From 711496adabddde8ad160b9ee6493fd13a7a31c2a Mon Sep 17 00:00:00 2001 From: Daniel Kennedy Date: Wed, 27 Apr 2022 15:40:27 -0600 Subject: [PATCH] move to campaign --- analysis/template.ipynb | 461 +++++------------------- {ppe_tools => ppe_analysis}/analysis.py | 224 +++++------- 2 files changed, 187 insertions(+), 498 deletions(-) rename {ppe_tools => ppe_analysis}/analysis.py (62%) diff --git a/analysis/template.ipynb b/analysis/template.ipynb index d1c3342..1ea2218 100644 --- a/analysis/template.ipynb +++ b/analysis/template.ipynb @@ -7,7 +7,7 @@ "source": [ "# PPE analysis template\n", "- Daniel Kennedy (djk2120@ucar.edu)\n", - "- updated January 23, 2022\n", + "- updated April 27, 2022\n", "- note that there are dependencies to other files in the repo" ] }, @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "f5222842-6944-4b0b-8dc8-d30c7b477562", "metadata": {}, "outputs": [], @@ -41,7 +41,7 @@ "### import some analysis functions we wrote for this project\n", "### note that you can inspect the code for these functions in ../ppe_tools/analysis.py\n", "import sys ; sys.path.append(\"..\")\n", - "from ppe_tools.analysis import *" + "from ppe_analysis.analysis import *" ] }, { @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "cf4dccb8-9382-483f-ad23-81884c0d8c9a", "metadata": {}, "outputs": [], @@ -75,7 +75,7 @@ " local_directory='$TMPDIR', # Use your local directory\n", " resource_spec='select=1:ncpus=1:mem=25GB', # Specify resources\n", " project=project, # Input your project ID here\n", - " walltime='01:00:00', # Amount of wall time\n", + " walltime='02:00:00', # Amount of wall time\n", " interface='ib0', # Interface to use\n", ")\n", "\n", @@ -88,14 +88,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "fb854d7d-b77d-4a27-adc4-5ffcc40f47c9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d27d4ab2ff99456f99344e7f5a0e5f45", + "model_id": "62223cf3864f4a00a667fb4025e4bd4f", "version_major": 2, "version_minor": 0 }, @@ -117,111 +117,70 @@ "id": "7e16e014-fe41-4798-a13e-cf414008e47d", "metadata": {}, "source": [ - "### Define some info about the ensemble" + "### Get some info about the ensemble" ] }, { "cell_type": "code", "execution_count": 6, - "id": "44af6ac8-d935-4c94-a2c7-672a455911af", + "id": "dc901a00-014f-4952-a550-4b8c8bd26c28", "metadata": {}, "outputs": [], "source": [ - "#define the directory structure to find files\n", - "def get_files(name,htape,keys):\n", - " topdir = '/glade/scratch/djk2120/PPEn11/' \n", - " thisdir = topdir+name+'/hist/'\n", - " files = [glob.glob(thisdir+'*'+key+'*'+htape+'*.nc')[0] for key in keys]\n", - " return files" + "ds0,la,lapft,attrs,paramkey,keys = ppe_init()" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "b387f5ad-15f4-420d-8c83-1860f8ef495d", + "cell_type": "markdown", + "id": "49fbe4bf-a9bb-4d51-8033-b23962582482", "metadata": {}, - "outputs": [], "source": [ - "#fetch the paraminfo\n", - "csv = '/glade/scratch/djk2120/PPEn11/surv.csv' \n", - "paramkey = pd.read_csv(csv)\n", - "keys = paramkey.key\n", - "\n", - "#fetch the sparsegrid landarea\n", - "la_file = '/glade/scratch/djk2120/PPEn08/sparsegrid_landarea.nc'\n", - "la = xr.open_dataset(la_file).landarea #km2\n", - "\n", - "#load conversion factors\n", - "attrs = pd.read_csv('agg_units.csv',index_col=0)" + "### load an ensemble to ds" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c741b021-cb34-44c4-9dd3-a5de32ba2b70", + "execution_count": 7, + "id": "847641e7-c2de-4fc1-b8dc-97dc00858865", "metadata": {}, "outputs": [], "source": [ - "#empty dataset, can be useful to have around\n", - "p,m = get_params(keys,paramkey)\n", - "ds0 = xr.Dataset()\n", - "ds0['param'] =xr.DataArray(p,dims='ens')\n", - "ds0['minmax'] =xr.DataArray(m,dims='ens')\n", - "ds0['key'] =xr.DataArray(keys,dims='ens')\n", - "whit = xr.open_dataset('./whit/whitkey.nc')\n", - "ds0['biome'] = whit['biome']\n", - "ds0['biome_name'] = whit['biome_name']" - ] - }, - { - "cell_type": "markdown", - "id": "9363e07e-ab45-4adb-b4a6-29f5b4ed8a8c", - "metadata": {}, - "source": [ - "## Example analyses:\n", - "### Working with annual means, at the global/biome/pft levels\n", - "- we have a helper function for this purpose (calc_mean)\n", - "- it also saves & writes intermediate data so you don't have to recalculate every session" + "data_vars = ['GPP','EFLX_LH_TOT']\n", + "ensemble = 'CTL2010'\n", + "htape = 'h0'\n", + "ds = get_ensemble(data_vars,ensemble,htape)\n", + "\n", + "#h1 = pft, h5=daily\n", + "#ensembles\n", + "# AF1855 - 1855 climate, CO2=367ppmv\n", + "# AF2095 - 2095 climate, CO2=367ppmv\n", + "# C285 - 2010 climate, CO2=285ppmv\n", + "# C867 - 2010 climate, CO2=867ppmv\n", + "# NDEP - 2010 climate, CO2=367ppmv, NDEP+=5g/m2" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "3279c314-1d45-4902-a2e7-7ae7d1bafdd8", + "execution_count": 9, + "id": "43e02c65-67d5-4b02-ad83-e408c1cdb2d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function calc_mean in module ppe_tools.analysis:\n", - "\n", - "calc_mean(ens_name, datavar, domain='global', overwrite=False, csv='/glade/scratch/djk2120/PPEn11/surv.csv')\n", - " Calculate the annual mean for given datavar across the ensemble.\n", - " ens_name, one of CTL2010,AF1855,AF2095,C285,C867,NDEP\n", - " datavar, e.g. GPP\n", - " domain, one of global,biome,pft\n", - " overwrite, option to rewrite existing saved data\n", - " returns xmean,xiav\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "help(calc_mean)" + "### here's the set of netcdf files, if useful\n", + "files = get_files('CTL2010','h0',keys)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "22dfbf45-5a9d-4685-a802-5c91f3eb24e0", + "execution_count": 30, + "id": "5ee5f995-7b88-4868-ad13-f376f9f8c78d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": { @@ -231,69 +190,31 @@ } ], "source": [ - "#global means, scatter plot\n", - "ens_name = 'AF1855'\n", - "datavar = 'GPP'\n", - "domain = 'global'\n", - "xmean,xiav = calc_mean(ens_name,datavar,domain=domain)\n", + "v='GPP'\n", + "cf1 = attrs['cf1'][v]\n", + "cf2 = float(attrs['cf2'][v])\n", + "unit = attrs['units'][v]\n", "\n", - "plt.figure(figsize=[9,3])\n", - "plt.subplot(121)\n", - "xmean.plot.line('.')\n", - "plt.title(ens_name+' annual mean')\n", - "plt.subplot(122)\n", - "xiav.plot.line('.')\n", - "plt.title(ens_name+' IAV')\n", - "plt.subplots_adjust(wspace=0.4)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1784c115-f8e2-4f01-bd64-936ab9e45d16", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## how to save a figure\n", - "filename = '../figs/example.png'\n", - "plt.figure(figsize=[9,3])\n", - "plt.subplot(121)\n", - "xmean.plot.line('.')\n", - "plt.title(ens_name+' annual mean')\n", - "plt.subplot(122)\n", - "xiav.plot.line('.')\n", - "plt.title(ens_name+' IAV')\n", - "plt.subplots_adjust(wspace=0.4)\n", - "plt.savefig(filename)\n", "\n", - "#note you can examine the printed figure by navigating \n", - "# to it in the left panel\n" + "xann = ann_mean(ds[v],cf1).mean(dim='year')\n", + "xglob = reg_mean(xann,la,cf=cf2)\n", + "xglob.name = v\n", + "xglob.attrs = {'units':unit}\n", + "xglob.plot.line('.')\n", + "plt.title(ensemble);\n" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "090c0771-f2a1-4868-97dd-657dea86cf9b", + "execution_count": 31, + "id": "b957cd2d-2cf2-4e1f-9333-a24cb291c1cd", "metadata": {}, "outputs": [ { "data": { - 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Fjx4xRlEF5syBTZu2b1u/HkaPhoED44mpBquVPfUUc81shZltAj4Ano/a5xMSM8ABwHOS5gMjgcMAzGw9cDHwAnC3mX2Qst1HzWyrmb1P+LBoX96AJOVIypeUv2rVqkoc2k5o3x5mzw69mVRffw0/+lF6Y3GuqhRN6IWWLUtvHLVEbe+pp/60t6ZMb+WHY/sz8Ecze0pSD2BMyjodgTXAfkW2a2VMl8jMcoFcgOzs7HKvVyVOPhmuuw4uvzz0zBs1gj/9Kfzyz5vnib2OOf/88wF4+OGHY46kkjIzQ8mlqFat0h5KbVDbe+rlsRtQePnHeYWNkloTyjCdgd6SjkpZ5xxJ9aK6fFtgcbqCrZR69WD6dPj2W2jTJtTUFyyAGTM8oddBLVu2pGXLlnGHUXljx0JGxvZtGRmh3e2gtvbUK9IDHgP8Q9InwBygjULh/EFghJl9KulCYKKkrtE6i4GZwN7Arwrr6ZKWALsCDSX1A04xs0VVcDxVZ4894KGH4MEHw3Q4R+DqoJtuuinuEKpGYd189OjwrbNVq5DQvZ5eLFnR+msNJ2kP4D9m1jruWMqSnZ1t+fn5cYfhnEsYSfPMLLu4ebWq/CJpP+A1wP9VzrkyDBo0iEGDBsUdhkuzWlV+MbNPgUPijsO52qBdu3Zxh+BiUKuSunOu/K6//vq4Q3AxqFXlF+ecc6XzpO5cQp177rmce+65cYfh0szLL84lVFZWVtwhuBh4UncuoUaNGhV3CC4GXn5xzrkE8aTuXEL179+f/v37xx2GSzMvvziXUMccc0zcIbgYeFJ3LqFGjKi5N+ly1cfLL845lyCe1J1LqL59+9K3b9+4w3Bp5uUX5xLqpJNOijsEFwNP6s4l1BVXXBF3CC4GXn5xzrkE8aTuXEL17t2b3r17xx2GSzMvv9Qk338Pu/iPxFWNPn36xB2Ci0Eie+qSMiUtiDuOcps4EQ4+GBo2hIMOCvcYda6Shg4dytChQ6t3J3l5kJkZbnqemRmmXay8Wxi3Rx4JN9H93/+FY46B11+H884LfyRDhsQdnXMly8uDnBxYvz5ML10apsFvCh2jWnfj6fKQlAk8bWYdJLUFHgf+DvwUqA90AO4AGgKDgU3AaWb2haSLgZxo3n+BwWa2XtKTwONm9jdJlwDdzazU39xy3Xi6Qwe45x44/ngYPhwKCmDtWli8GI48cmffAud46623AOjUqVP17GDOHNi0acf21q1hyZLq2acDEnTj6YqS1I6Q0M8HVhGS+S+AI4GxwHoz60y4mfUvo9WmmllXM+sEvANcGLXnADdI6gZcBfy6hH3mSMqXlL9q1aqyg1y8GI4+evu2XXeFDRsqcKTO7WjPvfZiz732qr4dFJfQAZYtq759ujIlufyyJ/Ak0N/MFkrKAl4ys2+AbyStBaZFy84HDo9ed5B0C9AcaAo8B2BmKyXdALwEnGlmXxS3UzPLBXIh9NTLjLJDB5g5E045BSZMCG3/+hdceSXMmFHBQ3buB/tV9w4yM0PJpahWrap7z64USe6prwU+JpRcCqV2LbamTG/lhw+4icBlZtYRuBFonLJOR2ANVfn3ct11cNFF8Mwz8M03MH06XHBBaHeuJhs7FjIytm/LyAjtLjZJ7ql/B/QDnpO0rgLrNQNWSGoADAQ+AZB0JNAb6AzMlPS8mX1U6Sj79w8nRX/3O1i0CA49FP74x9DuXCX06NEDgBnV9Y2v8GTo6NGh5NKqVUjofpI0VklO6pjZt5JOB14AJpVzteuB14GlhLJMM0mNgPuB883sU0lXAQ9JOtGq4kzzmWeGh3NVaEg6rp4aONCTeA2TyKtfaopyXf3inHMVVGevfnGuLtu8eTObN2+OOwyXZokuvzhXl5188slANdbUXY3kSd25hLroooviDsHFwJO6cwk1aNCguENwMfCaunMJtX79etYXjsvi6gzvqTuXUKeddhrgNfW6xpO6cwl16aWXxh2Ci4EndecSasCAAXGH4GLgNXXnEmrt2rWsXbs27jBcmnlP3bmEOuOMMwCvqdc1ntSdS6jLL7887hBcDDypO5dQZ511VtwhuBh4Td25hFq9ejWrV6+OOwyXZt5Tdy6hzj77bMBr6nWNJ3XnEuqqq66KOwQXA0/qziVUnz594g7BxcBr6s4l1GeffcZnn30Wdxguzbyn7uKxaVN47Lpr3JEk1rnnngt4Tb2uqZU9dUmzq3HbEyWdXcK8ayT9V9JiSadWVwyJ9s03kJMDLVrAvvvCkUfCK6/EHVUijRo1ilGjRsUdRnLk5UFmZrhRfGZmmK6BamVP3cyOTfc+Jf0EOBc4DNgPeFHSIWa2Jd2x1GqDBkHz5vDhh7D77jB1arjp9uzZcPDBcUeXKL169Yo7hOTIywudkcKhjJcuDdNQ4268XSuTuqR1wOnAjcBKIAuYCswHrgCaAP3M7ANJfYDrgIbAGmCgma2UdBew2sxuinrdo4Ee0S56SroC2Bv4jZk9DZwBTDazTcBHkv4LHAm8lo5jToT33oM33gh/EFdfDQUFoT0jA048EQ48MNbwkmbjpk0ANG7UKOZIEmDOnFAuTLV+PYweXeOSeq0sv6ToREjiHYHBwCFmdiTwAPDraJlXgKPNrDMwGbg6ah8FDJB0AnAXcL6ZbY3mZQLHA/8D3CupMbA/8HHKvpdHbduRlCMpX1L+qlWrquxAE+HDD6FjR2jYcPv2Zs1gw4Z4Ykqwd995h3ffeSfuMJKhaEIvtGxZeuMoh1rZU08x18xWAEj6AHg+ap8PnBC9PgCYImlfQm/9IwAzWy/pYuBl4Eoz+yBlu49GCf59SR8C7QEVs3/bocEsF8gFyM7O3mF+ndahA+Tnh7r6hAk/tA8dCnvuCTfeGFtoSbT6xRfDi5494w0kCTIzwzfMolq1SnsoZantPfXUj8+tKdNb+eED68/A3WbWEbgEaJyyTkdCSWa/ItstmoyN0DNvmdJ2APDpTkdeFx1wAAwYAH36hK+zy5bBLbfAk0+C39ChyvXs2ZOentCrxtixoUyYKiMjtNcwtT2pl8duwCfR6/MKGyW1Bq4COgO9JR2Vss45kupJOhBoCywGngLOldRIUhvgYOCNdBxAotx9d0jqF1wARx0V6uwvvwz77BN3ZInz4Ycf8uGHH8YdRjIMHAi5udC6NUjhOTe3xtXTofaWXypS1hgD/EPSJ8AcoI0kAQ8CI8zsU0kXAhMldY3WWQzMJJwo/ZWZbQQWSnoUWAR8DwzzK192Qv36cNVV4eGq1QUXXAD4depVZuDAGpnEi5JZ7Sr7StoD+I+ZtY47lrJkZ2dbfn5+3GG4OmrmzJkAHH/88TFH4qqapHlmll3cvFrVU5e0HzADGB9zKM7VeJ7M66ZaldTN7FPgkLjjcK42WLx4MQDt2rWLORKXTrUqqTvnyu+SSy4BvKZe13hSdy6hbr311rhDcDHwpO5cQh17bNqHSHI1QF24Tt25OmnBggUsWLAg7jBcmnlP3bmEuuyyywCvqdc1ntSdS6jbb7897hBcDDypO5dQXbt2LXshlzheU3cuoQoKCigoHLPe1RneU3cuoYYPHw54Tb2u8aTuXEJNSB2z3tUZntSdS6isrKy4Q3Ax8Jq6cwk1d+5c5s6dG3cYLs28p+5cQo0cORLwmnpd40nduYS6++674w7BxcCTunMJ1aFDh7hDcDHwmnpdYwYrV8JXX8Udiatms2fPZvbs2XGH4dIs7UldUnNJQyux/m8kLZL0tqR/RTeQLpy3RVJB9HhqJ7bdT9JPUqZvkpSc27HPmQNdu8Khh0KrVtCvH3z2WdxRuWpy7bXXcu2118Ydxo7y8iAzE+rVC895eXFHlChx9NSbAzud1IE3gWwzOxx4DPhDyrwNZpYVPfoWt7Kk+qVsux+wLamb2Q1m9mIlYq05PvkE+vaFkSNh9WpYsQJ+8hM4/fTQe3eJc99993HffffFHcb28vIgJweWLg2/d0uXhmlP7FUm7TeeljQZOANYDGwGNgBrgHbAy8BQM9sqaZ2ZNY3WORs43cyGFNlWZ+BuM/tpNL1tnSLLLQEeAk4B7gaaATlAQ+C/wGAgC3gaWBs9+gPXA0+b2WOSTiLcG3UXYC5wqZltKu1Ya9SNp2++OfTK77kHhg+Hwn8fnzsXDjkEdtstzuhcXTFnDmwq5s+mdWtYsiTt4dRWpd14Oo6e+ijgAzPLAkYCRwJXAR2BA4GzKrCtC4FnU6YbS8qXNEdSvyLLbjSz48xsMjDVzLqaWSfgHeBCM5sNPAWMjHr6HxSuKKkxMBEYYGYdCYn90uICkpQTxZC/atWqChxKNVu6FA4/fMf2pk1h48b0x+Oq3VdffcVXNe3cSXEJHWDZsvTGkWA14eqXN8zsQwBJ/wccRyirlErSICAbSL1leisz+1RSW+DfkuanJOcpKct1kHQLoRTUFHiujN21Az4ys/ei6f8FhgETii5oZrlALoSeelnHkTbZ2fDss3DJJVD47+MbN0KbNvD446EU4xKlX48eQA27Tj0zM3QwimrVKu2hJFVNuPqlaOKzYtobpy4QnbwcDfRNLYGY2afR84fADKBzymrfpryeCFwW9bpvLLr9YqiM+TXfoEGweDEMGwbz58OsWaGe3rOnJ/SEeuihh3jooYfiDmN7Y8dCRsb2bRkZod1ViTiS+jeEmnahIyW1kVQPGAC8ErWvlHRo1H5m4cJRHf0+QkL/PKX9x5IaRa9bAD8FFpUQQzNghaQGwMBSYiv0LpAp6aBoejAws1xHW1M0bQovvwwNG0L//nD55dC7Nzz8cNyRuWrStm1b2rZtG3cY2xs4EHJzQw1dCs+5uaHdVYm0nygFkPR34HDCSdJvgFWEmnrqidKzgd8DHwMLgKZmNkTSi9GyK6LNLTOzvpKOJST7rYQPqwlm9mC0vyWEK2ZWR9OXAlcDS4H5QLNo2z8F7gc2AWeTpBOlrs558cVw4VbPnsm5KtcFpZ0ojSWpb9u51AMYYWanxxZENfKk7uLUoybW1F2VKC2p14QTpc65avDII4/EHYKLQaxJ3cxmEE5oOueqWMuWLeMOwcWgJlz94pyrBtOnT2f69Olxh+HSzMsvziXUuHHjAOjVq1fMkbh08qTuXEJNnjw57hBcDDypO5dQ++yzT9whuBh4Td25hJo2bRrTpk2LOwyXZt5Tdy6h7rjjDgD69OkTcyQunTypO5dQjz1W5rh4LoE8qTuXUC1atIg7BBcDr6k7l1BTp05l6tSpcYfh0sx76s4l1F133QXAWWdV5L4zrrbzpO5cQj355JNxh+Bi4EnduYTaze87Wyd5Td25hJoyZQpTpkwpe0GXKN5Tdy6h/vrXvwIwYMCAmCNx6eRJ3bmEeuaZZ+IOwcXAyy+ubvr223Aj7m+/LXvZWiojI4OMojd5domXmKQuaaCkt6PHbEmdKrDuM5KaS8qUtKCEZdpIel3S+5KmSGpYddG7tNm6FW64AVq2hNNPD8/XXRfaE2bSpElMmjQp7jBqh7w8yMyEevXCc15e3BHttMQkdeAj4HgzOxy4Gcgt74pmdpqZfVXGYr8H7jSzg4EvgQt3NlAXozvvhOefh7ffhvffh/nz4aWXIBonJUkeeOABHnjggbjDqPny8iAnB5YuBbPwnJNTaxN7rDeeLomkq4GNZnaXpDuBTmZ2oqSTgPOBScCtQH1gtZmdVGT9HwMLzGz/srZlZoMkLQGygabAdOB1oDPwHvBLYAOwCtjHzL6XdAwwxsxOLe04/MbTNVBmJvzzn9C5MwwfDgUFoQQzfz4cfXTMwVWtrdHfdj0p5khquDlzYNOmHdtbt4YlS9IeTnmUduPpmtpTfxnoFr3OBppKagAcB8wH7gf6m1kn4Jxi1r8QeLYc25pVzLrtgNyox/81MBTYA/jKzL6PllkO7F9c4JJyJOVLyl+1alV5j9ely6efQvv227dlZBT/R13L1ZM8oZdHST/7ZcvSG0cVqalXv8wDukhqBmwC/kNIyN2AacDLZvYRgJl9kbqipBMISf24cmzr8mL2/bGZvRq9nhQt87/FLFfsVxwzyyUq/WRnZ9e8r0F13VFHwVNPwYABMGFCaHvsMdi8GWbMiDOyKjdx4kQAhgwZEmscNV5mZii5FNWqVdpDqQo1Mqmb2eaoJHI+MBt4GzgBOBBYAnQtbj1JhwMPAL3NbE05tvVOcbsvZno10FzSLlFv/QDg050/QhebW26Bc86B1avhuOPg1VdhzBhI4K3fPKmX09ixoYa+fv0PbRkZob0WqqnlFwhlkxHR8yzgV0ABITEfL6kNgKTdo+dWwFRgsJm9V55tWfEnFFpFNXOAnwOvRMu9BJwdtZ8H+MAatdHxx8P/+3/w8sswaFDonT/9NJx4YtyRVbkZM2YwI2HfPqrFwIGQmxtq6FJ4zs0N7bVQjTxRChCdyJwONDezbyW9B9xrZn+U1JtworQe8LmZnSzpAaA/UPg96vvCEwmlbSuavwToAjQDniEk/2OB9wkfEusltQUmA7sDbwKDzKzUQqyfKHXOVYfSTpTW2KSeLpLqA58TrmzZXJXb9qTu4nT//fcDcPHFF8cciatqtfHql3RaCDxQ1Qndubj5gF51U408UZpOZta+7KWcq31efPHFuENwMfCeunPOJYgndecS6i9/+Qt/+ctf4g7DpZkndecSatq0aUybNi3uMFya1fmaunNJ9eyzz5a9kEsc76k751yCeFJ3LqH+9Kc/8ac//SnuMFyaeVJ3LqH+9a9/8a9//SvuMFyaeU3duYR66qmn4g7BxcB76s45lyCe1J1LqPHjxzN+/Pi4w3Bp5uUX5xLqtddeizsEFwNP6s4l1OOPPx53CC4GXn5xzrkE8aTuXEKNGzeOcePGxR2GSzMvvziXUAUFBXGH4GLgSd1Vn08+gc8/h/btoUmTuKOpcyYn8GbarmxefnFV78svoV8/OPxw+OUvoVUruO++uKNy6ZCXB5mZUK9eeM7LizuiOqfGJ3VJzSUNjV73kPR0BdcfImm/KoxnP0mPVdX2EumCC2C//WD5cpg/H2bNgnHj4Pnn446sTrn55pu5+eab07fDvDzIyYGlS8EsPOfkeGJPs9pQfmkODAV2drT/IcAC4NOqCMbMPgXOroptJdInn4Qkvnw5jBoFhXXdRo3gF7+ADh1iDa8uOfudd8KLdI3/MmcObNq0fdv69TB6NAwcmJ4YXK1I6uOAAyUVAJuBb6OecgdgHjDIzExSF+CPQFNgNSGZ/xTIBvIkbQCOAUYCfYAmwGzgEjOz4nYs6SDgXmBPYAtwTvT8tJkVm50k5QA5AK1atarssdc+n38O++4LjRtv396kCXz3XTwx1VGHHnpoendYNKEXWrYsvXHUdWZWox9AJrAget0DWAscQCgdvQYcBzQgJOg9o+UGAA9Fr2cA2Snb2z3l9SNAn1L2/TpwZvS6MZCRGk9Zjy5dulids2GD2Z57mr3zzvbtl19uNnJkPDG59Gjd2iwUXrZ/tG4dd2SJA+RbCXmnNvTUi3rDzJYDRL33TOArQs/9BUkA9YEVJax/gqSrCQl6d2AhsMM9vyQ1A/Y3s38CmNnGqL3qjiSJGjeGW26BXr3ghhvgoIPg8cfhiSfC13OXNjfccAMAN910U3p2OHZsqKGvX/9DW0ZGaHdpUxuTeup3vC2EYxCw0MyOKW1FSY0JtflsM/tY0hhCD7zYxasg1ropJydc+XDvvfDZZ3DccSGh77tv3JHVKR9//HF6d1hYNx89OpRcWrUKCd3r6WlVG5L6N0CzMpZZDOwp6Rgze01SA+AQM1tYZP3CBL5aUlPCCc9ir2Qxs68lLZfUz8yekNSI8A3Alccpp4SHi83DDz+c/p0OHOhJPGY1Pqmb2RpJr0paAGwAVhazzHeSzgbukrQb4bgmEEorE4F7U06U3g/MB5YAc8vY/WDgPkk3EU7SngNsBYo9seqcc3GTFX/hhytB4VU2ZnZ8WctmZ2dbfn5+GqJybkfXXHMNALfddlvMkbiqJmmemWUXN6/G99RrEknZwN+BUXHH4lxZ1qxZE3cILgae1AFJ9xCuaU/1JzPbrihpZvnAIWkLzLlKyM3NjTsEFwNP6oCZDYs7Buecqwo1fuwX59zOGTFiBCNGjIg7DJdm3lN3LqE2bNgQdwguBp7UnUuoe+65J+4QXAy8/OKccwniSd25hBo+fDjDhw+POwyXZp7UnXMuQbym7lxCTZgwIe4QXAy8p+6ccwniSd25hBo2bBjDhvn/1dU1Xn5xLqGaNGkSdwguBp7UnUuo8ePHxx2Ci4GXX5xzLkE8qTuXUDk5OeTk5MQdhkszL784l1B77LFHene4Zg0sXAitW4eHi0WieuqSmksaWon1J0a3xXNJlJcXbohdr154zsuLO6Jqddttt6XnrkdmcM01cOCB4Tk7G845B779tvr37XaQqKQONAd2Oqm7BMvLg5wcWLo0JKGlS8N0whN7WuTmwosvwvvvw6uvwrJl0KgRXHll3JHVSYm6R6mkycAZwGLCjaI3AGuAdsDLwFAz2yppnZk1jdY5GzjdzIZImgh8DWQD+wBXm9ljkpoCTwI/BhoA15nZk2XF4/corUEyM0MiL6pRIzj66LSHkw7vvvsuAO3bt6/eHc2bF3rpzZuH6awsuO46aNsWPvsMMjKqd/91UF26R+kooIOZZUnqAUwHfgIsjV6fBTxWxjb2BY4D2gNPRctvBM40s68ltQDmSHrKivlElJQD5AC0atWqKo7JVYVly4pv37QpvXGkUaPGjdOzo82bw4djqj32gPr1QwnGk3paJS2pF/WGmX0IIOn/CMm6rKT+hJltBRZJ2jtqE3CrpO7AVmB/YG/gs6Irm1kukAuhp14lR+Eqr1Wr4nvqrVvDjBlpDycd2qRrR+edBwcfHHrnhZ59FvbfH1q0SFcULpL0pF40qVox7UW7M6ldN0XPA4E9gS5mtlnSkmLWczXZ2LGhhr5+/Q9tGRmh3VXO9ddD9+7h6pdevaCgAP74R/jb30Aqc3VXtZJ2ovQboFnK9JGS2kiqBwwAXonaV0o6NGo/sxzb3Q34PEroJwB+vVZtM3BgOKHXunVINK1bh+mBA+OOrNoMGjSIQYMGVf+ODjoI3ngDGjaE8ePhv/+F556DU0+t/n27HSSqp25mayS9KmkB4STpa8A4oCPhROk/o0VHAU8DHwMLgKZlbDoPmCYpHygA3q366F21Gzgw0Um8qHbt2qVvZwccAL//ffr250qUqKtfUkUnSkeY2elxxeBXvzjnqkNpV78krfzinHN1WqLKL6nMbAYwI+YwnIvNueeeC8DkyZNjjsSlU2KTunN1XVZWVtwhuBh4UncuoUaNGhV3CC4GXlN3zrkE8aTuXEL179+f/v37xx2GSzMvvziXUMccc0zcIbgYeFJ3LqFGjBgRdwguBl5+cc65BPGk7lxC9e3bl759+8YdhkszL784l1AnnXRS3CG4GHhSdy6hrrjiirhDcDHw8otzziWIJ3XnEqp379707t077jBcmnn5xbmE6tOnT9whuBh4UncuoYYOHRp3CC4GXn5xzrkE8aTuXEWsXRvuvzl3LtTwu4b17NmTnj17xh2GSzNP6s6V15//DJmZ4V6cgwdDVhZ88EHcUZVowIABDBgwIO4wypaXF97XevXCc15e3BHVbmZW6gPYQrjZcuEjExgC3F1kuRlAdvR6CfB4yryzgYml7KMR8GK0/QFlxVSOmC8H3iHcMLovMKqy29yZR5cuXcwlxIwZZq1amX34YZjeutVswgSzTp3Ca7dzJk0yy8gwC997wiMjI7S7EgH5VkLeKc+J0g1mlpXaIKk8nxfZkg4zs4XlWLYz0KDofqJ91TezLeXZYYqhQG8z+yiafqqC6+9gJ+NwSfHQQzBiBLRpA8OHQ0FBaF+8GLKzoVmzOKOrvebMgU2btm9bvx5Gj4aBA+OJqZarzvLLeODashaStBcwCciSVCDpQElLJN0g6RXgHEkXS5or6S1Jj0vKiNbdW9I/o/a3JB0r6V6gLfCUpCslDZF0d7T8k5J+Gb2+RFJe9PoUSa9J+o+kf0hqGrUXjeNySYskvS2p2Bs/SsqRlC8pf9WqVZV+E10N8dVXsO++O7Y3agTff5/2cMqjoKCAgsIPn5qqaEIvtGxZeuNIkPL01JtIKohef2RmZ5Zz248CQyUdVNpCZva5pIuAEWZ2Omz7JrDRzI6Lpvcws/uj17cAFwJ/Bu4CZprZmZLqA03N7FeSegEnmNlqSUNSdpcDvCrpI+Aq4GhJLYDrgJ5m9q2k3wK/AW6K1kmN41OgjZltktS8hOPJBXIBsrOza/aZNFd+PXvC3/4G/fvDhAmh7b//hSOPhFdeqZE99YKJEwHIGjIk1jhKlZkJS5fu2N6qVdpDSYqdKr8AJSWr1PYtwO3ANcCzFQ+NKSmvO0TJvDnQFHguaj8R+CVAVBpZW9oGzWylpBuAl4AzzewLSacDPyEke4CGwGslxPE2kCfpCeCJnTgmV1tdcAE88gj07QuDBsEnn8Cdd8Ktt9bIhA4wpCYn80Jjx0JOTii5FMrICO1up+xs+WUN8OMibbsDq4u0PQJ0B3bmY/fblNcTgcvMrCNwI9B4J7ZXqCMh/v2iaQEvmFlW9PiJmV1YQhz/A9wDdAHmSfJ/3qorfvQjmDEDeveGKVNg0aLw/KtfxR1ZiTZv3szmzZvjDqN0AwdCbi60bg1SeM7N9Xp6JexsUp8L/FTSPgCSsglXsHycupCZbQbuBIZXIkaAZsAKSQ2A1J/2v4BLoxjqS9q1tI1IOhLoTTgxO0JSG2BOdCwHRctkSDqkmHXrAS3N7CXgan741uDqiowMGDoUpk6FBx6AY4+NO6JSnXzyyZx88slxh1G2gQNhyRLYujU8e0KvlJ3qaUZljCuAZ6Jktw74uZltLWbxBwk168q4HngdWArMJyR5gCuAXEkXEso9l7J96WQbSY2A+4HzzexTSVcBDxFKOEOA/4uWIYr3vSKbqA9MkrQboXd/p5l9Vcnjcq7aXHTRRXGH4GIgq+H/FVebZWdnW35+ftxhOOcSRtI8M8subp7/R6lzCbV+/XrWp56AdHVCWk/0STqfUDJJ9aqZDUtnHM7VBaeddhoAM2bMiDcQl1ZpTepm9jDwcDr36Vxddemll8YdgouBX5LnXELVisG8XJXzmrpzCbV27VrWri31//FcAnlP3bmEOuOMMwCvqdc1ntSdS6jLL7887hBcDDypO5dQZ511VtwhuBh4Td25hFq9ejWrVxcdjsklnffUnUuos88+G/Cael3jSd25hLrqqqviDsHFwJO6cwnVp0+fuENwMfCaunMJ9dlnn/HZZ5/FHYZLM++pO5dQ5557LuA19brGk7pzCTVq1Ki4Q3Ax8KTuXEL16tUr7hBcDDypO5dQH38c7i7ZsmXLiq24ZQvMmgVffAHHHQd77VUN0bnqUitPlEqaXY3bnijp7OravqtmeXmQmQn16oXnvLy4I4rN4MGDGTx4cMVWevddOPRQ+M1v4OGHoV07uO226gnQVYta2VM3s5p9x18Xj7w8yMmBwrv9LF0apqFO3sz4uusqeGtgMzj7bBg5Ei6+OLStWBF669nZUBtuYu1q5z1KJa0DTgduBFYCWcBUwk2prwCaAP3M7ANJfQg3km4IrAEGRjfOvgtYbWY3SToVGA30INyM+msgG9gHuNrMHpPUFHgS+DHQALjOzJ4sLU6/R2maZWaGRF5Uo0Zw9NFpD6fW+eYbeOcdOPLIMJ2VBRMmwF//Cq++CpMmxRmdS5Hke5R2IiTxjsBg4BAzOxJ4APh1tMwrwNFm1hmYDFwdtY8CBkg6AbgLON/Mtkbz9gWOI3xwjIvaNgJnmtkRwAnAHZJUNCBJOZLyJeWvWrWqao/WlW7ZsuLbN21Kbxw1xIaNG9mwcWP5V9iyBRo02LG9RQv4+uuqC8xVq1pZfkkx18xWAEj6AHg+ap9PSLwABwBTJO1L6K1/BGBm6yVdDLwMXGlmH6Rs94kowS+StHfUJuBWSd2BrcD+wN7Adv/dYWa5QC6EnnpVHqwrQ6tWxffUW7eGOnitdu8ePYAKXKe+fj20bAn33AOHHRbatm6Fhx6Cvn2rJUZX9Wp7Uk/tgm1Nmd7KD8f2Z+CPZvaUpB7AmJR1OhJKMvuVst3C3vhAYE+gi5ltlrQEaFy58F2VGjt2+5o6QEZGaK+DbrzxxoqtkJERyi0nnQRDh8IBB4TzFN99B0OGVEeIrhrU9vJLeewGfBK9Pq+wUVJr4CqgM9Bb0lHl2M7nUUI/AWhdHcG6Shg4EHJzQ89cCs+5uXXyJCnA8ccfz/HHH1+xlQYPhunTw+WML70Upl98EZo0qZ4gXZWrrT31ipQ1xgD/kPQJMAdoE9XCHwRGmNmnki4EJkrqWsp28oBpkvKBAuDdnYrcVa+BA+tsEi9q8eLFALRr165iKxaeIHW1Uq27+kXSHsB/zKzG95T96hcXpx4Vram7WqO0q19qVU9d0n7ADGB8zKE4V+PdeuutcYfgYlCrkrqZfQocEnccztUGxx7r/6NXF9WFE6XO1UkLFixgwYIFcYfh0qxW9dSdc+V32WWXAV5Tr2s8qTuXULfffnvcIbgYeFJ3LqG6di3tCl2XVF5Tdy6hCgoKKCgoiDsMl2beU3cuoYYPHw54Tb2u8aTuXEJN8P8KrZM8qTuXUFlZWXGH4GLgST3NNm/ezPLly9lYkXGu66jGjRtzwAEH0KC4Mb5dmebOnQv4CdO6xpN6mi1fvpxmzZqRmZlJMffYcBEzY82aNSxfvpw2bdrEHU6tNHLkSMBr6nWNJ/U027hxoyf0cpDEHnvsgd89aufdfffdcYfgYuBJPQae0MvH36fK6dChQ9whuBj4dep13JgxYxg/vuRBL1etWsVRRx1F586dmTVrVoW3P3HixG3/rv7EE0+waNGinY7VVczs2bOZPXt23GG4NPOk7kr1r3/9i/bt2/Pmm2/SrVu3Sm3Lk3p6XXvttVx77bVxh+HSzJN6HTR27FjatWtHz549t90d54MPPqBXr1506dKFbt268e6771JQUMDVV1/NM888Q1ZWFhs2bODSSy8lOzubww47jN/97nfbtpmZmcnq1asByM/P33aDhkKzZ8/mqaeeYuTIkWRlZfHBBx/gqtd9993HfffdF3cYLs08qcesR48eTJw4EQiXO/bo0YNJkyYBsH79enr06MGUKVMAWLt2LT169GDq1KkArF69mh49ejBt2jQAPvvsszL3N2/ePCZPnsybb77J1KlTt132lpOTw5///GfmzZvH+PHjGTp0KFlZWdx0000MGDCAgoICmjRpwtixY8nPz+ftt99m5syZvP322+U6zmOPPZa+ffty++23U1BQwIEHHlih98lVXLt27Sp+K7uVK2HSJJg6dfsbeLtaw5N6CkmZkhI9APWsWbM488wzycjIYNddd6Vv375s3LiR2bNnc84555CVlcUll1zCihUril3/0Ucf5YgjjqBz584sXLgweeWUvDzIzIR69cJzXl7cEe20mTNnMnPmzPKvcNdd0L49PPkk3HtvOP6KrO9qBL/6JWap1xA3aNBgu+mMjIztpnfbbbftplu0aLHd9D777FOufRa9qmTr1q00b968zMGfPvroI8aPH8/cuXP58Y9/zJAhQ7b9E9Uuu+zC1q1bAWrvP1bl5UFOzg891KVLwzTUyptZF5bHynWd+rx5MG4cvPUWtGoV2l58Ec45J7wPTZpUX6CuStWopC7p98BSM/tLND0G+AaoDwwGtgLPmtkoSTOAN4EuwJ7AL4FrgI7AFDO7LtrGE0BLoDHwJzPLldQaeBE4BvgCmAncDLwH1Jd0P3As8AlwhpltkHQxkAM0BP4LDDazWvf9tHv37gwZMoRRo0bx/fffM23aNC655BLatGnDP/7xD8455xzMjLfffptOnTptt+7XX3/Nj370I3bbbTdWrlzJs88+u612npmZybx58+jduzePP/54sftu1qwZ33zzTXUf4s4bPXrHksP69XDhhXD//fHEVAnPFn64Fjm/UawPPoCGDeGXvwzTWVkwYQIcfjg89xz061c9QboqV9PKL5OBASnTPwNWA/2Ao8ysE/CHlPnfmVl34F7gSWAY0AEYImmPaJkLzKwLkA1cLmkPM1sK/D5a7ypgkZk9Hy1/MHCPmR0GfAX0j9qnmlnXKIZ3gAuLOwBJOZLyJeXXxH+cOeKIIxgwYABZWVn0799/2xUteXl5PPjgg3Tq1InDDjuMJ598cod1O3XqROfOnTnssMO44IIL+OlPf7pt3u9+9zuuuOIKunXrRv369Yvd97nnnsvtt99O586da+aJ0mXLim/ftCm9cVSRJo0b06Rx4/ItvGUL7FJMH2/XXb22XtuYWY16EBLmfkAn4FXgDuDiYpabAfw0en0i8ELKvJeBrOj1GOCt6LEWODplueeAD4Bm0XQm8H7K/N8C10WvjwdmAfOBj4B7yzqWLl26WFGLFi3aoc2VLK3vV+vWZrDjo3Xr9MVQhV544QV74YUXyrfwtGlmHTuabdjwQ9tHH5nttpvZypXVEZ6rBCDfSsg7Nar8EnkMOBvYh9BzbwNYCcsWdqG2prwunN5FUg+gJ3CMma2PSjaNASRlAAdEyzcllHkosp0tQGExcSLQz8zekjQE6FHRA3M13Nix29fUATIyQnstdMsttwDQs2fPshc+7TT4v/+DI4+EIUNg7VrIzQ3Hvtde1Ruoq1I1MalPBu4HWhB6x52AGyT9PUrMu5vZF+Xc1m7Al9F67YGjU+b9HsgDlkb7O72MbTUDVkhqAAwk1NtdkhSeDB09OpRiWrUKSa0WniQFeOSRR8q/cL168MgjMH06PP00/OhH8OyzobbuapUal9TNbKGkZsAnZraCkEizgHxJ3wHPAOX9N7npwK8kvQ0sBuYASDoe6Eoo32yR1F/S+cBLpWzreuB1wofAfEKSd0kzcGCtTeJFtWzZsmIr1KsXeuynnVY9Abm0UCjPuOqQnZ1t+fn527W98847HHrooTFFVPv4+7Xzpk+fDkCvXr1ijsRVNUnzzCy7uHk1rqfunKsa48aNAzyp1zWe1J1LqMmTJ8cdgotBTbtO3aXBkiVLih1r+4YbbuDFF18sdd2yhurdWUOGDOGxxx6r8u3WZfvss0+5/8vYJYcn9ZoujWOR3HTTTeW7/M3VCtOmTds22JurOzyp12SFY5EsXRr+DaZwLJIqSOxbtmzh4osv5rDDDuOUU05hw4YN2/WWn3nmGdq3b89xxx3H5Zdfzumn/3DF56JFi+jRowdt27blrrvu2mHb77zzDkceeeS26SVLlnD44YcD4YOja9eudOjQgZycHPxEffW54447uOOOO+IOw6WZJ/WarKSxSEaPrvSm33//fYYNG8bChQtp3rz5duO1bNy4kUsuuYRnn32WV155ZYf7hL777rs899xzvPHGG9x4441s3rx5u/mHHnoo3333HR9++CEAU6ZM4Wc/+xkAl112GXPnzmXBggVs2LCBp59+utLH4or32GOPeUmrDvKkXpOVNBZJSe0V0KZNG7Kifyzp0qULS5Ys2Tbv3XffpW3btrRp0waAn//859ut+z//8z80atSIFi1asNdee7Fy5codtv+zn/2MRx99FAhJfcCAMKTPSy+9xFFHHUXHjh3597//zcKFCyt9LK54LVq0oEWLFnGH4dLMk3pNVjgEannbK6BRo0bbXtevX5/vv/9+23RZJZHi1r3nnnvIysoiKyuLTz/9lAEDBvDoo4/y3nvvIYmDDz6YjRs3MnToUB577DHmz5/PxRdfXHuH6a0Fpk6duu2GKq7u8KRek40dG8YeSZWGsUjat2/Phx9+uK33XnjnpdIMGzaMgoICCgoK2G+//TjwwAOpX78+N99887ZeemECb9GiBevWrfPSQDW76667ij3n4ZLNr1OvyWIai6RJkyb85S9/oVevXrRo0WK7k54VMWDAAEaOHMlHH30EQPPmzbn44ovp2LEjmZmZdO3atSrDdkUUN3yySz4fJqAa1eZhAtatW0fTpk0xM4YNG8bBBx/MlVdemfY4asv75Vw6lTZMgJdfXLHuv/9+srKyOOyww1i7di2XXHJJ3CG5CpoyZUq5SmcuWbz84op15ZVXxtIzd1Xnr3/9K8C2cxqubvCk7lxCPfPMM3GH4GLgSd25hMooeuWUqxO8pu5cQk2aNIlJkybFHYZLM++pO5dQDzzwAACDBg2KORKXTp7U66CmTZuybt262PY/ZMgQTj/9dM4+++zYYqgLXnjhharb2KJFMHMm7LEH9OkDTZqUvY6LRZ0rv0hal/L6NEnvS2olaR9JkyV9IGmRpGckHSIpU5JJ+nXKendLGpKWgL/7DubOhcWL07I7V05pHBJ5ZzVo0IAGDRpUbiNmMGwYnHQS/Oc/8OCDcNBB8OabVROkq3J1LqkXknQS8GegF/Ax8E9ghpkdaGY/Idzceu9o8c+BKyQ1TGuQjz4a/ov0oovCH9Wxx0LKwFuVZWaMHDmSDh060LFjx23XNA8dOpSnnnoKgDPPPJMLLrgAgAcffJDrrrtuu23UyWF2q3FI5Ko0ceJEJk6cWLmNPPoovPYavPce3H8/PPccjB8PP/95OHZX49TJ8oukbsD9wGlm9oGkE4HNZnZv4TJmVhAtmwmsAl4FzovWq35vvQW//jU88wx06QJbtsAdd0C/fqGXJFV6F1OnTqWgoIC33nqL1atX07VrV7p370737t2ZNWsWffv25ZNPPmHFihUAvPLKK5x77rnbbSN1mN22bdvuMMzuDTfcAMDgwYN5+umn6dOnT6Xjjl1JQyJfeGFIfDVEVkFBeFGZxL5gAey5Zyi5AGRlwZ13wpgx4ffwiCMqF6SrcnWxp94IeBLoZ2bvRm0dgHllrDcOuEpS/dIWkpQjKV9SftFxyCvkwQfhsstCQgeoXx9GjgzlmNdf3/ntpnjllVf4+c9/Tv369dl77705/vjjmTt3Lt26dWPWrFksWrSIn/zkJ+y9996sWLGC1157jWOPPXaH7dS5YXZLGvp406b0xlGGwlEzK8Us/O6lkkJN/bvvKrdtVy3qYk99MzAbuBC4orwrmdlHkt4AflHGcrlALoSxX3Y6ys8/h6IDaUmhflvM+OU7o6RyyP7778+XX37J9OnT6d69O1988QWPPvooTZs2pVmzZtxzzz3cH/VIn3nmGQYMGMA555zDWWedtcMwu/n5+bRs2ZIxY8YkZ5jdVq1CyaWo1q1hxoy0h1Ot7rsvlGCeew52idLFzJmwejVkFzv0iItZXeypbwV+BnSVdG3UthDoUo51bwV+Szret+7dwx9TauJduRJmz4ajj66iXXRnypQpbNmyhVWrVvHyyy9vq48fc8wxTJgwge7du9OtWzfGjx9Pt27dAB9mN64hkWNx/vnQqFHoYIwbB5deCv37h2+Su9TFPmHNVxeTOma2HjgdGCjpQuDfQCNJFxcuI6mrpOOLrPcusChat3qddx4sXx5OSE2fDn/7G/ToAVdeCXvvXebq5XHmmWdy+OGH06lTJ0488UT+8Ic/bLv7fLdu3fj+++856KCDOOKII/jiiy+2JfXiDBgwgEmTJm2rp6cOs9uvX79kDbM7cCDk5oaeuRSec3OrfUjkWDRsCNOmwY03ht5569ZQUACnnhp3ZK4EdW7oXUnrzKxp9Lol8DIwHJgLTCD02DcCS6L2zcDTZtYhWqcT8CZwgZlNLG1flR569+uv4a9/DUl9t91Cr6lv3yo5SVpb+NC7zu2otKF369z3p8KEHr3+GGiTMvtnJazWIWWdt0jXN5xdd4Xf/jY8nHOuHOpk+cU555LKk7pzziWIJ/UY1LXzGDvL3yfnKs6Tepo1btyYNWvWeMIqg5mxZs0aGjduHHcoztUqde5EadwOOOAAli9fTqX+27SOaNy4MQcccEDcYThXq3hST7MGDRrQpk2bshd0zrmd4OUX55xLEE/qzjmXIJ7UnXMuQercMAHpJGkVUMxwflWiBbC6mrZdUTUpFqhZ8XgsxatJsUDNiqc8sbQ2sz2Lm+FJvZaSlF/S2A/pVpNigZoVj8dSvJoUC9SseCobi5dfnHMuQTypO+dcgnhSr71y4w4gRU2KBWpWPB5L8WpSLFCz4qlULF5Td865BPGeunPOJYgn9VpA0kOSPpe0IKXtHEkLJW2VlLaz9iXEcrukdyW9LemfkprHGMvNURwFkp6XtF86YikpnpR5IySZpBZxxSJpjKRPovemQNJpccUStf9a0uLo9/gPccUiaUrKe7JEUkE6YiklnixJc6J48iUdWdo2ivKkXjtMBHoVaVsAnEW4HV/csbwAdDCzw4H3gGtijOV2MzvczLKAp4Eb0hRLSfEU3jbxZGBZ3LEAd5pZVvR4Jq5YJJ0AnAEcbmaHAePjisXMBhS+J8DjwNQ0xVJsPMAfgBujeG6IpsvNk3otYGYvA18UaXvHzBbXkFieN7Pvo8k5QFqGViwhlq9TJn8EpO2kUXHxRO4Erq4hsaRdCbFcCowzs03RMp/HGAsAkkS4peX/pSOWUuIxYNfo9W7ApxXZpid1V9UuAJ6NMwBJYyV9DAwkvT314mLpC3wS3du2JrgsKk89JOnHMcZxCNBN0uuSZkrqGmMshboBK83s/ZjjGA7cHv0Oj6eC33w9qbsqI2k08D2QF2ccZjbazFpGcVwWVxySMoDRxPzBkuKvwIFAFrACuCPGWHYBfgwcDYwEHo16ynH6OWnspZfiUuDK6Hf4SuDBiqzsSd1VCUnnAacDA63mXCf7d6B/jPs/EGgDvCVpCaEs9R9J+8QRjJmtNLMtZrYVuB+o0Am4KrYcmGrBG8BWwpgnsZC0C+Ec1ZS4YkhxHj/U9f9BBX9OntRdpUnqBfwW6Gtm62OO5eCUyb7Au3HFYmbzzWwvM8s0s0xCIjvCzD6LIx5J+6ZMnkk42R6XJ4ATASQdAjQk3gG1egLvmtnyGGMo9ClwfPT6RKBi5SAz80cNfxC+Eq4ANhMSw4WEP8rlwCZgJfBcjLH8F/gYKIge98YYy+OEZPU2MA3YP86fU5H5S4AWMb43jwDzo/fmKWDfGGNpCEyKflb/AU6M82dEuArlV+n6XSnjvTkOmAe8BbwOdKnINv0/Sp1zLkG8/OKccwniSd055xLEk7pzziWIJ3XnnEsQT+rOOZcgntSdcy5BPKm7xJC0t6S/S/pQ0jxJr0k6M5rXQ9JaSW9KekfS70prr4kkbYmGY90vml4iab6kt6Jhhsv8T1VJv5S0IBrudpGkESnzjpF0fwXi6RZtI85/YnJFeFJ3iRCNG/IE8LKZtTWzLsC5bD9i5Cwz6wxkA4MkdSmjvabZYGGI2NRR+04ws05APnBtaStL6k0YLOoUC8PdHgGsTVmkFzC9PIFI2sXMZgFpGZPdlZ8ndZcUJwLfmdm9hQ1mttTM/lx0QTP7lvAfeweWp72QpCGSnpA0TdJHki6T9Juolz9H0u7RcgdKmh59W5glqX3U3icalfBNSS9K2jtqHxONmjgj+pZx+U4c/8vAQdH2LpT0XrS9+yXdHS1zDTCi8EPBzDaaWWrP/CTgxSjmrJTjflXS4VGcuZKeB/62EzG6NPCk7pLiMMK/m5dJ0h6E0QEXlqe9iA7ALwiDLI0F1ke9/NeAX0bL5AK/jr4tjAD+ErW/AhwdLT+ZMMZ6ofbAqdF2fyepQXmOJcXpwPyoNHN9dBwnR9tNjX1ecSsr3JFps5mtBR4AhkTthwCNzOztaNEuwBlm9osKxufSZJe4A3CuOki6hzCGxndmVjhWdzdJbxJGBBxnZgsl9SiuvZRNv2Rm3wDfSFpLGF8Gwpgqh0tqChwL/CNlJNlG0fMBwJRoYK2GwEcp2/1/Fm4YsUnS58DehLFAyvKSpC2E8VyuA3oAM83si+h9+Adh7PKynAI8H73+B3C9pJGE8fEnpiz3lJltKMf2XEw8qbukWEjKMLtmNizqfeanLDPLzE4vZt2S2ouzKeX11pTprYS/p3rAVxZuRVbUn4E/mtlT0YfJmBK2u4Xy/22eYGbbRjcsY0zyhYSe9r+Lmdcb+COAma2X9ALhdnM/I5xrKPRtOeNyMfHyi0uKfwONJV2a0paR7iAs3E7vI0nnQEiykjpFs3cDPolen1dNIbwBHC/px9EY4anjyd8G/KHwKhlJjSRdHn0QHE4YYbPQA8BdwNzCXr+rHTypu0SwMNxoP0JC+0jSG8D/EsZ5T7eBwIWS3iL0js+I2scQyjKzqKaxw83sE+BWwpCtLwKLiK5wsXCj6XsIJ0MXEurruxB6729aypCtZjYP+Bp4uDridNXHh951rpaQtM7MmpZjuaZmti7qqf8TeMjM/lnK8tcB/zWzySlt+wEzgPYW7pRU0rqZwNNm1qH8R+Kqk/fUnas9vk7956NSjJFUQLgBxUeE6/dLZGa3FEnovyT09EeXkdC7EU4Ux3nHIleE99SdK0LSqcDvizR/ZGZnxhGPcxXhSd055xLEyy/OOZcgntSdcy5BPKk751yCeFJ3zrkE8aTunHMJ8v8BevP5E069dOEAAAAASUVORK5CYII=\n", + "image/png": 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2dB8ZGRmWm5tbcUE55wp5+OGHAbj99tsjjsRVJElzzCyjLOtG/ju68EfdXxBc4JI0Sc45lxzeffdd3n333ajDcBGK/BZg4Q+/W0Qdx54ws6UEv/FzzlVTr732WtQhuIhFPqJzzjnnKpMnOudcrA0bNoxhw3b701YXc5FPXTrnXGX66KOPog7BRcwTnXMu1l566aWoQ3AR86lL55xzseaJzjkXaw888AAPPPBA1GG4CPnUpXMu1vLy8qIOwUXME51zLtbGjBkTdQguYj516ZxzLtY80TnnYm3IkCEMGTIk6jBchHzq0rlksGkTTJ4c1Pk76yxo3DjqiJLGZ599VvpKLtZiO6KTNL0S9z1K0mUlvHe3pC8kfSbp3MqKwdUg06ZB8+YwbBjk5MBRR8FTT0UdVdIYPXo0o0ePjjqM5JGTA2lpUKtW8JyTE3VEey22Izoz61TVxwwrMVwBtAYOByZLOqYCa+y5mmbjRrjsMnj+eTg3/N60ZAmcdhqceiq026V+sHN7LicHsrKCmQOAZcuCZUjqGpOxTXSSNhAUf70PWAWkA+OBecDtwH7ARWb2paQLgHuAfYG1QKaZrZL0CLDGzAaHo7MBQNfwEN0k3Q4cAvzWzCYAFwJjzGwzQUXzL4CTAL8HkdszkyZBmzZBkuvbFwoula9bF7p3h5Yto4wuKXy1dCkAzdPSIo0jKcyYAZs3F27Lz4cBA5I60cV26jJBO4LE1pagevgxZnYS8DRwa7jONOAUM2tPUHn8rrD9d0BPSWcAjwDXmtn28L00oAvwP8BTkuoBRwBfJxx7RdhWiKQsSbmSclevXl1hHXUx9OOP0KjRru377APbt+/a7naxedMmNm/aFHUYyaFokiuwfHnVxlHBYjuiSzDbzFYCSPoSeDtsnwecEb4+Ehgr6TCCUd1XAGaWL+lG4EPgDjP7MmG/48Kk97mkJcCxgIo5/i4l3M0sG8iGoML4XvbPxdlZZ8Ett8DXX8Pw4UHb5s1w8snBcvfuUUaXFI6NOoBkkpYWTFcWlZpa5aFUpJowokv8irI9YXk7OxP9o8BjZtYWuAmol7BNW4LpzMOL7LdogjKCEVzThLYjgW/3OHLnDj0UBg6EU06B+++HRx8NXrdqBeefH3V0Lm6GDoWUlMJtKSlBexKrCYmuLBoC34Svry5olNQMuBNoD/xC0skJ21wuqZaklgQV0j8DXgOukFRXUnPgaGBWVXTAxVjfvvDyy7B6NcydC4MGwT/+EVwV50p19913c/fdd0cdRnLIzITsbGjWDKTgOTs7qc/PQbynLsszJTgIeEHSN8AMoLkkAc8A/czsW0nXA6MkdQy3+QyYQnAxym/MbBOwQNI4YCGwFejjV1y6CnHSScHDldvatWujDiG5ZGYmfWIrSmbxO0UkqTHwsZk1izqW0mRkZFhubm7UYTjnXFKRNMfMMsqybuzmPiQdTnA5/7CoY3HOORe92E1dmtm3wDFRx+Gcqx769esHwLBh/t23popdonPOuUQbN26MOgQXMU90zrlYe/zxx6MOwUUsdufonHPOuUSe6Jxzsda3b1/69u0bdRguQp7onHPOxZqfo3POxdrwgnuEuhrLR3TOOedizROdcy7W+vTpQ58+faIOw0XIpy6dc7G23377RR2Ci5gnOudcrPkdUZwnOueci8rHHwePFi2ga1cvvVRJkvJTlfSBpIzw9VJJTSpw34Mk9auo/bkklJMTVFquVSt4zsmJOiK3F7KyssjKyoo6jMI2b4aLL4ZLLoF//hN++1vIyIBVq6KOLJZ8ROdcopwcyMqC/PxgedmyYBliV6OrpmjcuHHUIezqwQdh61b4/HOoUwfM4O67oU8fePHFqKOLnSpLdJLSgLeAacApwKfAc8B9wMFAJrAAeBRoG8Y2yMxelbRfuO7xwCJgl7PLkoYAa8zs4XB5KLAKmEtQWHUN0AaYA/QyM5P0ANCDoEjq22bWr8g+04GngBTgS+A6M/tO0gdAHnAScEDYPkvS/sXFvxcfm6tqAwbsTHIF8vPh+uth5MhoYnJ75X8LXnTtGmEURcyaBccdB2efHSynp8OQIXDYYfDjj7D//pGGFzdVPaI7CrgcyAJmA78Cfk6QbH5PUJn7PTO7TlIjYJakycBNQL6ZnSDpBODjYvb9DDAeeFhSLeAKgkTUFmgPtAa+Bf4JnCZpIXAxcGyY9BoVs8+/Area2RRJg4GBQN/wvf3NrJOk04FnCZLogOLiN7MfE3cqKSv8DEhNTS3zh+eqwPLlxbdv3ly1cbh4274datcu3FavXvC8ZUvVxxNzVZ3ovjKzeQCSFgDvhklmHpAGHAn0SDhHVg9IBU4HHgEws7mS5hbdsZktlbRWUnvgEOATM1srCWCWma0Ij5sXHmsGsAl4WtIbwITE/UlqCDQysylh0/PACwmr/CM87oeSDggT2zklxL+oSKzZQDYEFcbL8Lm5qpKaGkxXFtWsGXzwQZWH4/betddeC8Bzzz0XcSQJbrklOAf8yCM72555Btq3h0aNIgsrrqo60SV+Ld6esLw9jGUbcKmZfZa4UZisypIQngauAQ4lGGUVd9xtwD5mtlXSScBZBKO/W4Azy9qRYuIxQMXF75LI0KGFz9EBpKQE7S4pNW3aNOoQdnXvvdClC1xwAZx7LnzyCbzxBkyaFHVksVTdrrqcBNyqMLOFozOADwnO4SGpDXBCCdu/DJwHdAz3VSJJ9YGGZjaRYDoyPfF9M1sPfCepc9h0FTAlYZWe4X5+DqwP1y8pfpcsMjMhOzsYwUnBc3a2X4iSxAYPHszgwYOjDqOwgw+G3Fy48EJYtCg4XzdvHrRrF3VksVTdrrocAgwH5obJYinQHXgSeC6csswDZhW3sZn9JOl94L9mtq2UYzUAXpVUj2Akdkcx61wNPCUpBVgCXJvw3neSphNejFJK/C6ZZGZ6YnOVb//94YYboo6iRpBZfE4RhRehfAxcbmafV+JxPgD6mVnu3u4rIyPDcnP3ejfOuRL06tULgNGjR0cciatIkuaYWUZZ1q1uI7o9Jul4ggtKXq7MJOecSy6tWrWKOgQXsViN6JKRj+icc678yjOiq24XozjnnHMVyhOdcy7WrrjiCq644oqow3ARis05OuecK056enrUIbiIeaJzzsXa7373u6hDcBHzqUvnnHOx5onOORdrl156KZdeemnUYbgI+dSlcy7WTj311KhDcBHzROeci7V+/fqVvpKLNZ+6dM45F2ue6JxzsdajRw969OgRdRguQj516VwUli2DN98MqkpfeCH87GdRRxRbZ511VtQhuIjFdkQXltCprH2PknRZMe2NJb0vaYOkxyrr+C7JPfggnHgizJwJEyZAy5ZB0U1XKW6//XZuv/32qMMoLCcH0tKCKuNpacGyqzSxHdGZWacIDrsJ+APQJnw4V1huLjzyCMyfD4cdFrTNnAnnnw9Ll0KDBpGG56pATk7hKvbLlgXL4HUQK0lsE52kDQRFT+8DVhFUEB8PzANuB/YDLjKzLyVdANwD7AusBTLNbJWkR4A1ZjZY0rnAAKBreIhukm4HDgF+a2YTzOxHYJqko6qqny7JjBkDN94YJLm+fSEvb+d7J58cVJ52FWru3LkAnHDCCRFHEpoxAzZvLtyWnw8DBniiqySxTXQJ2gHHAesIqoQ/bWYnhUnqVqAvMA04xcxM0g3AXcCdwO+A2ZKmAo8A55vZ9qB4OGlAF6Al8L6ko8xsU1kCkpQFZAGkpqZWVD9dMtiyJTgvV1StWuAlsypF4yZNog6hsKJJrsDy5VUbRw1SExLdbDNbCSDpS+DtsH0ecEb4+khgrKTDCEZ1XwGYWb6kG4EPgTvM7MuE/Y4zs+3A55KWAMcCeWUJyMyygWwI6tHtRd9csrnoIrjpJujdG4YPD9q++AI6doSpU6G6/VGOgSOiDqCotLRgurIo/9JbaWJ7MUqCxK9P2xOWt7Mz0T8KPGZmbYGbgMSv3G0JpjMPL7LfognKE5YrXdeucPbZkJ4OgwbBnXdCp04wbJgnuZpi6FBISSnclpIStLtKURMSXVk0BL4JX19d0CipGcEUZnvgF5JOTtjmckm1JLUEWgCfVVWwLolJ8Nhj8Ne/wqZNcMABMG0aXH991JHFVrdu3ejWrVvUYeyUmQnZ2dCsWfDfQ7NmwbKfn6s0cZ66LM8IaxDwgqRvgBlAcwUn4p4B+pnZt5KuB0ZJ6hhu8xkwheBilN8UnJ+TtBQ4ANhX0kXAOWa2sAL64+JCgtNOCx6u0vXs2TPqEHaVmemJrQrJYngCXFJj4GMzaxZ1LKXJyMiw3NzcqMNwzrmkImmOmWWUZd3YTV1KOhz4CBgWdSzOOeeiF7upSzP7Fjgm6jicc9VD165dAfjggw8ijcNFJ3aJzjnnEl1zzTVRh+Ai5onOORdrnuhc7M7ROedcoi1btrBly5aow3AR8hGdcy7Wzj77bMDP0dVknuicc7F2ww03RB2Ci5gnOudcrPXq1SvqEFzE/Bydcy7W8vPzyS+o/eZqJB/ROedi7fzzzwf8HF1N5onOORdrN998c9QhuIh5onPOxVq1vKmzq1Ke6JyrbjZtgpdfhq+/hpNOgi5dgooHbo+sX78egIYNG0YciYuKX4ziXHXyr39Bq1YwahSsWhVUIv/FL4Lk5/bIhRdeyIUXXhh1GMXLyQkqjteqFTzn5EQdUSzFJtFJaiSpd/i6q6QJ5dz+mrDyQUXFc7ikFytqf66GuPFG6NcPJk2Chx6CuXOhbl34y1+ijixp3Xbbbdx2221Rh7GrnBzIyoJly8AseM7K8mRXCWJTj05SGjDBzNpI6kpQMLV7Obb/INymSovDeT06t8OqVXDcccFz//6Qlxe0r18Pn38OGWUqveWSxYwZsHnzru3NmsHSpVUeTrKpqfXoHgBaSsoD/gzUl/SipMWScsKK4UjqIGmKpDmSJkk6TNJlQAaQIylP0n6S7pU0W9J8SdkF2xdH0lGSJkv6VNLHklpKSpM0v4T1syTlSspdvXp1xX8SLjlt3x6ciyv6n5qfn9sr1fZel8UlOYDly6s2jhogziO6V4HWwLfAP4H+wExgCnChma2W1BM418yuKzqik3Sgma0LX/8NGGdmr5dw7JnAA2b2sqR6BF8gDi6IZ3dx+4jOFXLqqXDDDXD99cHy9u1w5ZVw/PEwcGC0sSWpaluPLi0tmK4sykd0ZVKeEV2cr7qcZWYrAMJRXhrwX6AN8E44QKsNrCxh+zMk3QWkAAcCC4BdEp2kBsARZvYygJltCtsrrieu5sjOhnPPhTffhDZt4I03oF49ePbZqCNLWnfeeWfUIRRv6NDgnFziXVtSUoJ2V6HinOgS5wW2EfRVwAIzO3V3G4ajsieADDP7WtIgoF5Jq1dArM4F2raFxYth7Njg5wX33gvnnw+1a0cdWdK64IILog6heJmZwfOAAcF0ZWpqkOQK2l2FiVOi+wFoUMo6nwEHSTrVzD6SVAc4xswWFNm+IKmtkVQfuAwo9gpKM/te0gpJF5nZK5LqEowUndszBxwQXH3pKsS///1vAA499NCIIylGZqYntioQm0RnZmsl/TO8AGQjsKqYdX4KLzx5RFJDgv4PJ5iWHAU8JWkjcCowEpgHLAVml3L4q4ARkgYDW4DLge1APE6AOpfErrjiCqAanqNzVSY2F6NUN5I6AP9nZl12t55fjOJc5XrrrbcAOO+88yKOxFUkvxglYpIygL8Dv4s6FudqOk9wzhNdOUh6HDitSPPDZvZcYkP4E4Vjqiww51yJvv76awCaNm0acSQuKp7oysHM+kQdg3OufK666irAz9HVZJ7onHOxds8990QdgouYJzrnXKx169Yt6hBcxOJ0r0vnnNvFkiVLWLJkSdRhuAj5iM45F2vXXXcd4OfoajJPdM65WLvvvvuiDsFFzBOdcy7WunTZ7T0bXA3g5+icc7H22Wef8dlnn0UdhouQj+icc7F20003AX6OribzROecK9nHH8PEibD//tCzJxx+eNQRldsf//jHqENwEatRU5eSMiXNDR/TJbUrx7YTJTWSlBZWSChuneaSZkr6XNJYSftWXPTOVSEz6NsXLrwQ1q+HBQuCWnkvvRR1ZOXWqVMnOnXqFHUY1VNOTlDpvFat4DknJ+qIKkVNG9F9BXQxs+8k/QLIBk4uy4Zmdj6ApEa7We1PwF/MbIykp4DrgSf3LmTnIvDuu0GV8/nzoWHDoK1PHzjrLDjnHGhQWunH6mP+/OB7aZs2bSKOpJrJySlc4XzZsmAZYlcjL2kTnaS7gE1m9oikvwDtzOxMSWcB1wKjgT8SFEFdY2Znmdn0hF3MAI4sy77MrJekpUBBSYh9JD0PtAf+BfyaoAbemcCvwnWeBwbhic4lo5degptuCpJc376Ql7fzvVNPhSZNooqs3LYWxJ6eHmUY1c+MGbB5c+G2/Pyg4nnMEl0yT11+CHQOX2cA9cOK4T8nKJg6ErjUzNoRFEIt6nrgzTLsa2ox27YCss3sBOB7oDfQGPivmW0N11kBHFFc4JKyJOVKyl29enVZ++tc1ZFg+/aoo6gQLVq2pEXLllGHUf0UTXIFli+v2jiqQNKO6IA5QAdJDYDNwMcESaoz8DrwoZl9BWBm6xI3lHQGQaL7eRn2dVsxx/7azP4Zvh4drvN8MesVW9XWzLIJpk3JyMjYZZ0tW7awYsUKNm3aVHzPXSH16tXjyCOPpE6dOlGHEh+XXRZMY11/PQwfHrTl5sK55wYjgfr1Iw2vPA6IOoDqKi0tmK4sKjW1ykOpbEmb6MxsSzideC0wHZgLnAG0BJYCHYvbTtIJwNPAL8xsbRn2tai4wxezvAZoJGmfcFR3JPDtnvRtxYoVNGjQgLS0NCTtyS5qDDNj7dq1rFixgubNm0cdTnyccQZcfDG0bg2XXw7ffQcTJsCoUUmV5ADywqnLdJ+6LGzo0MLn6ABSUoL2mEnmqUsIphz7hc9Tgd8AeQTJqouk5gCSDgyfU4HxwFVm9q+y7MvMihuVpUo6NXx9JTAtXO994LKw/Wrg1T3p1KZNm2jcuLEnuTKQROPGjX30W9Ek+POfYdIkOOIIOPlkWLwYevSIOrJy69u3L3379o06jOonMxOys6FZs+Dfu1mzYDlm5+cgiUd0oanAAOAjM/tR0iZgqpmtlpQFjJdUC/gPcDZwL8G5tCfCJLLVzDJ2t68ixytIeouAqyWNAD5n5wUn/w8YI+l+4BPgmT3tmCe5svPPqhK1bRs8ktjwgqlXt6vMzFgmtqKSOtGZ2btAnYTlYxJev8nOi00K2m4AbijvviTVBhoA34fTnceXsI8lwEl70hfnXOXwKUuX7FOXVWUB8LSZbYk6kKgMGjSIYcOGlfj+6tWrOfnkk2nfvj1TpxZ3oerujRo1iltuuQWAV155hYULF+5xrM4lmj17NrNnz446DBehpB7RVRUzOzbqGKq7d999l2OPPZbnny/u4tPyeeWVV+jevTvHH1/swNm5cunfvz/g97qsyXxElwS6du3KqFGjgOCnB127dmX06NEA5Ofn07VrV8aOHQvA+vXr6dq1K+PHjwdgzZo1dO3alddffx2Af//732U+7tChQ2nVqhXdunXbcff3L7/8kvPOO48OHTrQuXNnFi9eTF5eHnfddRcTJ04kPT2djRs3cvPNN5ORkUHr1q0ZOHDgjn2mpaWxZs0aAHJzc+natWuhY06fPp3XXnuN/v37k56ezpdffln+D8y5BI899hiPPfZY1GG4CPmIzhVrzpw5jBkzhk8++YStW7dy4okn0qFDB7Kysnjqqac4+uijmTlzJr179+a9995j8ODB5Obm7viDMnToUA488EC2bdvGWWedxdy5cznhhBNKPW6nTp3o0aMH3bt357LLLit1fedK47f+cp7okkDilEudOnUKLaekpBRabtiwYaHlJk2aFFo+9NBDy3TMqVOncvHFF5OSkgJAjx492LRpE9OnT+fyy3feaGZzCXdXGDduHNnZ2WzdupWVK1eycOHCMiU65yra9OnBnf/8xs41lyc6V6Kil+1v376dRo0a7fgBbkm++uorhg0bxuzZs/nZz37GNddcs+N3bvvssw/bw1tL+W/fXFX4/e9/D/g5uprMz9G5Yp1++um8/PLLbNy4kR9++IHXX3+dlJQUmjdvzgsvvAAEdyX59NNPd9n2+++/Z//996dhw4asWrWKN9/c+SuPtLQ05syZA8BLJZR8adCgAT/88EMl9MrVRCNGjGDEiBFRh+Ei5InOFevEE0+kZ8+epKenc+mll9K5c3DP65ycHJ555hnatWtH69atefXVXW/+0q5dO9q3b0/r1q257rrrOO2003a8N3DgQG6//XY6d+5M7dq1iz32FVdcwZ///Gfat2/vF6O4vdaqVStatWoVdRguQir+DleuqmRkZFhubm6htkWLFnHcccdFFFFy8s/MlWTKlCkAdOnSJeJIXEWSNCfhzla75efonHOxVvDzFj9HV3N5onPOxdqzzz4bdQguYp7onHOx1qJFi6hDcBHzROdcdbJuHfz97/D110FpnAsuAC8ou1cmT54MQLdu3SKOxEXFr7rcS5Juk7RI0j8kTZaUJ6mnpM6SFoTL+0Udp0sCn3wCxx8PH30EDRvCsGHQpQts2BB1ZEnt/vvv5/7776/6A+fkBFW8a9UKnnNyqj4GB/iIriL0Bn4BHAL8yczSASQ9BQwzs+cijM0lCzO48cag2OlVVwVtd98NV14ZJLxBgyINL5n97W9/q/qD5uQUrt69bFmwDDWi/lt144luL4TJrAUwETgG2CApj6AQ6y+BcyV1M7PK/S87JwcGDIDlyyE1FYYO3ev/mZYuXUr37t2ZP39+ofZ7772X008/fbfTQIMGDaJ+/fr069dvr2Io6pprronvPTC/+Sb4Y/irX0HfvlBw95nvv4cJE8CvGNxjTaM46IwZUPT2ePn5wf+nnuiqnCe6vWBmv5F0HtAZaAP0M7PuAJJOBSaY2YtFtwurn2cBpKam7l0QVfzNcfDgwRW+Twfssw9s2wbh7dF2MAOvoL5X1q1bB8CBBx5YdQct4R6wLF9edTG4HTzRRcDMsoFsCH4wvlc7GzBgZ5IrUEHfHLdt28aNN97I9OnTOeKII3j11Ve5+eabd4yqJk6cyG9/+1uaNGnCiSeeyJIlS5gwYQIACxcupGvXrixfvpy+ffty2223Fdr3okWLuPrqq5k1axYQjCB79OjB3LlzGTx4MK+//jobN26kU6dOjBgxYpf7bsbOoYdC27bwxBMwfHjQtmUL9OgRTF/edVek4SWzS8JSUFX6O7q0tOBLZ1F7+8XW7RG/GCXZlfQNsQK+OX7++ef06dOHBQsW0KhRo0L3pty0aRM33XQTb775JtOmTWP16tWFtl28eDGTJk1i1qxZ3HfffWzZUrg4+3HHHcdPP/3EkiVLABg7diy//OUvAbjllluYPXs28+fPZ+PGjTuSZ+w98ww8/DCceSbcemtwYcq++wZTmW6PjRkzhjFjxlTtQYcOhbDyxw4pKUG7q3Ke6JJdSd8QK+CbY/PmzUlPTwegQ4cOLF26dMd7ixcvpkWLFjRv3hyAK6+8stC2//M//0PdunVp0qQJBx98MKtWrdpl/7/85S8ZN24cECS6nj17AvD+++9z8skn07ZtW9577z0WLFiw131JCkcdBYsXQ58+0LIl/PWv8MorQbJze+zQQw8tc3mqCpOZCdnZ0KxZMPXcrFmw7OfnIuFTl8lu6NDC5+igwr451q1bd8fr2rVrs3Hjxh3Lpd0jtei2W7du5fHHH2fkyJEATJw4kZ49e3L55ZdzySWXIImjjz6aTZs20bt3b3Jzc2natCmDBg2qWeV89t0XLr006ihi5fXXXwfgggsuqNoDZ2Z6YqsmfES3l8wszczWmNkHBReihO3XFHchSoWL6Jvjsccey5IlS3aM8saOHVvqNn369CEvL4+8vDwOP/xwWrZsSe3atRkyZMiO0VxBUmvSpAkbNmzgxRcr/yN08fbQQw/x0EMPRR2Gi5CP6OIggm+O++23H0888QTnnXceTZo04aSTTtqj/fTs2ZP+/fvz1VdfAdCoUSNuvPFG2rZtS1paGh07dqzIsF0N5F+WnJfpiVgyl+nZsGED9evXx8zo06cPRx99NHfccUcksSTLZ+acqxjlKdPjU5duj40cOZL09HRat27N+vXruemmm6IOybldjB8/nvHjx0cdhouQT126PXbHHXdENoJzrqweeeQRAC655JKII3FR8UTnnIu1V199NeoQXMQ80TnnYq1hw4ZRh+Ai5ufonHOxNnbs2DL9/MXFl4/onHOx9uSTTwLs+K2mq3l8RBcHZjB7Nrz6alDupQLUr1+/Qvazp6655hr//ZOrEBMnTmTixIlRh+Ei5Iku2a1cCaecEvxgfOTI4A74/foFyc85R0pKCilFb7DsahRPdMnu2mvh7LODmwFPmABffAHvvQcVVFXZzOjfvz9t2rShbdu2O8519O7dm9deew2Aiy++mOuuuw6AZ555hnvuuafQPhYtWlTozilLly7lhBNOAIL6dh07dqRNmzZkZWWVeg9N58pr9OjRjB49OuowXIQ80SWzlSuDKcs//AFqhf+UBx4IAwfCqFEVcojx48eTl5fHp59+yuTJk+nfvz8rV67k9NNPZ+rUqQB88803LFy4EIBp06bRuXPnQvvwkjwuSk8//TRPP/101GG4CHmiS2bffw8HHAAJlQIAOPhgWL++Qg4xbdo0rrzySmrXrs0hhxxCly5dmD17Np07d2bq1KksXLiQ448/nkMOOYSVK1fy0Ucf0alTp1324yV59sB//gPDhsFttwUle2pSFYcK9M477/DOO+9EHYaLULVMdJIaSeq9F9v/VtJCSXMlvSupWcJ72yTlhY/X9mDfF0k6PmF5sKRuexrrXjnqqKBiQdHKyc89B+eeWyGHKGkq8YgjjuC7777jrbfe4vTTT6dz586MGzeO+vXr06BBAx5//HHS09NJT0/n22+/pWfPnowbN45//etfu5TkefHFF5k3bx433nhjzSrJsztz5gTnWxcuhObNg6noU06B776LOrKkU6dOHerUqVN5B8jJCSqK16oVPOfkVN6x3J4xs2r3ANKA+Xux/RlASvj6ZmBswnsbyrB97d28Nwq4rKL62qFDBytq4cKFu7SV6I03zA46yOwPfzAbPdrsssvMjj3WbPXqsu+jGPvvv7+Zmb300kt2zjnn2NatW+0///mPpaam2sqVK83M7Oqrr7amTZva559/bjNmzLAjjzzS+vbtW+I+MzIyrFevXvanP/3JzMy+++47O/jggy0/P99++OEHa926tQ0cOHDHvl944YUyx1uuzywZdOxo9re/7Vzevt3suuvM7rorupiS1HPPPWfPPfdc5ex89GizlBSz4PKv4JGSErS7SgXkWhn/zlbX39E9ALSUlAdsATYCa4FWwIdAbzPbLmmDmdUHkHQZ0N2COnDvJ+xrBtCrtANKWgo8C5wDPCapAZAF7At8AVwFpAM9gC6S7gEuBf4ATDCzFyWdBQwj+H3ibOBmM9u8Nx9Eqc4/H6ZMCa64fO01OO00eOaZYEqzAlx88cV89NFHtGvXDkk8+OCDO6o1d+7cmbfffpujjjqKZs2asW7dul3OzyXykjxl9O9/BxcVXXkl9O0LeXlB+4YNwQhv5swoo0s66QWfXwWdty5kxgzYXOR/8fx8GDDAi65WI9WyTI+kNILk0UZSV+At4HhgWfh6RJhYik10Rfb1GPBvM7s/XN4K5AFbgQfM7JWwfSnwhJk9GC43NrO14ev7gVVm9qikUWFsL4bvjQImhI/PgbPM7F+S/gp8bGbDi+lfFkESJTU1tcOyZcsKve8lZ8ovVp/ZunXBdOWqVfC73+1MdOvXw+efQ0aZKpO4qjBlSvHtEmzfXrWx1DDlKdNTXUd0Rc0ysyUAkv4B/Bwo9dfEknoBGUCXhOZUM/tWUgvgPUnzzOzL8L3E+wS1CRNcI6A+MKmUw7UCvjKzf4XLzwN9gOFFVzSzbCAbgnp0pfXD1TAHHgidOgUXogwfHrT99BNceGEwyrvrrkjDcwnS0qDIF1UAUlOrPBRXsmp5MUoxiiYDK6a9XuIK4QUiA4AeidOHZvZt+LwE+ABon7DZjwmvRwG3mFlb4L6i+y+GSnnfubLLzoa//x1OPhluuAGOOQb23z+YynTVx9ChUPTH6CkpQburNqprovsBaJCwfJKk5pJqAT2BaWH7KknHhe0XF6wsqT0wgiDJ/Seh/WeS6oavmwCnAQtLiKEBsFJSHSBxsr1obAUWA2mSjgqXrwJKmNcoXXWcUq6uYvlZNW0K8+bBffdBx44wfjy8+CLsu2/UkblEmZnBl5JmzYLpymbNgmU/P1etVMupSzNbK+mfkuYTXIjyEcEFKm0JLkZ5OVz1dwTnxr4G5hNMMQL8OXz9giSA5WbWAzgOGCFpO0GSf8DMSkp0fwBmEpwXnMfO5DYGGCnpNuCyhJg3Sbo2PGbBxShP7Un/69Wrx9q1a2ncuDFh/K4EZsbatWupV6+0AXcSql0bzjsv6ihcaTIzPbFVc9XyYpRE4cUo/cyse8ShVIqMjAzLzc0t1LZlyxZWrFjhvykro3r16nHkkUdW7m+lnHPVShwvRqlR6tSpQ/PmzaMOwznnYqHaJzoz+4DgohHnnHOu3KrrxSjOOedchfBE55xzLtaq/cUocSdpNcGVnXHSBFgTdRCVKM79874lrzj3r7i+NTOzg8qysSc6V+Ek5Zb1aqhkFOf+ed+SV5z7t7d986lL55xzseaJzjnnXKx5onOVITvqACpZnPvnfUtece7fXvXNz9E555yLNR/ROeecizVPdM4552LNE50rN0nPSvpPWF2ioO1ySQskbZeUUWT9uyV9IekzSedWfcRlV0Lf/ixpsaS5kl6W1CjhvaTpG5TYvyFh3/IkvS3p8IT3kqZ/xfUt4b1+kiwsz1XQltR9kzRI0jfhv1uepPMT3kuavkHJ/3aSbg37sEDSgwnt5eufmfnDH+V6AKcDJwLzE9qOI6iy/gGQkdB+PPApUBdoDnwJ1I66D+Xs2znAPuHrPwF/Ssa+7aZ/ByS8vg14Khn7V1zfwvamwCSCGzM0iUvfgEEElV2KrptUfdtN/84AJgN1w+WD97R/PqJz5WZmHwLrirQtMrPPiln9QmCMmW02s6+AL4CTqiDMPVJC3942s63h4gzgyPB1UvUNSuzf9wmL+wMFV6glVf+K61voL8Bd7OwXxKdvxUmqvkGJ/buZoGbo5nCdgiLa5e6fJzpX2Y4gKIxbYEXYlqyuA94MX8emb5KGSvoayATuDZuTvn+SegDfmNmnRd5K+r6FbgmnnZ+V9LOwLS59OwboLGmmpCmSOobt5e6fJzpX2YorkZ6Uv2mRNADYCuQUNBWzWlL2zcwGmFlTgr7dEjYndf8kpQAD2Jm4C71dTFvS9C30JNASSAdWAg+F7XHoGwRl5H4GnAL0B8ZJEnvQP090rrKtIDhHUuBI4NuIYtljkq4GugOZFp4oICZ9K+LvwKXh62TvX0uCczifSlpKEP/Hkg4l+fuGma0ys21mth0Yyc7pu6TvW2gFMN4Cs4DtBDd3Lnf/PNG5yvYacIWkupKaA0cDsyKOqVwknQf8P6CHmeUnvJX0fQOQdHTCYg9gcfg6qftnZvPM7GAzSzOzNII/kCea2b9J8r4BSDosYfFioOCKxaTvW+gV4EwASccA+xJUMCh3/6p9hXFX/Uj6B9AVaCJpBTCQ4ETyo8BBwBuS8szsXDNbIGkcsJBg2q+PmW2LKPRSldC3uwmu8HonmDlhhpn9Jtn6BiX273xJrQi+MS8DfgOQbP0rrm9m9kxx68ahb0BXSekE03ZLgZsg+foGJfbvWeDZ8CcHPwFXh7Mp5e6f3wLMOedcrPnUpXPOuVjzROeccy7WPNE555yLNU90zjnnYs0TnXPOuVjzROdcNSfpEEl/l7RE0hxJH0m6OHyvq6T1kj6RtEjSwN21F9lvwToTw+U0SRvDO+EvlPSUpN3+jZBUR9IDkj6XNF/SLEm/SHj/bkmZ5ejrHZKWS3qsrNs4VxpPdM5VY+Etj14BPjSzFmbWAbiCnTeWBphqZu2BDKCXpA6ltFNk2/MTlr80s3TgBIK7xF9USohDgMOANmbWBrgAaJDw/jnA26V2FJC0j5n9heJv2eXcHvNE51z1dibwk5k9VdBgZsvM7NGiK5rZj8Acgltfldq+O2G1hunAUZJqSXoirAk2QdJESZeF95K8Ebg14Q7zq8xsHICkAwjuZrFJ0leS6hS0S1oajgY/kPRHSVOA28vzwThXVp7onKveWgMfl2VFSY0JboC7oCztpewrBTgLmAdcAqQBbYEbgFPD1Y4Clhcp85OoG/Cumf1AUKfwf8L2K4CXzGxLuNzIzLqY2UPF7MO5veaJzrkkIulxSZ9Kmp3Q3FnSJwRThA+Y2YJS2nenpaQ84J/AG2b2JvBz4AUz2x7eJ/L9MoZ7HjtLGj0NXBu+vhZ4LmG9sWXcn3N7xO916Vz1toCd1QQwsz6SmgC5CetMNbPuxWxbUvvuFJyjS1RcWRQICl6mSmoQjtqKOomgeCZm9s/wYpcuBNWg5yes92M5Y3SuXHxE51z19h5QT9LNCW0pVRzDNODS8FzdIQQ33yWs5PAM8IikfSG4o76kXpJaA4uL3Gz3r8A/KDyac67SeaJzrhoL79Z+EdAlvKBjFvA8QdmgqvISQYmb+cAIYCawPnzvHmA1sDC8y/wr4fIvgLeK7CeHoJDmPyo/ZOd28uoFztVQkroC/coyvSmpvpltCC9smQWcFp6vK2n9d4Bfm9nKhLbLgAvN7KpSjnUNkGFmt+xuPefKys/ROVdz/QS0kTSxyG/pijNBUiOCnwsM2V2SAzCzsxOXJT1KMMrb7XEk3UFQD++lUuJxrsx8ROeccy7W/Bydc865WPNE55xzLtY80TnnnIs1T3TOOedizROdc865WPv/mpCiQ1TbPZgAAAAASUVORK5CYII=\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -303,85 +224,43 @@ } ], "source": [ - "#biome mean, rank_plot\n", - "\n", - "#calc biome-level annual means\n", - "ens_name = 'CTL2010'\n", - "datavar = 'GPP'\n", - "domain = 'biome'\n", - "xmean,xiav = calc_mean(ens_name,datavar,domain=domain)\n", - "\n", - "#select the temperate seasonal forest biome\n", - "biome=5\n", - "bname=str(ds0.biome_name.sel(biome_id=biome).values)\n", - "da = xmean.sel(biome=biome)\n", - "\n", - "#rank_plot will rank the parameters by their effects on the given datavar\n", - "plt.figure(figsize=[5,6])\n", - "rank_plot(da,ds0,12)\n", - "plt.title(ens_name+': '+bname);" + "rank_plot(xglob,ds0,10)" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "1f11f2a1-60a6-4a7f-9e32-da04485e7f0d", + "execution_count": 43, + "id": "6f8c3720-442f-4468-956a-5acaf36ba225", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['leafcn',\n", - " 'jmaxb1',\n", - " 'kmax',\n", + "['kmax',\n", + " 'leafcn',\n", + " 'medlynintercept',\n", " 'jmaxb0',\n", - " 'tpu25ratio',\n", - " 'wc2wjb0',\n", + " 'medlynslope',\n", + " 'jmaxb1',\n", " 'theta_cj',\n", - " 'tpuha',\n", - " 'FUN_fracfixers',\n", - " 'jmaxha']" + " 'wc2wjb0',\n", + " 'fff',\n", + " 'tpu25ratio']" ] }, - "execution_count": 17, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##access the top_n without plotting\n", - "da = xmean.sel(biome=biome)\n", + "da = xglob\n", "nx = 10\n", "xmins,xmaxs,pvals=top_n(da,nx,ds0.param,ds0.minmax)\n", "pvals[-1::-1] #list is in reverse order" ] }, - { - "cell_type": "markdown", - "id": "7bb32a26-18db-4f1b-ab6c-5430081e6220", - "metadata": {}, - "source": [ - "### Work more directly with the output data\n", - " - you'll probably want to activate the PBSCluster code from the beginning of the notebook \n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0cd1af91-fe26-4155-ac87-9379f292a814", - "metadata": {}, - "outputs": [], - "source": [ - "#load the ensemble\n", - "# monthly='h0', pft='h1', daily='h5'\n", - "keys = paramkey.key\n", - "htape = 'h0'\n", - "name = 'CTL2010'\n", - "files = get_files(name,htape,keys)\n", - "data_vars = ['GPP']\n", - "ds = get_ensemble(files,data_vars,keys,paramkey)" - ] - }, { "cell_type": "markdown", "id": "1d75f78b-1c3d-44dd-a4d0-92f8618623c9", @@ -392,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 44, "id": "ce7618cb-8004-4412-8b9c-f66a9578820f", "metadata": {}, "outputs": [ @@ -417,73 +296,38 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "c02b8680-bd30-440c-91fb-5d33daa3ffaa", + "execution_count": 21, + "id": "60510a5e-e69b-4ecb-aec3-15541772c4e4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function get_map in module ppe_tools.analysis:\n", + "\n", + "get_map(da)\n", + " Regrid from sparsegrid to standard lat/lon\n", + " \n", + " Better to do any dimension-reducing math before calling this function. \n", + " Could otherwise be pretty slow...\n", + "\n" + ] + } + ], "source": [ - "def get_map(da):\n", - " '''\n", - " Regrid from sparsegrid to standard lat/lon\n", - " \n", - " Better to do any dimension-reducing math before calling this function. \n", - " Could otherwise be pretty slow...\n", - " '''\n", - " \n", - " #ACCESS the sparsegrid info\n", - " thedir = '/glade/u/home/forrest/ppe_representativeness/output_v4/'\n", - " thefile = 'clusters.clm51_PPEn02ctsm51d021_2deg_GSWP3V1_leafbiomassesai_PPE3_hist.annual+sd.400.nc'\n", - " sg = xr.open_dataset(thedir+thefile)\n", - " \n", - " #DIAGNOSE the shape of the output map\n", - " newshape = []\n", - " coords=[]\n", - " # grab any dimensions that arent \"gridcell\" from da\n", - " for coord,nx in zip(da.coords,da.shape):\n", - " if nx!=400:\n", - " newshape.append(nx)\n", - " coords.append((coord,da[coord].values))\n", - " # grab lat/lon from sg\n", - " for coord in ['lat','lon']:\n", - " nx = len(sg[coord])\n", - " newshape.append(nx)\n", - " coords.append((coord,sg[coord].values))\n", - "\n", - " #INSTANTIATE the outgoing array\n", - " array = np.zeros(newshape)+np.nan\n", - " nd = len(array.shape)\n", - " \n", - " #FILL the array\n", - " ds = xr.open_dataset('/glade/scratch/djk2120/PPEn11/CTL2010/hist/PPEn08_CTL2010_OAAT0160.clm2.h0.2005-02-01-00000.nc')\n", - " for i in range(400):\n", - " lat=ds.grid1d_lat[i]\n", - " lon=ds.grid1d_lon[i]\n", - " cc = sg.rcent.sel(lat=lat,lon=lon,method='nearest')\n", - " ix = sg.cclass==cc\n", - " \n", - " \n", - " if nd==2:\n", - " array[ix]=da.isel(gridcell=i)\n", - " else:\n", - " nx = ix.sum().values\n", - " array[:,ix]=np.tile(da.isel(gridcell=i).values[:,np.newaxis],[1,nx])\n", - " \n", - " #OUTPUT as DataArray\n", - " da_map = xr.DataArray(array,name=da.name,coords=coords)\n", - " da_map.attrs=da.attrs\n", - "\n", - " return da_map" + "help(get_map)" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "77a6ef75-8e27-4dae-abc6-43e3c43264e5", + "execution_count": 41, + "id": "f8d368dc-1d08-4ac7-a02f-9d8b796ffcea", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -495,37 +339,13 @@ } ], "source": [ - "## but we have a function to map the sparsegrid to a global map: get_map\n", - "## it is a little bit slow.....\n", - "da_map = get_map(ds.GPP.isel(ens=0,time=6))\n", + "#slow, but should work...\n", + "da_map=get_map(xann.isel(ens=0))\n", + "da_map.name = v\n", + "da_map.attrs = {'units':'gC/m2/yr'}\n", "da_map.plot();" ] }, - { - "cell_type": "code", - "execution_count": 21, - "id": "60510a5e-e69b-4ecb-aec3-15541772c4e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_map in module ppe_tools.analysis:\n", - "\n", - "get_map(da)\n", - " Regrid from sparsegrid to standard lat/lon\n", - " \n", - " Better to do any dimension-reducing math before calling this function. \n", - " Could otherwise be pretty slow...\n", - "\n" - ] - } - ], - "source": [ - "help(get_map)" - ] - }, { "cell_type": "code", "execution_count": 30, @@ -565,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 45, "id": "a2202e19-f009-4a4d-ae25-bd4340a781c3", "metadata": {}, "outputs": [ @@ -591,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 47, "id": "6c251d62-854e-4e21-a566-3ba855098292", "metadata": {}, "outputs": [ @@ -623,83 +443,10 @@ "\n", "p = str(ds.param.isel(ens=ee).values)\n", "m = str(ds.minmax.isel(ens=ee).values)\n", - "plt.title(p+'-'+m+': ~'+str(latn)+'N, '+str(lonw)+'W');\n", + "plt.title(p+'-'+m+': ~'+str(lat)+'N, '+str(lonw)+'W');\n", " " ] }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4cfc3f1e-a3c1-40c8-a096-63cc990bf1f7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#compute the global annual cycle\n", - "cf1 = 24*60*60 #gc/m2/s -> /d\n", - "cf2 = 1/la.sum()\n", - "units = 'gC/m2/d'\n", - "\n", - "datavar='GPP'\n", - "x = cf1*cf2*(la*ds[datavar]).sum(dim='gridcell').groupby('time.month').mean()\n", - "\n", - "x.sel(ens=0).plot()\n", - "plt.ylim([0,5])\n", - "plt.ylabel(datavar+' ('+units+')')\n", - "plt.legend(['default parameterization'],loc=3)\n", - "plt.title('Global average annual cycle');" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "6a0285fb-2663-4367-a3e4-59e4b688cb07", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1tklJkdhxVAUTRlVy5ORtcxJ5ktRHVZZ6C7EPSWLcsHLGDStnxsTReaetb2xifZrwk6SfJP6W7g25K4QG1m6oZ11dA6tqkma05vE1bQ4W51NSJCrKksSTm/iTYUniqShN+8uKqczpbj1PSct0Q0qKaIqgvjFoaGqioTGob2yioSl5r28MGprf24zPHZ5Mt6m7vrEpGZ9O19AY1DcFG+obW21hr2nngHlbZSVFLU0cIypKGTdsCHuke6Ij0q3e4TnjR+T0Vw7gabflpcVsN6KY7UaUd2u+hsYm1mxIfiur0hXA6tr6lv7mlUVJRstVEAn+O/+9mLpu7JJ1RVlO4i8tLqKspVsE8NaqDa2aAADGDhvChFEV7LfzqCRxj0q3yEdXsN3w8sx3x6xnSouLkt3qXp6h09zu27IS2NDcbNBIbX0DtRubqNnYwIb65mFJe29t2t88/P31tWl3Q8v4+sb+39OWoLSoiJJiUVKUbBCV5GwYDSkpYnhFKRNGV7J3czIuL2VERUlLc0TSvylRb22nwJYUF7UcYxqQ+gek1j729Lc+Tn1TU7qlEWxsbNPdkGyl5HbXNzaxsb3uhmTe+jzdEcFhk8YxYZSbUWyTkuIiRlQUpcdFKvq07PrGJmrrG9mwcdPKIXelULOxgbr6JoqL1JKEN0/KoqSoqN3xJUWbNmiahxf7jKRBryAS/IjKwj7QaNa81Ty83L916zq3GZiZFSgneDOzApVpgpd0rqQXJS2UdIuk7h2CNjOzHssswUvaETgbmBERewPFwElZ1WdmZq1l3URTAlRIKgEqgTczrs/MzFKZJfiI+CtwBbAMeAtYHREPZFWfmZm1lmUTzSjgOGAXYAegStLn25lutqR5kuZVV1dnFY6Z2VYnyyaajwOvR0R1RNQDdwEHtp0oIuZExIyImDF27NgMwzEz27pkmeCXAQdIqlRyk5UjgMUZ1mdmZjmybIN/CrgDeBb4c1rXnKzqMzOz1jK9VUFEXARclGUdZmbWPl/JamZWoJzgzcwKlBO8mVmBcoI3MytQTvBmZgXKCd7MrEA5wZuZFSgneDOzAuUEb2ZWoJzgzcwKlBO8mVmBcoI3MytQTvBmZgXKCd7MrEA5wZuZFSgneDOzApXlQ7cnSVqQ81oj6etZ1WdmZq1l9kSniPgLMBVAUjHwV+DurOozM7PW+quJ5ghgSUS80U/1mZlt9forwZ8E3NLeCEmzJc2TNK+6urqfwjEzK3yZJ3hJZcAs4Pb2xkfEnIiYEREzxo4dm3U4ZmZbjf7Ygj8GeDYi3umHuszMLNUfCf5kOmieMTOz7GSa4CVVAp8A7sqyHjMz21xmp0kCREQNMCbLOszMrH2+ktXMrEA5wZuZFSgneDOzAuUEb2ZWoJzgzcwKlBO8mVmBcoI3MytQTvBmZgXKCd7MrEA5wZuZFSgneDOzAuUEb2ZWoJzgzcwKlBO8mVmBcoI3MytQWT/wY6SkOyS9JGmxpI9kWZ+ZmW2S6QM/gB8Bv4uIE9KHb1dmXJ+ZmaUyS/CShgOHAKcBRMRGYGNW9ZmZWWtZNtHsClQDP5f0nKRrJVW1nUjSbEnzJM2rrq7OMBwzs61LlxK8pFGSJkvaVVJXVwolwHTgJxExDVgPnN92ooiYExEzImLG2LFjuxy4mZnl12ETjaQRwJnAyUAZydZ4ObCtpCeB/4qIP+QpewWwIiKeSvvvoJ0Eb2Zm2cjXBn8H8Avg4IhYlTtC0n7AFyTtGhHXtTdzRLwtabmkSRHxF+AIYFEfxW1mZp3oMMFHxCfyjJsPzO9C+f8I3JyeQfMa8MVuR2hmZj2Sr4lmer4ZI+LZzgqPiAXAjO6HZWZmvZWvieYH6Xs5SZJ+HhCwL/AU8NFsQzMzs97o8IyYiDgsIg4D3gCmp2e67AdMA17trwDNzKxnunLK454R8efmnohYCEzNLCIzM+sTXbmSdbGka4GbgAA+DyzONCozM+u1riT4LwJfA85J+x8FfpJZRGZm1ifynUUzB7gfeCgirgSu7LeozMys1/K1wV8PTAH+R9LvJf2LpCn9FJeZmfVSvgudngSeBC6WNAY4EviGpH2A50huA3xb/4RpZmbd1aXbBUfESuCW9NV8q4KjM4zLzMx6Ke9pkpL2lHSEpKFtRo2NiO9mGJeZmfVShwle0tnAb0juJ7NQ0nE5oy/LOjAzM+udfE00/wDsFxHrJE0E7pA0MSJ+RHLLAjMz24LlS/DFEbEOICKWSjqUJMnvjBO8mdkWL18b/NuSpjb3pMn+WGAbYJ+M4zIzs17Kl+D/Hng7d0BENETE35M8TNvMzLZg+c6DX9HcLWkUMCFn+tquFC5pKbAWaAQaIsL3hjcz6yedngcv6V+B04AlJDcbI30/vIt1HBYR7/UoOjMz67GuXOj0OWC3iNiYdTBmZtZ3unI/+IXAyB6WH8ADkuZLmt3eBJJmS5onaV51dXUPqzEzs7a6sgX/b8BzkhYCdc0DI2JWF+Y9KCLelDQOeFDSSxHxaO4EETEHmAMwY8aMaK8QMzPrvq4k+BuB7wF/Bpq6U3hEvJm+vyvpbmAmyf3kzcwsY11J8O9FxI+7W7CkKqAoItam3UcCl3a3HDMz65muJPj5kv4NuJfWTTTPdjLftsDdkprr+VVE/K6ngZqZWfd0JcFPS98PyBnW6WmSEfEayQNDzMxsAOR7ZN9HgCcj4rB+jMfMzPpIvtMkTyVpnrlV0mmStuuvoMzMrPfy3argq5A89AM4BrhB0gjgD8DvgD9FRGO/RGlmZt3W6YVOEfFSRFwZEUeTtLs/BnwWeCrr4MzMrOfyPdFpf0nH5A6LiFqSrf4bfOMwM7MtW74t+O8Di9sZvigdZ2ZmW7B8CX5MRCxtOzAiXgXGZBaRmZn1iXwJviLPuKq+DsTMzPpWvgT/kKTvKr0UtZmkS4CHsw3LzMx6K9+VrN8ArgVelbQgHTYFmAd8JeO4zMysl/KdB78eOFnSrsDkdPCL6S0IzMxsC9eVe9GMBP7a3C1pOrAaeCMiGrIKzMzMeqcrCf6/gOnAC4CAvdPuMZK+GhEPZBifmZn1UFce2bcUmBYRMyJiP5K7Sy4EPg78e4axmZlZL3Qlwe8ZES8290TEIpKE77Z4M7MtWFeaaP4i6SfArWn/icDLkoYA9Z3NLKmY5Mybv0bEsT2O1MzMuqUrW/CnAa8CXwfOBV5Lh9UDXblX/Dm0f8sDMzPLUKdb8OkNxn6Qvtpal29eSeOBTwHfBf6pJwGamVnP5Lub5G8l/a2k0nbG7SrpUklf6qT8q4DzgKY89cyWNE/SvOrq6q7GbWZmncjXRPMPwMHAS5KekfQ/kh6W9DrwU2B+RFzf0cySjgXejYj5+QKIiDnpGTozxo4d25NlMDOzduS7kvVtkq3v8yRNBLYHaoGXI6KmC2UfBMyS9EmgHBgu6aaI+HzvwzYzs8505Swa0tsGL+1OwRHxTeCbAJIOBf7Zyd3MrP905SwaMzMbhLq0Bd9bETEXmNsfdZmZWSJvgpc0DdiN5C6SPpfdzGwQyXea5IXAr4HPAP8t6R/6LSozM+u1fFvwJwJTI6JG0hjgd8DP+icsMzPrrXwHWTc0nw4ZESs7mdbMzLYw+bbgd5N0b9qtNv1ExKxMIzMzs17Jl+CPa9N/RZaBmJlZ38p3JesjPovGzGzw8lk0ZmYFymfRmJkVKJ9FY2ZWoHwWjZlZgfJZNGZmBSrvWTT9GYiZmfWtfGfRHCfpzJz+pyS9lr5O6J/wzMysp/IdOD0PuDenfwiwP3Ao8LUMYzIzsz6Qrw2+LCKW5/Q/lp5Ns1JSVWcFSyoHHiVZMZQAd0TERb2K1szMuixfgh+V2xMRZ+X0duXp2HXA4RGxTlIp8Jik+yPiyR7EaWZm3ZSvieap9q5elXQ68HRnBUdiXdpbmr6iR1GamVm35duCPxe4R9LfAc+mw/YjaXL5dFcKl1QMzAd2B/4zIp5qZ5rZwGyAnXbaqcuBm5lZforIv1Et6XBgctr7YkQ83O1KpJHA3cA/RsTCjqabMWNGzJs3r7vFm5lttSTNj4gZ7Y3r9KHbaULvdlJvU8YqSXOBo4EOE7yZmfWdzO4vI2lsuuWOpArg48BLWdVnZmatdboF3wvbAzem7fBFwG0RcV+G9ZmZWY7MEnxEvABMy6p8MzPLz7cANjMrUE7wZmYFygnezKxAOcGbmRUoJ3gzswLlBG9mVqCc4M3MCpQTvJlZgXKCNzMrUE7wZmYFygnezKxAOcGbmRUoJ3gzswLlBG9mVqCc4M3MClSWT3SaIOkPkhZLelHSOVnVZWZmm8vyiU4NwDci4llJw4D5kh6MiEUZ1mlmZqnMtuAj4q2IeDbtXgssBnbMqj4zM2utX9rgJU0keXzfU+2Mmy1pnqR51dXV/RGOmdlWIfMEL2kocCfw9YhY03Z8RMyJiBkRMWPs2LFZh2NmttXINMFLKiVJ7jdHxF1Z1mVmZq1leRaNgOuAxRHxw6zqMTOz9mW5BX8Q8AXgcEkL0tcnM6zPzMxyZHaaZEQ8Biir8s3MLD9fyWpmVqCc4M3MCpQTvJlZgXKCNzMrUE7wZmYFygnezKxAOcGbmRUoJ3gzswLlBG9mVqCc4M3MCpQTvJlZgXKCNzMrUE7wZmYFygnezKxAOcGbmRWoLJ/odL2kdyUtzKoOMzPrWJZb8DcAR2dYvpmZ5ZFZgo+IR4H3syrfzMzycxu8mVmBGvAEL2m2pHmS5lVXVw90OGZmBSOzh253VUTMAeYAzJgxI9qOr6+vZ8WKFWzYsKHfY7OtW3l5OePHj6e0tHSgQzHrkQFP8J1ZsWIFw4YNY+LEiUga6HBsKxERrFy5khUrVrDLLrsMdDhmPZLlaZK3AE8AkyStkPTlnpSzYcMGxowZ4+Ru/UoSY8aM8Z6jDWqZbcFHxMl9VZaTuw0E/+5ssBvwg6xmZpYNJ/huuvjii7niiivyTlNdXc2HP/xhpk2bxh//+Mdu13HDDTdw1llnAXDPPfewaNGiHsU6UJYuXcqvfvWrHs174IEH9mi+yy67rE/KMSskTvAZ+P3vf8+ee+7Jc889x8EHH9yrsrJK8I2NjX1eZrOeJPjmeB5//PEe1dk2wfe0HLNCssWfRZPrkt++yKI31/RpmR/aYTgX/e3kvNN897vf5Re/+AUTJkxg7Nix7LfffgAsWbKEM888k+rqaiorK/nZz37Ghg0bOO+886itrWXq1Kk88cQT/NM//RPPPPMMtbW1nHDCCVxyySUATJw4kXnz5rHNNtswb948/vmf/5m5c+e21Pv4449z77338sgjj/Cd73yHO++8k912261l/GmnnUZ5eTkvvvgi77zzDj/84Q859thjWbp0KV/4whdYv349AFdffTUHHnggc+fO5ZJLLmH77bdnwYIFLFq0iE9/+tMsX76cDRs2cM455zB79mwAhg4dyplnnslDDz3EqFGjuOyyyzjvvPNYtmwZV111FbNmzaKxsZHzzz+fuXPnUldXx5lnnsnpp5/O+eefz+LFi5k6dSqnnnoqZ599drvTtRfP0KFDWbduHRdeeCH33nsvkOwRHXnkkfz85z9vN97zzz+/5fOePHkyN998c0s5EcF5553H/fffjyQuuOACTjzxRObOncvFF1/MNttsw8KFC9lvv/246aab3O5uBWVQJfiBMH/+fG699Vaee+45GhoamD59ekuCnz17Ntdccw177LEHTz31FGeccQYPP/wwl156KfPmzePqq68GkhXE6NGjaWxs5IgjjuCFF15g33337bTuAw88kFmzZnHsscdywgkntDvN0qVLeeSRR1iyZAmHHXYYr776KuPGjePBBx+kvLycV155hZNPPpl58+YB8PTTT7Nw4cKWU/+uv/56Ro8eTW1tLfvvvz+f+cxnGDNmDOvXr+fQQw/le9/7HscffzwXXHABDz74IIsWLeLUU09l1qxZXHfddYwYMYJnnnmGuro6DjroII488kguv/xyrrjiCu677z4A5syZ0+507cXT7NJLL+XSSy9l9erVHHzwwS1NVu3Fe/nll3P11VezYMGCzT6fu+66iwULFvD888/z3nvvsf/++3PIIYcA8Nxzz/Hiiy+yww47cNBBB/GnP/2Jj370o51+L2aDxaBK8J1taWfhj3/8I8cffzyVlZUAzJo1C4B169bx+OOP89nPfrZl2rq6unbLuO2225gzZw4NDQ289dZbLFq0qEsJvis+97nPUVRUxB577MGuu+7KSy+9xC677MJZZ53FggULKC4u5uWXX26ZfubMma2S6Y9//GPuvvtuAJYvX84rr7zCmDFjKCsr4+ijk3vF7bPPPgwZMoTS0lL22Wcfli5dCsADDzzACy+8wB133AHA6tWreeWVVygrK2sVY77p2saTKyI45ZRTOPfcc1tWqh3F25HHHnuMk08+meLiYrbddls+9rGP8cwzzzB8+HBmzpzJ+PHjAZg6dSpLly51greCMqgS/EBpb7e9qamJkSNHtrvVmOv111/niiuu4JlnnmHUqFGcdtppLedWl5SU0NTUBNDj863bxiaJK6+8km233Zbnn3+epqYmysvLW8ZXVVW1dM+dO5eHHnqIJ554gsrKSg499NCWOEpLS1vKLioqYsiQIS3dDQ0NQJKA/+M//oOjjjqqVQy5zUydTZcbT1sXX3wx48eP54tf/GKn8XYkYrOLo1s0LxNAcXFxy3KZFQofZO3EIYccwt13301tbS1r167lt7/9LQDDhw9nl1124fbbbweSRPL8889vNv+aNWuoqqpixIgRvPPOO9x///0t4yZOnMj8+fMBuPPOO9utf9iwYaxdu7bD+G6//XaamppYsmQJr732GpMmTWL16tVsv/32FBUV8ctf/rLDA6qrV69m1KhRVFZW8tJLL/Hkk0927UNJHXXUUfzkJz+hvr4egJdffpn169dvFnNH0+Vz33338eCDD/LjH/+4S/GWlpa2lJ/rkEMO4de//jWNjY1UV1fz6KOPMnPmzG4tp9lg5QTfienTp3PiiScydepUPvOZz7Q6K+bmm2/muuuuY8qUKUyePJnf/OY3m80/ZcoUpk2bxuTJk/nSl77EQQcd1DLuoosu4pxzzuHggw+muLi43fpPOukkvv/97zNt2jSWLFmy2fhJkybxsY99jGOOOYZrrrmG8vJyzjjjDG688UYOOOAAXn755Q63ko8++mgaGhrYd999+fa3v80BBxzQrc/mK1/5Ch/60IeYPn06e++9N6effnpLeSUlJUyZMoUrr7yyw+ny+cEPfsCbb77JzJkzmTp1KhdeeGHeeGfPns2+++7LKaec0qqc448/nn333ZcpU6Zw+OGH8+///u9st9123VpOs8FK+XZh+9uMGTOi+WBgs8WLF7PXXnsNUERbttNOOy3vAVjrPf/+bEsnaX5EzGhvnLfgzcwKlA+yDmI33HDDQIdgZluwQbEFvyU1I9nWw787G+y2+ARfXl7OypUr/WezftV8P/jcU0zNBpstvolm/PjxrFixAj/Oz/pb8xOdzAarLT7Bl5aW+ok6ZmY9kGkTjaSjJf1F0quSzs+yLjMzay3LR/YVA/8JHAN8CDhZ0oeyqs/MzFrLcgt+JvBqRLwWERuBW4HjMqzPzMxyZNkGvyOwPKd/BfDhthNJmg3MTnvXSfpLhjH1lW2A9wY6iAwV8vJ52QavQl6+3izbzh2NyDLBt/fkhM3OdYyIOcCcDOPoc5LmdXRpcCEo5OXzsg1ehbx8WS1blk00K4AJOf3jgTczrM/MzHJkmeCfAfaQtIukMuAk4N4M6zMzsxyZNdFERIOks4D/BYqB6yPixazq62eDqkmpBwp5+bxsg1chL18my7ZF3S7YzMz6zhZ/LxozM+sZJ3gzswLlBN8NkiZI+oOkxZJelHTOQMfU1yQVS3pO0n0DHUtfkzRS0h2SXkq/w48MdEx9RdK56W9yoaRbJA3q22BKul7Su5IW5gwbLelBSa+k76MGMsae6mDZvp/+Ll+QdLekkX1RlxN89zQA34iIvYADgDML8PYL5wCLBzqIjPwI+F1E7AlMoUCWU9KOwNnAjIjYm+SkhpMGNqpeuwE4us2w84HfR8QewO/T/sHoBjZftgeBvSNiX+Bl4Jt9UZETfDdExFsR8WzavZYkQew4sFH1HUnjgU8B1w50LH1N0nDgEOA6gIjYGBGrBjSovlUCVEgqASoZ5NecRMSjwPttBh8H3Jh23wh8uj9j6ivtLVtEPBARzU+if5LkuqFec4LvIUkTgWnAUwMcSl+6CjgPaBrgOLKwK1AN/DxtgrpWUtVAB9UXIuKvwBXAMuAtYHVEPDCwUWVi24h4C5KNLWDcAMeTlS8B9/dFQU7wPSBpKHAn8PWIWDPQ8fQFSccC70bE/IGOJSMlwHTgJxExDVjP4N3FbyVtiz4O2AXYAaiS9PmBjcp6QtL/I2kKvrkvynOC7yZJpSTJ/eaIuGug4+lDBwGzJC0lufPn4ZJuGtiQ+tQKYEVENO9x3UGS8AvBx4HXI6I6IuqBu4ADBzimLLwjaXuA9P3dAY6nT0k6FTgWOCX66AIlJ/hukCSSNtzFEfHDgY6nL0XENyNifERMJDlA93BEFMxWYES8DSyXNCkddASwaABD6kvLgAMkVaa/0SMokAPIbdwLnJp2nwr8ZgBj6VOSjgb+BZgVETV9Va4TfPccBHyBZOt2Qfr65EAHZV32j8DNkl4ApgKXDWw4fSPdK7kDeBb4M8n/elBf1i/pFuAJYJKkFZK+DFwOfELSK8An0v5Bp4NluxoYBjyY5pVr+qQu36rAzKwweQvezKxAOcGbmRUoJ3gzswLlBG9mVqCc4M3MCpQTvFkvpHeoPCOn/9BCvBOnDU5O8Ga9MxI4o7OJzAaCE7xtNSRNTO+5fW163/SbJX1c0p/Se4zPTO85fk96X+4nJe2bzntxeh/vuZJek3R2WuzlwG7pxSnfT4cNzbnv/M3p1aVm/S6zh26bbaF2Bz4LzAaeAf4O+CgwC/gWsBx4LiI+Lelw4BckV70C7AkcRnLF4V8k/YTkhmV7R8RUSJpoSO4yOpnklr1/IrkC+rHMl8ysDW/B29bm9Yj4c0Q0AS+SPEAiSC7xn0iS7H8JEBEPA2MkjUjn/e+IqIuI90hudLVtB3U8HREr0joWpOWa9TsneNva1OV0N+X0N5Hs0bbXnNJ8P4/ceRvpeA+4q9OZZcoJ3qy1R4FToKW55b1O7vm/lqTJxmyL4y0Ls9YuJnnq0wtADZtuT9uuiFiZHqRdSPIUnv/OPkSzrvHdJM3MCpSbaMzMCpQTvJlZgXKCNzMrUE7wZmYFygnezKxAOcGbmRUoJ3gzswL1/wGi6pXTHHVcEQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#compute the average annual cycle for a given biome\n", - "biome=1 #tropical rain forest\n", - "bname=str(ds0.biome_name.sel(biome_id=biome).values)\n", - "la_biome = la*(ds0.biome==biome)\n", - "cf1 = 24*60*60 #gc/m2/s -> /d\n", - "cf2 = 1/la_biome.sum()\n", - "units = 'gC/m2/d'\n", - "\n", - "datavar='GPP'\n", - "x = cf1*cf2*(la_biome*ds[datavar]).sum(dim='gridcell').groupby('time.month').mean()\n", - "\n", - "x.sel(ens=0).plot()\n", - "plt.ylim([0,9])\n", - "plt.ylabel(datavar+' ('+units+')');\n", - "plt.legend(['default parameterization'],loc=3)\n", - "plt.title(bname+' average annual cycle');" - ] - }, { "cell_type": "markdown", "id": "8318e545-9967-469d-b6c1-11358660b3e1", @@ -778,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 48, "id": "037d73c2-c915-4bf4-a3cb-559a9c35b306", "metadata": {}, "outputs": [ @@ -801,27 +548,27 @@ " ZWT_PERCH (time, gridcell) float32 ..." ] }, - "execution_count": 38, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "h0 = '/glade/scratch/djk2120/PPEn11/CTL2010/hist/PPEn11_CTL2010_OAAT0400.clm2.h0.2005-02-01-00000.nc'\n", + "h0 = get_files('CTL2010','h0',['OAAT0000'])[0]\n", "ds0 = xr.open_dataset(h0)\n", "ds0.data_vars" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 49, "id": "ee20e2d3-4eff-4a7d-98dd-01c4ee9f366f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Data variables: (12/89)\n", + "Data variables: (12/88)\n", " mcdate (time) int32 ...\n", " mcsec (time) int32 ...\n", " mdcur (time) int32 ...\n", @@ -837,24 +584,16 @@ " VPD_CAN (time, gridcell) float32 ..." ] }, - "execution_count": 39, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "h5 = '/glade/scratch/djk2120/PPEn11/CTL2010/hist/PPEn11_CTL2010_OAAT0400.clm2.h5.2005-01-01-00000.nc'\n", + "h5 = get_files('CTL2010','h5',['OAAT0000'])[0]\n", "ds5 = xr.open_dataset(h5)\n", "ds5.data_vars" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bfc901a-f0f6-4c2e-a4ad-5488a90a1022", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/ppe_tools/analysis.py b/ppe_analysis/analysis.py similarity index 62% rename from ppe_tools/analysis.py rename to ppe_analysis/analysis.py index 1ea4f6e..283c156 100644 --- a/ppe_tools/analysis.py +++ b/ppe_analysis/analysis.py @@ -9,8 +9,8 @@ #define the directory structure to find files def get_files(name,htape,keys): - topdir = '/glade/scratch/djk2120/PPEn11/hist/' - thisdir = topdir+name+'/' + topdir = '/glade/campaign/asp/djk2120/PPEn11/' + thisdir = topdir+name+'/hist/' files = [glob.glob(thisdir+'*'+key+'*'+htape+'*.nc')[0] for key in keys] return files @@ -21,7 +21,11 @@ def ppe_init(csv='/glade/scratch/djk2120/PPEn11/surv.csv'): #fetch the sparsegrid landarea la_file = '/glade/scratch/djk2120/PPEn08/sparsegrid_landarea.nc' la = xr.open_dataset(la_file).landarea #km2 - + # pft area + f = get_files('CTL2010','h1',keys[0])[0] + lapft = get_lapft(la,f) + + #load conversion factors attrs = pd.read_csv('agg_units.csv',index_col=0) @@ -35,14 +39,19 @@ def ppe_init(csv='/glade/scratch/djk2120/PPEn11/surv.csv'): ds0['biome'] = whit['biome'] ds0['biome_name'] = whit['biome_name'] - return ds0,la,attrs,paramkey,keys - -def get_ensemble(files,data_vars,keys,paramkey,p=True,extras=[]): + return ds0,la,lapft,attrs,paramkey,keys +def get_ensemble(data_vars,ensemble,htape, + csv='/glade/scratch/djk2120/PPEn11/surv.csv', + keys=[],paramkey='',p=True,extras=[]): def preprocess(ds): return ds[data_vars] + if csv: + ds0,la,lapft,attrs,paramkey,keys = ppe_init(csv=csv) + #read in the dataset + files = get_files(ensemble,htape,keys) ds = xr.open_mfdataset(files,combine='nested',concat_dim='ens', parallel=p,preprocess=preprocess) @@ -79,11 +88,12 @@ def preprocess(ds): whit = xr.open_dataset('./whit/whitkey.nc') ds['biome'] = whit['biome'] ds['biome_name'] = whit['biome_name'] + if htape=='h1': + ds['pft'] = ds.pfts1d_itype_veg return ds - def get_map(da): ''' Regrid from sparsegrid to standard lat/lon @@ -115,8 +125,9 @@ def get_map(da): array = np.zeros(newshape)+np.nan nd = len(array.shape) - #FILL the array #zqz, very brittle - ds = xr.open_dataset('/glade/scratch/djk2120/PPEn11/CTL2010/hist/PPEn08_CTL2010_OAAT0160.clm2.h0.2005-02-01-00000.nc') + #FILL the array + f = get_files('CTL2010','h0',['OAAT0000'])[0] + ds = xr.open_dataset(f) for i in range(400): lat=ds.grid1d_lat[i] lon=ds.grid1d_lon[i] @@ -171,154 +182,93 @@ def get_lapft(la,sample_h1): lapft.attrs['units'] = 'km2' return lapft -def get_cfs(attrs,datavar,ds,la): +def get_cfs(attrs,datavar,ds): if datavar in attrs.index: cf1 = attrs.cf1[datavar] cf2 = attrs.cf2[datavar] - if cf2=='1/lasum': - cf2 = 1/la.sum() - else: - cf2 = float(cf2) units = attrs.units[datavar] else: cf1 = 1/365 - cf2 = 1/la.sum() + cf2 = 0 #flag to use 1/la.sum() if datavar in ds: units = ds[datavar].attrs['units'] else: - units = 'tbd' + units = 'tbd' return cf1,cf2,units -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,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 - ''' +def ann_mean(x,cf=1/365): + xann = cf*(month_wts(10)*x).groupby('time.year').sum() + return xann + +def reg_mean(x,la,cf=0,g=[]): + if len(g)==0: #global mean + g = la.copy(deep=True)*0+1 + g.name = 'glob' + if cf==0: + cf = 1/la.groupby(g).sum() + xreg = cf*(la.values*x).groupby(g).sum().compute() + if 'glob' in xreg.dims: + xreg = xreg.isel(glob=0).drop('glob') + return xreg + +def calc_mean(datavar,ens,d='global', + csv='/glade/scratch/djk2120/PPEn11/surv.csv', + overwrite=False, + save=True): - ds0,la,attrs,paramkey,keys = ppe_init(csv=csv) + f = ens+'_'+datavar+'_ann.nc' + preload = '/glade/u/home/djk2120/clm5ppe/data/'+f - preload = ('/glade/u/home/djk2120/clm5ppe/pyth/data/'+ - ens_name+'_'+datavar+'_'+domain+'.nc') if not glob.glob(preload): - preload = './data/'+ens_name+'_'+datavar+'_'+domain+'.nc' - if not os.path.isdir('./data/'): - os.system('mkdir data') + preload = '../data/'+f + if overwrite: os.system('rm '+preload) - - #only calculate if not available on disk - if not glob.glob(preload): - longname='' - specials = ['ALTMAX','SCA_JJA','SCA_DJF'] - - if datavar not in specials: - - if domain=='pft': - xmean,xiav,longname,units=pft_mean(ens_name,datavar,la,attrs,keys,paramkey) - - if domain=='biome': - xmean,xiav,longname,units=biome_mean(ens_name,datavar,la,attrs,keys,paramkey) - - if domain=='global': - xmean,xiav,longname,units=gcell_mean(ens_name,datavar,la,attrs,keys,paramkey) - - else: - xmean,xiav,longname = calc_special(ens_name,datavar,la) - - #save the reduced data - out = xr.Dataset() - out[datavar+'_mean'] = xmean - out[datavar+'_mean'].attrs= {'units':units,'long_name':longname} - out[datavar+'_iav'] = xiav - out[datavar+'_iav'].attrs= {'units':units,'long_name':longname} - out['param'] = ds0.param - out['minmax'] = ds0.minmax - if domain=='biome': - out['biome_name']=ds0.biome_name - if domain=='pft': - pftkeys=['NV','NEMT','NEBT','NDBT','BETT','BEMT','BDTT','BDMT','BDBT', - 'BES','BDMS','BDBS','C3AG','C3NG','C4G','C3C','C3I'] - out['pftkey']=xr.DataArray(pftkeys,dims='pft') - out.load().to_netcdf(preload) - - #load from disk - ds = xr.open_dataset(preload) - v = datavar+'_iav' - xmean = ds[datavar+'_mean'] - if v in ds.data_vars: - xiav = ds[v] + + if glob.glob(preload): + xout = xr.open_dataset(preload) else: - xiav = [] + ds0,la,lapft,attrs,paramkey,keys = ppe_init(csv=csv) + dvs = datavar.split('-') + domains = {'h0':['global','biome'], + 'h1':['pft']} + xout = xr.Dataset() + for htape,la_x in zip(['h0','h1'],[la,lapft]): + ds = get_ensemble(dvs,ens,htape,csv='',keys=keys,paramkey=paramkey) + cf1,cf2,units = get_cfs(attrs,datavar,ds) + if dvs[0] in ds: + x = ds[dvs[0]] + if len(dvs)==2: + ix = ds[dvs[1]]>0 + x = (x/ds[dvs[1]]).where(ix).fillna(0) + + for domain in domains[htape]: + if domain=='global': + g=[] + else: + g=ds[domain] + + xann = ann_mean(x,cf1) + xann_reg = reg_mean(xann,la_x,cf2,g) + v = datavar+'_'+domain + xout[v]=xann_reg + + xout.attrs={'units':units} - return xmean,xiav - -def gcell_mean(ens,datavar,la,attrs,keys,paramkey): - - files = get_files(ens,'h0',keys) - dvs = datavar.split('-') - ds = get_ensemble(files,dvs,keys,paramkey) - - cf1,cf2,units = get_cfs(attrs,datavar,ds,la) - - x = ds[dvs[0]] - if len(dvs)==2: - ix = ds[dvs[1]]>0 - x = (x/ds[dvs[1]]).where(ix).fillna(0) - - xann = cf1*(month_wts(10)*x).groupby('time.year').sum() - xann_glob = cf2*(la*xann).sum(dim='gridcell').compute() - xmean = xann_glob.mean(dim='year') - xiav = xann_glob.std(dim='year') - longname = ds[dvs[0]].attrs['long_name'] - return xmean,xiav,longname,units - -def biome_mean(ens,datavar,la,attrs,keys,paramkey): - files = get_files(ens,'h0',keys) - dvs = datavar.split('-') - ds = get_ensemble(files,dvs,keys,paramkey) - - cf1,cf2,units = get_cfs(attrs,datavar,ds,la) - - x = ds[dvs[0]] - if len(dvs)==2: - ix = ds[dvs[1]]>0 - x = (x/ds[dvs[1]]).where(ix).fillna(0) - - xann = cf1*(month_wts(10)*x).groupby('time.year').sum() - xann_biome = cf2*(la*xann).groupby(ds.biome).sum().compute() - xmean = xann_biome.mean(dim='year') - xiav = xann_biome.std(dim='year') - longname = ds[dvs[0]].attrs['long_name'] - - return xmean,xiav,longname,units - -def pft_mean(ens,datavar,la,attrs,keys,paramkey): - files = get_files(ens,'h1',keys) - dvs = datavar.split('-') - ds = get_ensemble(files,dvs,keys,paramkey) + if not glob.glob(preload): + if save: + if not os.path.isdir('../data/'): + os.system('mkdir ../data') + xout.to_netcdf(preload) - cf1,cf2,units = get_cfs(attrs,datavar,ds,la) - lapft = get_lapft(la,files[0]) + ####### + v = datavar+'_'+d + x = xout[v] + xm = x.mean(dim='year') + xiav = x.std(dim='year') + units = xout.attrs['units'] - x = ds[dvs[0]] - if len(dvs)==2: - ix = ds[dvs[1]]>0 - x = (x/ds[dvs[1]]).where(ix).fillna(0) - - xann = cf1*(month_wts(10)*x).groupby('time.year').sum() - la_xann = (lapft*xann) - la_xann['pft'] = ds.pfts1d_itype_veg - xann_pft = cf2*(la_xann).groupby('pft').sum().compute() - xmean = xann_pft.mean(dim='year') - xiav = xann_pft.std(dim='year') - longname = ds[dvs[0]].attrs['long_name'] - - return xmean,xiav,longname,units + return xm,xiav,units def find_pair(da,params,minmax,p): '''