sp working

djk2120 · Feb 11, 2022 · e72a565 · e72a565
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diff --git a/analysis/sp.ipynb b/analysis/sp.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5d1eacf-f4ab-473a-a132-26ccf39aec6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import glob\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ade99a07-1ae3-4ac7-9e52-ef87ec1b3263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### import some analysis functions we wrote for this project\n",
+    "import sys ; sys.path.append(\"..\")\n",
+    "from ppe_analysis.analysis import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ad533cbe-be7a-41db-b814-d835936a8c08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup your PBSCluster\n",
+    "import dask\n",
+    "from dask_jobqueue import PBSCluster\n",
+    "from dask.distributed import Client\n",
+    "ncores=1\n",
+    "nmem='25GB'\n",
+    "cluster = PBSCluster(\n",
+    "    cores=ncores, # The number of cores you want\n",
+    "    memory=nmem, # Amount of memory\n",
+    "    processes=1, # How many processes\n",
+    "    queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)\n",
+    "    local_directory='$TMPDIR', # Use your local directory\n",
+    "    resource_spec='select=1:ncpus='+str(ncores)+':mem='+nmem, # Specify resources\n",
+    "    project='P93300641', # Input your project ID here\n",
+    "    walltime='02:00:00', # Amount of wall time\n",
+    "    interface='ib0', # Interface to use\n",
+    ")\n",
+    "\n",
+    "# Scale up\n",
+    "cluster.scale(30)\n",
+    "\n",
+    "# Setup your client\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d16b0dd-5cd1-43ee-8d6f-a6b41e3ce466",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d74970786cac4550b5b6dd3ac69c33f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(HTML(value='<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-outpu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "client.cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "19d0dbd6-ee7f-46fc-a738-bd0d28fc8d7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csv = '/glade/scratch/djk2120/PPEn11/SP_ensemble.csv'\n",
+    "ds0,la,attrs,paramkey,keys = ppe_init(csv=csv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "662eb71b-76c5-4521-b71f-3087481e91c7",
+   "metadata": {},
+   "source": [
+    "### calc via get_ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "10c46b55-28a7-43df-b060-06349784b21d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = get_files('CTL2010SP','h0',keys)\n",
+    "ds = get_ensemble(files,['FPSN'],keys,paramkey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "247ed494-0d1b-4426-8483-4a94a32e35df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=1/la.sum()*(la*ds.FPSN).mean(dim='time').sum(dim='gridcell').compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "17bde97a-30a3-43ca-a743-f521cd9f32a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2b19a2ead550>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x.plot.line('.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f661375-f821-436f-8006-741dc70bef20",
+   "metadata": {},
+   "source": [
+    "### via calc_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a2f9149e-090f-4f25-8d2e-ce7697f1e5d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da,iav = calc_mean('CTL2010SP','FPSN',csv=csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f8918bc-63f6-45d0-809e-cab5e0bd57c4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:miniconda3-ppe-py]",
+   "language": "python",
+   "name": "conda-env-miniconda3-ppe-py-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ppe_tools/analysis.py b/ppe_tools/analysis.py
@@ -9,14 +9,12 @@
 
 #define the directory structure to find files
 def get_files(name,htape,keys):
-
-    topdir     = '/glade/campaign/asp/djk2120/PPEn11/hist/'
+    topdir     = '/glade/scratch/djk2120/PPEn11/hist/' 
     thisdir    = topdir+name+'/'
     files      = [glob.glob(thisdir+'*'+key+'*'+htape+'*.nc')[0] for key in keys]
     return files
 
 def ppe_init(csv='/glade/scratch/djk2120/PPEn11/surv.csv'):
-
     paramkey = pd.read_csv(csv)
     keys = paramkey.key
 
@@ -191,17 +189,18 @@ def get_cfs(attrs,datavar,ds,la):
             units = 'tbd'
     return cf1,cf2,units
 
-def calc_mean(ens_name,datavar,domain='global',overwrite=False):
+def calc_mean(ens_name,datavar,domain='global',overwrite=False,
+              csv='/glade/scratch/djk2120/PPEn11/surv.csv'):
     '''
     Calculate the annual mean for given datavar across the ensemble.
-        ens_name, one of CTL2010,CTL2010SP,AF1855,AF2095,C285,C867,NDEP
+        ens_name, one of CTL2010,AF1855,AF2095,C285,C867,NDEP
         datavar, e.g. GPP
         domain, one of global,biome,pft
         overwrite, option to rewrite existing saved data
     returns xmean,xiav
     '''
 
-    ds0,la,attrs,paramkey,keys = ppe_init()
+    ds0,la,attrs,paramkey,keys = ppe_init(csv=csv)
 
     preload = ('/glade/u/home/djk2120/clm5ppe/pyth/data/'+
                ens_name+'_'+datavar+'_'+domain+'.nc')