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CDAT Migration: Update mp_partition_driver.py to use Dataset from dataset_xr.py #883

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merged 4 commits into from
Oct 31, 2024
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CDAT Migration: Update mp_partition_driver.py to use Dataset from dataset_xr.py #883

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fdeb516
Update `mp_partition_driver.py` to use Dataset from `dataset_xr.py`
tomvothecoder Oct 30, 2024
434ebd7
Update e3sm_diags import
tomvothecoder Oct 30, 2024
07c0d97
Add regression test material
tomvothecoder Oct 31, 2024
5059619
Move `mp_partition_plot.py` down a dir level
tomvothecoder Oct 31, 2024
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27 changes: 14 additions & 13 deletions e3sm_diags/driver/mp_partition_driver.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,9 @@
"""
This analysis set for mixed-phase cloud partition/T5050 metrics is requested by
the E3SM Aerosol Working Group. The script is integrated in e3sm_diags by Jill
Zhang and Yuying Zhang, with contribution from Yunpeng Shan, Jiwen Fan,
Xue Zheng and Susannah Burrows.
"""
from __future__ import annotations

import glob
Expand All @@ -9,8 +15,8 @@
import xarray as xr
from scipy.stats import binned_statistic

import e3sm_diags
from e3sm_diags.driver import utils
from e3sm_diags import INSTALL_PATH
from e3sm_diags.driver.utils.dataset_xr import Dataset
from e3sm_diags.logger import custom_logger
from e3sm_diags.plot.cartopy.mp_partition_plot import plot

Expand All @@ -20,8 +26,6 @@

logger = custom_logger(__name__)

# This analysis set for mixed-phase cloud partition/T5050 metrics is requested by the E3SM Aerosol Working Group. The script is integrated in e3sm_diags by Jill Zhang and Yuying Zhang, with contribution from Yunpeng Shan, Jiwen Fan, Xue Zheng and Susannah Burrows.


def flatten_array(var):
var_1d = var.stack(stacked=[...]).values
Expand Down Expand Up @@ -69,7 +73,7 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
# Read reference data first

benchmark_data_path = os.path.join(
e3sm_diags.INSTALL_PATH,
INSTALL_PATH,
"control_runs",
"mixed-phase_partition_data_1985-2014.json",
)
Expand All @@ -80,7 +84,7 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
# parse file
metrics_dict = json.loads(lcf_file)

test_data = utils.dataset.Dataset(parameter, test=True)
test_data = Dataset(parameter, data_type="test")
# test = test_data.get_timeseries_variable("LANDFRAC")
# print(dir(test))
# landfrac = test_data.get_timeseries_variable("LANDFRAC")(cdutil.region.domain(latitude=(-70.0, -30, "ccb")))
Expand Down Expand Up @@ -112,17 +116,16 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
)
raise

parameter.test_name_yrs = utils.general.get_name_and_yrs(
parameter, test_data, season
)
parameter.test_name_yrs = test_data.get_name_yrs_attr(season) # type: ignore

metrics_dict["test"] = {}
metrics_dict["test"]["T"], metrics_dict["test"]["LCF"] = compute_lcf(
cice, cliq, temp, landfrac
)

if run_type == "model-vs-model":
ref_data = utils.dataset.Dataset(parameter, ref=True)
ref_data = Dataset(parameter, data_type="ref")

ref_data_path = parameter.reference_data_path
start_year = parameter.ref_start_yr
end_year = parameter.ref_end_yr
Expand Down Expand Up @@ -162,9 +165,7 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
# cliq = ref_data.get_timeseries_variable("CLDLIQ")(
# cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
# )
parameter.ref_name_yrs = utils.general.get_name_and_yrs(
parameter, ref_data, season
)
parameter.ref_name_yrs = ref_data.get_name_yrs_attr(season) # type: ignore
metrics_dict["ref"] = {}
metrics_dict["ref"]["T"], metrics_dict["ref"]["LCF"] = compute_lcf(
cice, cliq, temp, landfrac
Expand Down
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