From fc07a76f6b530fc3afa25b88a0b7bf8ca754dd6e Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Tue, 22 Oct 2024 09:44:50 +0100
Subject: [PATCH 01/11] lower macos version requirement

---
 pixi.lock      | 2 +-
 pyproject.toml | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/pixi.lock b/pixi.lock
index ab55f66..87b5470 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -5374,7 +5374,7 @@ packages:
   name: emissions-harmonization-historical
   version: 0.1.0
   path: .
-  sha256: 5c35318d9cfb8a1a5cdc9b099acc336810eee20ce5e0de3531374688b9f110ff
+  sha256: e7ad60e96b845782cd9c59ff2bc93c49b3742d64661b5c204dad4b576008e03a
   requires_dist:
   - pre-commit==3.3.1
   - ptolemy-iamc @ git+https://github.com/gidden/ptolemy.git@015aa843c340bd57464eb7cfd5f3c8e397201266
diff --git a/pyproject.toml b/pyproject.toml
index b346f00..c9f7777 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,9 @@ platforms = [
     "win-64",
 ]
 
+[tool.pixi.system-requirements]
+macos = "12.7.6"
+
 [tool.pixi.tasks]
 
 [tool.pixi.dependencies]

From 2b941fb13559cc14826ded9a7f445348f4a03524 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Mon, 28 Oct 2024 13:23:11 +0000
Subject: [PATCH 02/11] WIP commit

---
 notebooks/0101_CEDS-prepare.py  | 2 ++
 notebooks/0102_GFED4-prepare.py | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/notebooks/0101_CEDS-prepare.py b/notebooks/0101_CEDS-prepare.py
index 8505222..ad1a19a 100644
--- a/notebooks/0101_CEDS-prepare.py
+++ b/notebooks/0101_CEDS-prepare.py
@@ -121,3 +121,5 @@
 
 # %%
 (ceds_ref.to_csv(ceds_processed_output_file))
+
+# %%
diff --git a/notebooks/0102_GFED4-prepare.py b/notebooks/0102_GFED4-prepare.py
index 7ac6490..ea741ad 100644
--- a/notebooks/0102_GFED4-prepare.py
+++ b/notebooks/0102_GFED4-prepare.py
@@ -279,3 +279,5 @@
 
 # %%
 (burningCMIP7_ref.to_csv(gfed_processed_output_file))
+
+# %%

From 45eada6d1aa34d006694cc78094e448c320b8a14 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Thu, 31 Oct 2024 12:41:16 +0000
Subject: [PATCH 03/11] CEDS emissions to IAMC format

---
 notebooks/0101_CEDS-prepare.py | 33 +++++++++++++++++++++++++++------
 1 file changed, 27 insertions(+), 6 deletions(-)

diff --git a/notebooks/0101_CEDS-prepare.py b/notebooks/0101_CEDS-prepare.py
index ad1a19a..61ba46f 100644
--- a/notebooks/0101_CEDS-prepare.py
+++ b/notebooks/0101_CEDS-prepare.py
@@ -40,8 +40,8 @@
 # Specify gases to processes
 
 # %%
-# use all gases covered in CEDS
-gases = [
+# use all species covered in CEDS
+species = [
     "BC",
     "CH4",
     "CO",
@@ -67,7 +67,8 @@
 
 # %%
 ceds = pd.concat(
-    read_CEDS(Path(ceds_data_folder) / f"{gas}_CEDS_emissions_by_country_sector_v{ceds_release}.csv") for gas in gases
+    read_CEDS(Path(ceds_data_folder) / f"{specie}_CEDS_emissions_by_country_sector_v{ceds_release}.csv")
+    for specie in species
 ).rename_axis(index={"region": "country"})
 ceds.attrs["name"] = "CEDS21"
 ceds = ceds.pix.semijoin(ceds_map, how="outer")
@@ -91,15 +92,19 @@
 ceds = add_global(ceds)
 
 # %%
+# See Hoesly et al. 2018 plots of NOx for suggestion that NOx units is NO2
+# https://gmd.copernicus.org/articles/11/369/2018/gmd-11-369-2018.pdf
+
 unit_wishes = pd.MultiIndex.from_tuples(
     [
         ("BC", "Mt BC/yr"),
         ("CH4", "Mt CH4/yr"),
         ("CO", "Mt CO/yr"),
         ("CO2", "Mt CO2/yr"),
+        ("N2O", "Mt N2O/yr"),
         ("NH3", "Mt NH3/yr"),
         ("NMVOC", "Mt NMVOC/yr"),
-        ("NOx", "Mt NOx/yr"),
+        ("NOx", "Mt NO2/yr"),  # CEDS is reported in NO2 units
         ("OC", "Mt OC/yr"),
         ("SO2", "Mt SO2/yr"),
     ],
@@ -112,12 +117,28 @@
 # %%
 ceds.pix.unique(unit_wishes.names).symmetric_difference(unit_wishes)
 
+# %%
+# reformat
+ceds_ref = (
+    ceds.droplevel("unit")
+    .pix.semijoin(unit_wishes, how="left")
+    .rename_axis(index={"em": "variable", "country": "region"})
+)
+
+# %%
+# rename to IAMC-style variable names including standard index order
+ceds_ref = (
+    ceds_ref.rename(index={"SO2": "Sulfur"}, level="variable")
+    .pix.format(variable="CMIP7 History|Emissions|{variable}|{sector}", drop=True)
+    .pix.assign(model="History", scenario=f"CEDSv{ceds_release}")
+    .reorder_levels(["model", "scenario", "region", "variable", "unit"])
+)
+
 # %% [markdown]
 # Save formatted CEDS data
 
 # %%
-# reformat
-ceds_ref = ceds.droplevel("unit").pix.semijoin(unit_wishes, how="left").rename_axis(index={"em": "gas"})
+ceds_ref
 
 # %%
 (ceds_ref.to_csv(ceds_processed_output_file))

From 9bea92d1d65d8bb9e749c2ddec56e44f02c96db8 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Thu, 31 Oct 2024 14:19:35 +0000
Subject: [PATCH 04/11] format CEDS data as IAMC

---
 notebooks/0102_GFED4-prepare.py | 31 +++++++++++++++++++------------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/notebooks/0102_GFED4-prepare.py b/notebooks/0102_GFED4-prepare.py
index ea741ad..973033d 100644
--- a/notebooks/0102_GFED4-prepare.py
+++ b/notebooks/0102_GFED4-prepare.py
@@ -228,18 +228,18 @@
 
 # %%
 # set units
-unit = pd.MultiIndex.from_tuples(
+unit_wishes = pd.MultiIndex.from_tuples(
     [
-        ("BC", "kt BC/yr"),
-        ("OC", "kt OC/yr"),
-        ("CO", "kt CO/yr"),
-        ("CO2", "kt CO2/yr"),
-        ("CH4", "kt CH4/yr"),
-        ("N2O", "kt N2O/yr"),
-        ("NH3", "kt NH3/yr"),
-        ("NOx", "kt NOx/yr"),
-        ("NMVOC", "kt VOC/yr"),
-        ("SO2", "kt SO2/yr"),
+        ("BC", "Mt BC/yr"),
+        ("OC", "Mt OC/yr"),
+        ("CO", "Mt CO/yr"),
+        ("CO2", "Mt CO2/yr"),
+        ("CH4", "Mt CH4/yr"),
+        ("N2O", "Mt N2O/yr"),
+        ("NH3", "Mt NH3/yr"),
+        ("NOx", "Mt NO/yr"),  # we know NO mass units, so label as such
+        ("NMVOC", "Mt VOC/yr"),
+        ("SO2", "Mt SO2/yr"),
     ],
     names=["em", "unit"],
 )
@@ -257,6 +257,8 @@
     .sum()
 )
 
+# format units
+burningCMIP7_ref = burningCMIP7_ref.droplevel("unit").pix.semijoin(unit_wishes, how="left")
 
 # rename to IAMC-style variable names
 burningCMIP7_ref = (
@@ -266,7 +268,12 @@
 )
 
 # add global level aggregation ("World")
-burningCMIP7_ref = add_global(burningCMIP7_ref, groups=["model", "scenario", "variable", "unit"])
+burningCMIP7_ref = add_global(burningCMIP7_ref, groups=["model", "scenario", "variable", "unit"]).rename_axis(
+    index={"country": "region"}
+)
+
+# fix order
+burningCMIP7_ref = burningCMIP7_ref.reorder_levels(["model", "scenario", "region", "variable", "unit"])
 
 # %%
 burningCMIP7.pix

From d9a7b6e3a90371787420e1410c1853c04918e5f3 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Wed, 20 Nov 2024 13:36:33 +0100
Subject: [PATCH 05/11] fight ruff

---
 notebooks/0101_CEDS-prepare.py          |    4 +-
 notebooks/0102_GFED4-prepare.py         |   47 +-
 notebooks/0103_GFED4-BB4CMIP-prepare.py |  298 +-
 pixi.lock                               | 8295 +++++++++++++----------
 pyproject.toml                          |    4 +-
 5 files changed, 4741 insertions(+), 3907 deletions(-)

diff --git a/notebooks/0101_CEDS-prepare.py b/notebooks/0101_CEDS-prepare.py
index 61ba46f..aee650e 100644
--- a/notebooks/0101_CEDS-prepare.py
+++ b/notebooks/0101_CEDS-prepare.py
@@ -37,7 +37,7 @@
 ceds_processed_output_file = DATA_ROOT / Path("national", "ceds", "processed", "ceds_cmip7_national_alpha.csv")
 
 # %% [markdown]
-# Specify gases to processes
+# Specify species to processes
 
 # %%
 # use all species covered in CEDS
@@ -132,7 +132,7 @@
     .pix.format(variable="CMIP7 History|Emissions|{variable}|{sector}", drop=True)
     .pix.assign(model="History", scenario=f"CEDSv{ceds_release}")
     .reorder_levels(["model", "scenario", "region", "variable", "unit"])
-)
+).sort_values(by=["region", "variable"])
 
 # %% [markdown]
 # Save formatted CEDS data
diff --git a/notebooks/0102_GFED4-prepare.py b/notebooks/0102_GFED4-prepare.py
index 973033d..a49c84d 100644
--- a/notebooks/0102_GFED4-prepare.py
+++ b/notebooks/0102_GFED4-prepare.py
@@ -16,6 +16,7 @@
 # import external packages and functions
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 import ptolemy
 import xarray as xr
@@ -62,11 +63,11 @@
 gfed_temp_file = DATA_ROOT / Path("national", "gfed", "processed", "gfed_temporaryfile.csv")
 
 # %% [markdown]
-# Specify gases to processes
+# Specify species to processes
 
 # %%
-# use all gases covered in CEDS
-gases = [
+# use all species covered in CEDS
+species = [
     "BC",
     "CH4",
     "CO",
@@ -105,6 +106,23 @@
 # show xarray
 emissions
 
+# %%
+dummy = xr.Dataset(
+    data_vars=dict(
+        DM=(["lat", "lon"], np.zeros((360, 720))),
+    ),
+    coords=dict(
+        lat=("lat", np.arange(-89.75, 90, 0.5)),
+        lon=("lon", np.arange(-179.75, 180, 0.5)),
+    ),
+)
+
+# %%
+dummy
+
+# %%
+emissions["DM"].regrid.conservative(dummy)
+
 # %% [markdown]
 # Get emissions factor for different species
 
@@ -138,7 +156,7 @@
 
 ef.loc["NMVOC"] = ef.multiply(nmvoc_factors, axis=0).sum()
 
-ef_per_DM = ef.loc[gases] / ef.loc["DM"]
+ef_per_DM = ef.loc[species] / ef.loc["DM"]
 # in kg {species} / kg DM
 ef_per_DM
 
@@ -151,18 +169,27 @@
 # 'chunks={"iso": 1}' uses Dask to enable chunking for memory efficiency, loading one ISO code at a time.
 idxr = xr.open_dataarray(gfed_isomask, chunks={"iso": 1})
 
+# %%
 # Step 2: Open a NetCDF file to use as a grid template for latitude and longitude coordinates.
 # The template file provides the lat/lon grid for regridding the emissions data.
-with xr.open_dataset(gfed_grid_template) as template:
-    # Interpolate the "DM" (Dry Matter) emissions data to the lat/lon grid from the template,
-    # using linear interpolation. This matches the emissions data to the same grid resolution.
-    dm_regrid = emissions["DM"].interp(lon=template.lon, lat=template.lat, method="linear")
 
+# Interpolate the "DM" (Dry Matter) emissions data to the lat/lon grid from the template,
+# using conservative interpolation. This matches the emissions data to the same grid resolution.
+dm_regrid = emissions["DM"].regrid.conservative(dummy)
+
+# %%
+dm_regrid
+
+# %%
 # Step 3: Compute the area of each grid cell using the 'ptolemy.cell_area' function.
 # This function calculates the area of each grid cell based on the interpolated lat/lon grid.
 # The resulting cell areas are stored in an xarray DataArray, with units of square meters ("m2").
 cell_area = xr.DataArray(ptolemy.cell_area(lats=dm_regrid.lat, lons=dm_regrid.lon), attrs=dict(unit="m2"))
 
+# %%
+cell_area
+
+# %%
 # calculate emissions by country by:
 # taking the country cell IDs (idxr), multiplying it by the area (cell_area),
 # and by the regridded lat/lon grid resummed to per year (dm_regrid.groupby("time.year").sum())
@@ -273,7 +300,9 @@
 )
 
 # fix order
-burningCMIP7_ref = burningCMIP7_ref.reorder_levels(["model", "scenario", "region", "variable", "unit"])
+burningCMIP7_ref = burningCMIP7_ref.reorder_levels(["model", "scenario", "region", "variable", "unit"]).sort_values(
+    by=["region", "variable"]
+)
 
 # %%
 burningCMIP7.pix
diff --git a/notebooks/0103_GFED4-BB4CMIP-prepare.py b/notebooks/0103_GFED4-BB4CMIP-prepare.py
index 6be6c07..5d584e8 100644
--- a/notebooks/0103_GFED4-BB4CMIP-prepare.py
+++ b/notebooks/0103_GFED4-BB4CMIP-prepare.py
@@ -14,25 +14,26 @@
 
 # %%
 from collections import defaultdict
+from pathlib import Path
 
-import cf_xarray.units
-import matplotlib.pyplot as plt
 import numpy as np
-import pint_xarray
+import ptolemy
 import scmdata
 import xarray as xr
+from tqdm import tqdm
 
 from emissions_harmonization_historical.constants import DATA_ROOT
 
-# %%
-pint_xarray.accessors.default_registry = pint_xarray.setup_registry(cf_xarray.units.units)
-
 # %%
 raw_data_path = DATA_ROOT / "national/gfed-bb4cmip/data_raw/data"
 raw_data_path
 
 # %%
-rcmip_data_path = DATA_ROOT / "global/rcmip/data_raw/rcmip-emissions-annual-means-v5-1-0.csv"
+gfed_processed_output_file = DATA_ROOT / Path("national", "gfed", "processed", "gfed-bb4cmip_cmip7_national_alpha.csv")
+
+# %%
+gfed_data_aux_folder = DATA_ROOT / Path("national", "gfed", "data_aux")
+gfed_isomask = Path(gfed_data_aux_folder, "iso_mask.nc")  # for aggregating to countries
 
 # %% [markdown]
 # Group raw data into variable groups which can be used for processing.
@@ -45,114 +46,57 @@
     variable = file.name.split("_")[0]
     bb4cmip_file_groups[variable].append(file)
 
-bb4cmip_file_groups.keys()
-
-# %% [markdown]
-# An example of loading data.
-
-# %%
-cell_area = xr.open_mfdataset(bb4cmip_file_groups["gridcellarea"])
-cell_area
-
-# %%
-gas = "BC"
-unit_target = "Mt / yr"
-unit_label = "Mt BC / yr"  # Have to be careful here, depends on the species
-rcmip_gas = "BC"
-
-# gas = "CO"
-# unit_target = "Mt / yr"
-# unit_label = "Mt CO / yr"
-# rcmip_gas = "CO"
-
-# # TODO: check. I don't think we used this last time so we may want to ignore this again.
-# gas = "CO2"
-# unit_target = "Mt / yr"
-# unit_label = "Mt CO2 / yr"
-# rcmip_gas = "CO2"
-
-# gas = "CH4"
-# unit_target = "Mt / yr"
-# unit_label = "Mt CH4 / yr"
-# rcmip_gas = "CH4"
-
-# # TODO: check. I don't think we used this last time.
-# gas = "N2O"
-# unit_target = "Mt / yr"
-# unit_label = "Mt N2O / yr"
-# rcmip_gas = "N2O"
-
-# gas = "OC"
-# unit_target = "Mt / yr"
-# unit_label = "Mt OC / yr"
-# rcmip_gas = "OC"
-
-# gas = "NH3"
-# unit_target = "Mt / yr"
-# unit_label = "Mt NH3 / yr"
-# rcmip_gas = "NH3"
-
-# This reveals the bug in NOx processing for RCMIP v0.5.1 data
-# first identified by Chris.
-# Long story short: NOx emissions were too low by ~50%.
-# In this round, let's report NOx emissions with units of Mt N / yr
-# to avoid the ambiguous Mt NOx / yr.
-gas = "NOx"
-unit_target = "Mt / yr"
-unit_label = "Mt NO / yr"  # File metadata has molecular weight of 30 so assume NO
-rcmip_gas = "NOx"
-
-# gas = "SO2"
-# unit_target = "Mt / yr"
-# unit_label = "Mt SO2 / yr"
-# rcmip_gas = "Sulfur"
-
 # %%
-gas_ds = xr.open_mfdataset(bb4cmip_file_groups[gas], combine_attrs="drop_conflicts")
-gas_ds
-
-# %% [markdown]
-# Do a unit-aware sum
-
-# %%
-emissions_da = gas_ds[gas].pint.quantify()
-cellarea_da = cell_area["gridcellarea"].pint.quantify(gridcellarea="m^2")
-global_sum_emissions = (emissions_da * cellarea_da).sum(["latitude", "longitude"]).compute()
-global_sum_emissions
-
-# %%
-global_sum_emissions.plot()
-
-# %%
-# Should really weight the mean by the length of the month.
-# One for the future.
-global_sum_emissions_annual_mean = global_sum_emissions.groupby("time.year").mean()
-
-# Check that the sum matches the data provider's check values
-for original_file in bb4cmip_file_groups[gas]:
-    orig_ds = xr.open_dataset(original_file)
-    timerange = original_file.stem.split("_")[-1].split("-")
-
-    for i, timestamp in enumerate(timerange):
-        year = int(timestamp[:4])
-        if i < 1:
-            compare_val = float(orig_ds.attrs["annual_total_first_year_Tg_yr"])
-        elif i == 1:
-            compare_val = float(orig_ds.attrs["annual_total_last_year_Tg_yr"])
-        else:
-            raise NotImplementedError()
-
-        np.testing.assert_allclose(
-            global_sum_emissions_annual_mean.sel(year=year).pint.to("Tg / yr").data.m,
-            compare_val,
-            rtol=5e-2,
-        )
-
-global_sum_emissions_annual_mean.plot()
-
-
-# %%
-def gfed_to_scmrun(in_da: xr.DataArray, *, unit_target: str, unit_label: str, region: str) -> scmdata.ScmRun:
+species_data = {
+    "BC": {
+        "unit_label": "Mt BC / yr",
+        "filename_label": "BC",
+    },
+    "NMVOC": {
+        "unit_label": "Mt NMVOC / yr",
+        "filename_label": "NMVOC_bulk",
+    },
+    "CO2": {
+        "unit_label": "Mt CO2 / yr",
+        "filename_label": "CO2",
+    },
+    "CH4": {
+        "unit_label": "Mt CH4 / yr",
+        "filename_label": "CH4",
+    },
+    "N2O": {
+        "unit_label": "Mt N2O / yr",
+        "filename_label": "N2O",
+    },
+    "OC": {
+        "unit_label": "Mt OC / yr",
+        "filename_label": "OC",
+    },
+    "NH3": {
+        "unit_label": "Mt NH3 / yr",
+        "filename_label": "NH3",
+    },
+    "NOx": {
+        "unit_label": "Mt NO / yr",
+        "filename_label": "NOx",
+    },
+    "SO2": {
+        "unit_label": "Mt SO2 / yr",
+        "filename_label": "SO2",
+    },
+}
+
+# %%
+# cell areas of GFED map
+cell_area_gfed = xr.open_mfdataset(bb4cmip_file_groups["gridcellarea"]).rename({"latitude": "lat", "longitude": "lon"})
+# not sure needed
+
+# get iso map at half degree
+idxr = xr.open_dataarray(gfed_isomask, chunks="auto")  # chunks={"iso": 1})
+
+
+# %%
+def gfed_to_scmrun(in_da: xr.DataArray, *, unit_label: str, world: bool = False) -> scmdata.ScmRun:
     """
     Convert an `xr.DataArray` to `scmdata.ScmRun`
 
@@ -163,87 +107,89 @@ def gfed_to_scmrun(in_da: xr.DataArray, *, unit_target: str, unit_label: str, re
     in_da
         Input `xr.DataArray` to convert
 
-    unit_target
-        The unit to which to convert the data to.
-
-        This is done based on the original data unit,
-        so almost always has to exclude the gas.
-        I.e. this should be e.g. "Mt / yr", not "Mt BC / yr".
-
     unit_label
         The label to apply to the unit as it will be handled in the output.
 
-        This typically includes the gas, e.g. "Mt BC / yr"
-
-    region
-        The region to which the data applies.
+        This typically includes the species, e.g. "Mt BC / yr"
 
-        Typically "World".
+    world
+        Do global emissions
 
     Returns
     -------
     :
         `scmdata.ScmRun`
     """
-    df = in_da.pint.to(unit_target).pint.dequantify().to_pandas().to_frame().T
-    df["model"] = "GFED-BB4CMIP"
-    df["scenario"] = "historical-CMIP6Plus"
-    df["region"] = region
-    df["unit"] = unit_label
-    df["variable"] = f"Emissions|{gas}|BB4CMIP"
-    out = scmdata.ScmRun(df)
+    df = in_da.to_numpy()
+
+    if world:
+        region = "World"
+    else:
+        region = in_da.iso.to_numpy()
+
+    out = scmdata.ScmRun(
+        df,
+        columns=dict(
+            model="GFED-BB4CMIP",
+            scenario="historical-CMIP6Plus",
+            region=region,
+            unit=unit_label,
+            variable=f"Emissions|{species}|BB4CMIP",
+        ),
+        index=np.arange(1750, 2023, dtype=int),
+    )  # .interpolate(target_times=np.arange(1750, 2023, dtype=int)).timeseries(time_axis="year")
 
     return out
 
 
 # %%
-gfed_to_plot = gfed_to_scmrun(
-    global_sum_emissions_annual_mean, unit_target=unit_target, unit_label=unit_label, region="World"
-)
-gfed_to_plot.timeseries()
+# out = []
+for species in tqdm(species_data):
+    species_ds = xr.open_mfdataset(bb4cmip_file_groups[species], combine_attrs="drop_conflicts").rename(
+        {"latitude": "lat", "longitude": "lon"}
+    )
+    # species_ds
 
-# %%
-rcmip_data = scmdata.ScmRun(str(rcmip_data_path), lowercase_cols=True)
-rcmip_data
+    # First get the emissions on to an annual grid
+    emissions_da = species_ds[species].chunk("auto")  # kg / m2 / s
 
-# %%
-tmp = rcmip_data.filter(
-    variable=[
-        f"Emissions|{rcmip_gas}",
-        f"Emissions|{rcmip_gas}|MAGICC AFOLU",
-        f"Emissions|{rcmip_gas}|MAGICC AFOLU|*Burning*",
-    ],
-    region="World",
-    scenario=["historical", "ssp*"],
-)
-
-tmp_total = tmp.filter(variable=[f"Emissions|{rcmip_gas}", f"Emissions|{rcmip_gas}|MAGICC AFOLU"])
-tmp_total_burning = tmp.filter(variable=f"Emissions|{rcmip_gas}|MAGICC AFOLU|*Burning*", log_if_empty=False)
-if tmp_total_burning.empty:
-    # No RCMIP burning emissions
-    tmp = scmdata.run_append([tmp_total])
-
-else:
-    tmp_total_burning = tmp_total_burning.process_over(
-        "variable", "sum", op_cols={"variable": f"Emissions|{rcmip_gas}|MAGICC AFOLU|Burning"}, as_run=True, min_count=1
+    # weight by month length
+    seconds_per_month = (
+        np.tile([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], len(emissions_da.time) // 12) * 24 * 60 * 60
     )
-    tmp = scmdata.run_append([tmp_total, tmp_total_burning])
+    weights = xr.DataArray(seconds_per_month, dims=("time",), coords=emissions_da.coords["time"].coords)
 
-rcmip_to_plot = scmdata.run_append(
-    [
-        tmp.filter(scenario="historical"),
-        tmp.filter(scenario="historical", keep=False).filter(year=range(2014, 3000 + 1)),
-    ]
-)
+    # get annual sum emissions in each grid cell
+    gridded_annual_emissions_rate = (emissions_da * weights).groupby("time.year").sum().compute()  # kg / m2 / yr
 
-# %%
-to_plot = scmdata.run_append([gfed_to_plot, rcmip_to_plot]).convert_unit(unit_label)
+    # regrid to half degree
+    regridded_annual_emissions_rate = gridded_annual_emissions_rate.regrid.conservative(
+        idxr
+    )  # still kg / m2 / yr, now coarser grid
+
+    # get cell areas of new grid
+    cell_area = xr.DataArray(
+        ptolemy.cell_area(lats=regridded_annual_emissions_rate.lat, lons=regridded_annual_emissions_rate.lon)
+    )  # m2
+
+    # emissions in each country, annual time series
+    country_emissions = ((regridded_annual_emissions_rate * cell_area * idxr).sum(["lat", "lon"])).compute() / 1e9
+    country_emissions  # Mt / yr in each country, the 1e9 is kg to Mt
+    world_emissions = (regridded_annual_emissions_rate * cell_area).sum(["lat", "lon"]).compute() / 1e9
+
+    out_world = gfed_to_scmrun(world_emissions, unit_label=species_data[species]["unit_label"], world=True)
+    out_country = gfed_to_scmrun(country_emissions, unit_label=species_data[species]["unit_label"])
 
-ax = to_plot.filter(scenario="hist*").lineplot(style="variable", alpha=0.5, linewidth=2)
-ax.legend(loc="center left", bbox_to_anchor=(1.05, 0.5))
-ax.set_ylim(ymin=0)
-plt.show()
+    # write out temporary files to save RAM and process combined dataset later
+    out_world = out_world.append(out_country)
 
-ax = to_plot.filter(variable=f"Emissions|{rcmip_gas}", keep=False).lineplot(style="variable", alpha=0.5, linewidth=2)
-ax.legend(loc="center left", bbox_to_anchor=(1.05, 0.5))
-ax.set_ylim(ymin=0)
+    gfed_temp_file = DATA_ROOT / Path("national", "gfed-bb4cmip", "processed", f"{species}.csv")
+    out_world = out_world.interpolate(target_times=np.arange(1750, 2023, dtype=int)).timeseries(time_axis="year")
+    out_world.to_csv(gfed_temp_file)
+
+
+# %%
+# out = scmdata.run_append(out).interpolate(target_times=np.arange(1750, 2023, dtype=int)).timeseries(time_axis="year")
+
+# %%
+# out.to_csv(gfed_processed_output_file)
diff --git a/pixi.lock b/pixi.lock
index 87b5470..25b6b31 100644
--- a/pixi.lock
+++ b/pixi.lock
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
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@@ -59,31 +59,33 @@ environments:
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
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   timestamp: 1726487214495
 - kind: conda
   name: json5
-  version: 0.9.25
-  build: pyhd8ed1ab_0
+  version: 0.9.28
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/json5-0.9.25-pyhd8ed1ab_0.conda
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-  md5: 5d8c241a9261e720a34a07a3e1ac4109
+  url: https://conda.anaconda.org/conda-forge/noarch/json5-0.9.28-pyhff2d567_0.conda
+  sha256: 402586e586761e0d51dd590fb71786f7f4e21c16353ca7d1c559358a1f849b26
+  md5: b5fd1ac9269dd22e003eaac27e249d97
   depends:
-  - python >=3.7,<4.0
+  - python >=3.9
   license: Apache-2.0
   license_family: APACHE
   purls:
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-  size: 27995
-  timestamp: 1712986338874
+  size: 28525
+  timestamp: 1731366079831
 - kind: conda
   name: jsonpointer
   version: 3.0.0
@@ -7529,18 +7734,17 @@ packages:
   timestamp: 1710262791393
 - kind: conda
   name: jupyterlab
-  version: 4.2.5
-  build: pyhd8ed1ab_0
+  version: 4.3.1
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.2.5-pyhd8ed1ab_0.conda
-  sha256: db08036a6fd846c178ebdce7327be1130bda10ac96113c17b04bce2bc4d67dda
-  md5: 594762eddc55b82feac6097165a88e3c
+  url: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.3.1-pyhff2d567_0.conda
+  sha256: ff1035eb0020dbaf4e332ef4b81a7068b595dfc57dde3313e9c4a37583772644
+  md5: b4f3d579fc21a44518d52c52507461b4
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   - httpx >=0.25.0
-  - importlib_metadata >=4.8.3
-  - importlib_resources >=1.4
+  - importlib-metadata >=4.8.3
   - ipykernel >=6.5.0
   - jinja2 >=3.0.3
   - jupyter-lsp >=2.0.0
@@ -7549,7 +7753,7 @@ packages:
   - jupyterlab_server >=2.27.1,<3
   - notebook-shim >=0.2
   - packaging
-  - python >=3.8
+  - python >=3.9
   - setuptools >=40.1.0
   - tomli >=1.2.2
   - tornado >=6.2.0
@@ -7558,8 +7762,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/jupyterlab?source=hash-mapping
-  size: 7361961
-  timestamp: 1724745262468
+  size: 7101932
+  timestamp: 1731776859245
 - kind: conda
   name: jupyterlab_pygments
   version: 0.3.0
@@ -7941,12 +8145,12 @@ packages:
 - kind: conda
   name: ld_impl_linux-64
   version: '2.43'
-  build: h712a8e2_1
-  build_number: 1
+  build: h712a8e2_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_1.conda
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-  md5: 83e1364586ceb8d0739fbc85b5c95837
+  url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda
+  sha256: 7c91cea91b13f4314d125d1bedb9d03a29ebbd5080ccdea70260363424646dbe
+  md5: 048b02e3962f066da18efe3a21b77672
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   - __glibc >=2.17,<3.0.a0
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   license: GPL-3.0-only
   license_family: GPL
   purls: []
-  size: 669616
-  timestamp: 1727304687962
+  size: 669211
+  timestamp: 1729655358674
 - kind: conda
   name: ld_impl_linux-aarch64
   version: '2.43'
-  build: h80caac9_1
-  build_number: 1
+  build: h80caac9_2
+  build_number: 2
   subdir: linux-aarch64
-  url: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.43-h80caac9_1.conda
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-  md5: 5019b8e4dd2433395270cc0838ad4065
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.43-h80caac9_2.conda
+  sha256: 80ec7e8f006196808fac5bd4b3773a652847f97bbf08044cd87731424ac64f8b
+  md5: fcbde5ea19d55468953bf588770c0501
   constrains:
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   license: GPL-3.0-only
   license_family: GPL
   purls: []
-  size: 698534
-  timestamp: 1727304686383
+  size: 698245
+  timestamp: 1729655345825
 - kind: conda
   name: lerc
   version: 4.0.0
@@ -8353,16 +8557,16 @@ packages:
 - kind: conda
   name: libarrow
   version: 17.0.0
-  build: h2432dd3_22_cpu
-  build_number: 22
+  build: h2409f62_31_cpu
+  build_number: 31
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-17.0.0-h2432dd3_22_cpu.conda
-  sha256: 3838b41c5a047ba89dd5e93a00202204c42ffb3e111023b2f56a2a719417980f
-  md5: 38b6616ec9246997e7ae1473da833fbb
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-17.0.0-h2409f62_31_cpu.conda
+  sha256: ea4b2a9a0742434ebb59f8588a77ff4b952df51902ca3f859cbf4d4754ec22d5
+  md5: 936bffec143f916b2812cc97ce9e4d73
   depends:
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-  - aws-crt-cpp >=0.28.3,<0.28.4.0a0
-  - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
+  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
@@ -8374,166 +8578,166 @@ packages:
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   - libbrotlienc >=1.1.0,<1.2.0a0
   - libcxx >=18
-  - libgoogle-cloud >=2.30.0,<2.31.0a0
-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
-  - libre2-11 >=2023.9.1
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
+  - libre2-11 >=2024.7.2
   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
+  - parquet-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5311194
-  timestamp: 1728880564494
+  size: 5309292
+  timestamp: 1731789752130
 - kind: conda
   name: libarrow
   version: 17.0.0
-  build: h3475b9b_22_cpu
-  build_number: 22
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-17.0.0-h3475b9b_22_cpu.conda
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-  md5: 3caf581f511584b19f33c54e8fa3ebac
+  build: h3b997a5_31_cpu
+  build_number: 31
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-h3b997a5_31_cpu.conda
+  sha256: b2d5164e625548a7b1ca039c3c8965c7675e08b837011e0e9d4e19620db42ade
+  md5: f2e1e2ac4a19f1e2b3e9365272257ee2
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-  - aws-crt-cpp >=0.28.3,<0.28.4.0a0
-  - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
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   - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=18
-  - libgoogle-cloud >=2.30.0,<2.31.0a0
-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
-  - libre2-11 >=2023.9.1
+  - libgcc >=13
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
+  - libre2-11 >=2024.7.2
+  - libstdcxx >=13
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   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
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   - arrow-cpp <0.0a0
   - apache-arrow-proc =*=cpu
+  - parquet-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5908335
-  timestamp: 1728880977173
+  size: 8513702
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   name: libarrow
   version: 17.0.0
-  build: h6949a90_22_cpu
-  build_number: 22
-  subdir: linux-aarch64
-  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-17.0.0-h6949a90_22_cpu.conda
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-  md5: 719cab7475b951ad2ad47d9fcb467b78
+  build: h7b593d6_31_cpu
+  build_number: 31
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-17.0.0-h7b593d6_31_cpu.conda
+  sha256: 23c65722a3ba557acfe53336b0b421a1ad52cf5e81f1f62c0d995f2e2623816a
+  md5: 3fa5a9cbaaa0093e783ec015c90d9543
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-  - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
+  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
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-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
-  - libre2-11 >=2023.9.1
-  - libstdcxx >=13
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+  - libcurl >=8.10.1,<9.0a0
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+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
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   constrains:
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   - apache-arrow-proc =*=cpu
   - parquet-cpp <0.0a0
+  - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 7826104
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-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-17.0.0-ha019072_22_cpu.conda
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+  build: hd4c0275_31_cpu
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+  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-17.0.0-hd4c0275_31_cpu.conda
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+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
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+  - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
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-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
-  - libre2-11 >=2023.9.1
+  - libcxx >=18
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
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-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
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   - snappy >=1.2.1,<1.3.0a0
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   constrains:
-  - arrow-cpp <0.0a0
-  - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
+  - parquet-cpp <0.0a0
+  - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5092813
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 - kind: conda
   name: libarrow
   version: 17.0.0
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-  build_number: 22
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-ha07344c_22_cpu.conda
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-  md5: 041e55887514cd414ec7df03d68210fb
+  build: hf19f309_31_cpu
+  build_number: 31
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-17.0.0-hf19f309_31_cpu.conda
+  sha256: 0a1a98b35634bac691c8e4b9b8d27bfd42254f9854675300761cbe21dd59b287
+  md5: 8bb500153ee110c1f7e56c21f774ccff
   depends:
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-  - aws-crt-cpp >=0.28.3,<0.28.4.0a0
-  - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
+  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
@@ -8546,14 +8750,14 @@ packages:
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   - libbrotlienc >=1.1.0,<1.2.0a0
   - libgcc >=13
-  - libgoogle-cloud >=2.30.0,<2.31.0a0
-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
-  - libre2-11 >=2023.9.1
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
+  - libre2-11 >=2024.7.2
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   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
@@ -8564,428 +8768,532 @@ packages:
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 8527600
-  timestamp: 1728881566531
+  size: 7803046
+  timestamp: 1731790864250
 - kind: conda
   name: libarrow-acero
   version: 17.0.0
-  build: h240833e_22_cpu
-  build_number: 22
+  build: h240833e_31_cpu
+  build_number: 31
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-17.0.0-h240833e_22_cpu.conda
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-  md5: 1ba76b42451a767e28bea7c9c89d83c6
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-  - libgcc >=13
-  - libprotobuf >=5.27.5,<5.27.6.0a0
-  - libstdcxx >=13
+  - libarrow 17.0.0 h7b593d6_31_cpu
+  - libarrow-acero 17.0.0 hac47afa_31_cpu
+  - libarrow-dataset 17.0.0 hac47afa_31_cpu
+  - libprotobuf >=5.28.2,<5.28.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.42.34433
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 515581
-  timestamp: 1728881756281
-- kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 22_osx64_openblas
-  build_number: 22
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-22_osx64_openblas.conda
-  sha256: d72060239f904b3a81d2329efcf84dc62c2dfd66dbc4efc8dcae1afdf8f02b59
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-  - libopenblas >=0.3.27,<0.3.28.0a0
-  - libopenblas >=0.3.27,<1.0a0
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-  - liblapacke 3.9.0 22_osx64_openblas
-  - blas * openblas
-  - libcblas 3.9.0 22_osx64_openblas
-  - liblapack 3.9.0 22_osx64_openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 14749
-  timestamp: 1712542279018
+  size: 361278
+  timestamp: 1731791604736
 - kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 24_linux64_openblas
-  build_number: 24
+  name: libavif16
+  version: 1.1.1
+  build: h1909e37_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-24_linux64_openblas.conda
-  sha256: 3097f7913bda527d4fe9f824182b314e130044e582455037fca6f4e97965d83c
-  md5: 80aea6603a6813b16ec119d00382b772
+  url: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda
+  sha256: e06da844b007a64a9ac35d4e3dc4dbc66583f79b57d08166cf58f2f08723a6e8
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   depends:
-  - libopenblas >=0.3.27,<0.3.28.0a0
-  - libopenblas >=0.3.27,<1.0a0
-  constrains:
-  - blas * openblas
-  - liblapack 3.9.0 24_linux64_openblas
-  - libcblas 3.9.0 24_linux64_openblas
-  - liblapacke 3.9.0 24_linux64_openblas
-  license: BSD-3-Clause
+  - __glibc >=2.17,<3.0.a0
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - libgcc >=13
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 14981
-  timestamp: 1726668454790
+  size: 116202
+  timestamp: 1730268687453
 - kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 24_linuxaarch64_openblas
-  build_number: 24
+  name: libavif16
+  version: 1.1.1
+  build: h3b0c220_2
+  build_number: 2
   subdir: linux-aarch64
-  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-24_linuxaarch64_openblas.conda
-  sha256: a0a86754a6dcdf5917735d3095a34aab7adce56dd3fda5258e8526f0e1cf0164
-  md5: f763daad76fe32da91acfdf3e476ec0d
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libavif16-1.1.1-h3b0c220_2.conda
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-  - libopenblas >=0.3.27,<0.3.28.0a0
-  - libopenblas >=0.3.27,<1.0a0
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-  - liblapacke 3.9.0 24_linuxaarch64_openblas
-  - libcblas 3.9.0 24_linuxaarch64_openblas
-  - blas * openblas
-  license: BSD-3-Clause
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - libgcc >=13
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 14991
-  timestamp: 1726668539439
+  size: 116097
+  timestamp: 1730268695528
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-  name: libblas
-  version: 3.9.0
-  build: 24_osxarm64_openblas
-  build_number: 24
+  name: libavif16
+  version: 1.1.1
+  build: h45b7238_2
+  build_number: 2
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-24_osxarm64_openblas.conda
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-  md5: 35cb711e7bc46ee5f3dd67af99ad1986
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h45b7238_2.conda
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-  - libopenblas >=0.3.27,<0.3.28.0a0
-  - libopenblas >=0.3.27,<1.0a0
-  constrains:
-  - liblapack 3.9.0 24_osxarm64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 24_osxarm64_openblas
-  - libcblas 3.9.0 24_osxarm64_openblas
-  license: BSD-3-Clause
+  - __osx >=11.0
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 15144
-  timestamp: 1726668802976
+  size: 96781
+  timestamp: 1730268761553
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-  name: libblas
-  version: 3.9.0
-  build: 24_win64_mkl
-  build_number: 24
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-24_win64_mkl.conda
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-  md5: ea127210707251a33116b437c22b8dad
+  name: libavif16
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+  build: h4d049a7_2
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+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/libavif16-1.1.1-h4d049a7_2.conda
+  sha256: f74662ac8325dedbc786bf4f3faef39ad4981739cf0239c2ea2d80c791b04de5
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+  - dav1d >=1.2.1,<1.2.2.0a0
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 97828
+  timestamp: 1730269135854
+- kind: conda
+  name: libavif16
+  version: 1.1.1
+  build: h71406da_2
+  build_number: 2
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/libavif16-1.1.1-h71406da_2.conda
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+  md5: 804f440fd71e1a903215710826cf98aa
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-  - mkl 2024.1.0 h66d3029_694
+  - __osx >=10.13
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+  - dav1d >=1.2.1,<1.2.2.0a0
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 108913
+  timestamp: 1730268731759
+- kind: conda
+  name: libblas
+  version: 3.9.0
+  build: 25_linux64_openblas
+  build_number: 25
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
+  sha256: d6d12dc437d060f838820e9e61bf73baab651f91935ac594cf10beb9ef1b4450
+  md5: 8ea26d42ca88ec5258802715fe1ee10b
+  depends:
+  - libopenblas >=0.3.28,<0.3.29.0a0
+  - libopenblas >=0.3.28,<1.0a0
+  constrains:
+  - liblapack 3.9.0 25_linux64_openblas
+  - libcblas 3.9.0 25_linux64_openblas
+  - blas * openblas
+  - liblapacke 3.9.0 25_linux64_openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 15677
+  timestamp: 1729642900350
+- kind: conda
+  name: libblas
+  version: 3.9.0
+  build: 25_linuxaarch64_openblas
+  build_number: 25
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-25_linuxaarch64_openblas.conda
+  sha256: 5c08f78312874bb61307f5ea737377df2d0f6e7f7833ded21ca58d8820c794ca
+  md5: f9b8a4a955ed2d0b68b1f453abcc1c9e
+  depends:
+  - libopenblas >=0.3.28,<0.3.29.0a0
+  - libopenblas >=0.3.28,<1.0a0
+  constrains:
+  - blas * openblas
+  - liblapacke 3.9.0 25_linuxaarch64_openblas
+  - liblapack 3.9.0 25_linuxaarch64_openblas
+  - libcblas 3.9.0 25_linuxaarch64_openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 15808
+  timestamp: 1729643002627
+- kind: conda
+  name: libblas
+  version: 3.9.0
+  build: 25_osx64_openblas
+  build_number: 25
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-25_osx64_openblas.conda
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+  md5: da0a6f87958893e1d2e2bbc7e7a6541f
+  depends:
+  - libopenblas >=0.3.28,<0.3.29.0a0
+  - libopenblas >=0.3.28,<1.0a0
+  constrains:
+  - liblapack 3.9.0 25_osx64_openblas
+  - liblapacke 3.9.0 25_osx64_openblas
+  - blas * openblas
+  - libcblas 3.9.0 25_osx64_openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 15952
+  timestamp: 1729643159199
+- kind: conda
+  name: libblas
+  version: 3.9.0
+  build: 25_osxarm64_openblas
+  build_number: 25
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-25_osxarm64_openblas.conda
+  sha256: f1fb9a11af0b2878bd8804b4c77d3733c40076218bcbdb35f575b1c0c9fddf11
+  md5: f8cf4d920ff36ce471619010eff59cac
+  depends:
+  - libopenblas >=0.3.28,<0.3.29.0a0
+  - libopenblas >=0.3.28,<1.0a0
+  constrains:
+  - blas * openblas
+  - liblapack 3.9.0 25_osxarm64_openblas
+  - liblapacke 3.9.0 25_osxarm64_openblas
+  - libcblas 3.9.0 25_osxarm64_openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 15913
+  timestamp: 1729643265495
+- kind: conda
+  name: libblas
+  version: 3.9.0
+  build: 25_win64_mkl
+  build_number: 25
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-25_win64_mkl.conda
+  sha256: 5468bb91c44b41ce060bbd997c797b2f91e2b7ce91a7cbf4ddf7e7b734a8dc98
+  md5: 499208e81242efb6e5abc7366c91c816
+  depends:
+  - mkl 2024.2.2 h66d3029_14
   constrains:
   - blas * mkl
-  - liblapack 3.9.0 24_win64_mkl
-  - libcblas 3.9.0 24_win64_mkl
-  - liblapacke 3.9.0 24_win64_mkl
+  - libcblas 3.9.0 25_win64_mkl
+  - liblapack 3.9.0 25_win64_mkl
+  - liblapacke 3.9.0 25_win64_mkl
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 5183540
-  timestamp: 1726669397923
+  size: 3736641
+  timestamp: 1729643534444
 - kind: conda
   name: libbrotlicommon
   version: 1.1.0
@@ -9248,103 +9556,103 @@ packages:
 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 22_osx64_openblas
-  build_number: 22
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-22_osx64_openblas.conda
-  sha256: 6a2ba9198e2320c3e22fe3d121310cf8a8ac663e94100c5693b34523fcb3cc04
-  md5: b9fef82772330f61b2b0201c72d2c29b
+  build: 25_linux64_openblas
+  build_number: 25
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
+  sha256: ab87b0477078837c91d9cda62a9faca18fba7c57cc77aa779ae24b3ac783b5dd
+  md5: 5dbd1b0fc0d01ec5e0e1fbe667281a11
   depends:
-  - libblas 3.9.0 22_osx64_openblas
+  - libblas 3.9.0 25_linux64_openblas
   constrains:
-  - liblapacke 3.9.0 22_osx64_openblas
+  - liblapack 3.9.0 25_linux64_openblas
   - blas * openblas
-  - liblapack 3.9.0 22_osx64_openblas
+  - liblapacke 3.9.0 25_linux64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 14636
-  timestamp: 1712542311437
+  size: 15613
+  timestamp: 1729642905619
 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 24_linux64_openblas
-  build_number: 24
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-24_linux64_openblas.conda
-  sha256: 2a52bccc5b03cdf014d856d0b85dbd591faa335ab337d620cd6aded121d7153c
-  md5: f5b8822297c9c790cec0795ca1fc9be6
+  build: 25_linuxaarch64_openblas
+  build_number: 25
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-25_linuxaarch64_openblas.conda
+  sha256: fde797e5528040fed0e9228dd75331be0cf5cbb0bc63641f53c3cca9eb86ec16
+  md5: db6af51123c67814572a8c25542cb368
   depends:
-  - libblas 3.9.0 24_linux64_openblas
+  - libblas 3.9.0 25_linuxaarch64_openblas
   constrains:
   - blas * openblas
-  - liblapack 3.9.0 24_linux64_openblas
-  - liblapacke 3.9.0 24_linux64_openblas
+  - liblapacke 3.9.0 25_linuxaarch64_openblas
+  - liblapack 3.9.0 25_linuxaarch64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 14910
-  timestamp: 1726668461033
+  size: 15700
+  timestamp: 1729643006729
 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 24_linuxaarch64_openblas
-  build_number: 24
-  subdir: linux-aarch64
-  url: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-24_linuxaarch64_openblas.conda
-  sha256: d7e0e459184ea92a4c0ece4ff5b4088bfa14811e3b650d948befc1cdab82fce2
-  md5: fe7560187584eaae4f115d471b62c09c
+  build: 25_osx64_openblas
+  build_number: 25
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-25_osx64_openblas.conda
+  sha256: b04ae297aa5396df3135514866db72845b111c92524570f923625473f11cfbe2
+  md5: ab304b75ea67f850cf7adf9156e3f62f
   depends:
-  - libblas 3.9.0 24_linuxaarch64_openblas
+  - libblas 3.9.0 25_osx64_openblas
   constrains:
-  - liblapack 3.9.0 24_linuxaarch64_openblas
+  - liblapack 3.9.0 25_osx64_openblas
+  - liblapacke 3.9.0 25_osx64_openblas
   - blas * openblas
-  - liblapacke 3.9.0 24_linuxaarch64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 14901
-  timestamp: 1726668544814
+  size: 15842
+  timestamp: 1729643166929
 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 24_osxarm64_openblas
-  build_number: 24
+  build: 25_osxarm64_openblas
+  build_number: 25
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-24_osxarm64_openblas.conda
-  sha256: 40dc3f7c44af5cd5a2020386cb30f92943a9d8f7f54321b4d6ae32b2e54af9a4
-  md5: c8977086a19233153e454bb2b332a920
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-25_osxarm64_openblas.conda
+  sha256: d9fa5b6b11252132a3383bbf87bd2f1b9d6248bef1b7e113c2a8ae41b0376218
+  md5: 4df0fae81f0b5bf47d48c882b086da11
   depends:
-  - libblas 3.9.0 24_osxarm64_openblas
+  - libblas 3.9.0 25_osxarm64_openblas
   constrains:
-  - liblapack 3.9.0 24_osxarm64_openblas
   - blas * openblas
-  - liblapacke 3.9.0 24_osxarm64_openblas
+  - liblapack 3.9.0 25_osxarm64_openblas
+  - liblapacke 3.9.0 25_osxarm64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15062
-  timestamp: 1726668809379
+  size: 15837
+  timestamp: 1729643270793
 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 24_win64_mkl
-  build_number: 24
+  build: 25_win64_mkl
+  build_number: 25
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-24_win64_mkl.conda
-  sha256: 297e858e9a2e6c4d9846fc101607ad31b778d8bde8591f9207e72d728a9f00a7
-  md5: a42c7390d3249698c0ffb6040e9396e7
+  url: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-25_win64_mkl.conda
+  sha256: 21528cdfe67dafdb2d21925515a167f13963e002c2b6d06d68984767f731850c
+  md5: 3ed189ba03a9888a8013aaee0d67c49d
   depends:
-  - libblas 3.9.0 24_win64_mkl
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   license_family: Apache
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 - kind: conda
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   version: 3.9.0
-  build: 22_osx64_openblas
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-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-22_osx64_openblas.conda
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+  url: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
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+  - liblapacke 3.9.0 25_linux64_openblas
+  - libcblas 3.9.0 25_linux64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 14657
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+  - liblapacke 3.9.0 25_linuxaarch64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 14911
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   license_family: BSD
   purls: []
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   subdir: osx-arm64
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-  md5: 49a3241f76cdbe705e346204a328f66c
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-  - libcblas 3.9.0 24_osxarm64_openblas
+  - liblapacke 3.9.0 25_osxarm64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15063
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   name: liblapack
   version: 3.9.0
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-24_win64_mkl.conda
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-  build: h66d3029_694
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+  version: 2024.2.2
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+  version: 3.4.2
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   - numpy >=1.24
   - packaging >=23.1
   - pandas >=2.1
   - python >=3.10
   constrains:
-  - seaborn-base >=0.12
-  - distributed >=2023.9
-  - scipy >=1.11
+  - matplotlib-base >=3.7
   - netcdf4 >=1.6.0
-  - toolz >=0.12
-  - nc-time-axis >=1.4
+  - numba >=0.57
+  - hdf5 >=1.12
+  - sparse >=0.14
   - cftime >=1.6
-  - h5netcdf >=1.2
-  - matplotlib-base >=3.7
-  - h5py >=3.8
   - zarr >=2.16
-  - hdf5 >=1.12
-  - numba >=0.57
+  - flox >=0.7
+  - pint >=0.22
+  - toolz >=0.12
+  - scipy >=1.11
+  - seaborn-base >=0.12
+  - distributed >=2023.9
   - iris >=3.7
   - cartopy >=0.22
-  - dask-core >=2023.9
-  - flox >=0.7
   - bottleneck >=1.3
-  - pint >=0.22
-  - sparse >=0.14
+  - dask-core >=2023.9
+  - h5py >=3.8
+  - h5netcdf >=1.2
+  - nc-time-axis >=1.4
   license: Apache-2.0
   license_family: APACHE
   purls:
   - pkg:pypi/xarray?source=hash-mapping
-  size: 801066
-  timestamp: 1728453306227
+  size: 813754
+  timestamp: 1729867978934
+- kind: pypi
+  name: xarray-regrid
+  version: 0.4.0
+  url: https://files.pythonhosted.org/packages/a9/cd/7e8753b9ef31c4e18d8c206019be052380bd2e8eb389180b6b39dc3f0c1e/xarray_regrid-0.4.0-py3-none-any.whl
+  sha256: 26a61fc4e72440c735e23f1d2060503cceffb4d7eb7abbd8ad1599fe1c928a29
+  requires_dist:
+  - flox
+  - numpy
+  - scipy
+  - xarray
+  - dask[distributed] ; extra == 'accel'
+  - opt-einsum ; extra == 'accel'
+  - sparse ; extra == 'accel'
+  - aiohttp ; extra == 'benchmarking'
+  - cftime ; extra == 'benchmarking'
+  - h5netcdf ; extra == 'benchmarking'
+  - matplotlib ; extra == 'benchmarking'
+  - pooch ; extra == 'benchmarking'
+  - requests ; extra == 'benchmarking'
+  - zarr ; extra == 'benchmarking'
+  - hatch ; extra == 'dev'
+  - mypy ; extra == 'dev'
+  - pandas-stubs ; extra == 'dev'
+  - pytest ; extra == 'dev'
+  - pytest-cov ; extra == 'dev'
+  - ruff ; extra == 'dev'
+  - coverage[toml] ; extra == 'docs'
+  - myst-nb ; extra == 'docs'
+  - myst-parser ; extra == 'docs'
+  - sphinx ; extra == 'docs'
+  - sphinx-autoapi ; extra == 'docs'
+  - sphinx-rtd-theme ; extra == 'docs'
+  requires_python: '>=3.10'
 - kind: conda
   name: xerces-c
   version: 3.2.5
@@ -20599,12 +21456,12 @@ packages:
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h3b0a872_6
-  build_number: 6
+  build: h3b0a872_7
+  build_number: 7
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_6.conda
-  sha256: e67288b1c98a31ee58a5c07bdd873dbe08e75f752e1ad605d5e8c0697339903e
-  md5: 113506c8d2d558e733f5c38f6bf08c50
+  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
+  sha256: a4dc72c96848f764bb5a5176aa93dd1e9b9e52804137b99daeebba277b31ea10
+  md5: 3947a35e916fcc6b9825449affbf4214
   depends:
   - __glibc >=2.17,<3.0.a0
   - krb5 >=1.21.3,<1.22.0a0
@@ -20614,17 +21471,17 @@ packages:
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 335528
-  timestamp: 1728364029042
+  size: 335400
+  timestamp: 1731585026517
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h5efb499_6
-  build_number: 6
+  build: h5efb499_7
+  build_number: 7
   subdir: linux-aarch64
-  url: https://conda.anaconda.org/conda-forge/linux-aarch64/zeromq-4.3.5-h5efb499_6.conda
-  sha256: 7cf61f742757ebb8773c5c96a9d768e06a288a0b9bd95ba212dccd17fae25abb
-  md5: c395b75ab44b4f82e5531de2cf9d20ba
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/zeromq-4.3.5-h5efb499_7.conda
+  sha256: a6003096dc0570a86492040ba32b04ce7662b159600be2252b7a0dfb9414e21c
+  md5: f2f3282559a4b87b7256ecafb4610107
   depends:
   - krb5 >=1.21.3,<1.22.0a0
   - libgcc >=13
@@ -20633,36 +21490,36 @@ packages:
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 371083
-  timestamp: 1728368602099
+  size: 371419
+  timestamp: 1731589490850
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h9f5b81c_6
-  build_number: 6
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h9f5b81c_6.conda
-  sha256: 5c5061c976141eccbbb2aec21483ddd10fd1df4fd9bcf638e3fd57b2bd85721f
-  md5: 84121ef1717cdfbecedeae70142706cc
+  build: h7130eaa_7
+  build_number: 7
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-h7130eaa_7.conda
+  sha256: b932dce8c9de9a8ffbf0db0365d29677636e599f7763ca51e554c43a0c5f8389
+  md5: 6a0a76cd2b3d575e1b7aaeb283b9c3ed
   depends:
-  - __osx >=11.0
+  - __osx >=10.13
   - krb5 >=1.21.3,<1.22.0a0
-  - libcxx >=17
+  - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 280870
-  timestamp: 1728363954972
+  size: 292112
+  timestamp: 1731585246902
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: ha9f60a1_6
-  build_number: 6
+  build: ha9f60a1_7
+  build_number: 7
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-ha9f60a1_6.conda
-  sha256: c37130692742cc43eedf4e23270c7d1634235acff50760025e9583f8b46b64e6
-  md5: 33a78bbc44d6550c361abb058a0556e2
+  url: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-ha9f60a1_7.conda
+  sha256: 15cc8e2162d0a33ffeb3f7b7c7883fd830c54a4b1be6a4b8c7ee1f4fef0088fb
+  md5: e03f2c245a5ee6055752465519363b1c
   depends:
   - krb5 >=1.21.3,<1.22.0a0
   - libsodium >=1.0.20,<1.0.21.0a0
@@ -20672,27 +21529,27 @@ packages:
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 2701749
-  timestamp: 1728364260886
+  size: 2527503
+  timestamp: 1731585151036
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: he4ceba3_6
-  build_number: 6
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-he4ceba3_6.conda
-  sha256: 0e2a6ced111fd99b66b76ec797804ab798ec190a88a2779060f7a8787c343ee0
-  md5: 00ec9f2a5e21bbbd22ffbbc12b3df286
+  build: hc1bb282_7
+  build_number: 7
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
+  sha256: 9e585569fe2e7d3bea71972cd4b9f06b1a7ab8fa7c5139f92a31cbceecf25a8a
+  md5: f7e6b65943cb73bce0143737fded08f1
   depends:
-  - __osx >=10.13
+  - __osx >=11.0
   - krb5 >=1.21.3,<1.22.0a0
-  - libcxx >=17
+  - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 290634
-  timestamp: 1728364170966
+  size: 281565
+  timestamp: 1731585108039
 - kind: conda
   name: zict
   version: 3.0.0
@@ -20712,21 +21569,21 @@ packages:
   timestamp: 1681770298596
 - kind: conda
   name: zipp
-  version: 3.20.2
+  version: 3.21.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
-  sha256: 1e84fcfa41e0afdd87ff41e6fbb719c96a0e098c1f79be342293ab0bd8dea322
-  md5: 4daaed111c05672ae669f7036ee5bba3
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
+  sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec
+  md5: fee389bf8a4843bd7a2248ce11b7f188
   depends:
   - python >=3.8
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/zipp?source=hash-mapping
-  size: 21409
-  timestamp: 1726248679175
+  size: 21702
+  timestamp: 1731262194278
 - kind: conda
   name: zlib
   version: 1.3.1
diff --git a/pyproject.toml b/pyproject.toml
index c9f7777..7da1237 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,12 +3,13 @@ authors = [
     { name = "Jarmo S. Kikstra", email = "kikstra@iiasa.ac.at"},
     { name = "Jonas Hörsch" },
     { name = "Zebedee Nicholls", email = "zebedee.nicholls@climate-energy-college.org"},
+    { name = "Chris Smith", email = "chrisroadmap@gmail.com"}
 ]
 requires-python = "==3.11"
 dependencies = [
     "pre-commit==3.3.1",
     "ptolemy-iamc @ git+https://github.com/gidden/ptolemy.git@015aa843c340bd57464eb7cfd5f3c8e397201266",
-    "aneris-iamc @ git+https://github.com/iiasa/aneris.git@9e5aa002b523f1d5d0ac32554988816e8c20cb44", "openscm-units>=0.6.2", "scmdata>=0.17.0,<0.18",
+    "aneris-iamc @ git+https://github.com/iiasa/aneris.git@9e5aa002b523f1d5d0ac32554988816e8c20cb44", "openscm-units>=0.6.2", "scmdata>=0.17.0,<0.18", "xarray-regrid==0.4.0",
 ]
 name = "emissions-harmonization-historical"
 version = "0.1.0"
@@ -55,6 +56,7 @@ pooch = ">=1.8.2,<2"
 nc-time-axis = ">=1.4.1,<2"
 pint-xarray = ">=0.4,<0.5"
 seaborn = ">=0.13.2,<0.14"
+tqdm = ">=4.67.0,<5"
 
 [tool.pixi.pypi-dependencies]
 emissions_harmonization_historical = { path = ".", editable = true }

From 37ca32f66adf4876866433d3c29b4fe0ef994c59 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Wed, 20 Nov 2024 17:51:00 +0100
Subject: [PATCH 06/11] fight ruff

---
 notebooks/0103_GFED4-BB4CMIP-prepare.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/notebooks/0103_GFED4-BB4CMIP-prepare.py b/notebooks/0103_GFED4-BB4CMIP-prepare.py
index 5d584e8..c880200 100644
--- a/notebooks/0103_GFED4-BB4CMIP-prepare.py
+++ b/notebooks/0103_GFED4-BB4CMIP-prepare.py
@@ -20,6 +20,7 @@
 import ptolemy
 import scmdata
 import xarray as xr
+import xarray_regrid  # noqa: F401
 from tqdm import tqdm
 
 from emissions_harmonization_historical.constants import DATA_ROOT

From f9f52303346a386cafc1645bf5f453daa7d54985 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Thu, 21 Nov 2024 16:45:53 +0100
Subject: [PATCH 07/11] add CO to GFED-BB4CMIP

---
 notebooks/0103_GFED4-BB4CMIP-prepare.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/notebooks/0103_GFED4-BB4CMIP-prepare.py b/notebooks/0103_GFED4-BB4CMIP-prepare.py
index c880200..97a7e37 100644
--- a/notebooks/0103_GFED4-BB4CMIP-prepare.py
+++ b/notebooks/0103_GFED4-BB4CMIP-prepare.py
@@ -57,6 +57,10 @@
         "unit_label": "Mt NMVOC / yr",
         "filename_label": "NMVOC_bulk",
     },
+    "CO": {
+        "unit_label": "Mt CO / yr",
+        "filename_label": "CO",
+    },
     "CO2": {
         "unit_label": "Mt CO2 / yr",
         "filename_label": "CO2",

From 8f6129572a9200477f65224b88dc7c992f1a150c Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Thu, 21 Nov 2024 18:07:49 +0100
Subject: [PATCH 08/11] commit README of data destination for BB4CMIP

---
 data/national/gfed-bb4cmip/processed/README.txt | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 data/national/gfed-bb4cmip/processed/README.txt

diff --git a/data/national/gfed-bb4cmip/processed/README.txt b/data/national/gfed-bb4cmip/processed/README.txt
new file mode 100644
index 0000000..fe7ca57
--- /dev/null
+++ b/data/national/gfed-bb4cmip/processed/README.txt
@@ -0,0 +1 @@
+This location will host processed data from BB4CMIP.

From 71dcad465e7369362ab3dc30b41019c7cef3ea9a Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Thu, 21 Nov 2024 18:10:26 +0100
Subject: [PATCH 09/11] put NMVOC into GFED data

---
 notebooks/0102_GFED4-prepare.py            |  5 +-
 notebooks/0104_GFED-BB4CMIP-postprocess.py | 75 ++++++++++++++++++++++
 2 files changed, 78 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/0104_GFED-BB4CMIP-postprocess.py

diff --git a/notebooks/0102_GFED4-prepare.py b/notebooks/0102_GFED4-prepare.py
index a49c84d..f5074db 100644
--- a/notebooks/0102_GFED4-prepare.py
+++ b/notebooks/0102_GFED4-prepare.py
@@ -20,6 +20,7 @@
 import pandas as pd
 import ptolemy
 import xarray as xr
+import xarray_regrid  # noqa: F401
 from pandas_indexing import set_openscm_registry_as_default
 
 from emissions_harmonization_historical.constants import DATA_ROOT
@@ -218,7 +219,7 @@
         ("N2O", "kg N2O"),
         ("NH3", "kg NH3"),
         ("NOx", "kg NOx"),
-        ("VOC", voc_unit),
+        ("NMVOC", voc_unit),
         ("SO2", "kg SO2"),
     ],
     names=["em", "unit"],
@@ -265,7 +266,7 @@
         ("N2O", "Mt N2O/yr"),
         ("NH3", "Mt NH3/yr"),
         ("NOx", "Mt NO/yr"),  # we know NO mass units, so label as such
-        ("NMVOC", "Mt VOC/yr"),
+        ("NMVOC", "Mt NMVOC/yr"),
         ("SO2", "Mt SO2/yr"),
     ],
     names=["em", "unit"],
diff --git a/notebooks/0104_GFED-BB4CMIP-postprocess.py b/notebooks/0104_GFED-BB4CMIP-postprocess.py
new file mode 100644
index 0000000..1782848
--- /dev/null
+++ b/notebooks/0104_GFED-BB4CMIP-postprocess.py
@@ -0,0 +1,75 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# The 0103 script dumped the files into separate CSVs; here we combine and make consistenct with other emissions files
+# and IAMC format
+
+# %%
+import pandas as pd
+
+from emissions_harmonization_historical.constants import DATA_ROOT
+
+# %%
+data_path = DATA_ROOT / "national/gfed-bb4cmip/processed"
+
+# %%
+species = [
+    "BC",
+    "NMVOC",
+    "CO",
+    "CO2",
+    "CH4",
+    "N2O",
+    "OC",
+    "NH3",
+    "NOx",
+    "SO2",
+]
+
+# %%
+df_list = []
+# Rename variable in place
+for specie in species:
+    df_in = pd.read_csv(data_path / f"{specie}.csv")
+    df_in.variable = f"CMIP7 History|Emissions|{specie}|Biomass Burning"
+    df_list.append(df_in)
+
+# %%
+df = pd.concat(df_list)
+
+# %%
+df["model"] = "History"
+df
+
+# %%
+# sort order: region, variable
+df_sorted = df.sort_values(["region", "variable"])
+
+# %%
+df_sorted
+
+# %%
+# fix column order
+df_reordered = df_sorted.reindex(
+    columns=["model", "scenario", "region", "variable", "unit"] + [str(i) for i in range(1750, 2101)]
+)
+
+# %%
+df_reordered
+
+# %%
+df_reordered.to_csv(data_path / "gfed-bb4cmip_cmip7_national_alpha.csv", index=False)
+
+# %%

From 6832868f46b17e1db23c49caa5dc46579a49d15a Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Fri, 22 Nov 2024 19:52:47 +0100
Subject: [PATCH 10/11] GCB CO2 emissions

---
 .gitignore                                    |   5 +
 .../gcb/Global_Carbon_Budget_2023v1.1.xlsx    | Bin 421952 -> 0 bytes
 data/national/gcb/data_raw/README.txt         |   9 +
 data/national/gcb/processed/README.txt        |   1 +
 notebooks/0105_GCB-prepare.py                 | 193 ++++++++++++++++++
 5 files changed, 208 insertions(+)
 delete mode 100644 data/global/gcb/Global_Carbon_Budget_2023v1.1.xlsx
 create mode 100644 data/national/gcb/data_raw/README.txt
 create mode 100644 data/national/gcb/processed/README.txt
 create mode 100644 notebooks/0105_GCB-prepare.py

diff --git a/.gitignore b/.gitignore
index 35f75ab..bb00a2f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 data/national/ceds/processed
 data/national/gfed/processed
 data/national/gfed-bb4cmip/processed
+data/national/gcb/processed
 
 # RCMIP data
 data/global/rcmip/data_raw
@@ -13,6 +14,10 @@ data/national/ceds/data_raw
 data/national/gfed/data_raw
 data/national/gfed-bb4cmip/data_raw
 
+# GCB data
+data/national/gcb/data_raw
+data/global/gcb/data_raw
+
 # pixi stuff
 .pixi
 
diff --git a/data/global/gcb/Global_Carbon_Budget_2023v1.1.xlsx b/data/global/gcb/Global_Carbon_Budget_2023v1.1.xlsx
deleted file mode 100644
index 07ff50f937eee4a095817a87803a6148d991dea4..0000000000000000000000000000000000000000
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diff --git a/data/national/gcb/data_raw/README.txt b/data/national/gcb/data_raw/README.txt
new file mode 100644
index 0000000..b258297
--- /dev/null
+++ b/data/national/gcb/data_raw/README.txt
@@ -0,0 +1,9 @@
+Download data from source:
+https://globalcarbonbudgetdata.org/latest-data.html
+https://zenodo.org/records/14106218
+
+Download the following links:
+"Global Carbon Budget v2024"
+GCB2024v18_MtCO2_flat.csv
+
+Last accessed: 20.11.2024
diff --git a/data/national/gcb/processed/README.txt b/data/national/gcb/processed/README.txt
new file mode 100644
index 0000000..fc3b8e4
--- /dev/null
+++ b/data/national/gcb/processed/README.txt
@@ -0,0 +1 @@
+The processed data from Global Carbon Budget goes here.
diff --git a/notebooks/0105_GCB-prepare.py b/notebooks/0105_GCB-prepare.py
new file mode 100644
index 0000000..2cf3766
--- /dev/null
+++ b/notebooks/0105_GCB-prepare.py
@@ -0,0 +1,193 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# Process Global Carbon Budget data
+#
+# We use the version from 10.5281/zenodo.14106218, since the Excel sheet of fossil fuel production by country may have
+# errors (in any case the sum of country emissions and bunkers does not equal the global total in the Excel sheets).
+# See https://bsky.app/profile/cjsmith.be/post/3lbhxt4chqc2x.
+
+# %%
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scmdata
+
+from emissions_harmonization_historical.constants import DATA_ROOT
+
+# %%
+raw_data_path = DATA_ROOT / "national/gcb/data_raw/"
+raw_data_path
+
+# %%
+gcb_processed_output_file = DATA_ROOT / Path("national", "gcb", "processed", "gcb_cmip7_national_fossil_alpha.csv")
+
+# %%
+df = pd.read_csv(
+    raw_data_path / "GCB2024v18_MtCO2_flat.csv"
+)  # , sheet_name='Historical Budget', skiprows=15, index_col="Year")
+
+# %%
+# rename Global to World
+df.loc[df["Country"] == "Global", "Country"] = "World"
+df
+
+# %%
+# for country in df.Country.unique():
+#     # find NaNs in ISO3 and set these empty values equal to the country name
+#     if type(df[(df['Country']==country) & (df.Year==1750)]['ISO 3166-1 alpha-3'].values[0]) is float:
+#         #print(country)
+#         df.loc[(df['Country']==country), 'ISO 3166-1 alpha-3'] = country
+
+# %%
+times = df.Year.unique()
+nt = len(times)
+nt
+
+# %%
+# iso_list = df['ISO 3166-1 alpha-3'].unique()
+# niso = len(iso_list)
+# niso
+countries = df.Country.unique()
+nc = len(countries)
+
+# %%
+# iso_list
+
+# %%
+data = np.zeros((nt, nc))
+for i, country in enumerate(countries):
+    data[:, i] = df.loc[(df["Country"] == country)].Total
+
+# %%
+# World on top
+countries = np.concatenate((countries[-1:], countries[:-1]))
+data = np.concatenate((data[:, -1:], data[:, :-1]), axis=1)
+data
+
+# %%
+# convert nan to zero
+data[np.isnan(data)] = 0
+
+# %%
+countries
+
+# %%
+data
+
+# %%
+df_out = (
+    scmdata.ScmRun(
+        data,
+        index=times,
+        columns={
+            "variable": [
+                "CMIP7 History|Emissions|CO2|Fossil Fuel and Industrial",
+            ],
+            "unit": ["Mt CO2 / yr"],
+            "region": countries,
+            "model": "History",
+            "scenario": "Global Carbon Budget",
+        },
+    )
+    .interpolate(target_times=np.arange(1750, 2023, dtype=int))
+    .timeseries(time_axis="year")
+)
+
+# %%
+df_out.to_csv(gcb_processed_output_file)
+
+# %%
+df_lu_blue = pd.read_excel(
+    raw_data_path / "National_LandUseChange_Carbon_Emissions_2024v1.0.xlsx", sheet_name="BLUE", skiprows=7, index_col=0
+)
+df_lu_hc2023 = pd.read_excel(
+    raw_data_path / "National_LandUseChange_Carbon_Emissions_2024v1.0.xlsx",
+    sheet_name="H&C2023",
+    skiprows=7,
+    index_col=0,
+)
+df_lu_oscar = pd.read_excel(
+    raw_data_path / "National_LandUseChange_Carbon_Emissions_2024v1.0.xlsx", sheet_name="OSCAR", skiprows=7, index_col=0
+)
+df_lu_luce = pd.read_excel(
+    raw_data_path / "National_LandUseChange_Carbon_Emissions_2024v1.0.xlsx", sheet_name="LUCE", skiprows=7, index_col=0
+)
+
+# %%
+df_lu4 = pd.concat([df_lu_blue, df_lu_hc2023, df_lu_oscar, df_lu_luce])
+df_lu = df_lu4.groupby(level=0).mean()
+
+df_lu.drop(columns={"EU27"}, inplace=True)
+df_lu.rename(columns={"Global": "World"}, inplace=True)
+# df_lu4 = pd.Panel([df_lu_blue, df_lu_hc2023, df_lu_oscar, df_lu_luce])
+# print('Mean of stacked DFs:\n{df}'.format(df=panel.mean(axis=0)))
+# # df_lu.head()
+df_lu
+
+# %%
+# World on top
+cols = list(df_lu.columns)
+cols = cols[-1:] + cols[:-1]
+df_lu_ordered = df_lu[cols]
+df_lu_ordered
+
+# %%
+# these ones are in fossil data but not in country LUC data. At least it looks like there are no naming conflicts.
+print([i for i in df.Country.unique() if i not in df_lu.columns])
+
+# %%
+# df_lu_extended.loc[1750:1849] = [np.nan] * 200
+# df_lu_extended = pd.concat(pd.DataFrame(np.nan,index=np.arange(1750, 1850, dtype="int"),columns=df.columns))
+df_lu_extended = pd.concat([df_lu_ordered, pd.DataFrame(index=np.arange(1750, 1850))])
+df_lu_extended.loc[1750:1849, "World"] = np.linspace(3, 597, 100)
+df_lu_extended
+
+# %%
+df_lu_extended.sort_index(inplace=True)
+df_lu_extended
+
+# %%
+df_lu_extended
+
+# %%
+for i in range(100):
+    df_lu_extended.iloc[i, 1:] = df_lu_extended.iloc[100, 1:] * df_lu_extended.iloc[i, 0] / df_lu_extended.iloc[100, 0]
+
+# %%
+df_lu_out = (
+    scmdata.ScmRun(
+        df_lu_extended,
+        index=times,
+        columns={
+            "variable": [
+                "CMIP7 History|Emissions|CO2|Land Use Change",
+            ],
+            "unit": ["Mt CO2 / yr"],
+            "region": df_lu_ordered.columns,
+            "model": "History",
+            "scenario": "Global Carbon Budget",
+        },
+    )
+    .interpolate(target_times=np.arange(1750, 2024, dtype=int))
+    .timeseries(time_axis="year")
+)
+
+# %%
+df_lu_out
+
+# %%
+df_lu_out.to_csv(DATA_ROOT / Path("national", "gcb", "processed", "gcb_cmip7_national_luc_alpha.csv"))

From fd3b312de567eaf615b3ba67d7b4cb9ae8cf1674 Mon Sep 17 00:00:00 2001
From: Chris Smith <chrisroadmap@gmail.com>
Date: Fri, 3 Jan 2025 12:32:24 +0100
Subject: [PATCH 11/11] GCB

---
 notebooks/0105_GCB-prepare.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/notebooks/0105_GCB-prepare.py b/notebooks/0105_GCB-prepare.py
index 2cf3766..4020f6e 100644
--- a/notebooks/0105_GCB-prepare.py
+++ b/notebooks/0105_GCB-prepare.py
@@ -65,7 +65,7 @@
 nc = len(countries)
 
 # %%
-# iso_list
+countries
 
 # %%
 data = np.zeros((nt, nc))
@@ -103,7 +103,7 @@
             "scenario": "Global Carbon Budget",
         },
     )
-    .interpolate(target_times=np.arange(1750, 2023, dtype=int))
+    .interpolate(target_times=np.arange(1750, 2024, dtype=int))
     .timeseries(time_axis="year")
 )
 
@@ -167,10 +167,13 @@
 for i in range(100):
     df_lu_extended.iloc[i, 1:] = df_lu_extended.iloc[100, 1:] * df_lu_extended.iloc[i, 0] / df_lu_extended.iloc[100, 0]
 
+# %%
+df_lu_converted = df_lu_extended * 3.664
+
 # %%
 df_lu_out = (
     scmdata.ScmRun(
-        df_lu_extended,
+        df_lu_converted,
         index=times,
         columns={
             "variable": [
@@ -191,3 +194,5 @@
 
 # %%
 df_lu_out.to_csv(DATA_ROOT / Path("national", "gcb", "processed", "gcb_cmip7_national_luc_alpha.csv"))
+
+# %%