy/8787/status\n", + " \n", + " Workers: 12\n", + "
    \n", + " Total threads: 12\n", + " \n", + " Total memory: 0 B\n", + "
    Status: runningUsing processes: True
    \n", "\n", - " \n", + "
    \n", + " \n", + "

    Scheduler Info

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

    Scheduler

    \n", + "

    Scheduler-a1226526-d7a0-40e7-afed-550ca9e916aa

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " Comm: tcp://127.0.0.1:33961\n", + " \n", + " Workers: 12\n", + "
    \n", + " Dashboard: /proxy/8787/status\n", + " \n", + " Total threads: 12\n", + "
    \n", + " Started: Just now\n", + " \n", + " Total memory: 0 B\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", + " \n", + "

    Workers

    \n", + "     \n", "\n", - "
    \n", - " Comm: tcp://127.0.0.1:43107\n", - " \n", - " Total threads: 1\n", - "
    \n", - " Dashboard: /proxy/36175/status\n", - " \n", - " Memory: 0 B\n", - "
    \n", - " Nanny: tcp://127.0.0.1:46843\n", - "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-8waprgwb\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", " \n", "
    \n", "
    \n", "
    \n", "
    \n", " \n", - "

    Worker: 36

    \n", + "

    Worker: 0

    \n", "     \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1842,12 +238,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 37

    \n", + "

    Worker: 1

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:42559\n", + " Comm: tcp://127.0.0.1:45051\n", " \n", " Total threads: 1\n", @@ -1810,7 +206,7 @@ "
    \n", - " Dashboard: /proxy/37495/status\n", + " Dashboard: /proxy/36751/status\n", " \n", " Memory: 0 B\n", @@ -1818,13 +214,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:33841\n", + " Nanny: tcp://127.0.0.1:42849\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-ci4arahq\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-stjt_sim\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1887,12 +283,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 38

    \n", + "

    Worker: 2

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:44337\n", + " Comm: tcp://127.0.0.1:46369\n", " \n", " Total threads: 1\n", @@ -1855,7 +251,7 @@ "
    \n", - " Dashboard: /proxy/37197/status\n", + " Dashboard: /proxy/40825/status\n", " \n", " Memory: 0 B\n", @@ -1863,13 +259,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44949\n", + " Nanny: tcp://127.0.0.1:43079\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-oaqn_sqn\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-i3idwoah\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1932,12 +328,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 39

    \n", + "

    Worker: 3

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:44023\n", + " Comm: tcp://127.0.0.1:36191\n", " \n", " Total threads: 1\n", @@ -1900,7 +296,7 @@ "
    \n", - " Dashboard: /proxy/42029/status\n", + " Dashboard: /proxy/43407/status\n", " \n", " Memory: 0 B\n", @@ -1908,13 +304,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:38731\n", + " Nanny: tcp://127.0.0.1:39541\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-_lej3rzu\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-yxb5ywjp\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1977,12 +373,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 40

    \n", + "

    Worker: 4

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:41611\n", + " Comm: tcp://127.0.0.1:44797\n", " \n", " Total threads: 1\n", @@ -1945,7 +341,7 @@ "
    \n", - " Dashboard: /proxy/43283/status\n", + " Dashboard: /proxy/41637/status\n", " \n", " Memory: 0 B\n", @@ -1953,13 +349,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35387\n", + " Nanny: tcp://127.0.0.1:43047\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-kqqf83i6\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-jfg_p5ry\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2022,12 +418,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 41

    \n", + "

    Worker: 5

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:45835\n", + " Comm: tcp://127.0.0.1:37169\n", " \n", " Total threads: 1\n", @@ -1990,7 +386,7 @@ "
    \n", - " Dashboard: /proxy/32959/status\n", + " Dashboard: /proxy/33519/status\n", " \n", " Memory: 0 B\n", @@ -1998,13 +394,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:34925\n", + " Nanny: tcp://127.0.0.1:46253\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-xx2cisgd\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-5lwgfupm\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2067,12 +463,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 42

    \n", + "

    Worker: 6

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:41785\n", + " Comm: tcp://127.0.0.1:38317\n", " \n", " Total threads: 1\n", @@ -2035,7 +431,7 @@ "
    \n", - " Dashboard: /proxy/44317/status\n", + " Dashboard: /proxy/35177/status\n", " \n", " Memory: 0 B\n", @@ -2043,13 +439,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:40949\n", + " Nanny: tcp://127.0.0.1:33527\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-sy8iipu1\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-op7c0gi_\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2112,12 +508,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 43

    \n", + "

    Worker: 7

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:34363\n", + " Comm: tcp://127.0.0.1:34161\n", " \n", " Total threads: 1\n", @@ -2080,7 +476,7 @@ "
    \n", - " Dashboard: /proxy/45161/status\n", + " Dashboard: /proxy/42657/status\n", " \n", " Memory: 0 B\n", @@ -2088,13 +484,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:39795\n", + " Nanny: tcp://127.0.0.1:39743\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-3dkpfhsc\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-9fl6hvpe\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2157,12 +553,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 44

    \n", + "

    Worker: 8

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:46215\n", + " Comm: tcp://127.0.0.1:41545\n", " \n", " Total threads: 1\n", @@ -2125,7 +521,7 @@ "
    \n", - " Dashboard: /proxy/40665/status\n", + " Dashboard: /proxy/39899/status\n", " \n", " Memory: 0 B\n", @@ -2133,13 +529,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:43837\n", + " Nanny: tcp://127.0.0.1:42287\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-0wcv4hfc\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-bsqwhxah\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2202,12 +598,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 45

    \n", + "

    Worker: 9

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:41671\n", + " Comm: tcp://127.0.0.1:41257\n", " \n", " Total threads: 1\n", @@ -2170,7 +566,7 @@ "
    \n", - " Dashboard: /proxy/42979/status\n", + " Dashboard: /proxy/44357/status\n", " \n", " Memory: 0 B\n", @@ -2178,13 +574,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35759\n", + " Nanny: tcp://127.0.0.1:41333\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-x_o60uqr\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-58o82vlb\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2247,12 +643,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 46

    \n", + "

    Worker: 10

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:34885\n", + " Comm: tcp://127.0.0.1:39053\n", " \n", " Total threads: 1\n", @@ -2215,7 +611,7 @@ "
    \n", - " Dashboard: /proxy/38773/status\n", + " Dashboard: /proxy/42649/status\n", " \n", " Memory: 0 B\n", @@ -2223,13 +619,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:42795\n", + " Nanny: tcp://127.0.0.1:41337\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-mc87qnke\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-ajufq7lo\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2292,12 +688,12 @@ "
    \n", "
    \n", " \n", - "

    Worker: 47

    \n", + "

    Worker: 11

    \n", "     \n", "
    \n", - " Comm: tcp://127.0.0.1:44357\n", + " Comm: tcp://127.0.0.1:32769\n", " \n", " Total threads: 1\n", @@ -2260,7 +656,7 @@ "
    \n", - " Dashboard: /proxy/36337/status\n", + " Dashboard: /proxy/41561/status\n", " \n", " Memory: 0 B\n", @@ -2268,13 +664,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:44301\n", + " Nanny: tcp://127.0.0.1:36537\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-gd2dowuz\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-eorm014a\n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -2346,7 +742,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -2372,7 +768,15 @@ "cell_type": "code", "execution_count": 3, "id": "f7120133-0beb-441f-9c9f-caa81649227d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:25.479028Z", + "iopub.status.busy": "2024-10-07T22:21:25.478537Z", + "iopub.status.idle": "2024-10-07T22:21:39.044028Z", + "shell.execute_reply": "2024-10-07T22:21:39.030979Z", + "shell.execute_reply.started": "2024-10-07T22:21:25.478979Z" + } + }, "outputs": [ { "data": { @@ -2408,7 +812,6 @@ "}\n", "\n", "html[theme=dark],\n", - "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -2748,7 +1151,7 @@ " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", "Data variables:\n", - " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(1, 216, 240), meta=np.ndarray>\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array<chunksize=(12, 216, 240), meta=np.ndarray>\n", "Attributes: (12/16)\n", " filename: ocean_month.nc\n", " title: ACCESS-OM2\n", @@ -2762,11 +1165,11 @@ " intake_esm_attrs:filename: ocean_month.nc\n", " intake_esm_attrs:file_id: ocean_month\n", " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon
    \n", - " Comm: tcp://127.0.0.1:45973\n", + " Comm: tcp://127.0.0.1:38723\n", " \n", " Total threads: 1\n", @@ -2305,7 +701,7 @@ "
    \n", - " Dashboard: /proxy/36235/status\n", + " Dashboard: /proxy/40007/status\n", " \n", " Memory: 0 B\n", @@ -2313,13 +709,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:46107\n", + " Nanny: tcp://127.0.0.1:38523\n", "
    \n", - " Local directory: /jobfs/126166311.gadi-pbs/dask-scratch-space/worker-9vhw_awp\n", + " Local directory: /jobfs/126368780.gadi-pbs/dask-scratch-space/worker-c1eala1k\n", "
    \n", + " dtype='datetime64[ns]')
    • sst
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(12, 216, 240), meta=np.ndarray>
      long_name :
      Potential temperature
      units :
      K
      valid_range :
      [-10. 500.]
      cell_methods :
      time: mean
      time_avg_info :
      average_T1,average_T2,average_DT
      standard_name :
      sea_surface_temperature
    • \n", " \n", "
    \n", " \n", @@ -2782,17 +1185,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2820,17 +1223,17 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "\n", @@ -2845,17 +1248,17 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "\n", @@ -2898,12 +1301,12 @@ "\n", " \n", " \n", - "
    Bytes 4.24 GiB 202.50 kiB 2.37 MiB
    Shape (732, 1080, 1440) (1, 216, 240) (12, 216, 240)
    Dask graph 21960 chunks in 123 graph layers 1830 chunks in 123 graph layers
    Data type
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
+       "
    • xt_ocean
      PandasIndex
      PandasIndex(Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625, -278.375,\n",
        "       -278.125, -277.875, -277.625,\n",
        "       ...\n",
        "         77.625,   77.875,   78.125,   78.375,   78.625,   78.875,   79.125,\n",
        "         79.375,   79.625,   79.875],\n",
-       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
+       "      dtype='float64', name='xt_ocean', length=1440))
    • yt_ocean
      PandasIndex
      PandasIndex(Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
        "        -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
        "        -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
        "       -80.12661353099249,\n",
@@ -2912,7 +1315,7 @@
        "        89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
        "          89.630486559435,  89.73608514863992,  89.84168373784476,\n",
        "        89.94728232704986],\n",
-       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
+       "      dtype='float64', name='yt_ocean', length=1080))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1958-01-14 12:00:00', '1958-02-13 00:00:00',\n",
        "               '1958-03-14 12:00:00', '1958-04-14 00:00:00',\n",
        "               '1958-05-14 12:00:00', '1958-06-14 00:00:00',\n",
        "               '1958-07-14 12:00:00', '1958-08-14 12:00:00',\n",
@@ -2923,7 +1326,7 @@
        "               '2018-07-14 12:00:00', '2018-08-14 12:00:00',\n",
        "               '2018-09-14 00:00:00', '2018-10-14 12:00:00',\n",
        "               '2018-11-14 00:00:00', '2018-12-14 12:00:00'],\n",
-       "              dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " + " dtype='datetime64[ns]', name='time', length=732, freq=None))
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ " Size: 5GB\n", @@ -2933,7 +1336,7 @@ " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * time (time) datetime64[ns] 6kB 1958-01-14T12:00:00 ... 2018-12-14T12...\n", "Data variables:\n", - " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", + " sst (time, yt_ocean, xt_ocean) float32 5GB dask.array\n", "Attributes: (12/16)\n", " filename: ocean_month.nc\n", " title: ACCESS-OM2\n", @@ -2957,7 +1360,9 @@ ], "source": [ "experiment = '025deg_jra55_iaf_omip2_cycle6'\n", - "sst = catalog[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask()\n", + "sst = catalog[experiment].search(frequency=\"1mon\", variable=\"sst\").to_dask(\n", + " xarray_open_kwargs={'chunks':{'time':-1}}\n", + ")\n", "sst" ] }, @@ -2973,7 +1378,15 @@ "cell_type": "code", "execution_count": 4, "id": "92072502-a7c6-4d54-9778-768c531df475", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:39.059498Z", + "iopub.status.busy": "2024-10-07T22:21:39.050238Z", + "iopub.status.idle": "2024-10-07T22:21:39.261986Z", + "shell.execute_reply": "2024-10-07T22:21:39.260837Z", + "shell.execute_reply.started": "2024-10-07T22:21:39.059448Z" + } + }, "outputs": [ { "data": { @@ -3009,7 +1422,6 @@ "}\n", "\n", "html[theme=dark],\n", - "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -3363,11 +1775,11 @@ " intake_esm_attrs:filename: ocean_month.nc\n", " intake_esm_attrs:file_id: ocean_month\n", " intake_esm_attrs:_data_format_: netcdf\n", - " intake_esm_dataset_key: ocean_month.1mon
  • filename :
    ocean_month.nc
    title :
    ACCESS-OM2
    grid_type :
    mosaic
    grid_tile :
    1
    intake_esm_vars :
    ['sst']
    intake_esm_attrs:realm :
    ocean
    intake_esm_attrs:variable :
    pbot_t,patm_t,rho_dzt,dht,sea_level,sea_level_sq,pot_temp,temp,sst,sst_sq,bottom_temp,salt,sss,sss_sq,bottom_salt,age_global,mld,mld_max,mld_min,mld_sq,psiu,psiv,bv_freq,buoyfreq2_wt,hblt_max,pot_rho_0,pot_rho_2,rho,eta_t,u,v,wt,tx_trans,ty_trans,tz_trans,tx_trans_gm,ty_trans_gm,tx_trans_submeso,ty_trans_submeso,tx_trans_rho,ty_trans_rho,tx_trans_rho_gm,ty_trans_rho_gm,tx_trans_nrho_submeso,ty_trans_nrho_submeso,tx_trans_int_z,ty_trans_int_z,temp_xflux_adv_int_z,temp_yflux_adv_int_z,temp_yflux_gm_int_z,temp_xflux_gm_int_z,temp_xflux_ndiffuse_int_z,temp_yflux_ndiffuse_int_z,temp_yflux_submeso_int_z,temp_xflux_submeso_int_z,lprec,fprec,evap,runoff,melt,pme_river,wfimelt,wfiform,pme_net,sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore,sfc_salt_flux_coupler,sfc_hflux_from_water_prec,sfc_hflux_from_water_evap,sfc_hflux_from_runoff,fprec_melt_heat,frazil_3d_int_z,lw_heat,evap_heat,sens_heat,swflx,sw_heat,mh_flux,liceht,net_sfc_heating,temp_rivermix,sfc_hflux_coupler,sfc_hflux_pme,tau_x,tau_y,bmf_u,bmf_v,vert_pv,usq,vsq,bih_fric_u,bih_fric_v,u_dot_grad_vert_pv,ekman_we,eta_nonbouss,surface_pot_temp_max,surface_pot_temp_min,average_T1,average_T2,average_DT,time_bounds
    intake_esm_attrs:frequency :
    1mon
    intake_esm_attrs:variable_long_name :
    bottom pressure on T cells [Boussinesq (volume conserving) model],applied pressure on T cells,t-cell rho*thickness,t-cell thickness,effective sea level (eta_t + patm/(rho0*g)) on T cells,square of effective sea level (eta_t + patm/(rho0*g)) on T cells,Potential temperature,Conservative temperature,Potential temperature,squared Potential temperature,Conservative temperature,Practical Salinity,Practical Salinity,squared Practical Salinity,Practical Salinity,Age (global),mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,mixed layer depth determined by density criteria,squared mixed layer depth determined by density criteria,quasi-barotropic strmfcn psiu (compatible with tx_trans),quasi-barotropic strmfcn psiv (compatible with ty_trans),buoy freq at T-cell centre for use in neutral physics,Squared buoyancy frequency at T-cell bottom,T-cell boundary layer depth from KPP,potential density referenced to 0 dbar,potential density referenced to 2000 dbar,in situ density,surface height on T cells [Boussinesq (volume conserving) model],i-current,j-current,dia-surface velocity T-points,T-cell i-mass transport,T-cell j-mass transport,T-cell k-mass transport,T-cell mass i-transport from GM,T-cell mass j-transport from GM,T-cell mass i-transport from submesoscale param,T-cell mass j-transport from submesoscale param,T-cell i-mass transport on pot_rho,T-cell j-mass transport on pot_rho,T-cell i-mass transport from GM on pot_rho,T-cell j-mass transport from GM on pot_rho,T-cell i-mass transport from submesoscale param on neutral rho,T-cell j-mass transport from submesoscale param on neutral rho,T-cell i-mass transport vertically summed,T-cell j-mass transport vertically summed,z-integral of cp*rho*dyt*u*temp,z-integral of cp*rho*dxt*v*temp,z-integral cp*gm_yflux*dyt*rho_dzt*temp,z-integral cp*gm_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp,z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_yflux*dxt*rho_dzt*temp,z-integral cp*submeso_xflux*dyt*rho_dzt*temp,liquid precip (including ice melt/form) into ocean (>0 enters ocean),snow falling onto ocean (>0 enters ocean),mass flux from evaporation/condensation (>0 enters ocean),mass flux of liquid river runoff entering ocean,water flux transferred with sea ice form/melt (>0 enters ocean),mass flux of precip-evap+river via sbc (liquid, frozen, evaporation),water into ocean due to ice melt (>0 enters ocean),water out of ocean due to ice form (>0 enters ocean),precip-evap into ocean (total w/ restore + normalize),sfc_salt_flux_ice,sfc_salt_flux_runoff,sfc_salt_flux_restore: flux from restoring term,sfc_salt_flux_coupler: flux from the coupler,heat flux from precip transfer of water across ocean surface,heat flux from evap transfer of water across ocean surface,heat flux (relative to 0C) from liquid river runoff,heat flux to melt frozen precip (<0 cools ocean),Vertical sum of ocn frazil heat flux over time step,longwave flux into ocean (<0 cools ocean),latent heat flux into ocean (<0 cools ocean),sensible heat into ocean (<0 cools ocean),shortwave flux into ocean (>0 heats ocean),penetrative shortwave heating,heat into ocean due to melting ice (>0 heats ocean),heat into ocean due to land ice discharge-melt (>0 heats ocean),surface ocean heat flux coming through coupler and mass transfer,cp*rivermix*rho_dzt*temp,surface heat flux coming through coupler,heat flux (relative to 0C) from pme transfer of water across ocean surface,i-directed wind stress forcing u-velocity,j-directed wind stress forcing v-velocity,Bottom u-stress via bottom drag,Bottom v-stress via bottom drag,vertical piece of Ertel PV: (f+zeta)*N^2,i-current,j-current,Thickness and rho wghtd horz bih frict on u-zonal,Thickness and rho wghtd horz bih frict on v-merid,3d velocity dot product with 3d gradient of vertical piece of Ertel PV: u.grad((f+zeta)*N^2),Ekman vertical velocity averaged to wt-point,surface height including steric contribution,Potential temperature,Potential temperature,Start time for average period,End time for average period,Length of average period,time axis boundaries
    intake_esm_attrs:variable_standard_name :
    sea_water_pressure_at_sea_floor,sea_water_pressure_at_sea_water_surface,sea_water_mass_per_unit_area,cell_thickness,sea_surface_height_above_geoid,square_of_sea_surface_height_above_geoid,sea_water_potential_temperature,sea_water_conservative_temperature,sea_surface_temperature,square_of_sea_surface_temperature,,sea_water_salinity,sea_surface_salinity,square_of_sea_surface_salinity,,sea_water_age_since_surface_contact,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_mixed_layer_thickness_defined_by_sigma_t,square_of_ocean_mixed_layer_thickness_defined_by_sigma_t,ocean_barotropic_mass_streamfunction,,,,ocean_mixed_layer_thickness_defined_by_mixing_scheme,sea_water_potential_density,,,,sea_water_x_velocity,sea_water_y_velocity,,ocean_mass_x_transport,ocean_mass_y_transport,upward_ocean_mass_transport,,,,,,,,,,,,,,,,,,,,,rainfall_flux,snowfall_flux,water_evaporation_flux,water_flux_into_sea_water_from_rivers,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,water_flux_into_sea_water,icemelt_flux,iceform_flux,,downward_sea_ice_basal_salt_flux,salt_flux_into_sea_water_from_rivers,,,temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,heat_flux_into_sea_water_due_to_snow_thermodynamics,,surface_net_downward_longwave_flux,surface_downward_latent_heat_flux,surface_downward_sensible_heat_flux,surface_net_downward_shortwave_flux,downwelling_shortwave_flux_in_sea_water,mh_flux,liceht_flux,,,,,surface_downward_x_stress,surface_downward_y_stress,,,,sea_water_x_velocity,sea_water_y_velocity,,,,,,sea_surface_temperature,sea_surface_temperature,,,,
    intake_esm_attrs:variable_cell_methods :
    time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean_pow(02),time: mean_pow(02),time: mean,time: mean,time: mean,time: mean,time: mean,time: max,time: min,,,,
    intake_esm_attrs:variable_units :
    dbar,Pa,(kg/m^3)*m,m,meter,m^2,K,K,K,squared K,deg_C,psu,psu,squared psu,psu,yr,m,m,m,m^2,kg/s,kg/s,1/s,1/s^2,m,kg/m^3,kg/m^3,kg/m^3,meter,m/sec,m/sec,m/sec,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,kg/s,Watts,Watts,Watt,Watt,Watt,Watt,Watt,Watt,(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),(kg/m^3)*(m/sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),kg/(m^2*sec),Watts/m^2,Watts/m^2,Watts/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,(W/m^2),(W/m^2),Watts/m^2,Watt/m^2,Watts/m^2,Watts/m^2,N/m^2,N/m^2,N/m^2,N/m^2,1/sec^3,m/sec,m/sec,(kg/m^3)*(m^2/s^2),(kg/m^3)*(m^2/s^2),1/sec^4,m/s,meter,K,K,days since 0001-01-01 00:00:00,days since 0001-01-01 00:00:00,days,days
    intake_esm_attrs:filename :
    ocean_month.nc
    intake_esm_attrs:file_id :
    ocean_month
    intake_esm_attrs:_data_format_ :
    netcdf
    intake_esm_dataset_key :
    ocean_month.1mon
    • " ], "text/plain": [ " Size: 5GB\n", @@ -3537,7 +1949,15 @@ "cell_type": "code", "execution_count": 5, "id": "a094eebf-28f6-4226-8a40-29b2964d7cd7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:39.263145Z", + "iopub.status.busy": "2024-10-07T22:21:39.262917Z", + "iopub.status.idle": "2024-10-07T22:21:39.268505Z", + "shell.execute_reply": "2024-10-07T22:21:39.267744Z", + "shell.execute_reply.started": "2024-10-07T22:21:39.263125Z" + } + }, "outputs": [], "source": [ "def get_trend(time, timeseries):\n", @@ -3579,7 +1999,15 @@ "cell_type": "code", "execution_count": 6, "id": "e1c5f18b-8439-4135-bc7b-663121e65663", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:39.269451Z", + "iopub.status.busy": "2024-10-07T22:21:39.269243Z", + "iopub.status.idle": "2024-10-07T22:21:39.309624Z", + "shell.execute_reply": "2024-10-07T22:21:39.309120Z", + "shell.execute_reply.started": "2024-10-07T22:21:39.269431Z" + } + }, "outputs": [ { "data": { @@ -3615,7 +2043,6 @@ "}\n", "\n", "html[theme=dark],\n", - "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -3953,7 +2380,7 @@ "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", - "Dimensions without coordinates: stat_type" + " dtype='float64', name='yt_ocean', length=1080))
    • " ], "text/plain": [ " Size: 25MB\n", @@ -4123,14 +2550,22 @@ "cell_type": "code", "execution_count": 7, "id": "58dd2ab9-b505-4885-bed5-37567a403fc4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:21:39.310638Z", + "iopub.status.busy": "2024-10-07T22:21:39.310442Z", + "iopub.status.idle": "2024-10-07T22:24:36.087108Z", + "shell.execute_reply": "2024-10-07T22:24:36.086375Z", + "shell.execute_reply.started": "2024-10-07T22:21:39.310621Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 31.5 s, sys: 3.98 s, total: 35.5 s\n", - "Wall time: 1min 6s\n" + "CPU times: user 25.1 s, sys: 3.31 s, total: 28.4 s\n", + "Wall time: 2min 56s\n" ] }, { @@ -4167,7 +2602,6 @@ "}\n", "\n", "html[theme=dark],\n", - "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -4510,19 +2944,19 @@ " [nan, nan, nan, ..., nan, nan, nan]])\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", - " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95
    • " ], "text/plain": [ " Size: 12MB\n", @@ -4568,14 +3002,22 @@ "id": "437d4d93-c95a-4606-a483-c5409ac37619", "metadata": {}, "source": [ - "Plot the calculated slope, stippling all regions that are significant at $p<0.05$. Before we plot we need to load the unmasked coordinates and attach them to the dataarray otherwise regions near the poles are distorted (see the [Making_Maps_with_Cartopy](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Making_Maps_with_Cartopy.html#Fixing-the-tripole) tutorial)." + "Plot the calculated slope, stippling all regions that are significant at $p<0.05$. Before we plot we need to load the unmasked coordinates of geographic latitude and longitude and attach them to the dataset. If we don't use these cooridings the regions near the poles are distorted (see the [Making_Maps_with_Cartopy](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Making_Maps_with_Cartopy.html#Fixing-the-tripole) tutorial)." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "836b90e1-6089-4b74-b73a-ad98f8eac1cf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:24:36.092129Z", + "iopub.status.busy": "2024-10-07T22:24:36.091519Z", + "iopub.status.idle": "2024-10-07T22:24:39.574540Z", + "shell.execute_reply": "2024-10-07T22:24:39.573722Z", + "shell.execute_reply.started": "2024-10-07T22:24:36.092107Z" + } + }, "outputs": [], "source": [ "geolon_t = xr.open_dataset(\"/g/data/ik11/grids/ocean_grid_025.nc\").geolon_t\n", @@ -4587,9 +3029,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "id": "539a6348-94b0-491c-94bf-a55ca72a25bd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:24:39.575882Z", + "iopub.status.busy": "2024-10-07T22:24:39.575307Z", + "iopub.status.idle": "2024-10-07T22:24:57.967387Z", + "shell.execute_reply": "2024-10-07T22:24:57.965850Z", + "shell.execute_reply.started": "2024-10-07T22:24:39.575859Z" + } + }, "outputs": [ { "data": { @@ -4628,9 +3078,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "id": "f27843df", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-07T22:24:57.970452Z", + "iopub.status.busy": "2024-10-07T22:24:57.969747Z", + "iopub.status.idle": "2024-10-07T22:24:59.456148Z", + "shell.execute_reply": "2024-10-07T22:24:59.455197Z", + "shell.execute_reply.started": "2024-10-07T22:24:57.970398Z" + } + }, "outputs": [], "source": [ "client.close()" @@ -4639,9 +3097,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:analysis3-24.07] *", + "display_name": "Python [conda env:analysis3-24.04] *", "language": "python", - "name": "conda-env-analysis3-24.07-py" + "name": "conda-env-analysis3-24.04-py" }, "language_info": { "codemirror_mode": {