AR6 climate assessment on postprocessing

requirements.txt

@@ -4,3 +4,6 @@
 # |  AGPL-3.0, you are granted additional permissions described in the
 # |  REMIND License Exception, version 1.0 (see LICENSE file).
 # |  Contact: [email protected]
+climate-assessment
+netcdf4
+pandas<2

scripts/utils/climate_assessment/source_climate_assessment.py

@@ -0,0 +1,112 @@
+# %%
+import os
+import os.path
+import sys
+
+import pandas as pd
+import pandas.testing as pdt
+import tempfile
+
+import climate_assessment
+from climate_assessment.cli import run_workflow
+import pyam
+
+# %%
+# User setup
+
+excel_file = sys.argv[1]
+# mif_file = "REMIND_gabrielAR6SHAPE_2023-05-17_05.12.52.mif"
+
+# Base folder containing the MAGICC binary and parameter sets
+os.environ["MAGICC_ROOT_FILES_DIR"] = "/p/projects/rd3mod/climate-assessment-files/"
+
+# How many MAGICC workers can run in parallel?
+os.environ["MAGICC_WORKER_NUMBER"] = "12"
+
+# Where should the MAGICC workers be located on the filesystem (you need about
+# 500Mb space per worker at the moment, they're removed after use)
+os.environ["MAGICC_WORKER_ROOT_DIR"] = tempfile.gettempdir()
+
+
+# %%
+# Derived setup
+# Where is the binary
+os.environ["MAGICC_EXECUTABLE_7"] = os.path.join(
+    os.environ["MAGICC_ROOT_FILES_DIR"], "magicc-v7.5.3", "bin", "magicc"
+)
+infilling_database_file = os.path.join(
+    os.environ["MAGICC_ROOT_FILES_DIR"],"1652361598937-ar6_emissions_vetted_infillerdatabase_10.5281-zenodo.6390768.csv"
+)
+infilling_database_file
+
+# %%
+model = "magicc"
+model_version = "v7.5.3"
+probabilistic_file = os.path.join(
+    os.environ["MAGICC_ROOT_FILES_DIR"],"magicc-ar6-0fd0f62-f023edb-drawnset/0fd0f62-derived-metrics-id-f023edb-drawnset.json"
+)
+
+# Use fewer (e.g. 10) if you just want to do a test run but note that this breaks
+# the stats of the probabilistic ensemble
+num_cfgs = 600
+# num_cfgs = 10
+# Set to True if you're not using the full MAGICC ensemble
+test_run = False
+# test_run = True
+# How many scenarios do you want to run in one go?
+# Usually better to use more MAGICC workers. 
+# scenario_batch_size = 20
+scenario_batch_size = 1
+
+# Where should the output be saved? Create if it doesn't exist
+outdir = os.path.join("output_climate")
+if not(os.path.exists(outdir) and os.path.isdir(outdir)):
+    os.makedirs(outdir)
+
+outdir
+
+#%%
+print("Reading "+ excel_file)
+print("Keep in mind that the initial read of the Excel time can take up to minutes, so dont expect any output for a while")
+inxls = pyam.IamDataFrame(excel_file)
+
+#%%
+csv_file = os.path.splitext(excel_file)[0] + ".csv"
+print("Filtering regions and writing to " + csv_file)
+inxls.filter(region="World").to_csv(csv_file)
+#%%
+# %%
+# FIXME: This is commented, but is needed if we use mifs
+# instead of xls files. The definitive solution will definitely
+# use mifs, as reading xls in pandas is way too slow for use every iteration. 
+# # Get the mif file with the proper format for pyam to read it
+# inmif = pd.read_csv(mif_file, delimiter=";")
+# inmif = inmif.query('Region == "World"')
+# # inmif["Model"] = "REMIND-MAgPIE"
+
+# if 'Unnamed' in inmif.columns[len(inmif.columns)-1]:
+#     inmif = inmif.iloc[:,:-1]
+
+# # inmif = inmif[inmif["Scenario"].isin(usescens)]
+
+# csv_file = os.path.splitext(mif_file)[0] + ".csv"
+
+# inmif.to_csv(csv_file, index = False)
+
+# # Check if pyam will read the file
+# testread = pyam.IamDataFrame(csv_file)
+# testread
+
+# %%
+print("Starting workflow using "+ excel_file)
+run_workflow(
+    csv_file,
+    outdir,
+    model=model,
+    model_version=model_version,
+    probabilistic_file=probabilistic_file,
+    num_cfgs=num_cfgs,
+    infilling_database=infilling_database_file,
+    scenario_batch_size=scenario_batch_size,
+)
+

scripts/utils/climate_assessment/submit_climate_assessment.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --qos=priority
+#SBATCH --time=06:00:00
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=12
+#SBATCH --mem=60000
+#SBATCH --job-name=test_remind_2
+#SBATCH --output=PYTHONLOG-%x.%j.out
+source ~/.bashrc
+# Replace this with the resulting xls of output.R -> export -> xlsx_IIASA -> AR6
+filename="../../../output/export/REMIND_gabrielAR6SHAPE_2023-05-17_05.12.52.xlsx"
+python source_climate_assessment.py $filename