Link to LDF output

The output from the stand-alone LDF configuration is in the link below

Note that LDF is currently run by users. Temporarily, we suggest users walk through the following process:

1. Install LDF and activate cupid-analysis

2. Use the CUPiD/helper_scripts/generate_ldf_config_file.py script to generate a LDF config file based on a CUPiD configuration file.

   • cd CUPiD/examples/external_diag_packages

   • ../../helper_scripts/generate_ldf_config_file.py --cupid-config-loc . --ldf-template ../../externals/LDF/config_clm_unstructured_plots.yaml --out-file LDF_config.yaml

3. Run LDF with the newly created configuration file.

   • ../../externals/LDF/run_adf_diag LDF_config.yaml

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import os

from IPython.core.display import HTML, Image
from IPython.display import display
import pandas as pd

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ldf_root = "."
case_name = None
base_case_name = None
start_date = ""
end_date = ""
base_start_date = None
base_end_date = None
key_plots = None
# ldf_root will be external_diag_packages/computed_notebooks/LDF/

# Parameters
case_name = "b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.192"
base_case_name = "b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188"
CESM_output_dir = "/glade/derecho/scratch/hannay/archive"
base_case_output_dir = "/glade/derecho/scratch/gmarques/archive"
start_date = "0002-01-01"
end_date = "0021-12-01"
base_start_date = "0002-01-01"
base_end_date = "0021-12-01"
obs_data_dir = (
    "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data"
)
ts_dir = None
lc_kwargs = {"threads_per_worker": 1}
serial = True
ldf_root = "../../examples/key_metrics/LDF_output/"
defaults_file = "../../externals/LDF/lib/ldf_variable_defaults.yaml"
key_plots = [
    "GPP_ANN_LatLon_Mean.png",
    "ELAI_ANN_LatLon_Mean.png",
    "TOTECOSYSC_GlobalMean_ANN_TimeSeries_Mean.png",
    "RegionalClimo_Amazonia_RegionalClimo_Mean.png",
]
subset_kwargs = {}
product = "/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks//lnd/link_to_LDF.ipynb"

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# Want some base case parameter defaults to equal control case values
if base_case_name is not None:
    if base_start_date is None:
        base_start_date = start_date

    if base_end_date is None:
        base_end_date = end_date

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# convert start-date and end-date to year range
case_year_range = [int(start_date.split("-")[0]), int(end_date.split("-")[0]) - 1]

base_case_year_range = [
    int(base_start_date.split("-")[0]),
    int(base_end_date.split("-")[0]) - 1,
]

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ldf_comparison_name = f"{case_name}_{case_year_range[0]}_{case_year_range[1]}_vs_{base_case_name}_{base_case_year_range[0]}_{base_case_year_range[1]}"
ldf_root = os.path.join(ldf_root, ldf_comparison_name)
display(
    HTML(
        f'<a href="../LDF/{ldf_comparison_name}/website/index.html">Full LDF output</a>'
    )
)

Full LDF output

Key Metrics from LDF

Some important things to look at from LDF include a comparison table and a few maps:

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comparison_table = os.path.join(ldf_root, "amwg_table_comp.csv")
if os.path.isfile(comparison_table):
    table = pd.read_csv(comparison_table)
    display(HTML(table.to_html(index=False, float_format="{:6g}".format)))

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for path_to_key_plot in key_plots:
    full_path = os.path.join(ldf_root, path_to_key_plot)
    if os.path.isfile(full_path):
        display(Image(full_path))