diff --git a/adf_quick_run.ipynb b/adf_quick_run.ipynb new file mode 100644 index 000000000..a3ec2fbcb --- /dev/null +++ b/adf_quick_run.ipynb @@ -0,0 +1,1467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ca2a6f5-bd5a-4520-8380-baf5cb536d41", + "metadata": {}, + "source": [ + "## ADF Diagnostics In Jupyter\n", + "This notebook will run the Atmospheric Diagnostic Framework using the settings in a config.yaml file in your ADF directory. \n", + "\n", + "Note that it was developed to run on Cheyenne/Caspar *with the NPL (conda) kernel*" + ] + }, + { + "cell_type": "markdown", + "id": "7eea8d1a-d6a7-4464-bd6d-8194aa2368d0", + "metadata": { + "tags": [] + }, + "source": [ + "### Setup\n", + "#### Required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4d0264e2-c116-4e18-af33-0ce3adabcd60", + "metadata": {}, + "outputs": [], + "source": [ + "import os.path\n", + "from pathlib import Path\n", + "import sys" + ] + }, + { + "cell_type": "markdown", + "id": "6566766f-9d8f-4428-bf01-3c36a1ffeb63", + "metadata": {}, + "source": [ + "#### Paths" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66a55cfa-2d81-4298-8623-4ceaf3c0fb1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current working directory = /glade/work/richling/ADF/adf-tutorials/multiscale_modeling\n", + "ADF path = /glade/work/richling/ADF/ADF/\n" + ] + } + ], + "source": [ + "# Determine ADF directory path\n", + "# If it is in your cwd, set adf_path = local_path, \n", + "# otherwise set adf_path appropriately\n", + "\n", + "local_path = os.path.abspath('')\n", + "adf_path = \"/glade/work/richling/ADF/ADF/\"\n", + "\n", + "# Set up the ADF for your location/user name\n", + "#user = \"richling\" \n", + "#adf_path = f\"/glade/work/{user}/ADF/\"\n", + "\n", + "print(f\"current working directory = {local_path}\")\n", + "print(f\"ADF path = {adf_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1689b448-daf6-44ca-9c9e-b1345490b552", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The lib scripts live here, right? /glade/work/richling/ADF/ADF/lib\n" + ] + } + ], + "source": [ + "#set path to ADF lib\n", + "lib_path = os.path.join(adf_path,\"lib\")\n", + "print(f\"The lib scripts live here, right? {lib_path}\")\n", + "\n", + "#Add paths to python path:\n", + "sys.path.append(lib_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f927cc3e-7fa9-4347-b44e-956c7ebdc742", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The plotting scripts live here, right? /glade/work/richling/ADF/ADF/scripts/plotting\n" + ] + } + ], + "source": [ + "#set path to ADF plotting scripts directory\n", + "plotting_scripts_path = os.path.join(adf_path,\"scripts\",\"plotting\")\n", + "print(f\"The plotting scripts live here, right? {plotting_scripts_path}\")\n", + "\n", + "#Add paths to python path:\n", + "sys.path.append(plotting_scripts_path)" + ] + }, + { + "cell_type": "markdown", + "id": "159be7f2-8593-483a-b200-daf8c3c992c8", + "metadata": {}, + "source": [ + "#### Import config file into ADF object\n", + "If there are errors, here, it is likely due to path errors above" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f553607f-cd31-4aa3-8142-4959b3569a1a", + "metadata": {}, + "outputs": [], + "source": [ + "#import ADF diagnostics object\n", + "from adf_diag import AdfDiag\n", + "\n", + "# If this fails, check your paths output in the cells above,\n", + "# and that you are running the NPL (conda) Kernel\n", + "# You can see all the paths being examined by un-commenting the following:\n", + "#sys.path" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cd4918d2-dcc0-417b-8867-d368c4cb1d78", + "metadata": {}, + "outputs": [], + "source": [ + "# Set path for config YAML file\n", + "#config_path = \"/path/to/your/yaml/file/\"\n", + "config_path = \"/glade/work/richling/NCAR-ADF/ADF/\"\n", + "\n", + "#Your config file path\n", + "#/glade/work/${user}/adf-tutorial/ \n", + "\n", + "# Set name of config YAML file:\n", + "config_fil_str = \"config_quick_run.yaml\n", + "\"\n", + "\n", + "config_file=os.path.join(config_path,config_fil_str)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4ac46243-f980-41e1-9f8c-8d8ad361bb89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Initialize ADF object\n", + "adf = AdfDiag(config_file)\n", + "adf" + ] + }, + { + "cell_type": "markdown", + "id": "c7d266a8-f006-468d-96d5-51d9a93dd6dd", + "metadata": { + "tags": [] + }, + "source": [ + "### ADF Standard Work Flow" + ] + }, + { + "cell_type": "markdown", + "id": "1b1d075b-232d-4466-a6be-192c457ae5a9", + "metadata": {}, + "source": [ + "#### Calculate the Time Series files\n", + "\n", + "* NOTE: If not comparing against observations, you must run _create_time_series()_ again with baseline flag" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5f056e6d-5828-434d-b70b-f60627f30ba1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Generating CAM time series files...\n", + "\t Processing time series for case 'f.cam6_3_106.FLTHIST_v0a.ne30.dcs_effgw_rdg.001' :\n", + " ...CAM time series file generation has finished successfully.\n", + "\n", + " Generating CAM time series files...\n", + "\t Processing time series for case 'f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001' :\n", + " ...CAM time series file generation has finished successfully.\n" + ] + } + ], + "source": [ + "#Create model time series.\n", + "adf.create_time_series()\n", + "\n", + "#Create model baseline time series (if needed):\n", + "if not adf.compare_obs:\n", + " adf.create_time_series(baseline=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4ec72ce9-8167-435c-8334-728e2df24a01", + "metadata": {}, + "source": [ + "#### Calculate the Climo files\n", + "\n", + "* NOTE: Do not need to specify or repeat for baseline case unlike time series generation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "85a611c7-a90b-4d24-a518-046407564e35", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Calculating CAM climatologies...\n", + "\t Calculating climatologies for case 'f.cam6_3_106.FLTHIST_v0a.ne30.dcs_effgw_rdg.001' :\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDHGH and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDICE and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDLIQ and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDLOW and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDMED and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDTOT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLOUD and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNTC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNTC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LHFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LWCF and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping OMEGA500 and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PBLH and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PSL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping QFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping Q and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping RELHUM and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SHFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SST and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SWCF and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping T and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TAUX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TAUY and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TGCLDIWP and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TGCLDLWP and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TMQ and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TREFHT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping U and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping U10 and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping ICEFRAC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping OCNFRAC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LANDFRAC and moving to next variable.\n", + "\t Calculating climatologies for case 'f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001' :\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDHGH and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDICE and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDLIQ and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDLOW and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDMED and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLDTOT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping CLOUD and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FLNTC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping FSNTC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LHFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LWCF and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping OMEGA500 and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PBLH and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECSC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PRECC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping PSL and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping QFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping Q and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping RELHUM and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SHFLX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SST and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping SWCF and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping T and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TAUX and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TAUY and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TGCLDIWP and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TGCLDLWP and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TMQ and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TREFHT and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping TS and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping U and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping U10 and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping ICEFRAC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping OCNFRAC and moving to next variable.\n", + "\t INFO: Found climo file and clobber is False, so skipping LANDFRAC and moving to next variable.\n", + " ...CAM climatologies have been calculated successfully.\n" + ] + } + ], + "source": [ + "#Create model climatology (climo) files.\n", + "adf.create_climo()" + ] + }, + { + "cell_type": "markdown", + "id": "0dc28ffb-f501-4a42-8e21-cd8b993163ea", + "metadata": {}, + "source": [ + "#### Regrid the Climo files" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9673eada-6eeb-4c68-b59a-bd7baf62aca1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Regridding CAM climatologies...\n", + "\t Regridding case 'f.cam6_3_106.FLTHIST_v0a.ne30.dcs_effgw_rdg.001' :\n", + "\t - regridding PS (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDHGH (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDICE (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDLIQ (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDLOW (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDMED (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLDTOT (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding CLOUD (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FLNS (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FLNT (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FLNTC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FSNS (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FSNT (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding FSNTC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding LHFLX (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding LWCF (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding OMEGA500 (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PBLH (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECL (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECT (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECSL (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECSC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECSC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PRECC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding PSL (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding QFLX (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding Q (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding RELHUM (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding SHFLX (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding SST (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding SWCF (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding T (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TAUX (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TAUY (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TGCLDIWP (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TGCLDLWP (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TMQ (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TREFHT (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding TS (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding U (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding U10 (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding ICEFRAC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding OCNFRAC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + "\t - regridding LANDFRAC (known targets: ['f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001'])\n", + "\t Regridded file already exists, so skipping...\n", + " ...CAM climatologies have been regridded successfully.\n" + ] + } + ], + "source": [ + "#Regrid model climatology files to match either\n", + "#observations or CAM baseline climatologies.\n", + "#This call uses the \"regridding_scripts\" specified in the config file:\n", + "adf.regrid_climo()" + ] + }, + { + "cell_type": "markdown", + "id": "56434dfe-187d-43a8-967c-5cf027e1b05b", + "metadata": {}, + "source": [ + "#### Run statistics on Time Series files" + ] + }, + { + "cell_type": "markdown", + "id": "e40c90be-b041-46b6-b268-3f3f02ea855a", + "metadata": { + "tags": [] + }, + "source": [ + "#Perform analyses on the simulation(s).\n", + "#This call uses the \"analysis_scripts\" specified in the config file:\n", + "adf.perform_analyses()" + ] + }, + { + "cell_type": "markdown", + "id": "e12c5b56-2a54-49c9-816c-9d447d511a9d", + "metadata": {}, + "source": [ + "#### Create Plots" + ] + }, + { + "cell_type": "markdown", + "id": "386acebc-2654-4889-9a01-306948928881", + "metadata": { + "tags": [] + }, + "source": [ + "#Create plots.\n", + "#This call uses the \"plotting_scripts\" specified in the config file:\n", + "adf.create_plots()\n" + ] + }, + { + "cell_type": "markdown", + "id": "898b1090-aad9-45b2-a0f2-2882ae8f9834", + "metadata": {}, + "source": [ + "#### Generate HTML files\n", + "\n", + "* This will create html files that you can view via webbrower either:\n", + " - in Casper/Cheyenne\n", + " - pushing it to CGD projects webpage through Tungsten" + ] + }, + { + "cell_type": "markdown", + "id": "f391772b-8f17-40bc-b87d-d9b27b0a8bba", + "metadata": { + "tags": [] + }, + "source": [ + "#Create website.\n", + "if adf.create_html:\n", + " adf.create_website()" + ] + }, + { + "cell_type": "markdown", + "id": "11d4e7cd-49f5-4a68-85a1-ad18fa2a6706", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "29b2605d-519c-4305-ab2b-47c5c053a00c", + "metadata": {}, + "source": [ + "## ADF Helpful Methods and Structures \n", + "\n", + "Demonstration of a few methods to get information from the ADF object" + ] + }, + { + "cell_type": "markdown", + "id": "cdd8063d-195b-42bc-99a4-37ea0f9a62a0", + "metadata": {}, + "source": [ + "#### List all adf object related methods" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d3d9703e-5053-4a3f-9745-2a6cddfa4f6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['_AdfBase__debug_log',\n", + " '_AdfConfig__config_dict',\n", + " '_AdfConfig__create_search_dict',\n", + " '_AdfConfig__expand_yaml_var_ref',\n", + " '_AdfConfig__kword_pattern',\n", + " '_AdfConfig__search_dict',\n", + " '_AdfDiag__analysis_scripts',\n", + " '_AdfDiag__cvdp_info',\n", + " '_AdfDiag__diag_scripts_caller',\n", + " '_AdfDiag__function_caller',\n", + " '_AdfDiag__plotting_scripts',\n", + " '_AdfDiag__regridding_scripts',\n", + " '_AdfDiag__time_averaging_scripts',\n", + " '_AdfInfo__base_nickname',\n", + " '_AdfInfo__basic_info',\n", + " '_AdfInfo__cam_bl_climo_info',\n", + " '_AdfInfo__cam_climo_info',\n", + " '_AdfInfo__compare_obs',\n", + " '_AdfInfo__diag_var_list',\n", + " '_AdfInfo__eyear_baseline',\n", + " '_AdfInfo__eyears',\n", + " '_AdfInfo__num_cases',\n", + " '_AdfInfo__num_procs',\n", + " '_AdfInfo__plot_location',\n", + " '_AdfInfo__syear_baseline',\n", + " '_AdfInfo__syears',\n", + " '_AdfInfo__test_nicknames',\n", + " '_AdfObs__var_obs_dict',\n", + " '_AdfObs__variable_defaults',\n", + " '_AdfWeb__case_web_paths',\n", + " '_AdfWeb__plot_type_multi',\n", + " '_AdfWeb__plot_type_order',\n", + " '_AdfWeb__website_data',\n", + " '__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'add_diag_var',\n", + " 'add_website_data',\n", + " 'baseline_climo_dict',\n", + " 'basic_info_dict',\n", + " 'cam_climo_dict',\n", + " 'case_nicknames',\n", + " 'climo_yrs',\n", + " 'compare_obs',\n", + " 'create_climo',\n", + " 'create_html',\n", + " 'create_plots',\n", + " 'create_time_series',\n", + " 'create_website',\n", + " 'debug_log',\n", + " 'diag_var_list',\n", + " 'end_diag_fail',\n", + " 'expand_references',\n", + " 'get_baseline_info',\n", + " 'get_basic_info',\n", + " 'get_cam_info',\n", + " 'get_cvdp_info',\n", + " 'num_cases',\n", + " 'num_procs',\n", + " 'perform_analyses',\n", + " 'plot_location',\n", + " 'plotting_scripts',\n", + " 'read_config_var',\n", + " 'regrid_climo',\n", + " 'setup_run_cvdp',\n", + " 'var_obs_dict',\n", + " 'variable_defaults']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(adf)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b6f3971d-14de-4d7d-996b-39e4bda99d1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adf.num_procs" + ] + }, + { + "cell_type": "markdown", + "id": "4583376e-6ac3-4f0f-81d0-f4e92ce13124", + "metadata": {}, + "source": [ + "### Get information from the subsections of the config yaml file\n", + "\n", + "Remember the different sub-sections?" + ] + }, + { + "cell_type": "markdown", + "id": "ad924a19-81ba-49dc-8e3b-2bb44f2b5114", + "metadata": {}, + "source": [ + "#### Basic Info Section" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e3904cb9-a618-43af-9605-e6fcd4e7fd59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "diag_loc: /glade/scratch/${user}/ADF_output/\n", + "climo_loc: /glade/scratch/richling/ADF/tutorials/data/output/climos/\n", + "ts_loc: /glade/scratch/richling/ADF/tutorials/data/output/timeseries/\n", + "hist_str: cam.h0\n", + "compare_obs: False\n", + "create_html: True\n", + "obs_data_loc: /glade/work/nusbaume/SE_projects/model_diagnostics/ADF_obs\n", + "cam_regrid_loc: /glade/scratch/richling/ADF/tutorials/data/output/regrid/\n", + "cam_overwrite_regrid: False\n", + "cam_diag_plot_loc: ${diag_loc}diag-plot/\n", + "use_defaults: True\n", + "plot_press_levels: [200, 850]\n", + "central_longitude: 180\n", + "weight_season: True\n", + "num_procs: 8\n", + "redo_plot: False\n" + ] + } + ], + "source": [ + "basic_info_dict = adf.read_config_var(\"diag_basic_info\")\n", + "\n", + "for key,val in basic_info_dict.items():\n", + " print(f\"{key}: {val}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e8b1de2f-2a36-472f-b0ae-517845245473", + "metadata": {}, + "source": [ + "#### Test Case Info Section" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cccdb0b2-ba50-45d5-8ce5-af6a9161c762", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calc_cam_climo: True\n", + "cam_overwrite_climo: False\n", + "cam_case_name: f.cam6_3_106.FLTHIST_v0a.ne30.dcs_effgw_rdg.001\n", + "case_nickname: ${diag_cam_climo.cam_case_name}\n", + "cam_hist_loc: /glade/scratch/richling/ADF/tutorials/data/${diag_cam_climo.cam_case_name}/\n", + "cam_climo_loc: ${climo_loc}${diag_cam_climo.cam_case_name}/\n", + "start_year: 1995\n", + "end_year: 2012\n", + "cam_ts_done: False\n", + "cam_ts_save: True\n", + "cam_overwrite_ts: False\n", + "cam_ts_loc: ${ts_loc}${diag_cam_climo.cam_case_name}/\n" + ] + } + ], + "source": [ + "test_dict = adf.read_config_var(\"diag_cam_climo\")\n", + "\n", + "for key,val in test_dict.items():\n", + " print(f\"{key}: {val}\")" + ] + }, + { + "cell_type": "markdown", + "id": "33a7da55-13a0-49a6-988b-ab7451b49dac", + "metadata": {}, + "source": [ + "#### Baseline Case Info Section" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9cf8b802-300f-41f6-b6ad-32c4f08c463f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calc_cam_climo: True\n", + "cam_overwrite_climo: False\n", + "cam_case_name: f.cam6_3_106.FLTHIST_v0a.ne30.dcs_non-ogw.001\n", + "case_nickname: ${diag_cam_baseline_climo.cam_case_name}\n", + "cam_hist_loc: /glade/scratch/richling/ADF/tutorials/data/${diag_cam_baseline_climo.cam_case_name}/\n", + "cam_climo_loc: ${climo_loc}${diag_cam_baseline_climo.cam_case_name}/\n", + "start_year: 1995\n", + "end_year: 2012\n", + "cam_ts_done: False\n", + "cam_ts_save: True\n", + "cam_overwrite_ts: False\n", + "cam_ts_loc: ${ts_loc}${diag_cam_baseline_climo.cam_case_name}/\n" + ] + } + ], + "source": [ + "baseline_dict = adf.read_config_var(\"diag_cam_baseline_climo\")\n", + "\n", + "for key,val in baseline_dict.items():\n", + " print(f\"{key}: {val}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b878fc07-a39a-4ef6-a04c-499280d3d6cb", + "metadata": {}, + "source": [ + "### Get information not directly from the subsections of the config yaml file\n", + "\n", + "This just represents a different way to get some ADF info" + ] + }, + { + "cell_type": "markdown", + "id": "721ba2f9-29c8-40ee-a813-181194f46b40", + "metadata": {}, + "source": [ + "#### Get Case/Baseline Names\n", + "\n", + "This is a different wat to get case names than from the *adf.read_config_var()* method that read in data from sub-sections above" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "55b72978-0af4-4d04-87d6-0c56de58400b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['f.cam6_3_119.FLTHIST_ne30.r328_gamma0.33_soae_numcin3.001']\n", + "f.cam6_3_119.FLTHIST_ne30.r328_gamma0.33_soae.001\n" + ] + } + ], + "source": [ + "#List of case names (list by default)\n", + "case_names = adf.get_cam_info(\"cam_case_name\",required=True)\n", + "print(case_names)\n", + "\n", + "base_name = adf.get_baseline_info(\"cam_case_name\")\n", + "print(base_name)" + ] + }, + { + "cell_type": "markdown", + "id": "898f441c-b001-4165-a614-766183b638ed", + "metadata": {}, + "source": [ + "#### Get Case/Baseline Climo file locations\n", + "\n", + "Here we are calling directly from the config file, no subsection " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "77ba4bcb-70d7-47cd-a7c5-0b1827baaaf3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(['/glade/scratch/richling/adf-output/ADF-data/climos/f.cam6_3_119.FLTHIST_ne30.r328_gamma0.33_soae_numcin3.001/1995-2005/'],\n", + " '/glade/scratch/richling/adf-output/ADF-data/climos/f.cam6_3_119.FLTHIST_ne30.r328_gamma0.33_soae.001/1995-2005/')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case_climo_loc = adf.get_cam_info('cam_climo_loc', required=True)\n", + "base_climo_loc = adf.get_baseline_info(\"cam_climo_loc\")\n", + "case_climo_loc,base_climo_loc" + ] + }, + { + "cell_type": "markdown", + "id": "864e9b1f-fef8-4d45-94b6-5e4cc8e099c9", + "metadata": {}, + "source": [ + "#### Get Desired Variable Names\n", + "\n", + "Here we are calling directly from the config file, no subsection " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ff7a34b1-6795-46f3-a3b5-a40507b6aa52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['CLDHGH', 'CLDICE', 'CLDLIQ', 'CLDLOW', 'CLDMED', 'CLDTOT', 'CLOUD', 'FLNS', 'FLNT', 'FLNTC', 'FSNS', 'FSNT', 'FSNTC', 'LHFLX', 'LWCF', 'OMEGA500', 'PBLH', 'PRECL', 'PRECT', 'PRECSL', 'PRECSC', 'PRECSC', 'PRECC', 'PS', 'PSL', 'QFLX', 'Q', 'RELHUM', 'SHFLX', 'SST', 'SWCF', 'T', 'TAUX', 'TAUY', 'TGCLDIWP', 'TGCLDLWP', 'TMQ', 'TREFHT', 'TS', 'U', 'U10', 'ICEFRAC', 'OCNFRAC', 'LANDFRAC']\n" + ] + } + ], + "source": [ + "var_list = adf.diag_var_list\n", + "print(var_list)" + ] + }, + { + "cell_type": "markdown", + "id": "e8504642-fcfc-482c-b5a8-7494f9fde680", + "metadata": {}, + "source": [ + "#### Get variable defaults from adf_variable_defaults.yaml\n", + "\n", + "Take a look at what defaults are for TS" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a3a780fd-6234-4574-8535-ebf6e7bba3fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'colormap': 'Blues',\n", + " 'contour_levels_range': [220, 320, 5],\n", + " 'diff_colormap': 'BrBG',\n", + " 'diff_contour_range': [-10, 10, 1],\n", + " 'scale_factor': 1,\n", + " 'add_offset': 0,\n", + " 'new_unit': 'K',\n", + " 'mpl': {'colorbar': {'label': 'K'}},\n", + " 'obs_file': 'ERAI_all_climo.nc',\n", + " 'obs_name': 'ERAI',\n", + " 'obs_var_name': 'TS',\n", + " 'category': 'Surface variables'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adf.variable_defaults[\"TS\"]" + ] + }, + { + "cell_type": "markdown", + "id": "b2701110-87da-44be-8cc5-a67fe759870e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4e847946-aed1-4226-be42-46b46fc945ea", + "metadata": {}, + "source": [ + "## Exploration of the Output Data\n", + "\n", + "Now that the ADF has created all the necessary timeseries/climo/regridded data let's run a quick set of functions to plot time series for RESTOM, TS, SST, and ICEFRAC" + ] + }, + { + "cell_type": "markdown", + "id": "5eddc165-065b-4874-a53d-0a53486c1817", + "metadata": {}, + "source": [ + "##### Let's grab the case names, time series locations, variable defaults dictionary and climo years" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "001f31de-2897-4cfd-a7a2-a63a47b63827", + "metadata": {}, + "outputs": [], + "source": [ + "case_names = adf.get_cam_info('cam_case_name', required=True)\n", + "case_names_len = len(case_names)\n", + "data_name = adf.get_baseline_info('cam_case_name', required=False)\n", + "\n", + "case_ts_locs = adf.get_cam_info(\"cam_ts_loc\", required=True)\n", + "data_ts_loc = adf.get_baseline_info(\"cam_ts_loc\", required=False)\n", + "\n", + "res = adf.variable_defaults # dict of variable-specific plot preferences\n", + "# or an empty dictionary if use_defaults was not specified in YAML.\n", + "\n", + "start_year = adf.climo_yrs[\"syears\"]\n", + "end_year = adf.climo_yrs[\"eyears\"]" + ] + }, + { + "cell_type": "markdown", + "id": "40e35c52-2044-4677-8b7c-d8266bd19b20", + "metadata": {}, + "source": [ + "### Time Series Plotting Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a123c21a-e27b-4b2b-b674-c77b7912d996", + "metadata": {}, + "outputs": [], + "source": [ + "def _load_dataset(fils):\n", + " if len(fils) == 0:\n", + " print(\"Input file list is empty.\")\n", + " return None\n", + " elif len(fils) > 1:\n", + " return xr.open_mfdataset(fils, combine='by_coords')\n", + " else:\n", + " sfil = str(fils[0])\n", + " return xr.open_dataset(sfil)\n", + " #End if\n", + "#End def" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "eff599aa-4ad7-43b0-8381-a94177e40c6e", + "metadata": {}, + "outputs": [], + "source": [ + "def _data_calcs(ts_loc,var,subset=None):\n", + " \"\"\"\n", + " args\n", + " ----\n", + " - ts_loc: Path\n", + " path to time series file\n", + " \n", + " - var: str\n", + " name of variable\n", + " \n", + " - subset (optional): dict \n", + " lat/lon extents (south, north, east, west)\n", + " \"\"\"\n", + " fils = sorted(list(Path(ts_loc).glob(f\"*{var}*.nc\")))\n", + "\n", + " ts_ds = _load_dataset(fils)\n", + " \n", + " time = ts_ds['time']\n", + " time = xr.DataArray(ts_ds['time_bnds'].load().mean(dim='nbnd').values, dims=time.dims, attrs=time.attrs)\n", + " ts_ds['time'] = time\n", + " ts_ds.assign_coords(time=time)\n", + " ts_ds = xr.decode_cf(ts_ds)\n", + " \n", + " if subset != None:\n", + " ts_ds = ts_ds.sel(lat=slice(subset[\"s\"],subset[\"n\"]), lon=slice(subset[\"w\"],subset[\"e\"])) \n", + " \n", + " data = ts_ds[var].squeeze()\n", + " unit = data.units\n", + " \n", + " # global weighting\n", + " w = np.cos(np.radians(data.lat))\n", + " avg = data.weighted(w).mean(dim=(\"lat\",\"lon\"))\n", + " \n", + " yrs = np.unique([str(val.item().timetuple().tm_year).zfill(4) for _,val in enumerate(ts_ds[\"time\"])])\n", + "\n", + " return avg,yrs,unit" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8141a995-7a66-4562-9ea6-472cde4e21f2", + "metadata": {}, + "outputs": [], + "source": [ + "def ts_plot(ax, name, vals, yrs, unit, color_dict,linewidth=None,zorder=1):\n", + " \"\"\"\n", + " args\n", + " ----\n", + " - color_dict: dict\n", + " color and marker style for variable\n", + " \"\"\"\n", + "\n", + " ax.plot(yrs, vals, color_dict[\"marker\"], c=color_dict[\"color\"],label=name,linewidth=linewidth,zorder=zorder)\n", + "\n", + " ax.set_xlabel(\"Years\",fontsize=15,labelpad=20)\n", + " ax.set_ylabel(unit,fontsize=15,labelpad=20) \n", + "\n", + " # For the minor ticks, use no labels; default NullFormatter.\n", + " ax.tick_params(which='major', length=7)\n", + " ax.tick_params(which='minor', length=5)\n", + " \n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d7aaeed2-b21f-4610-8b2e-0530d5556b69", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_var_details(ax, var, vals_cases, vals_base):\n", + " \n", + " mins = []\n", + " maxs = []\n", + " for i,val in enumerate(vals_cases):\n", + "\n", + " mins.append(np.nanmin(vals_cases[i]))\n", + " maxs.append(np.nanmax(vals_cases[i]))\n", + "\n", + " mins.append(np.nanmin(vals_base))\n", + " maxs.append(np.nanmax(vals_base))\n", + "\n", + " if var == \"SST\": \n", + " ax.set_ylabel(\"K\",fontsize=20,labelpad=12)\n", + " tick_spacing = 0.5\n", + " ax.yaxis.set_major_locator(MultipleLocator(1))\n", + " ax.set_title(f\"Time Series Global: {var} - ANN\",loc=\"left\",fontsize=22)\n", + " \n", + " if var == \"TS\":\n", + " ax.set_ylabel(\"K\",fontsize=20,labelpad=12)\n", + " tick_spacing = 0.5\n", + " ax.yaxis.set_minor_locator(MultipleLocator(0.5))\n", + " ax.set_title(f\"Time Series Global: {var} - ANN\",loc=\"left\",fontsize=22)\n", + "\n", + " if var == \"ICEFRAC\":\n", + " ax.set_ylabel(\"frac\",fontsize=20,labelpad=12)\n", + " tick_spacing = 0.1\n", + " ax.set_ylim(np.floor(min(mins)),np.ceil(max(maxs)))\n", + " ax.set_title(f\"Time Series LabSea: {var} - ANN\",loc=\"left\",fontsize=22)\n", + "\n", + " if var == \"RESTOM\":\n", + " ax.set_ylabel(\"W/m2\",fontsize=20,labelpad=12)\n", + " tick_spacing = 0.5\n", + " ax.yaxis.set_minor_locator(MultipleLocator(0.1))\n", + " ax.set_title(f\"Time Series Global: {var} - ANN\",loc=\"left\",fontsize=22)\n", + " \n", + " # Set label to show if RESTOM is 1 or 5-yr avg\n", + " line_1yr = Line2D([], [], label='1-yr avg', color='k', linewidth=1,marker='*',) \n", + " line_5yr = Line2D([], [], label='5-yr avg', color='k', linewidth=1,)\n", + " ax.legend(handles=[line_1yr,line_5yr], bbox_to_anchor=(0.99, 0.99))\n", + " \n", + " # Add extra space on the y-axis, except for ICEFRAC\n", + " if var != \"ICEFRAC\":\n", + " ax.set_ylim(np.floor(min(mins)),np.ceil(max(maxs))+tick_spacing)\n", + " \n", + " ax.yaxis.set_major_locator(MultipleLocator(tick_spacing))\n", + " \n", + " ax.tick_params(axis='y', which='major', labelsize=16)\n", + " ax.tick_params(axis='y', which='minor', labelsize=16)\n", + " \n", + " ax.tick_params(axis='x', which='major', labelsize=14)\n", + " ax.tick_params(axis='x', which='minor', labelsize=14)\n", + " \n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26eb6dfb-eec3-4c89-9964-6495891ca572", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1fa7a0d8-6d8f-45e1-b606-9507a11764dc", + "metadata": {}, + "source": [ + "### Plot the time series!" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "67e22347-a3b8-4b3c-a68f-38a84ff58454", + "metadata": {}, + "outputs": [], + "source": [ + "ts_var_list = [\"RESTOM\",\"TS\",\"SST\",\"ICEFRAC\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "90848bf6-403d-4a3b-a69a-b4ff12f3891b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting variable: RESTOM\n", + "Plotting variable: TS\n", + "Plotting variable: SST\n", + "Plotting variable: ICEFRAC\n", + "CPU times: user 14.6 s, sys: 8.64 s, total: 23.3 s\n", + "Wall time: 26 s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.ticker as ticker\n", + "from matplotlib.ticker import MultipleLocator\n", + "from matplotlib.lines import Line2D\n", + "\n", + "fig = plt.figure(figsize=(30,15))\n", + "\n", + "# Change the layout/number of subplots based off number of variables desired\n", + "rows = 2\n", + "cols = 3\n", + "gs = fig.add_gridspec(rows, cols, hspace=.3, wspace=.2)\n", + "\n", + "# Rough subset for Lab Sea\n", + "w = -63.5+360\n", + "e = -47.5+360\n", + "s = 53.5\n", + "n = 65.5\n", + "subset = {\"s\":s,\"n\":n,\"e\":e,\"w\":w}\n", + "\n", + "# Add more colors as needed for number of test cases\n", + "# ** Baseline is already added as green dashed line in plotting function **\n", + "# matplotlib colors here: https://matplotlib.org/stable/gallery/color/named_colors.html\n", + "colors = [\"k\", \"aqua\", \"orange\", \"b\", \"magenta\", \"goldenrod\", \"slategrey\", \"rosybrown\"]\n", + "\n", + "# Setup plotting\n", + "#---------------\n", + "\n", + "# Loop over variables:\n", + "for i,var in enumerate(ts_var_list):\n", + " \n", + " print(\"Plotting variable:\",var)\n", + " \n", + " if var == \"RESTOM\":\n", + " ax = plt.subplot(gs[0, 0])\n", + " if var == \"TS\":\n", + " ax = plt.subplot(gs[0, 1])\n", + " if var == \"SST\":\n", + " ax = plt.subplot(gs[0, 2])\n", + " if var == \"ICEFRAC\":\n", + " ax = plt.subplot(gs[1, 0])\n", + " \n", + " \n", + " # Grab baseline case:\n", + " #--------------------\n", + "\n", + " if var == \"RESTOM\": \n", + " avg_base_FSNT,yrs_base,unit = _data_calcs(data_ts_loc,'FSNT')\n", + " avg_base_FLNT,_,_ = _data_calcs(data_ts_loc,\"FLNT\")\n", + " if len(yrs_base) < 5:\n", + " print(f\"Not a lot of climo years for {data_name}, only doing 1-yr avg for RESTOM...\")\n", + " FSNT_base = avg_base_FSNT\n", + " FLNT_base = avg_base_FLNT\n", + " else:\n", + " FSNT_base = avg_base_FSNT.rolling(time=60,center=True).mean()\n", + " FLNT_base = avg_base_FLNT.rolling(time=60,center=True).mean()\n", + "\n", + " avg_base = FSNT_base - FLNT_base\n", + " \n", + " if (var == \"TS\" or var == \"SST\"):\n", + " avg_base,yrs_base,unit = _data_calcs(data_ts_loc,var)\n", + " \n", + " if var == \"ICEFRAC\":\n", + " avg_base,yrs_base,unit = _data_calcs(data_ts_loc,var,subset)\n", + " \n", + " # Get int of years for plotting on x-axis\n", + " yrs_base_int = yrs_base.astype(int)\n", + "\n", + " # Create yearly averages\n", + " vals_base = [avg_base.sel(time=i).mean() for i in yrs_base]\n", + " \n", + " # Plot baseline data\n", + " color_dict = {\"color\":\"g\",\"marker\":\"--\"}\n", + " ax = ts_plot(ax, data_name, vals_base, yrs_base_int, unit, color_dict)\n", + "\n", + " # Loop over test cases:\n", + " #----------------------\n", + " # Create lists to hold all sets of years (for each case) and\n", + " # sets of var data (for each case)\n", + " vals_cases = []\n", + " yrs_cases = []\n", + " for case_idx, case_name in enumerate(case_names):\n", + "\n", + " if var == \"RESTOM\":\n", + " avg_case_FSNT,yrs_case,unit = _data_calcs(case_ts_locs[case_idx],'FSNT')\n", + " avg_case_FLNT,_,_ = _data_calcs(case_ts_locs[case_idx],\"FLNT\")\n", + " if len(yrs_case) < 5:\n", + " print(f\"Not a lot of climo years for {case_name}, only doing 1-yr avg for RESTOM...\")\n", + " FSNT_case = avg_case_FSNT\n", + " FLNT_case = avg_case_FLNT\n", + " color_dict = {\"color\":colors[case_idx],\"marker\":\"-*\"}\n", + " else:\n", + " FSNT_case = avg_case_FSNT.rolling(time=60,center=True).mean()\n", + " FLNT_case = avg_case_FLNT.rolling(time=60,center=True).mean()\n", + " color_dict = {\"color\":colors[case_idx],\"marker\":\"-\"}\n", + "\n", + " avg_case = FSNT_case - FLNT_case\n", + "\n", + " if (var == \"TS\" or var == \"SST\"):\n", + " avg_case,yrs_case,unit = _data_calcs(case_ts_locs[case_idx],var)\n", + " color_dict = {\"color\":colors[case_idx],\"marker\":\"-\"}\n", + " \n", + " if var == \"ICEFRAC\":\n", + " avg_case,yrs_case,unit = _data_calcs(case_ts_locs[case_idx],var,subset)\n", + " color_dict = {\"color\":colors[case_idx],\"marker\":\"-\"}\n", + " \n", + " # Get yearly averages for all available years\n", + " vals_case = [avg_case.sel(time=i).mean() for i in yrs_case]\n", + " vals_cases.append(vals_case)\n", + " \n", + " # Get int of years for plotting on x-axis\n", + " yrs_case_int = yrs_case.astype(int)\n", + " yrs_cases.append(yrs_case_int)\n", + " \n", + " # Add case to plot (ax)\n", + " ax = ts_plot(ax, case_name, vals_case, yrs_case_int, unit, color_dict)\n", + "\n", + " # End for (case names)\n", + "\n", + " # Get variable details\n", + " ax = plot_var_details(ax, var, vals_cases, vals_base)\n", + "\n", + " #Grab all unique years and find min/max years\n", + " uniq_yrs = sorted(x for v in yrs_cases for x in v)\n", + " max_year = int(max(uniq_yrs))\n", + " min_year = int(min(uniq_yrs))\n", + "\n", + " last_year = max_year - max_year % 5\n", + " if (max_year > 5) and (last_year < max_year):\n", + " last_year += 5\n", + "\n", + " first_year = min_year - min_year % 5\n", + " if min_year < 5:\n", + " first_year = 0\n", + "\n", + " ax.set_xlim(first_year, last_year)\n", + " ax.set_xlabel(\"Years\",fontsize=15,labelpad=20)\n", + " # Set the x-axis plot limits\n", + " # to guarantee data from all cases (including baseline) are on plot\n", + " ax.set_xlim(min_year, max_year+1)\n", + "\n", + " # x-axis ticks and numbers\n", + " if max_year-min_year > 120:\n", + " ax.xaxis.set_major_locator(MultipleLocator(20))\n", + " ax.xaxis.set_minor_locator(MultipleLocator(10))\n", + " if 10 <= max_year-min_year <= 120:\n", + " ax.xaxis.set_major_locator(MultipleLocator(5))\n", + " ax.xaxis.set_minor_locator(MultipleLocator(1))\n", + " if 0 < max_year-min_year < 10:\n", + " ax.xaxis.set_major_locator(MultipleLocator(1))\n", + " ax.xaxis.set_minor_locator(MultipleLocator(1))\n", + " \n", + " # End for (case loop)\n", + "# End for (variables loop)\n", + "\n", + "# Set up legend\n", + "# Gather labels based on case names and plotted line format (color, style, etc)\n", + "lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n", + "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n", + "fig.legend(lines[:case_names_len+1], labels[:case_names_len+1],\n", + " loc=\"center left\",fontsize=18,\n", + " bbox_to_anchor=(0.365, 0.25,.02,.05)) #bbox_to_anchor(x0, y0, width, height)\n", + " \n", + "#plt.savefig(\"TimeSeries_ANN.png\", facecolor='w',bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b037d2f-85c0-4849-a8f1-f4a18d3ddc31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023a", + "language": "python", + "name": "npl-2023a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}