diff --git a/.virtual_documents/rime/pp_combined_example.ipynb b/.virtual_documents/rime/pp_combined_example.ipynb
new file mode 100644
index 0000000..b3f1821
--- /dev/null
+++ b/.virtual_documents/rime/pp_combined_example.ipynb
@@ -0,0 +1,407 @@
+
+
+
+from rime.process_config import *
+from rime.rime_functions import *
+from rime.utils import *
+
+import dask
+import dask.dataframe as dd
+# from dask.diagnostics import ProgressBar
+from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler, ProgressBar
+from dask.distributed import Client
+
+# from dask.distributed import Client # uncomment this to open Dask client to see performance
+import glob
+import numpy as np
+import pandas as pd
+import pyam
+import re
+import time
+import xarray as xr
+import yaml
+
+
+# Load a yaml file that will help select multiple climate indicator files (not necessarily needed)
+with open("indicator_params.yml", "r") as f:
+    params = yaml.full_load(f)
+
+
+dask.config.set(scheduler="processes")
+dask.config.set(num_workers=num_workers)
+client = Client()  # uncomment this to open Dask client
+
+
+
+
+
+
+
+
+os.getcwd()
+
+
+df_scens_in = pyam.IamDataFrame('test_data/emissions_temp_AR6_small.xlsx')
+dft = df_scens_in.filter(variable=temp_variable) # temp_variable as defined in the config file, = 'AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile'
+
+# Replace & fill missing SSP scenario allocation
+dft = ssp_helper(dft, ssp_meta_col="Ssp_family", default_ssp="SSP2")
+
+
+
+
+
+scenarios = ["SSP1-26", "SSP2-45", "SSP3-Baseline", "SSP5-Baseline"]
+variable = (
+    "AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile"
+)
+ssps = df_scens_in.filter(scenario=scenarios, model="IMAGE*", variable=variable)
+imps = df_scens_in.filter(
+    IMP_marker=["CurPol", "ModAct", "SP", "GS", "Neg"], variable=variable
+)  # ,'Ren-2.0','Neg-2.0'
+ssps_imp = ssps.append(imps)
+ssps_imp = ssp_helper(ssps_imp, ssp_meta_col="Ssp_family", default_ssp="SSP2")
+ssps_imp
+
+
+
+
+
+
+
+
+# Requires some configuration - need to download the data from https://zenodo.org/records/10212339, and set the file paths in the file process_config.py
+
+
+
+# ind = "pr"
+# var = "pr"
+# short = "r10"  # params["indicators"][ind][var]["short_name"]
+ssp = "ssp2"
+ftype = "abs"
+
+
+# published data
+
+# # Prepare data
+# impact_data_dir = 'test_data//'
+# files = glob.glob(f"{impact_data_dir}/{var}*{short}_*{ftype}.nc4")
+# mapdata = xr.open_mfdataset(
+#     files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gmt"
+# )
+# mapdata = xr.concat([mapdata[x] for x in mapdata.data_vars], dim='gmt').to_dataset(name=f'{var}_{short}')
+# mapdata = tidy_mapdata(mapdata)
+# mapdata = mapdata.sortby("gmt").drop_duplicates("gmt")
+
+
+# impact_data_dir = '/mnt/c/users/byers/iiasa/ECE.prog - Documents/Research Theme - NEXUS/Hotspots_Explorer_2p0/rcre_testing/rime_testdata'
+# files = glob.glob(f"{impact_data_dir}/*{var}*{short}_*{ftype}.nc4")
+# files
+
+
+
+# Prepare data (using split_files on sharepoint)
+# impact_data_dir = '/mnt/c/users/byers/iiasa/ECE.prog - Documents/Research Theme - NEXUS/Hotspots_Explorer_2p0/rcre_testing/rime_testdata'
+impact_data_dir = 'test_data'
+# indicators = ['dri','iavar']
+ind = 'dri_qtot'
+
+files = glob.glob(f"{impact_data_dir}/*{ind}_*{ftype}.nc4")
+mapdata = xr.open_mfdataset(
+    files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gwl"
+)
+mapdata
+# mapdata = xr.concat([mapdata[x] for x in mapdata.data_vars], dim='gwl').to_dataset(name=f'{var}_{short}')
+mapdata = tidy_mapdata(mapdata)
+# mapdata = mapdata.sortby("gwl").drop_duplicates("gwl")
+
+
+mapdata
+
+
+print("Test multiple IAM scenarios, 1 indicator")
+
+start = time.time()
+
+# Run function
+# Using dask for small numbers of scenarios ~<10 can be slower due to spawning of workers.
+# use_dask=True currently broken 
+map_out_MS = map_transform_gwl_wrapper(
+    ssps_imp,
+    mapdata,
+    years,
+    caution_checks=True,
+    use_dask=False,
+    include_orig_gwl=False,
+    gwl_name="gwl",
+    drawdown_max=0.15,
+    temp_min=1.2,
+    temp_max=3.0,
+    interpolation=0.01,
+)
+
+comp = dict(zlib=True, complevel=5)
+encoding = {var: comp for var in map_out_MS.data_vars}
+filename = f"scenario_maps_multiscenario_{ftype}_test_notebook.nc"
+map_out_MS.to_netcdf(filename, encoding=encoding)
+
+print("FINISHED Test multiple scenarios, 1 indicator")
+print(f"{time.time()-start}")
+
+
+map_out_MS
+
+
+
+
+
+
+
+
+# Doesn't work -0
+# AttributeError: 'DataArray' object has no attribute 'rename_vars'
+# lines 230-233 in map_transform_gmt
+
+gwl_name = "gwl"
+print("Test 1 scenario, multiple indicators")
+start = time.time()
+ssp = "ssp2"
+mapdata = xr.Dataset()
+
+# impact_data_dir = '/mnt/p/watxene/ISIMIP_postprocessed/cse/split_files_fixed'
+impact_data_dir = 'test_data'
+indicators = ['dri','iavar']
+for ind in indicators:
+
+    print(ind)
+    files = glob.glob(f"{impact_data_dir}/ISI*{ind}*_{ftype}*.nc4")
+        
+    a = xr.open_mfdataset(
+            files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gwl")
+    for x in a.data_vars:
+        mapdata[x] = a[x]
+
+
+mapdata = tidy_mapdata(mapdata)
+mapdata
+
+
+os.getcwd()
+
+
+
+
+
+map_out_MI = map_transform_gwl_wrapper(
+    ssps_imp.filter(IMP_marker="CurPol"),
+    mapdata,
+    years,
+    use_dask=False,
+    include_orig_gwl=False,
+    gwl_name="gwl",
+    drawdown_max=0.15,
+    temp_min=1.2,
+    temp_max=3.0,
+    interpolation=0.01,
+)
+
+comp = dict(zlib=True, complevel=5)
+encoding = {var: comp for var in map_out_MI.data_vars}
+filename = f"scenario_maps_multiindicator_{ftype}_test_notebook.nc"
+# map_out_MI.to_netcdf(filename, encoding=encoding)
+
+print("FINISHED 1 scenario, multiple indicators")
+print(f"{time.time()-start}")
+
+
+
+
+
+map_out_MI
+
+
+
+
+
+filename = "test_map_notebook.html"
+plot_maps_dashboard(
+    map_out_MI,
+    filename=filename,
+    year=2063,
+    cmap="magma_r",
+    shared_axes=True,
+    clim=None,
+)
+#os.startfile(filename) # Windows
+import webbrowser
+webbrowser.open(f'file://{os.path.realpath(filename)}')
+
+
+
+
+
+
+
+
+filesall = glob.glob(fname_input_climate)
+files = filesall  # [:6]
+f = files[-0]
+# load input climate impacts data file
+ds = xr.open_mfdataset(f)
+ds = ds.sel(year=years)
+ds
+
+
+varis = list(ds.data_vars.keys())[:lvaris]
+dsi = ds[varis]
+print(f"# of variables = {len(varis)}")
+
+
+
+
+
+mode = "GMT"
+if mode == "GMT":
+    dfp = df_scens_in.filter(variable=temp_variable)
+elif mode == "bypass":
+    dfp = prepare_cumulative(df_scens_in, years=years, use_dask=True)
+    ts = dfp.timeseries().apply(co2togmt_simple)
+    ts = pyam.IamDataFrame(ts)
+    ts.rename(
+        {
+            "variable": {ts.variable[0]: "RIME|GSAT_tcre"},
+            "unit": {ts.unit[0]: "°C"},
+        },
+        inplace=True,
+    )
+    # Export data to check error and relationships
+    # ts.append(dfp).to_csv('c://users//byers//downloads//tcre_gmt_output.csv')
+    dfp = ts
+    dfp.meta = df_scens_in.meta.copy()
+dfp = dfp.filter(year=years)
+
+
+# For testing on C1 scenarios
+# few_scenarios = True
+# very_few_scenarios = False
+
+# if few_scenarios:
+#     dfp = dfp.filter(Category=["C1*"])
+#     if very_few_scenarios:
+#         dfp = dfp.filter(model="REMIND 2.1*", scenario="*")
+
+dfp = ssps_imp
+
+
+# pre-prepare the dataset into correct format
+#  Assign SSP to meta and select SSP2 in case SSP not present in name
+dfp = ssp_helper(dfp, ssp_meta_col="Ssp_family", default_ssp="SSP2", keep_meta=False)
+
+dft = dfp.timeseries()
+dft = dft.join(dfp.meta["Ssp_family"]).reset_index()
+# dft = dft.apply(fix_duplicate_temps, years=years, axis=1)
+
+
+
+
+
+start = time.time()
+year_res = 10
+parallel = True
+if parallel:
+    """
+    For parallel processing, convert dft as a wide IAMC pd.Dataframe
+    into a dask.DataFrame.
+    """
+    ddf = dd.from_pandas(dft, npartitions=1000)
+
+    # dfx = dft.iloc[0].squeeze()  # FOR DEBUIGGING THE FUNCTION
+    outd = ddf.apply(
+        table_impacts_gmt,
+        dsi=dsi,
+        ssp_meta_col="Ssp_family",
+        axis=1,
+        meta=("result", None),
+    )
+
+    with ProgressBar():
+        # try:
+        df_new = outd.compute(num_workers=num_workers)
+else:
+    df_new = dft.apply(table_impacts_gmt, dsi=dsi, axis=1)
+
+expandeddGMT = pd.concat([df_new[x] for x in df_new.index])
+print(f" Done:  {time.time()-start}")
+
+filename = f"RIME_output_{region}_{year_res}yr.csv"
+
+# expandedd.to_csv(filename, encoding="utf-8", index=False)
+print(f" Saved: {region} yrs={year_res}\n  {time.time()-start}")
+print(f"{len(dsi.data_vars)} variables, {len(dfp.meta)} scenarios")
+
+
+
+
+
+expandeddGMT = pyam.IamDataFrame(expandeddGMT)
+
+
+expandeddGMT.variable
+
+
+# model = "IMAG*"
+scenario = [
+    "CO_Bridge",
+    "EN_INDCi2030_3000f",
+    "EN_NPi2020_400f_lowBECCS",
+    # 'NGFS2_Current Policies',
+    "SSP1-26",
+    "SSP2-45",
+    # 'SSP3-Baseline',
+    # 'SSP5-Baseline',
+    "SusDev_SDP-PkBudg1000",
+]
+variable = "RIME|sdii|Hazard|Risk score|Population weighted"
+# variable = 'RIME|wsi|Exposure|Population|%'
+variable = "RIME|cdd|Exposure|Population|%"
+expandeddGMT.filter(variable=variable, scenario=scenario, region="GBR").plot()
+
+
+expandeddGMT.filter(variable=variable, scenario=scenario, region="PAK").plot()
+
+
+expandeddGMT.variable
+
+
+
+
+
+imps.scenario
+
+
+color_map = {
+    "SSP1-26": "AR6-SSP1-2.6",
+    "SSP2-45": "AR6-SSP2-4.5",
+    "SSP3-Baseline": "AR6-SSP3-7.0",
+    "SSP5-Baseline": "AR6-SSP5-8.5",
+    "NGFS2_Current Policies": "AR6-IP-CurPol",
+    # 'EN_NPi2020_900f': 'AR6-IMP-Neg2.0',
+    "EN_INDCi2030_3000f": "AR6-IP-ModAct",
+    # 'SSP2_openres_lc_50': 'AR6-IMP-Ren2.0',
+    # AR6-IMP-GS',
+    "CO_Bridge": "AR6-IMP-GS",
+    "EN_NPi2020_400f_lowBECCS": "AR6-IMP-Neg",
+    "SusDev_SDP-PkBudg1000": "AR6-IMP-SP",
+}
+
+pyam.run_control().update({"color": {"scenario": color_map}})
+
+ssps_imp.filter(
+    variable="AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile",
+    scenario="SSP*",
+).plot(legend=False, fill_between=True, color="scenario", title="")
+
+
+ssps_imp.filter(
+    variable="AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile"
+).plot(legend=False, fill_between=True, color="scenario", title="")
diff --git a/poetry.lock b/poetry.lock
index 57b2da4..9b39fe8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1242,49 +1242,62 @@ files = [
 
 [[package]]
 name = "matplotlib"
-version = "3.8.3"
+version = "3.7.0"
 description = "Python plotting package"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-3.8.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:cf60138ccc8004f117ab2a2bad513cc4d122e55864b4fe7adf4db20ca68a078f"},
-    {file = "matplotlib-3.8.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5f557156f7116be3340cdeef7f128fa99b0d5d287d5f41a16e169819dcf22357"},
-    {file = "matplotlib-3.8.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f386cf162b059809ecfac3bcc491a9ea17da69fa35c8ded8ad154cd4b933d5ec"},
-    {file = "matplotlib-3.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3c5f96f57b0369c288bf6f9b5274ba45787f7e0589a34d24bdbaf6d3344632f"},
-    {file = "matplotlib-3.8.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:83e0f72e2c116ca7e571c57aa29b0fe697d4c6425c4e87c6e994159e0c008635"},
-    {file = "matplotlib-3.8.3-cp310-cp310-win_amd64.whl", hash = "sha256:1c5c8290074ba31a41db1dc332dc2b62def469ff33766cbe325d32a3ee291aea"},
-    {file = "matplotlib-3.8.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:5184e07c7e1d6d1481862ee361905b7059f7fe065fc837f7c3dc11eeb3f2f900"},
-    {file = "matplotlib-3.8.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d7e7e0993d0758933b1a241a432b42c2db22dfa37d4108342ab4afb9557cbe3e"},
-    {file = "matplotlib-3.8.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:04b36ad07eac9740fc76c2aa16edf94e50b297d6eb4c081e3add863de4bb19a7"},
-    {file = "matplotlib-3.8.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7c42dae72a62f14982f1474f7e5c9959fc4bc70c9de11cc5244c6e766200ba65"},
-    {file = "matplotlib-3.8.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:bf5932eee0d428192c40b7eac1399d608f5d995f975cdb9d1e6b48539a5ad8d0"},
-    {file = "matplotlib-3.8.3-cp311-cp311-win_amd64.whl", hash = "sha256:40321634e3a05ed02abf7c7b47a50be50b53ef3eaa3a573847431a545585b407"},
-    {file = "matplotlib-3.8.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:09074f8057917d17ab52c242fdf4916f30e99959c1908958b1fc6032e2d0f6d4"},
-    {file = "matplotlib-3.8.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5745f6d0fb5acfabbb2790318db03809a253096e98c91b9a31969df28ee604aa"},
-    {file = "matplotlib-3.8.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b97653d869a71721b639714b42d87cda4cfee0ee74b47c569e4874c7590c55c5"},
-    {file = "matplotlib-3.8.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:242489efdb75b690c9c2e70bb5c6550727058c8a614e4c7716f363c27e10bba1"},
-    {file = "matplotlib-3.8.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:83c0653c64b73926730bd9ea14aa0f50f202ba187c307a881673bad4985967b7"},
-    {file = "matplotlib-3.8.3-cp312-cp312-win_amd64.whl", hash = "sha256:ef6c1025a570354297d6c15f7d0f296d95f88bd3850066b7f1e7b4f2f4c13a39"},
-    {file = "matplotlib-3.8.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:c4af3f7317f8a1009bbb2d0bf23dfaba859eb7dd4ccbd604eba146dccaaaf0a4"},
-    {file = "matplotlib-3.8.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4c6e00a65d017d26009bac6808f637b75ceade3e1ff91a138576f6b3065eeeba"},
-    {file = "matplotlib-3.8.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e7b49ab49a3bea17802df6872f8d44f664ba8f9be0632a60c99b20b6db2165b7"},
-    {file = "matplotlib-3.8.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6728dde0a3997396b053602dbd907a9bd64ec7d5cf99e728b404083698d3ca01"},
-    {file = "matplotlib-3.8.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:813925d08fb86aba139f2d31864928d67511f64e5945ca909ad5bc09a96189bb"},
-    {file = "matplotlib-3.8.3-cp39-cp39-win_amd64.whl", hash = "sha256:cd3a0c2be76f4e7be03d34a14d49ded6acf22ef61f88da600a18a5cd8b3c5f3c"},
-    {file = "matplotlib-3.8.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:fa93695d5c08544f4a0dfd0965f378e7afc410d8672816aff1e81be1f45dbf2e"},
-    {file = "matplotlib-3.8.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e9764df0e8778f06414b9d281a75235c1e85071f64bb5d71564b97c1306a2afc"},
-    {file = "matplotlib-3.8.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:5e431a09e6fab4012b01fc155db0ce6dccacdbabe8198197f523a4ef4805eb26"},
-    {file = "matplotlib-3.8.3.tar.gz", hash = "sha256:7b416239e9ae38be54b028abbf9048aff5054a9aba5416bef0bd17f9162ce161"},
+    {file = "matplotlib-3.7.0-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:3da8b9618188346239e51f1ea6c0f8f05c6e218cfcc30b399dd7dd7f52e8bceb"},
+    {file = "matplotlib-3.7.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:c0592ba57217c22987b7322df10f75ef95bc44dce781692b4b7524085de66019"},
+    {file = "matplotlib-3.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:21269450243d6928da81a9bed201f0909432a74e7d0d65db5545b9fa8a0d0223"},
+    {file = "matplotlib-3.7.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eb2e76cd429058d8954121c334dddfcd11a6186c6975bca61f3f248c99031b05"},
+    {file = "matplotlib-3.7.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:de20eb1247725a2f889173d391a6d9e7e0f2540feda24030748283108b0478ec"},
+    {file = "matplotlib-3.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c5465735eaaafd1cfaec3fed60aee776aeb3fd3992aa2e49f4635339c931d443"},
+    {file = "matplotlib-3.7.0-cp310-cp310-win32.whl", hash = "sha256:092e6abc80cdf8a95f7d1813e16c0e99ceda8d5b195a3ab859c680f3487b80a2"},
+    {file = "matplotlib-3.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:4f640534ec2760e270801056bc0d8a10777c48b30966eef78a7c35d8590915ba"},
+    {file = "matplotlib-3.7.0-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:f336e7014889c38c59029ebacc35c59236a852e4b23836708cfd3f43d1eaeed5"},
+    {file = "matplotlib-3.7.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:3a10428d4f8d1a478ceabd652e61a175b2fdeed4175ab48da4a7b8deb561e3fa"},
+    {file = "matplotlib-3.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:46ca923e980f76d34c1c633343a72bb042d6ba690ecc649aababf5317997171d"},
+    {file = "matplotlib-3.7.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c849aa94ff2a70fb71f318f48a61076d1205c6013b9d3885ade7f992093ac434"},
+    {file = "matplotlib-3.7.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:827e78239292e561cfb70abf356a9d7eaf5bf6a85c97877f254009f20b892f89"},
+    {file = "matplotlib-3.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:691ef1f15360e439886186d0db77b5345b24da12cbc4fc57b26c4826db4d6cab"},
+    {file = "matplotlib-3.7.0-cp311-cp311-win32.whl", hash = "sha256:21a8aeac39b4a795e697265d800ce52ab59bdeb6bb23082e2d971f3041074f02"},
+    {file = "matplotlib-3.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:01681566e95b9423021b49dea6a2395c16fa054604eacb87f0f4c439750f9114"},
+    {file = "matplotlib-3.7.0-cp38-cp38-macosx_10_12_universal2.whl", hash = "sha256:cf119eee4e57389fba5ac8b816934e95c256535e55f0b21628b4205737d1de85"},
+    {file = "matplotlib-3.7.0-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:21bd4033c40b95abd5b8453f036ed5aa70856e56ecbd887705c37dce007a4c21"},
+    {file = "matplotlib-3.7.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:111ef351f28fd823ed7177632070a6badd6f475607122bc9002a526f2502a0b5"},
+    {file = "matplotlib-3.7.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f91d35b3ef51d29d9c661069b9e4ba431ce283ffc533b981506889e144b5b40e"},
+    {file = "matplotlib-3.7.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:0a776462a4a63c0bfc9df106c15a0897aa2dbab6795c693aa366e8e283958854"},
+    {file = "matplotlib-3.7.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0dfd4a0cbd151f6439e6d7f8dca5292839ca311e7e650596d073774847ca2e4f"},
+    {file = "matplotlib-3.7.0-cp38-cp38-win32.whl", hash = "sha256:56b7b79488209041a9bf7ddc34f1b069274489ce69e34dc63ae241d0d6b4b736"},
+    {file = "matplotlib-3.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:8665855f3919c80551f377bc16df618ceabf3ef65270bc14b60302dce88ca9ab"},
+    {file = "matplotlib-3.7.0-cp39-cp39-macosx_10_12_universal2.whl", hash = "sha256:f910d924da8b9fb066b5beae0b85e34ed1b6293014892baadcf2a51da1c65807"},
+    {file = "matplotlib-3.7.0-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:cf6346644e8fe234dc847e6232145dac199a650d3d8025b3ef65107221584ba4"},
+    {file = "matplotlib-3.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3d1e52365d8d5af699f04581ca191112e1d1220a9ce4386b57d807124d8b55e6"},
+    {file = "matplotlib-3.7.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c869b646489c6a94375714032e5cec08e3aa8d3f7d4e8ef2b0fb50a52b317ce6"},
+    {file = "matplotlib-3.7.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f4ddac5f59e78d04b20469bc43853a8e619bb6505c7eac8ffb343ff2c516d72f"},
+    {file = "matplotlib-3.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb0304c1cd802e9a25743414c887e8a7cd51d96c9ec96d388625d2cd1c137ae3"},
+    {file = "matplotlib-3.7.0-cp39-cp39-win32.whl", hash = "sha256:a06a6c9822e80f323549c6bc9da96d4f233178212ad9a5f4ab87fd153077a507"},
+    {file = "matplotlib-3.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:cb52aa97b92acdee090edfb65d1cb84ea60ab38e871ba8321a10bbcebc2a3540"},
+    {file = "matplotlib-3.7.0-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:3493b48e56468c39bd9c1532566dff3b8062952721b7521e1f394eb6791495f4"},
+    {file = "matplotlib-3.7.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d0dcd1a0bf8d56551e8617d6dc3881d8a1c7fb37d14e5ec12cbb293f3e6170a"},
+    {file = "matplotlib-3.7.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51fb664c37714cbaac69c16d6b3719f517a13c96c3f76f4caadd5a0aa7ed0329"},
+    {file = "matplotlib-3.7.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:4497d88c559b76da320b7759d64db442178beeea06a52dc0c629086982082dcd"},
+    {file = "matplotlib-3.7.0-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:9d85355c48ef8b9994293eb7c00f44aa8a43cad7a297fbf0770a25cdb2244b91"},
+    {file = "matplotlib-3.7.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:03eb2c8ff8d85da679b71e14c7c95d16d014c48e0c0bfa14db85f6cdc5c92aad"},
+    {file = "matplotlib-3.7.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71b751d06b2ed1fd017de512d7439c0259822864ea16731522b251a27c0b2ede"},
+    {file = "matplotlib-3.7.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b51ab8a5d5d3bbd4527af633a638325f492e09e45e78afdf816ef55217a09664"},
+    {file = "matplotlib-3.7.0.tar.gz", hash = "sha256:8f6efd313430d7ef70a38a3276281cb2e8646b3a22b3b21eb227da20e15e6813"},
 ]
 
 [package.dependencies]
 contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
-kiwisolver = ">=1.3.1"
-numpy = ">=1.21,<2"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.20"
 packaging = ">=20.0"
-pillow = ">=8"
+pillow = ">=6.2.0"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
@@ -1796,20 +1809,20 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
 name = "pyam-iamc"
-version = "1.7.0"
+version = "1.9.0"
 description = "Analysis & visualization of integrated-assessment scenarios"
 optional = false
-python-versions = "<3.11,>=3.7.1"
+python-versions = "<3.12,>=3.7.1"
 files = [
-    {file = "pyam-iamc-1.7.0.tar.gz", hash = "sha256:0af22a23585ed30363e8755a7e2ac4a646df71c5375f57b3a9124fb567cd32e4"},
-    {file = "pyam_iamc-1.7.0-py3-none-any.whl", hash = "sha256:512a67ea145a09ab7db34b49ca70e81800f602ee46ad75a179c51f0a95cafb9b"},
+    {file = "pyam-iamc-1.9.0.tar.gz", hash = "sha256:a37f853b4e563628e78ff79a639d5636e25cdc2b5db35d13b5ac7c27ccdf6333"},
+    {file = "pyam_iamc-1.9.0-py3-none-any.whl", hash = "sha256:91f6841326197c5b32a511c33198e7dcf32cd47e02bb105bb3b0764bf7419acd"},
 ]
 
 [package.dependencies]
 httpx = {version = "*", extras = ["http2"]}
 iam-units = ">=2020.4.21"
 importlib-metadata = "*"
-matplotlib = ">=3.2.0"
+matplotlib = ">=3.5.0,<3.7.1"
 numpy = ">=1.19.0,<1.24"
 openpyxl = "*"
 pandas = ">=1.2.0"
@@ -1826,11 +1839,11 @@ wquantiles = "*"
 xlsxwriter = "*"
 
 [package.extras]
-docs = ["autodocsumm", "kaleido", "nbsphinx", "numpydoc", "pillow", "sphinx", "sphinx-gallery", "sphinxcontrib-bibtex (<2.0)", "sphinxcontrib-programoutput"]
+docs = ["autodocsumm", "kaleido", "nbsphinx", "numpydoc", "pillow", "sphinx", "sphinx-gallery"]
 optional-io-formats = ["datapackage", "pandas-datareader", "unfccc-di-api (>=3.0.1)", "xlrd"]
 optional-plotting = ["plotly"]
 tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-mpl"]
-tutorials = ["ipykernel", "jupyter-client", "nbconvert", "nbformat", "pypandoc"]
+tutorials = ["ipykernel", "jupyter-client", "nbconvert (>=7.3)", "nbformat", "pypandoc"]
 
 [[package]]
 name = "pybtex"
@@ -2918,4 +2931,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.11"
-content-hash = "24b78587cb44cf03d5d2a74b5599ba9824a78fcfa04b127f70cf5a98220ca3b0"
+content-hash = "a07d3ecb9ec6479240718b42fec57b544c2fc7f14fdacad466050d3ac6ae72a3"
diff --git a/pyproject.toml b/pyproject.toml
index 83a478f..e9f5114 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,10 +17,10 @@ dask = "^2024.2.0"
 distributed = "^2024.2.0"
 holoviews = "^1.18.3"
 hvplot = "^0.9.2"
-matplotlib = "^3.8.3"
+matplotlib = "^3.6"
 numpy = "^1.23"
 pandas = "^2.2.0"
-pyam-iamc = "^1.7"
+pyam-iamc = "^1.8"
 pyyaml = "^6.0.1"
 rich = "^13.7.0"
 scipy = "^1.12.0"
diff --git a/rime/rime_functions.py b/rime/rime_functions.py
index e778677..4ce439b 100644
--- a/rime/rime_functions.py
+++ b/rime/rime_functions.py
@@ -145,16 +145,16 @@ def table_impacts_gwl(
         tgt_g = xr.DataArray(tt[years].values, dims="points")
 
         # Do selection (lookup) of datapoints in dsd ix along the years & gwl points.
-        try:
-            agh = dsd.sel(year=tgt_y, gwl=tgt_g, method="nearest").to_dataframe(
-                name=indicator
-            )
-            agh = agh.reset_index()
-            aghp = agh.pivot_table(index="region", columns="year", values=indicator)
-            edf = edf.join(aghp, on="region")
+        # try:
+        agh = dsd.sel(year=tgt_y, gwl=tgt_g, method="nearest").to_dataframe(
+            name=indicator
+        )
+        agh = agh.reset_index()
+        aghp = agh.pivot_table(index="region", columns="year", values=indicator)
+        edf = edf.join(aghp, on="region")
 
-        except Exception as e:
-            print(f"skip {model} {scenario}")
+        # except Exception as e:
+            # print(f"skip {model} {scenario}")
 
         idf = pd.concat([idf, edf]).reset_index(drop=True)
 
@@ -222,8 +222,8 @@ def map_transform_gwl(
 
     """
 
-    if len(df1.variable) > 1:
-        raise Exception("Error: more than 1 variable in DataFrame")
+    # if len(df1.variable) > 1:
+    #     raise Exception("Error: more than 1 variable in DataFrame")
     if len(df1.meta) > 1:
         raise Exception("Error: more than 1 model-scenario in DataFrame")
 
diff --git a/rime/utils.py b/rime/utils.py
index 8e18a11..08a5b3c 100644
--- a/rime/utils.py
+++ b/rime/utils.py
@@ -119,6 +119,8 @@ def tidy_mapdata(mapdata):
     dvs = mapdata.data_vars
     if "threshold" in mapdata.coords:
         mapdata = mapdata.rename({"threshold": "gwl"})
+    if "gmt" in mapdata.coords:
+        mapdata = mapdata.rename({"gmt": "gwl"})        
     mapdata = mapdata.set_index(
         {"lon": "lon", "lat": "lat", "gwl": "gwl"}
     ).reset_coords()