From 54a561fbcf8e89398babbe163c16bb10e41fdd0c Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Mon, 27 Jan 2025 22:07:38 -0800 Subject: [PATCH 01/10] required functions added --- pcmdi_metrics/enso/lib/__init__.py | 2 + .../summary_plot_lib/EnsoCollectionsLib.py | 995 ++++++ .../summary_plot_lib/EnsoErrorsWarnings.py | 483 +++ .../enso/lib/summary_plot_lib/EnsoPlotLib.py | 2829 +++++++++++++++++ .../enso/lib/summary_plot_lib/KeyArgLib.py | 24 + .../enso/lib/summary_plot_lib/__init__.py | 0 6 files changed, 4333 insertions(+) create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/__init__.py diff --git a/pcmdi_metrics/enso/lib/__init__.py b/pcmdi_metrics/enso/lib/__init__.py index b403b6dbf..450206c89 100644 --- a/pcmdi_metrics/enso/lib/__init__.py +++ b/pcmdi_metrics/enso/lib/__init__.py @@ -8,3 +8,5 @@ sort_human, tree, ) + +from .summary_plot_lib.EnsoPlotLib import plot_param # noqa diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py new file mode 100644 index 000000000..c34e264d7 --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py @@ -0,0 +1,995 @@ +# -*- coding:UTF-8 -*- +# +# Define ENSO metrics collections as a function of science question/realm +# +# Draft version +# + + +# Define metrics collections +def defCollection(mc=True): + # Name, list of metrics + metrics_collection = { + 'ENSO_perf': { + 'long_name': 'Metrics Collection for ENSO performance', + 'metrics_list': { + 'BiasPrLatRmse': { + 'variables': ['pr'], + 'regions': {'pr': 'nino3_LatExt'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasPrLonRmse': { + 'variables': ['pr'], + 'regions': {'pr': 'equatorial_pacific'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasSshLatRmse': { + 'variables': ['ssh'], + 'regions': {'ssh': 'nino3_LatExt'}, + 'obs_name': {'ssh': ['AVISO']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasSshLonRmse': { + 'variables': ['ssh'], + 'regions': {'ssh': 'equatorial_pacific'}, + 'obs_name': {'ssh': ['AVISO']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasSstLatRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3_LatExt'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasTauxLatRmse': { + 'variables': ['taux'], + 'regions': {'taux': 'equatorial_pacific_LatExt'}, + 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasTauxLonRmse': { + 'variables': ['taux'], + 'regions': {'taux': 'equatorial_pacific'}, + 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalPrLatRmse': { + 'variables': ['pr'], + 'regions': {'pr': 'nino3_LatExt'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalPrLonRmse': { + 'variables': ['pr'], + 'regions': {'pr': 'equatorial_pacific'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalSshLatRmse': { + 'variables': ['ssh'], + 'regions': {'ssh': 'nino3_LatExt'}, + 'obs_name': {'ssh': ['AVISO']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalSshLonRmse': { + 'variables': ['ssh'], + 'regions': {'ssh': 'equatorial_pacific'}, + 'obs_name': {'ssh': ['AVISO']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalSstLatRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3_LatExt'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalTauxLatRmse': { + 'variables': ['taux'], + 'regions': {'taux': 'equatorial_pacific_LatExt'}, + 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'SeasonalTauxLonRmse': { + 'variables': ['taux'], + 'regions': {'taux': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoAmpl': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoDuration': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'nbr_years_window': 6, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSeasonality': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSstDiversity_1': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoSstDiversity_2': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoSstSkew': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + # 'EnsoPrTsRmse': { + # 'variables': ['sst', 'pr'], + # 'regions': {'sst': 'nino3.4', 'pr': 'nino3'}, + # 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'pr': ['ERA-Interim', 'GPCPv2.3']}, + # 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + # 'normalization': True}, + # 'nbr_years_window': 6, + # 'smoothing': {'window': 5, 'method': 'triangle'}, + # 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + # 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + # }, + 'EnsoSstTsRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'nbr_years_window': 6, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + # 'EnsoTauxTsRmse': { + # 'variables': ['sst', 'taux'], + # 'regions': {'sst': 'nino3.4', 'taux': 'nino4'}, + # 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'taux': ['ERA-Interim', 'Tropflux']}, + # 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + # 'normalization': True}, + # 'nbr_years_window': 6, + # 'smoothing': {'window': 5, 'method': 'triangle'}, + # 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + # 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + # }, + 'NinoSstDiversity_1': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'NinoSstDiversity_2': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + }, + 'common_collection_parameters': { + 'detrending': {'method': 'linear'}, + 'frequency': 'monthly', + 'min_time_steps': 204, + 'normalization': False, + 'project_interpreter': 'CMIP', + 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), + 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), + }, + 'plot_order': ['BiasPrLatRmse', 'BiasPrLonRmse', 'BiasSstLonRmse', 'BiasTauxLonRmse', + 'SeasonalPrLatRmse', 'SeasonalPrLonRmse', 'SeasonalSstLonRmse', 'SeasonalTauxLonRmse', + 'EnsoSstLonRmse', 'EnsoSstTsRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoSstSkew', + 'EnsodDuration', 'EnsoSstDiversity_2'], + 'description': 'Describe which science question this collection is about', + }, + 'ENSO_tel': { + 'long_name': 'Metrics Collection for ENSO teleconnections', + 'metrics_list': { + 'EnsoAmpl': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoSeasonality': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoPrMapDjf': { + 'variables': ['sst', 'pr'], + 'regions': {'pr': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoPrMapJja': { + 'variables': ['sst', 'pr'], + 'regions': {'pr': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSlpMapDjf': { + 'variables': ['sst', 'slp'], + 'regions': {'slp': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSlpMapJja': { + 'variables': ['sst', 'slp'], + 'regions': {'slp': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSstMapDjf': { + 'variables': ['sst'], + 'regions': {'sst': 'global'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSstMapJja': { + 'variables': ['sst'], + 'regions': {'sst': 'global'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + }, + 'common_collection_parameters': { + 'detrending': {'method': 'linear'}, + 'frequency': 'monthly', + 'min_time_steps': 204, + 'normalization': False, + 'project_interpreter': 'CMIP', + 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), + 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), + }, + 'plot_order': ['EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoPrMapDjfRmse', 'EnsoPrMapJjaRmse', + 'EnsoSstMapDjfRmse', 'EnsoSstMapJjaRmse'], + 'description': 'Describe which science question this collection is about', + }, + 'ENSO_proc': { + 'long_name': 'Metrics Collection for ENSO processes', + 'metrics_list': { + 'BiasSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'BiasTauxLonRmse': { + 'variables': ['taux'], + 'regions': {'taux': 'equatorial_pacific'}, + 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoAmpl': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoSstLonRmse': { + 'variables': ['sst'], + 'regions': {'sst': 'equatorial_pacific'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSeasonality': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsoSstSkew': { + 'variables': ['sst'], + 'regions': {'sst': 'nino3.4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + 'metric_computation': 'abs_relative_difference', + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + }, + 'EnsodSstOce_1': { + 'variables': ['sst', 'thf'], + 'regions': {'sst': 'nino3', 'thf': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsodSstOce_2': { + 'variables': ['sst', 'thf'], + 'regions': {'sst': 'nino3', 'thf': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': True}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSshSst': { + 'variables': ['sst', 'ssh'], + 'regions': {'sst': 'nino3', 'ssh': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'ssh': ['AVISO']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstLhf': { + 'variables': ['sst', 'lhf'], + 'regions': {'sst': 'nino3', 'lhf': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'lhf': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstLwr': { + 'variables': ['sst', 'lwr'], + 'regions': {'sst': 'nino3', 'lwr': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'lwr': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstShf': { + 'variables': ['sst', 'shf'], + 'regions': {'sst': 'nino3', 'shf': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'shf': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstSwr': { + 'variables': ['sst', 'swr'], + 'regions': {'sst': 'nino3', 'swr': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'swr': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstTaux': { + 'variables': ['sst', 'taux'], + 'regions': {'sst': 'nino3', 'taux': 'nino4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'taux': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbSstThf': { + 'variables': ['sst', 'thf'], + 'regions': {'sst': 'nino3', 'thf': 'nino3'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + 'EnsoFbTauxSsh': { + 'variables': ['taux', 'ssh'], + 'regions': {'ssh': 'nino3', 'taux': 'nino4'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'ssh': ['AVISO']}, + 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', + 'newgrid_name': 'generic_1x1deg'}, + 'metric_computation': 'abs_relative_difference', + }, + }, + 'common_collection_parameters': { + 'detrending': {'method': 'linear'}, + 'frequency': 'monthly', + 'min_time_steps': 204, + 'normalization': False, + 'project_interpreter': 'CMIP', + 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), + 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), + }, + 'plot_order': ['BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', + 'EnsoSstSkew', 'EnsodSstOce_2', 'EnsoFbSstThf', 'EnsoFbSstTaux', 'EnsoFbTauxSsh', + 'EnsoFbSshSst'], + 'description': 'Describe which science question this collection is about', + }, + 'test_tel': { + 'long_name': 'Metrics Collection for ENSO teleconnections', + 'metrics_list': { + 'EnsoPrMapDjf': { + 'variables': ['sst', 'pr'], + 'regions': {'pr': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoPrMapJja': { + 'variables': ['sst', 'pr'], + 'regions': {'pr': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSlpMapDjf': { + 'variables': ['sst', 'slp'], + 'regions': {'slp': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSlpMapJja': { + 'variables': ['sst', 'slp'], + 'regions': {'slp': 'global', 'sst': 'nino3.4'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSstMapDjf': { + 'variables': ['sst'], + 'regions': {'sst': 'global'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + 'EnsoSstMapJja': { + 'variables': ['sst'], + 'regions': {'sst': 'global'}, + 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, + 'normalization': False}, + 'smoothing': {'window': 5, 'method': 'triangle'}, + 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', + 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, + }, + }, + 'common_collection_parameters': { + 'detrending': {'method': 'linear'}, + 'frequency': 'monthly', + 'min_time_steps': 204, + 'normalization': False, + 'project_interpreter': 'CMIP', + 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), + 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), + }, + 'plot_order': ['EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoPrMapDjfRmse', 'EnsoPrMapJjaRmse', + 'EnsoSstMapDjfRmse', 'EnsoSstMapJjaRmse'], + 'description': 'Describe which science question this collection is about', + }, + } + if mc is True: + return metrics_collection + else: + return metrics_collection[mc] + + +# List of reference observations for each variables +def ReferenceObservations(dataset=True): + dict_ref_obs = { + '20CRv2': { + 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.monolevel.mm.html', + 'file_name': '' + '*.mon.mean.nc', + 'variable_name_in_file': { + 'landmask': {'var_name': 'land'}, + 'lhf': {'var_name': 'lhtfl'}, + # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # lwr = dlwrf - ulwrf + 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, + 'pr': {'var_name': 'prate'}, + 'slp': {'var_name': 'press'}, + 'shf': {'var_name': 'shtfl'}, + 'sst': {'var_name': 'air'}, + # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # swr = dswrf - uswrf + 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, + 'taux': {'var_name': 'uflx'}, + 'tauy': {'var_name': 'vflx'}, + # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # tfh = lhtfl + shtfl + dlwrf - ulwrf + dswrf - uswrf + 'thf': { + 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], + 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + }, + }, + }, + '20CRv3': { + 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV3.monolevel.html', + 'file_name': '' + '*.mon.mean.nc', + 'variable_name_in_file': { + 'landmask': {'var_name': 'land'}, + 'lhf': {'var_name': 'lhtfl'}, + # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # lwr = dlwrf - ulwrf + 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, + 'pr': {'var_name': 'prate'}, + 'slp': {'var_name': 'prmsl'}, + 'shf': {'var_name': 'shtfl'}, + 'sst': {'var_name': 'skt'}, + # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # swr = dswrf - uswrf + 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, + # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # tfh = lhtfl + shtfl + dlwrf - ulwrf + dswrf - uswrf + 'thf': { + 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], + 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + }, + }, + }, + 'AVISO': { + 'website': 'https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global.html', + 'file_name': 'dt_global_allsat_msla_h_y????_m??.nc', + 'variable_name_in_file': {'ssh': {'var_name': 'sla'}, }, + }, + 'CFSR': { + 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', + 'file_name': '' + '_Omon_reanalysis_CFSR_*.nc', + 'variable_name_in_file': { + 'ssh': {'var_name': 'zos'}, + 'so': {'var_name': 'so'}, + 'thetao': {'var_name': 'thetao'}, + 'thf': {'var_name': 'hfds'}, # I'm not sure yet if it is the total heat flux + 'uo': {'var_name': 'uo'}, + 'vo': {'var_name': 'vo'}, + }, + }, + 'CMAP': { + 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html', + 'file_name': '' + '.mon.mean.nc', + 'variable_name_in_file': { + 'pr': {'var_name': 'precip'}, + }, + }, + 'ERA-Interim': { + 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', + 'file_name': '' + '_Amon_reanalysis_IFS-Cy31r2_*.nc', + 'variable_name_in_file': { + 'landmask': {'var_name': 'lsmask'}, + 'lhf': {'var_name': 'hfls'}, + # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # lwr = rlds - rlus + # sometimes lwr is included in the datasets in a variable called 'rls' + 'lwr': {'var_name': ['rlds', 'rlus'], 'algebric_calculation': ['plus', 'minus']}, + 'pr': {'var_name': 'pr'}, + 'slp': {'var_name': 'psl'}, + 'shf': {'var_name': 'hfss'}, + 'sst': {'var_name': 'ts'}, + # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # swr = rsds - rsus + # sometimes swr is included in the datasets in a variable called 'rss' + 'swr': {'var_name': ['rsds', 'rsus'], 'algebric_calculation': ['plus', 'minus']}, + 'taux': {'var_name': 'tauu'}, + 'tauy': {'var_name': 'tauv'}, + # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # tfh = hfls + hfss + rlds - rlus + rsds - rsus + # sometimes rls = rlds - rlus and rss = rsds - rsus + # sometimes thf is included in the datasets in a variable called 'hfds', 'netflux', 'thflx',... + 'thf': { + 'var_name': ['hfls', 'hfss', 'rlds', 'rlus', 'rsds', 'rsus'], + 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + }, + 'uas': {'var_name': 'uas'}, + 'vas': {'var_name': 'vas'}, + }, + }, + 'ERSSTv5': { + 'website': 'see https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/netcdf/', + 'file_name': 'ersst.v5.' + '' + '.nc', + 'variable_name_in_file': { + 'sst': {'var_name': 'sst'}, + }, + }, + 'GODAS': { + 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html', + 'file_name': '' + '_YYYY.nc', + 'variable_name_in_file': { + 'ssh': {'var_name': 'sshg'}, + 'taux': {'var_name': 'uflx'}, + 'tauy': {'var_name': 'vflx'}, + 'thf': {'var_name': 'thflx'}, + }, + }, + 'GPCPv2.3': { + 'website': 'see https://www.esrl.noaa.gov/psd/cgi-bin/db_search/DBSearch.pl?Dataset=GPCP+Version+2.3+' + + 'Combined+Precipitation+Dataset&group=0&submit=Search', + 'file_name': 'precip.mon.mean.nc', + 'variable_name_in_file': { + 'landmask': {'var_name': 'lsmask'}, + 'pr': {'var_name': 'precip'}, + }, + }, + 'HadISST': { + 'website': 'see https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html', + 'file_name': 'HadISST_' + '' + '.nc', + 'variable_name_in_file': { + 'sst': {'var_name': 'sst'}, + }, + }, + 'NCEP2': { + 'website': 'see https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.gaussian.html', + 'file_name': '' + '.sfc.mon.mean.nc', + 'variable_name_in_file': { + 'landmask': {'var_name': 'land'}, + 'lhf': {'var_name': 'lhtfl'}, + # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # lwr = rlds - rlus + 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, + 'pr': {'var_name': 'prate'}, + 'shf': {'var_name': 'shtfl'}, + 'slp': {'var_name': 'pres'}, + 'sst': {'var_name': 'skt'}, + # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # swr = rsds - rsus + 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, + 'taux': {'var_name': 'uflx'}, + 'tauy': {'var_name': 'vflx'}, + 'thf': { + 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], + 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + }, + }, + }, + 'OAFlux': { + 'website': 'see ftp://ftp.whoi.edu/pub/science/oaflux/data_v3/monthly/turbulence/', + 'file_name': '' + '_oaflux_*.nc', + 'variable_name_in_file': { + 'lhf': {'var_name': 'lhtfl'}, + 'shf': {'var_name': 'shtfl'}, + 'sst': {'var_name': 'tmpsf'}, + }, + }, + 'OISST': { + 'website': 'see https://www.earthsystemcog.org/search/obs4mips/?template=obs4mips&limit=200', + 'file_name': '' + '_OISST_L4_AVHRR-only-v2_*-*.nc', + 'variable_name_in_file': { + 'sst': {'var_name': 'sst'}, + }, + }, + 'ORAS4': { + 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', + 'file_name': '' + '_Omon_ORAreanalysis_ORAS4_*.nc', + 'variable_name_in_file': { + 'so': {'var_name': 'so'}, + 'thetao': {'var_name': 'thetao'}, + 'uo': {'var_name': 'uo'}, + 'vo': {'var_name': 'vo'}, + }, + }, + 'SODA3.4.2': { + 'website': 'see https://www.atmos.umd.edu/~ocean/index_files/soda3.4.2_mn_download_b.htm', + 'file_name': 'soda3.4.2_mn_ocean_reg_????.nc', + 'variable_name_in_file': { + 'so': {'var_name': 'salt'}, + 'ssh': {'var_name': 'ssh'}, + 'taux': {'var_name': 'taux'}, + 'thetao': {'var_name': 'temp'}, + 'thf': {'var_name': 'net_heating'}, + 'uo': {'var_name': 'u'}, + 'vo': {'var_name': 'v'}, + }, + }, + 'Tropflux': { + 'website': 'see https://incois.gov.in/tropflux/tf_products.jsp', + 'file_name': '' + '_tropflux_1m_*.nc', + 'variable_name_in_file': { + 'lhf': {'var_name': 'lhf'}, + 'lwr': {'var_name': 'lwr'}, + 'shf': {'var_name': 'shf'}, + 'sst': {'var_name': 'sst'}, + 'swr': {'var_name': 'swr'}, + 'taux': {'var_name': 'taux'}, + 'thf': {'var_name': 'netflux'}, + }, + }, + } + if dataset is True: + return dict_ref_obs + else: + return dict_ref_obs[dataset] + + +def ReferenceRegions(region=True): + dict_reference_regions = { + 'global': {'long_name': 'Global 60S-60N', 'latitude': (-60., 60.), 'longitude': (0., 360.)}, + 'global2': {'long_name': 'Global', 'latitude': (-90., 90.), 'longitude': (0., 360.)}, + 'tropical_pacific': { + 'long_name': 'Tropical Pacific (TP)', 'latitude': (-30., 30.), 'longitude': (120., 280.), + }, + 'equatorial_pacific': { + 'long_name': 'Equatorial Pacific (EP)', 'latitude': (-5., 5.), 'longitude': (150., 270.), + }, + 'equatorial_pacific_LatExt': { + 'long_name': 'Equatorial Pacific (EP)', 'latitude': (-15., 15.), 'longitude': (150., 270.), + }, + 'equatorial_pacific_LatExt2': { + 'long_name': 'Equatorial Pacific extended in latitude', 'latitude': (-15., 15.), 'longitude': (120., 285.), + }, + 'eastern_equatorial_pacific': { + 'long_name': 'Western Equatorial Pacific (WEP)', 'latitude': (-5., 5.), 'longitude': (205., 280.), + }, + 'western_equatorial_pacific': { + 'long_name': 'Eastern Equatorial Pacific (EEP)', 'latitude': (-5., 5.), 'longitude': (120., 205.), + }, + 'nino1+2': {'long_name': 'Niño 1+2', 'latitude': (-10., 0.), 'longitude': (270., 280.)}, + 'nino3': {'long_name': 'Niño 3', 'latitude': (-5., 5.), 'longitude': (210., 270.)}, + 'nino3_LatExt': { + 'long_name': 'Niño 3 extended in latitude', 'latitude': (-15., 15.), 'longitude': (210., 270.) + }, + 'nino3.4': {'long_name': 'Niño 3.4', 'latitude': (-5., 5.), 'longitude': (190., 240.)}, + 'nino4': {'long_name': 'Niño 4', 'latitude': (-5., 5.), 'longitude': (160., 210.)}, + # AR5 reference regions + 'ALA': {'long_name': 'Alaska/N.W. Canada', 'latitude': (60., 72.6), 'longitude': (192., 255.), + 'maskland': False, 'maskocean': True}, + # 'AMZ': {'long_name': 'Amazon', 'polygon shaped region, I do not know how to select it'}, + # 'CAM': {'long_name': 'Central America/Mexico', 'polygon shaped region, I do not know how to select it'}, + 'CAS': {'long_name': 'Central Asia', 'latitude': (30., 50.), 'longitude': (60., 75.), 'maskland': False, + 'maskocean': True}, + # 'CEU': {'long_name': 'Central Europe', 'polygon shaped region, I do not know how to select it'}, + 'CGI': {'long_name': 'Canada/Greenland/Iceland', 'latitude': (50., 85.), 'longitude': (255., 350.), + 'maskland': False, 'maskocean': True}, + 'CNA': {'long_name': 'Central North America', 'latitude': (28.6, 50.), 'longitude': (255., 275.), + 'maskland': False, 'maskocean': True}, + 'EAF': {'long_name': 'East Africa', 'latitude': (-11.4, 15.), 'longitude': (25., 52.), 'maskland': False, + 'maskocean': True}, + 'EAS': {'long_name': 'East Asia', 'latitude': (20., 50.), 'longitude': (100., 145.), 'maskland': False, + 'maskocean': True}, + 'ENA': {'long_name': 'East North America', 'latitude': (25., 50.), 'longitude': (275., 300.), 'maskland': False, + 'maskocean': True}, + 'MED': {'long_name': 'South Europe/Mediterranean', 'latitude': (30., 45.), 'longitude': (350., 400.), + 'maskland': False, 'maskocean': True}, + 'NAS': {'long_name': 'North Asia', 'latitude': (50., 70.), 'longitude': (40., 180.), 'maskland': False, + 'maskocean': True}, + 'NAU': {'long_name': 'North Australia', 'latitude': (-30., -10.), 'longitude': (110., 155.), 'maskland': False, + 'maskocean': True}, + 'NEB': {'long_name': 'North-East Brazil', 'latitude': (-20., 0.), 'longitude': (310., 326.), 'maskland': False, + 'maskocean': True}, + # 'NEU': {'long_name': 'North Europe', 'polygon shaped region, I do not know how to select it'}, + 'SAF': {'long_name': 'Southern Africa', 'latitude': (-35., -11.4), 'longitude': (350., 412.), 'maskland': False, + 'maskocean': True}, + 'SAH': {'long_name': 'Sahara', 'latitude': (15., 30.), 'longitude': (340., 400.), 'maskland': False, + 'maskocean': True}, + # 'SAS': {'long_name': 'South Asia', 'polygon shaped region, I do not know how to select it'}, + 'SAU': {'long_name': 'South Australia/New Zealand', 'latitude': (-50., -30.), 'longitude': (110., 180.), + 'maskland': False, 'maskocean': True}, + 'SEA': {'long_name': 'Southeast Asia', 'latitude': (-10., 20.), 'longitude': (95., 155.), 'maskland': False, + 'maskocean': False}, + # 'SSA': {'long_name': 'Southeastern South America', 'polygon shaped region, I do not know how to select it'}, + 'TIB': {'long_name': 'Tibetan Plateau', 'latitude': (30., 50.), 'longitude': (75., 100.), 'maskland': False, + 'maskocean': True}, + 'WAF': {'long_name': 'West Africa', 'latitude': (-11.4, 15.), 'longitude': (340., 385.), 'maskland': False, + 'maskocean': True}, + 'WAS': {'long_name': 'West Asia', 'latitude': (15., 50.), 'longitude': (40., 60.), 'maskland': False, + 'maskocean': True}, + 'WNA': {'long_name': 'West North America', 'latitude': (28.6, 60.), 'longitude': (230., 255.), + 'maskland': False, 'maskocean': True}, + # 'WSA': {'long_name': 'West Coast South America', 'polygon shaped region, I do not know how to select it'}, + # non-SREX reference regions + 'ANT': {'long_name': 'Antarctica', 'latitude': (-90., -50.), 'longitude': (0., 360.), 'maskland': False, + 'maskocean': False}, + 'ARC': {'long_name': 'Arctic', 'latitude': (67.5, 90.), 'longitude': (0., 360.), 'maskland': False, + 'maskocean': False}, + # 'CAR': { + # 'long_name': 'Caribbean', 'polygon shaped region, I do not know how to select it' + # }, + 'NTP': {'long_name': 'Northern Tropical Pacific', 'latitude': (5., 25.), 'longitude': (155., 210.)}, + 'STP': {'long_name': 'Southern Topical Pacific', 'latitude': (-25., -5.), 'longitude': (155., 230.)}, + 'ETP': {'long_name': 'Equatorial Tropical Pacific', 'latitude': (-5., 5.), 'longitude': (155., 210.)}, + 'WIO': {'long_name': 'West Indian Ocean', 'latitude': (-25., 5.), 'longitude': (52., 75.), 'maskland': False, + 'maskocean': False}, + # Power and Delage's (2018) oceanic regions + 'CEP': {'long_name': 'Central Equatorial Pacific', 'latitude': (-5., 5.), 'longitude': (180., 220.), + 'maskland': True, 'maskocean': False}, + 'CNP': {'long_name': 'Central Northern Tropical Pacific', 'latitude': (5., 15.), 'longitude': (180., 220.), + 'maskland': True, 'maskocean': False}, + 'CSP': {'long_name': 'Central Southern Tropical Pacific', 'latitude': (-15., -5.), 'longitude': (180., 220.), + 'maskland': True, 'maskocean': False}, + 'INO': {'long_name': 'Indian Ocean', 'latitude': (-25., 0.), 'longitude': (55., 95.), 'maskland': True, + 'maskocean': False}, + # YYP regions + 'africaSE': {'long_name': 'South and East Africa', 'latitude': (-40, 15.), 'longitude': (0., 55.), + 'maskland': False, 'maskocean': True}, + 'americaN': {'long_name': 'North America', 'latitude': (10., 60.), 'longitude': (235., 300.), + 'maskland': False, 'maskocean': True}, + 'americaS': {'long_name': 'South America', 'latitude': (-60., 15.), 'longitude': (275., 330.), + 'maskland': False, 'maskocean': True}, + 'asiaS': {'long_name': 'South Asia', 'latitude': (-10., 30.), 'longitude': (65., 130.), + 'maskland': False, 'maskocean': True}, + 'oceania': {'long_name': 'oceania', 'latitude': (-50., 0.), 'longitude': (110., 180.), 'maskland': False, + 'maskocean': True}, + } + if region is True: + return dict_reference_regions + else: + return dict_reference_regions[region] + + +def CmipVariables(): + dict_cmip_variables = { + 'reference': 'http://cfconventions.org/Data/cf-standard-names/46/build/cf-standard-name-table.html', + 'variable_name_in_file': { + # line keys: + # '':{'var_name':'','cf_name':, + # 'cf_unit':''} + # areacell + 'areacell': {'var_name': 'areacella', 'cf_name': 'cell_area', 'cf_units': 'm2'}, + # landmask + 'landmask': {'var_name': 'sftlf', 'cf_name': 'cell_area', 'cf_units': '1'}, + # latent heat flux (on ocean grid or ocean points only) + 'lhf': {'var_name': 'hfls', 'cf_name': 'surface_upward_latent_heat_flux', 'cf_units': 'W m-2'}, + # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) + # lwr = rlds - rlus + # sometimes lwr is included in the datasets in a variable called 'rls' + 'lwr': { + 'var_name': ['rlds', 'rlus'], + 'cf_name': ['surface_downwelling_longwave_flux_in_air', 'surface_upwelling_longwave_flux_in_air'], + 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'minus']}, + # Rainfall Flux + 'pr': {'var_name': 'pr', 'cf_name': 'rainfall_flux', 'cf_units': 'kg m-2 s-1'}, + # Sea Level Pressure + 'slp': {'var_name': 'psl', 'cf_name': 'air_pressure_at_mean_sea_level', 'cf_units': 'Pa'}, + # sensible heat flux (on ocean grid or ocean points only) + 'shf': {'var_name': 'hfss', 'cf_name': 'surface_upward_sensible_heat_flux', 'cf_units': 'W m-2'}, + # sea surface height + 'ssh': {'var_name': 'zos', 'cf_name': 'sea_surface_height_above_geoid', 'cf_units': 'm'}, + # sea surface temperature + 'sst': {'var_name': 'ts', 'cf_name': 'sea_surface_temperature', 'cf_units': 'K'}, + # shortwave radiation computed from these variables IN THAT ORDER + # swr = rsds - rsus + # sometimes swr is included in the datasets in a variable called 'rss' + 'swr': { + 'var_name': ['rsds', 'rsus'], + 'cf_name': ['surface_downwelling_shortwave_flux_in_air', 'surface_upwelling_shortwave_flux_in_air'], + 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'minus'] + }, + # zonal surface wind stress + 'taux': {'var_name': 'tauu', 'cf_name': 'surface_downward_eastward_stress', 'cf_units': 'Pa'}, + # total heat flux computed from these variables IN THAT ORDER + # tfh = hfls + hfss + rlds - rlus + rsds - rsus + # sometimes rls = rlds - rlus and rss = rsds - rsus + # sometimes thf is included in the datasets in a variable called 'hfds', 'netflux', 'thflx',... + 'thf': { + 'var_name': ['hfls', 'hfss', 'rlds', 'rlus', 'rsds', 'rsus'], + 'cf_name': ['surface_upward_latent_heat_flux', 'surface_upward_sensible_heat_flux', + 'surface_downwelling_longwave_flux_in_air', 'surface_upwelling_longwave_flux_in_air', + 'surface_downwelling_shortwave_flux_in_air', 'surface_upwelling_shortwave_flux_in_air'], + 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'] + }, + }, + } + return dict_cmip_variables diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py new file mode 100644 index 000000000..97262ca66 --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py @@ -0,0 +1,483 @@ +# -*- coding:UTF-8 -*- +from __future__ import print_function +from sys import exit as sys_exit + + +# ---------------------------------------------------# +# colors for printing +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + + +# ---------------------------------------------------------------------------------------------------------------------# +# +# Set of defined errors and warning functions used in EnsoUvcdatToolsLib.py +# +def plus_comma_space(string): + """ + ################################################################################# + Description: + Adds a comma and a space if the string is not empty or if the string is not composed of space only + ################################################################################# + + :param string: string + string to which comma_space could be added + + :return string: string + given string+', ' if applicable + + Examples + ---------- + string = string('') + print string + '' + # or + string = string(' ') + print string + ' ' + # or + string = string('Where there’s a will') + print string + 'Where there’s a will, ' + """ + if string.isspace(): + return string + elif not string: + return string + else: + return string+', ' + + +def message_formating(inspect_stack): + """ + ################################################################################# + Description: + Formats inspect.stack() as ' File filename, line n, in module' + ################################################################################# + + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + + :return string: string + formatted inspect.stack() in a string (using PlusCommaSpace) + + Examples + ---------- + string = message_formating(inspect_stack) + print string + ' File filename, line n, in module' + """ + string = ' ' + # adds file's name + if inspect_stack[0][1] != '': + string = plus_comma_space(string) + 'File ' + str(inspect_stack[0][1]) + # adds line number + string = plus_comma_space(string) + 'line ' + str(inspect_stack[0][2]) + # adds module's name + if inspect_stack[0][3] != '': + string = plus_comma_space(string) + 'in ' + str(inspect_stack[0][3]) + return string + + +def my_warning(list_strings): + """ + ################################################################################# + Description: + Prints the strings in 'list_strings' and continues + ################################################################################# + + :param list_strings: list + list of strings to print + :return: + """ + for ii in range(2): + print(bcolors.WARNING + "") + print(str().ljust(5) + "%%%%% ----- %%%%%") + for string in list_strings: + print(str().ljust(5) + str(string)) + print(str().ljust(5) + "%%%%% ----- %%%%%") + for ii in range(2): + print("" + bcolors.ENDC) + return + + +# ---------------------------------------------------------------------------------------------------------------------# +# +# ERRORS +# +def my_error(list_strings): + """ + ################################################################################# + Description: + Prints the strings in 'list_strings' and exits + ################################################################################# + + :param list_strings: list + list of strings to print + :return: + """ + for ii in range(2): + print(bcolors.FAIL + "") + print(str().ljust(5) + "%%%%% ----- %%%%%") + for string in list_strings: + print(str().ljust(5) + str(string)) + print(str().ljust(5) + "%%%%% ----- %%%%%") + for ii in range(2): + print("" + bcolors.ENDC) + sys_exit("") + return + + +def mismatch_shapes_error(tab1, tab2, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_error' in the case of array shape error + Prints strings and exits + ################################################################################# + + :param tab1: masked_array + :param tab2: masked_array + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + try: name1 = tab1.name + except: name1 = 'no_name' + try: name2 = tab2.name + except: name2 = 'no_name' + list_strings = ["ERROR " + message_formating(inspect_stack) + ": array shape", + str().ljust(5) + "arrays shapes mismatch: " + str(name1) + " = " + str(tab1.shape) + "', and " + + str(name2) + " = " + str(tab2.shape)] + my_warning(list_strings) + return + + +def object_type_error(parameter_name, type_parameter, type_parameter_should_be, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_error' in the case of object type error + Prints strings and exits + ################################################################################# + + :param parameter_name: string + name of a parameter from which the error comes from + :param type_parameter: string + parameter's type + :param type_parameter_should_be: string + what the parameter's type should be + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR " + message_formating(inspect_stack) + ": object type", + str().ljust(5) + str(parameter_name) + ": should be '" + str(type_parameter_should_be) + "', not '" + + str(type_parameter) + "'"] + my_warning(list_strings) + return + + +def too_short_time_period(metric_name, length, minimum_length, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_warning' in the case of a too short time-period + Prints strings and exits + ################################################################################# + + :param metric_name: string + name of the metric from which the error comes from + :param length: integer + length of the time axis of the variable + :param minimum_length: integer + minimum length of the time axis for the metric to make sens (defined in the metrics collection) + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR " + message_formating(inspect_stack) + ": too short time-period", + str().ljust(5) + str(metric_name) + ": the time-period is too short: " + str(length) + + " (minimum time-period: " + str(minimum_length) + ")"] + my_warning(list_strings) + return + + +def unlikely_units(var_name, name_in_file, units, min_max, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_warning' in the case of unlikely units + Prints strings and exits + ################################################################################# + + :param var_name: string + generic name of the variable that has unlikely units + :param name_in_file: string + name of the variable in the file (usually the short_name) that has unlikely units + :param units: string + units of the variable + :param min_max: list + minimum and maximum values of 'var_name' + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR " + message_formating(inspect_stack) + ": units", + str().ljust(5) + "the file says that " + str(var_name) + " (" + str(name_in_file) + + ") is in " + str(units) + " but it seems unlikely (" + str(min_max) + ")"] + my_warning(list_strings) + return + + +def unknown_averaging(average, known_average, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_error' in the case of unknown frequency + Prints strings + ################################################################################# + + :param average: string + averaging method (axis) (should by horizontal, meridional, temporal or zonal) + :param known_average: string + list of defined averaging method (axis) + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR" + message_formating(inspect_stack) + ": averaging method", + str().ljust(5) + "unkwown averaging method (axis): " + str(average), + str().ljust(10) + "known averaging method: " + str(sorted(known_average))] + my_error(list_strings) + return + + +def unknown_frequency(frequency, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_error' in the case of unknown frequency + Prints strings + ################################################################################# + + :param frequency: string + frequency of a dataset (should by daily, monthly or yearly) + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR" + message_formating(inspect_stack) + ": frequency", + str().ljust(5) + "unknown frequency: " + str(frequency)] + my_error(list_strings) + return + + +def unknown_key_arg(arg, inspect_stack): + """ + ################################################################################# + Description: + Function 'my_error' in the case of unknown argument + Prints strings + ################################################################################# + + :param arg: string + argument of a function + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR" + message_formating(inspect_stack) + ": argument", + str().ljust(5) + "unknown argument(s): " + str(arg)] + my_warning(list_strings) + return + + +def unknown_units(var_name, name_in_file, units, inspect_stack): + """ + ################################################################################# + Description: + Function 'MyError' in the case of unknown units + Prints strings and exits + ################################################################################# + + :param var_name: string + generic name of the variable that has unlikely units + :param name_in_file: string + name of the variable in the file (usually the short_name) that has unlikely units + :param units: string + units of the variable + :param inspect_stack: array + list of information about the program/module/line,... created using inspect.stack() + :return: + """ + list_strings = ["ERROR" + message_formating(inspect_stack) + ": units", + str().ljust(5) + "unknown units: " + str(var_name) + " (" + str(name_in_file) + + ") is in " + str(units)] + my_warning(list_strings) + return +# ---------------------------------------------------------------------------------------------------------------------# + + +# ---------------------------------------------------------------------------------------------------------------------# +# Just prints +def debug_mode(color, title, nbr_spaces, axes1='', axes2='', axes3='', axes4='', file1='', file2='', file3='', file4='', + line1='', line2='', line3='', line4='', nina1='', nina2='', nina3='', nina4='', nino1='', nino2='', + nino3='', nino4='', shape1='', shape2='', shape3='', shape4='', time1='', time2='', time3='', time4='', + var1='', var2='', var3='', var4=''): + """ + ################################################################################# + Description: + Prints strings to ease debugging + ################################################################################# + + :param color: string + color code (e.g. '\033[94m' is blue, '\033[92m' is green) + :param title: string + name of the section that is printed + :param nbr_spaces: int + number of leading spaces before printing the title + :param axes1: string, optional + axis list of variable 1 + :param axes2: string, optional + axis list of variable 2 + :param axes3: string, optional + axis list of variable 3 + :param axes4: string, optional + axis list of variable 4 + :param file1: string, optional + file name of variable 1 + :param file2: string, optional + file name of variable 2 + :param file3: string, optional + file name of variable 3 + :param file4: string, optional + file name of variable 4 + :param line1: string, optional + just a line to print 1 + :param line2: string, optional + just a line to print 2 + :param line3: string, optional + just a line to print 3 + :param line4: string, optional + just a line to print 4 + :param nina1: string, optional + list of nina years 1 + :param nina2: string, optional + list of nina years 2 + :param nina3: string, optional + list of nina years 3 + :param nina4: string, optional + list of nina years 4 + :param nino1: string, optional + list of nino years 1 + :param nino2: string, optional + list of nino years 2 + :param nino3: string, optional + list of nino years 3 + :param nino4: string, optional + list of nino years 4 + :param shape1: string, optional + shape of the array containing variable 1 + :param shape2: string, optional + shape of the array containing variable 2 + :param shape3: string, optional + shape of the array containing variable 3 + :param shape4: string, optional + shape of the array containing variable 4 + :param time1: string, optional + time bounds of variable 1 + :param time2: string, optional + time bounds of variable 2 + :param time3: string, optional + time bounds of variable 3 + :param time4: string, optional + time bounds of variable 4 + :param var1: string, optional + variable name 1 + :param var2: string, optional + variable name 2 + :param var3: string, optional + variable name 3 + :param var4: string, optional + variable name 4 + :return: + """ + # first variable + print(color + str().ljust(nbr_spaces) + title + bcolors.ENDC) + if file1: + print(color + str().ljust(nbr_spaces+5) + 'file name 1: ' + file1 + bcolors.ENDC) + if var1: + print(color + str().ljust(nbr_spaces+5) + 'variable name 1: ' + var1 + bcolors.ENDC) + if axes1: + print(color + str().ljust(nbr_spaces+5) + 'axes list 1: ' + axes1 + bcolors.ENDC) + if time1: + print(color + str().ljust(nbr_spaces+5) + 'time bounds 1: ' + time1 + bcolors.ENDC) + if shape1: + print(color + str().ljust(nbr_spaces+5) + 'shape 1: ' + shape1 + bcolors.ENDC) + if nina1: + print(color + str().ljust(nbr_spaces+5) + 'nina year 1: ' + nina1 + bcolors.ENDC) + if nino1: + print(color + str().ljust(nbr_spaces+5) + 'nino year 1: ' + nino1 + bcolors.ENDC) + if line1: + print(color + str().ljust(nbr_spaces+5) + line1 + bcolors.ENDC) + # second variable + if file2: + print(color + str().ljust(nbr_spaces+5) + 'file name 2: ' + file2 + bcolors.ENDC) + if var2: + print(color + str().ljust(nbr_spaces+5) + 'variable name 2: ' + var2 + bcolors.ENDC) + if axes2: + print(color + str().ljust(nbr_spaces+5) + 'axes list 2: ' + axes2 + bcolors.ENDC) + if time2: + print(color + str().ljust(nbr_spaces+5) + 'time bounds 2: ' + time2 + bcolors.ENDC) + if shape2: + print(color + str().ljust(nbr_spaces+5) + 'shape 2: ' + shape2 + bcolors.ENDC) + if nina2: + print(color + str().ljust(nbr_spaces+5) + 'nina year 2: ' + nina2 + bcolors.ENDC) + if nino2: + print(color + str().ljust(nbr_spaces+5) + 'nino year 2: ' + nino2 + bcolors.ENDC) + if line2: + print(color + str().ljust(nbr_spaces+5) + line2 + bcolors.ENDC) + # third variable + if file3: + print(color + str().ljust(nbr_spaces + 5) + 'file name 3: ' + file3 + bcolors.ENDC) + if var3: + print(color + str().ljust(nbr_spaces + 5) + 'variable name 3: ' + var3 + bcolors.ENDC) + if axes3: + print(color + str().ljust(nbr_spaces + 5) + 'axes list 3: ' + axes3 + bcolors.ENDC) + if time3: + print(color + str().ljust(nbr_spaces + 5) + 'time bounds 3: ' + time3 + bcolors.ENDC) + if shape3: + print(color + str().ljust(nbr_spaces + 5) + 'shape 3: ' + shape3 + bcolors.ENDC) + if nina3: + print(color + str().ljust(nbr_spaces + 5) + 'nina year 3: ' + nina3 + bcolors.ENDC) + if nino3: + print(color + str().ljust(nbr_spaces + 5) + 'nino year 3: ' + nino3 + bcolors.ENDC) + if line3: + print(color + str().ljust(nbr_spaces + 5) + line3 + bcolors.ENDC) + # fourth variable + if file4: + print(color + str().ljust(nbr_spaces + 5) + 'file name 4: ' + file4 + bcolors.ENDC) + if var4: + print(color + str().ljust(nbr_spaces + 5) + 'variable name 4: ' + var4 + bcolors.ENDC) + if axes4: + print(color + str().ljust(nbr_spaces + 5) + 'axes list 4: ' + axes4 + bcolors.ENDC) + if time4: + print(color + str().ljust(nbr_spaces + 5) + 'time bounds 4: ' + time4 + bcolors.ENDC) + if shape4: + print(color + str().ljust(nbr_spaces + 5) + 'shape 4: ' + shape4 + bcolors.ENDC) + if nina4: + print(color + str().ljust(nbr_spaces + 5) + 'nina year 4: ' + nina4 + bcolors.ENDC) + if nino4: + print(color + str().ljust(nbr_spaces + 5) + 'nino year 4: ' + nino4 + bcolors.ENDC) + if line4: + print(color + str().ljust(nbr_spaces + 5) + line4 + bcolors.ENDC) + return +# ---------------------------------------------------------------------------------------------------------------------# diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py new file mode 100644 index 000000000..155d188a5 --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py @@ -0,0 +1,2829 @@ +# -*- coding:UTF-8 -*- +# +# Define ENSO metrics plots +# +from copy import deepcopy +from numpy import arange as NUMPYarange +# ENSO_metrics functions +from .EnsoCollectionsLib import defCollection +from .KeyArgLib import default_arg_values + + +dict_colorbar = { + 'amplitude': 'amp', + 'anomalies': 'balance', + 'PR': 'rain', + 'SST': 'thermal', +} + +dict_label = { + 'amplitude': [round(ii, 1) for ii in NUMPYarange(0, 2.1, 0.5)], + 'amplitude5': list(range(0, 6, 1)), + 'amplitude10': [round(ii, 1) for ii in NUMPYarange(0, 10.1, 2.5)], + 'amplitude15': list(range(0, 16, 5)), + 'amplitude60': list(range(0, 61, 20)), + 'PR': list(range(0, 13, 4)), + 'PRA': [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], + 'REG03': [round(ii, 1) for ii in NUMPYarange(-0.3, 0.35, 0.1)], + 'REG05': [round(ii, 2) for ii in NUMPYarange(-0.5, 0.55, 0.25)], + 'REG12': [round(ii, 1) for ii in NUMPYarange(-1.2, 1.4, 0.6)], + 'REG2': list(range(-2, 3, 1)), + 'REG25': [round(ii, 1) for ii in NUMPYarange(-2.5, 2.6, 1.0)], + 'REG3': list(range(-3, 4, 1)), + 'REG4': list(range(-4, 5, 1)), + 'REG5': [round(ii, 1) for ii in NUMPYarange(-5, 6, 2.5)], + 'REG20': list(range(-20, 25, 10)), + 'REG30': list(range(-30, 35, 15)), + 'REG50': list(range(-50, 55, 25)), + 'REG60': list(range(-60, 65, 30)), + 'REG80': list(range(-80, 85, 40)), + 'SKEW': [round(ii, 1) for ii in NUMPYarange(-1.5, 1.6, 0.5)], + 'dSST': list(range(-2, 3, 1)), + 'SST': list(range(21, 31, 3)), + 'SSTA': [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], + 'TAUX': list(range(-100, 110, 50)), +} + +plot_parameters = { + 'BiasPrLatRmse': { + 'netcdf_variables': ['pr_lat__', 'pr_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean PR', # 'a) Mean meridional PR', # + 'varpattern': 'pr_lat__', + 'xname': 'latitude', + 'yname': 'PR', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['PR'], + 'label': dict_label['PR'], + "maskland": True, + 'title': ['Mean PR', 'Mean PR'], + 'varpattern': 'pr_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PR', + }, + }, + 'BiasPrLonRmse': { + 'netcdf_variables': ['pr_lon__', 'pr_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean PR', + 'varpattern': 'pr_lon__', + 'xname': 'longitude', + 'yname': 'PR', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['PR'], + 'label': dict_label['PR'], + "maskland": True, + 'title': ['Mean PR', 'Mean PR'], + 'varpattern': 'pr_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PR', + }, + }, + 'BiasPrRmse': { + 'netcdf_variables': ['pr_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['PR'], + 'label': dict_label['PR'], + "maskland": True, + 'title': ['Mean PR', 'Mean PR'], + 'varpattern': 'pr_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PR', + }, + }, + 'BiasSshLatRmse': { + 'netcdf_variables': ['ssh_lat__', 'ssh_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean SSH', + 'varpattern': 'ssh_lat__', + 'xname': 'latitude', + 'yname': 'SSH', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SSH', 'Mean SSH'], + 'varpattern': 'ssh_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSH', + }, + }, + 'BiasSshLonRmse': { + 'netcdf_variables': ['ssh_lon__', 'ssh_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean SSH', + 'varpattern': 'ssh_lon__', + 'xname': 'longitude', + 'yname': 'SSH', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SSH', 'Mean SSH'], + 'varpattern': 'ssh_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSH', + }, + }, + 'BiasSshRmse': { + 'netcdf_variables': ['ssh_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SSH', 'Mean SSH'], + 'varpattern': 'ssh_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSH', + }, + }, + 'BiasSstLatRmse': { + 'netcdf_variables': ['sst_lat__', 'sst_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean SST', + 'varpattern': 'sst_lat__', + 'xname': 'latitude', + 'yname': 'SST', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SST', 'Mean SST'], + 'varpattern': 'sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SST', + }, + }, + 'BiasSstLonRmse': { + 'netcdf_variables': ['sst_lon__', 'sst_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean SST', + 'varpattern': 'sst_lon__', + 'xname': 'longitude', + 'yname': 'SST', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SST', 'Mean SST'], + 'varpattern': 'sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SST', + }, + }, + 'BiasSstRmse': { + 'netcdf_variables': ['sst_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], + 'label': dict_label['SST'], + "maskland": True, + 'title': ['Mean SST', 'Mean SST'], + 'varpattern': 'sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SST', + }, + }, + 'BiasTauxLatRmse': { + 'netcdf_variables': ['taux_lat__', 'taux_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean TAUX', + 'varpattern': 'taux_lat__', + 'xname': 'latitude', + 'yname': 'TAUX', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['TAUX'], + "maskland": True, + 'title': ['Mean TAUX', 'Mean TAUX'], + 'varpattern': 'taux_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TAUX', + }, + }, + 'BiasTauxLonRmse': { + 'netcdf_variables': ['taux_lon__', 'taux_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Mean TAUX', + 'varpattern': 'taux_lon__', + 'xname': 'longitude', + 'yname': 'TAUX', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['TAUX'], + "maskland": True, + 'title': ['Mean TAUX', 'Mean TAUX'], + 'varpattern': 'taux_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TAUX', + }, + }, + 'BiasTauxRmse': { + 'netcdf_variables': ['taux_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['TAUX'], + "maskland": True, + 'title': ['Mean TAUX', 'Mean TAUX'], + 'varpattern': 'taux_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TAUX', + }, + }, + 'EnsoAmpl': { + 'netcdf_variables': ['sstStd_lon__', 'sstStd_map__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO amplitude', + 'varpattern': 'diagnostic', + 'yname': 'SSTA std', + }, + 'dive_down01': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSTA standard deviation', + 'varpattern': 'sstStd_lon__', + 'xname': 'longitude', + 'yname': 'SSTA std', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['SSTA standard deviation', 'SSTA standard deviation'], + 'varpattern': 'sstStd_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA std', + }, + }, + 'EnsodSstOce': { + 'netcdf_variables': ['dSST_ts__', 'dSSTthf_ts__', 'dSSToce_ts__', 'dSSTthf_lon__', 'dSSToce_lon__', + 'dSST_hov__', 'dSSTthf_hov__', 'dSSToce_hov__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO ocean-driven SST change', + 'varpattern': 'diagnostic', + 'yname': 'normalized dSSToce', + }, + 'dive_down01': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'ENSO SST change', + 'varpattern': ['dSST_ts__', 'dSSTthf_ts__', 'dSSToce_ts__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['dSST', 'dSSTthf', 'dSSToce'], + 'xname': 'months', + 'yname': 'normalized dSST', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'ENSO SST change', + 'varpattern': ['dSSTthf_lon__', 'dSSToce_lon__'], + 'colors': {"model": ["red", "blue"], "reference": ["red", "blue"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['dSSTthf', 'dSSToce'], + 'xname': 'longitude', + 'yname': 'normalized dSST', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['dSST'], + 'title': ['ENSO dSST', 'ENSO heat flux dSST', 'ENSO ocean dSST'], + 'varpattern': ['dSST_hov__', 'dSSTthf_hov__', 'dSSToce_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'normalized dSST', + }, + + }, + 'EnsoDuration': { + 'netcdf_variables': ["sst_against_sst_ts__", 'Nina_duration__', 'Nino_duration__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO duration', + 'varpattern': 'diagnostic', + 'yname': 'duration (reg>0.25)', + }, + 'dive_down01': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'ENSO life-cycle', + 'varpattern': "sst_against_sst_ts__", #'sst_over_sst_ts__', + 'xname': 'months', + 'yname': 'reg(SSTA, SSTA)', + }, + 'dive_down02': { + 'plot_type': 'boxplot', + 'nbr_panel': 2, + 'title': ['La Nina duration', 'El Nino duration'], + 'varpattern': ['Nina_duration__', 'Nino_duration__'], + 'yname': ['duration (SSTA<-0.5)', 'duration (SSTA>0.5)'], + }, + }, + 'EnsoFbSshSst': { + 'netcdf_variables': ['ssh__', 'sst__', 'ssh_over_sst_lon__', 'sshPOS_over_sst_lon__', 'sshNEG_over_sst_lon__', + 'ssh_over_sst_hov__', 'sshPOS_over_sst_hov__', 'sshNEG_over_sst_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'SSH-to-SST coupling', + 'varpattern': ['ssh__', 'sst__'], + 'xname': 'SSHA', + 'yname': 'SSTA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['ssh__', 'sst__'], + 'xname': 'SSHA', + 'yname': 'SSTA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Thermocline feedback', + #'varpattern': ['ssh_over_sst_lon__', 'sshPOS_over_sst_lon__', 'sshNEG_over_sst_lon__'], + 'varpattern': ['reg_sst_over_ssh_lon__', 'reg_sst_over_POSssh_lon__', 'reg_sst_over_NEGssh_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSHA>0', 'SSHA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSHA, SSTA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG03'], + 'title': ['reg(SSHA, SSTA)', 'reg(SSHA>0, SSTA)', 'reg(SSHA<0, SSTA)'], + #'varpattern': ['ssh_over_sst_hov__', 'sshPOS_over_sst_hov__', 'sshNEG_over_sst_hov__'], + 'varpattern': ['reg_sst_over_ssh_hov__', 'reg_sst_over_POSssh_hov__', 'reg_sst_over_NEGssh_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstLhf': { + 'netcdf_variables': ['sst__', 'lhf__', 'sst_over_lhf_lon__', 'sstPOS_over_lhf_lon__', 'sstNEG_over_lhf_lon__', + 'sst_over_lhf_hov__', 'sstPOS_over_lhf_hov__', 'sstNEG_over_lhf_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Latent heat feedback', + 'varpattern': ['sst__', 'lhf__'], + 'xname': 'SSTA', + 'yname': 'LHFA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'lhf__'], + 'xname': 'SSTA', + 'yname': 'LHFA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Latent heat feedback', + #'varpattern': ['sst_over_lhf_lon__', 'sstPOS_over_lhf_lon__', 'sstNEG_over_lhf_lon__'], + 'varpattern': ['reg_lhf_over_sst_lon__', 'reg_lhf_over_POSsst_lon__', 'reg_lhf_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, LHFA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG20'], + 'title': ['reg(SSTA, LHFA)', 'reg(SSTA>0, LHFA)', 'reg(SSTA<0, LHFA)'], + #'varpattern': ['sst_over_lhf_hov__', 'sstPOS_over_lhf_hov__', 'sstNEG_over_lhf_hov__'], + 'varpattern': ['reg_lhf_over_sst_hov__', 'reg_lhf_over_POSsst_hov__', 'reg_lhf_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstLwr': { + 'netcdf_variables': ['sst__', 'lwr__', 'sst_over_lwr_lon__', 'sstPOS_over_lwr_lon__', 'sstNEG_over_lwr_lon__', + 'sst_over_lwr_hov__', 'sstPOS_over_lwr_hov__', 'sstNEG_over_lwr_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Longwave feedback', + 'varpattern': ['sst__', 'lwr__'], + 'xname': 'SSTA', + 'yname': 'LWRA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'lwr__'], + 'xname': 'SSTA', + 'yname': 'LWRA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Longwave feedback', + #'varpattern': ['sst_over_lwr_lon__', 'sstPOS_over_lwr_lon__', 'sstNEG_over_lwr_lon__'], + 'varpattern': ['reg_lwr_over_sst_lon__', 'reg_lwr_over_POSsst_lon__', 'reg_lwr_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, LWRA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG20'], + 'title': ['reg(SSTA, LWRA)', 'reg(SSTA>0, LWRA)', 'reg(SSTA<0, LWRA)'], + #'varpattern': ['sst_over_lwr_hov__', 'sstPOS_over_lwr_hov__', 'sstNEG_over_lwr_hov__'], + 'varpattern': ['reg_lwr_over_sst_hov__', 'reg_lwr_over_POSsst_hov__', 'reg_lwr_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstShf': { + 'netcdf_variables': ['sst__', 'shf__', 'sst_over_shf_lon__', 'sstPOS_over_shf_lon__', 'sstNEG_over_shf_lon__', + 'sst_over_shf_hov__', 'sstPOS_over_shf_hov__', 'sstNEG_over_shf_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Sensible heat feedback', + 'varpattern': ['sst__', 'shf__'], + 'xname': 'SSTA', + 'yname': 'SHFA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'shf__'], + 'xname': 'SSTA', + 'yname': 'SHFA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Sensible heat feedback', + #'varpattern': ['sst_over_shf_lon__', 'sstPOS_over_shf_lon__', 'sstNEG_over_shf_lon__'], + 'varpattern': ['reg_shf_over_sst_lon__', 'reg_shf_over_POSsst_lon__', 'reg_shf_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, SHFA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG5'], + 'title': ['reg(SSTA, SHFA)', 'reg(SSTA>0, SHFA)', 'reg(SSTA<0, SHFA)'], + #'varpattern': ['sst_over_shf_hov__', 'sstPOS_over_shf_hov__', 'sstNEG_over_shf_hov__'], + 'varpattern': ['reg_shf_over_sst_hov__', 'reg_shf_over_POSsst_hov__', 'reg_shf_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstSwr': { + 'netcdf_variables': ['sst__', 'swr__', 'sst_over_swr_lon__', 'sstPOS_over_swr_lon__', 'sstNEG_over_swr_lon__', + 'sst_over_swr_hov__', 'sstPOS_over_swr_hov__', 'sstNEG_over_swr_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Shortwave feedback', + 'varpattern': ['sst__', 'swr__'], + 'xname': 'SSTA', + 'yname': 'SWRA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'swr__'], + 'xname': 'SSTA', + 'yname': 'SWRA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Shortwave feedback', + #'varpattern': ['sst_over_swr_lon__', 'sstPOS_over_swr_lon__', 'sstNEG_over_swr_lon__'], + 'varpattern': ['reg_swr_over_sst_lon__', 'reg_swr_over_POSsst_lon__', 'reg_swr_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, SWRA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG50'], + 'title': ['reg(SSTA, SWRA)', 'reg(SSTA>0, SWRA)', 'reg(SSTA<0, SWRA)'], + #'varpattern': ['sst_over_swr_hov__', 'sstPOS_over_swr_hov__', 'sstNEG_over_swr_hov__'], + 'varpattern': ['reg_swr_over_sst_hov__', 'reg_swr_over_POSsst_hov__', 'reg_swr_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstTaux': { + 'netcdf_variables': [ + 'sst__', 'taux__', 'sst_over_taux_lon__', 'sstPOS_over_taux_lon__', 'sstNEG_over_taux_lon__', + 'sst_over_taux_hov__', 'sstPOS_over_taux_hov__', 'sstNEG_over_taux_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'SST-to-Taux coupling', + 'varpattern': ['sst__', 'taux__'], + 'xname': 'SSTA', + 'yname': 'TAUXA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'taux__'], + 'xname': 'SSTA', + 'yname': 'TAUXA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Wind-SST feedback', + #'varpattern': ['sst_over_taux_lon__', 'sstPOS_over_taux_lon__', 'sstNEG_over_taux_lon__'], + 'varpattern': ['reg_taux_over_sst_lon__', 'reg_taux_over_POSsst_lon__', 'reg_taux_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, TAUXA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG20'], + 'title': ['reg(SSTA, TAUXA)', 'reg(SSTA>0, TAUXA)', 'reg(SSTA<0, TAUXA)'], + #'varpattern': ['sst_over_taux_hov__', 'sstPOS_over_taux_hov__', 'sstNEG_over_taux_hov__'], + 'varpattern': ['reg_taux_over_sst_hov__', 'reg_taux_over_POSsst_hov__', 'reg_taux_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbSstThf': { + 'netcdf_variables': ['sst__', 'thf__', 'sst_over_thf_lon__', 'sstPOS_over_thf_lon__', 'sstNEG_over_thf_lon__', + 'sst_over_thf_hov__', 'sstPOS_over_thf_hov__', 'sstNEG_over_thf_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Total heat feedback', + 'varpattern': ['sst__', 'thf__'], + 'xname': 'SSTA', + 'yname': 'THFA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['sst__', 'thf__'], + 'xname': 'SSTA', + 'yname': 'THFA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'Total heat feedback', + #'varpattern': ['sst_over_thf_lon__', 'sstPOS_over_thf_lon__', 'sstNEG_over_thf_lon__'], + 'varpattern': ['reg_thf_over_sst_lon__', 'reg_thf_over_POSsst_lon__', 'reg_thf_over_NEGsst_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'SSTA>0', 'SSTA<0'], + 'xname': 'longitude', + 'yname': 'reg(SSTA, THFA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG50'], + 'title': ['reg(SSTA, THFA)', 'reg(SSTA>0, THFA)', 'reg(SSTA<0, THFA)'], + #'varpattern': ['sst_over_thf_hov__', 'sstPOS_over_thf_hov__', 'sstNEG_over_thf_hov__'], + 'varpattern': ['reg_thf_over_sst_hov__', 'reg_thf_over_POSsst_hov__', 'reg_thf_over_NEGsst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoFbTauxSsh': { + 'netcdf_variables': [ + 'taux__', 'ssh__', 'taux_over_ssh_lon__', 'tauxPOS_over_ssh_lon__', 'tauxNEG_over_ssh_lon__', + 'taux_over_ssh_hov__', 'tauxPOS_over_ssh_hov__', 'tauxNEG_over_ssh_hov__'], + 'diagnostic': { + 'plot_type': 'scatterplot', + 'nbr_panel': 1, + 'title': 'Taux-to-SSH coupling', + 'varpattern': ['taux__', 'ssh__'], + 'xname': 'TAUXA', + 'yname': 'SSHA', + }, + 'dive_down01': { + 'plot_type': 'scatterplot', + 'nbr_panel': 2, + 'title': 'nonlinarity', + 'varpattern': ['taux__', 'ssh__'], + 'xname': 'TAUXA', + 'yname': 'SSHA', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSH-Wind feedback', + #'varpattern': ['taux_over_ssh_lon__', 'tauxPOS_over_ssh_lon__', 'tauxNEG_over_ssh_lon__'], + 'varpattern': ['reg_ssh_over_taux_lon__', 'reg_ssh_over_POStaux_lon__', 'reg_ssh_over_NEGtaux_lon__'], + 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, + 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + 'legend': ['All', 'TAUXA>0', 'TAUXA<0'], + 'xname': 'longitude', + 'yname': 'reg(TAUXA, SSHA)', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 6, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG05'], + 'title': ['reg(TAUXA, SSHA)', 'reg(TAUXA>0, SSHA)', 'reg(TAUXA<0, SSHA)'], + #'varpattern': ['taux_over_ssh_hov__', 'tauxPOS_over_ssh_hov__', 'tauxNEG_over_ssh_hov__'], + 'varpattern': ['reg_ssh_over_taux_hov__', 'reg_ssh_over_POStaux_hov__', 'reg_ssh_over_NEGtaux_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + }, + 'EnsoPrMap': { + 'netcdf_variables': ['reg_pr_over_sst_map__', 'reg_pr_over_sst_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_pr_over_sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + 'varpattern': 'reg_pr_over_sst_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + 'varpattern': 'reg_pr_over_sst_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + 'varpattern': 'reg_pr_over_sst_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + 'varpattern': 'reg_pr_over_sst_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + 'varpattern': 'reg_pr_over_sst_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + }, + 'EnsoPrMapDjf': { + 'netcdf_variables': ['reg_pr_over_sst_djf_map__', 'reg_pr_over_sst_djf_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_pr_over_sst_djf_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map__", "pr_nino_djf_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + 'varpattern': 'reg_pr_over_sst_djf_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + 'varpattern': 'reg_pr_over_sst_djf_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + 'varpattern': 'reg_pr_over_sst_djf_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + 'varpattern': 'reg_pr_over_sst_djf_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], + 'varpattern': 'reg_pr_over_sst_djf_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map_africaSE__", "pr_nino_djf_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map_americaN__", "pr_nino_djf_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map_americaS__", "pr_nino_djf_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map_asiaS__", "pr_nino_djf_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], + 'varpattern': ["pr_nina_djf_map_oceania__", "pr_nino_djf_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + }, + 'EnsoPrMapJja': { + 'netcdf_variables': ['reg_pr_over_sst_jja_map__', 'reg_pr_over_sst_jja_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_pr_over_sst_jja_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map__", "pr_nino_jja_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + 'varpattern': 'reg_pr_over_sst_jja_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + 'varpattern': 'reg_pr_over_sst_jja_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + 'varpattern': 'reg_pr_over_sst_jja_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + 'varpattern': 'reg_pr_over_sst_jja_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], + 'varpattern': 'reg_pr_over_sst_jja_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map_africaSE__", "pr_nino_jja_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map_americaN__", "pr_nino_jja_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map_americaS__", "pr_nino_jja_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map_asiaS__", "pr_nino_jja_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], + 'varpattern': ["pr_nina_jja_map_oceania__", "pr_nino_jja_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + }, + 'EnsoPrTsRmse': { + 'netcdf_variables': ['pr_over_sst_ts__', 'pr_over_sst_hov__', 'Nina_pr_ts__', 'Nino_pr_ts__', 'Nina_pr_hov__', + 'Nino_pr_hov__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO's PRA life-cycle", + #'varpattern': 'pr_over_sst_ts__', + 'varpattern': 'sst_against_pr_ts__', + 'xname': 'months', + 'yname': 'reg(ENSO SSTA, PRA)', + }, + 'dive_down01': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], + #'varpattern': 'pr_over_sst_hov__', + 'varpattern': 'sst_against_pr_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO's PRA life-cycle", + 'varpattern': ['Nina_pr_ts__', 'Nino_pr_ts__'], + 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['La Nina', 'El Nino'], + 'xname': 'months', + 'yname': 'ENSO PRA', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG5'], + 'title': ['La Nina PRA', 'El Nino PRA'], + 'varpattern': ['Nina_pr_hov__', 'Nino_pr_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'PRA', + }, + }, + # 'EnsoSlpMap': { + # 'netcdf_variables': ['reg_slp_over_sst_map__', 'reg_slp_over_sst_map__'], + # 'diagnostic': { + # 'plot_type': 'map', + # 'nbr_panel': 2, + # 'colorbar': dict_colorbar['anomalies'], + # 'label': dict_label['REG2'], + # "maskland": False, + # 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + # #'varpattern': 'ts_over_sst_map__', + # 'varpattern': 'reg_slp_over_sst_map__', + # 'xname': 'longitude', + # 'yname': 'latitude', + # 'zname': 'regression', + # }, + # }, + 'EnsoSlpMap': { + 'netcdf_variables': ['reg_slp_over_sst_map__', 'reg_slp_over_sst_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], + 'varpattern': 'reg_slp_over_sst_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + }, + 'EnsoSlpMapDjf': { + 'netcdf_variables': ['reg_slp_over_sst_djf_map__', 'reg_slp_over_sst_djf_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + # 'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_slp_over_sst_djf_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map__", "slp_nino_djf_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + 'varpattern': 'reg_slp_over_sst_djf_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + 'varpattern': 'reg_slp_over_sst_djf_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + 'varpattern': 'reg_slp_over_sst_djf_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + 'varpattern': 'reg_slp_over_sst_djf_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], + 'varpattern': 'reg_slp_over_sst_djf_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map_africaSE__", "slp_nino_djf_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map_americaN__", "slp_nino_djf_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map_americaS__", "slp_nino_djf_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map_asiaS__", "slp_nino_djf_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], + 'varpattern': ["slp_nina_djf_map_oceania__", "slp_nino_djf_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + }, + 'EnsoSlpMapJja': { + 'netcdf_variables': ['reg_slp_over_sst_jja_map__', 'reg_slp_over_sst_jja_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + # 'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_slp_over_sst_jja_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map__", "slp_nino_jja_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + 'varpattern': 'reg_slp_over_sst_jja_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + 'varpattern': 'reg_slp_over_sst_jja_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + 'varpattern': 'reg_slp_over_sst_jja_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + 'varpattern': 'reg_slp_over_sst_jja_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], + 'varpattern': 'reg_slp_over_sst_jja_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map_africaSE__", "slp_nino_jja_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map_americaN__", "slp_nino_jja_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map_americaS__", "slp_nino_jja_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map_asiaS__", "slp_nino_jja_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], + 'varpattern': ["slp_nina_jja_map_oceania__", "slp_nino_jja_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + }, + 'EnsoSstLonRmse': { + 'netcdf_variables': ['sst_over_sst_lon__', 'sst_over_sst_map__', 'Nina_sst_lon__', 'Nino_sst_lon__', + 'Nina_sst_map__', 'Nino_sst_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO pattern", + #'varpattern': 'sst_over_sst_lon__', + 'varpattern': 'sst_against_sst_lon__', + 'xname': 'longitude', + 'yname': 'reg(ENSO SSTA, SSTA)', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": True, + 'title': ['reg(ENSO SSTA, SSTA)', 'reg(ENSO SSTA, SSTA)'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'sst_against_sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO's SSTA pattern", + 'varpattern': ['Nina_sst_lon__', 'Nino_sst_lon__'], + 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['La Nina', 'El Nino'], + 'xname': 'longitude', + 'yname': 'ENSO SSTA', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG25'], + "maskland": True, + 'title': ['La Nina SSTA', 'El Nino SSTA'], + 'varpattern': ['Nina_sst_map__', 'Nino_sst_map__'], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA', + }, + }, + # 'EnsoSstMap': { + # 'netcdf_variables': ['reg_ts_over_sst_map__', 'reg_ts_over_sst_map__'], + # 'diagnostic': { + # 'plot_type': 'map', + # 'nbr_panel': 2, + # 'colorbar': dict_colorbar['anomalies'], + # 'label': dict_label['PRA'], + # "maskland": False, + # 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + # #'varpattern': 'ts_over_sst_map__', + # 'varpattern': 'reg_ts_over_sst_map__', + # 'xname': 'longitude', + # 'yname': 'latitude', + # 'zname': 'regression', + # }, + # }, + 'EnsoSstMap': { + 'netcdf_variables': ['reg_ts_over_sst_map__', 'reg_ts_over_sst_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], + 'varpattern': 'reg_ts_over_sst_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + }, + 'EnsoSstMapDjf': { + 'netcdf_variables': ['reg_ts_over_sst_djf_map__', 'reg_ts_over_sst_djf_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + # 'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_ts_over_sst_djf_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map__", "ts_nino_djf_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + 'varpattern': 'reg_ts_over_sst_djf_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + 'varpattern': 'reg_ts_over_sst_djf_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + 'varpattern': 'reg_ts_over_sst_djf_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + 'varpattern': 'reg_ts_over_sst_djf_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], + 'varpattern': 'reg_ts_over_sst_djf_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map_africaSE__", "ts_nino_djf_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map_americaN__", "ts_nino_djf_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map_americaS__", "ts_nino_djf_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map_asiaS__", "ts_nino_djf_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], + 'varpattern': ["ts_nina_djf_map_oceania__", "ts_nino_djf_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + }, + 'EnsoSstMapJja': { + 'netcdf_variables': ['reg_ts_over_sst_jja_map__', 'reg_ts_over_sst_jja_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + # 'varpattern': 'sst_over_sst_map__', + 'varpattern': 'reg_ts_over_sst_jja_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map__", "ts_nino_jja_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + 'varpattern': 'reg_ts_over_sst_jja_map_africaSE__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down03': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + 'varpattern': 'reg_ts_over_sst_jja_map_americaN__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + 'varpattern': 'reg_ts_over_sst_jja_map_americaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down05': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + 'varpattern': 'reg_ts_over_sst_jja_map_asiaS__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down06': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], + 'varpattern': 'reg_ts_over_sst_jja_map_oceania__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'regression', + }, + 'dive_down07': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map_africaSE__", "ts_nino_jja_map_africaSE__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down08': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map_americaN__", "ts_nino_jja_map_americaN__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down09': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map_americaS__", "ts_nino_jja_map_americaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down10': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map_asiaS__", "ts_nino_jja_map_asiaS__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + 'dive_down11': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['PRA'], + "maskland": False, + "maskocean": True, + 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], + 'varpattern': ["ts_nina_jja_map_oceania__", "ts_nino_jja_map_oceania__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TSA', + }, + }, + 'EnsoSstTsRmse': { + 'netcdf_variables': ['sst_over_sst_ts__', 'sst_over_sst_hov__', 'Nina_sst_ts__', 'Nino_sst_ts__', + 'Nina_sst_hov__', 'Nino_sst_hov__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO life-cycle", + #'varpattern': 'sst_over_sst_ts__', + 'varpattern': 'sst_against_sst_ts__', + 'xname': 'months', + 'yname': 'reg(ENSO SSTA, SSTA)', + }, + 'dive_down01': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG12'], + 'title': ['reg(ENSO SSTA, SSTA)', 'reg(ENSO SSTA, SSTA)'], + #'varpattern': 'sst_over_sst_hov__', + 'varpattern': 'sst_against_sst_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO's SSTA life-cycle", + 'varpattern': ['Nina_sst_ts__', 'Nino_sst_ts__'], + 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['La Nina', 'El Nino'], + 'xname': 'months', + 'yname': 'ENSO SSTA', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + 'title': ['La Nina SSTA', 'El Nino SSTA'], + 'varpattern': ['Nina_sst_hov__', 'Nino_sst_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSTA', + }, + }, + 'EnsoTauxTsRmse': { + 'netcdf_variables': ['taux_over_sst_ts__', 'taux_over_sst_hov__', 'Nina_taux_ts__', 'Nino_taux_ts__', + 'Nina_taux_hov__', 'Nino_taux_hov__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO life-cycle", + #'varpattern': 'taux_over_sst_ts__', + 'varpattern': 'sst_against_taux_ts__', + 'xname': 'months', + 'yname': 'reg(ENSO SSTA, TAUXA)', + }, + 'dive_down01': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG20'], + 'title': ['reg(ENSO SSTA, TAUXA)', 'reg(ENSO SSTA, TAUXA)'], + #'varpattern': 'taux_over_sst_hov__', + 'varpattern': 'sst_against_taux_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'regression', + }, + 'dive_down02': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "ENSO's TAUXA life-cycle", + 'varpattern': ['Nina_taux_ts__', 'Nino_taux_ts__'], + 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['La Nina', 'El Nino'], + 'xname': 'months', + 'yname': 'ENSO TAUXA', + }, + 'dive_down03': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG30'], + 'title': ['La Nina TAUXA', 'El Nino TAUXA'], + 'varpattern': ['Nina_taux_hov__', 'Nino_taux_hov__'], + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSTA', + }, + }, + 'EnsoSeasonality': { + 'netcdf_variables': ['sstStd_monthly__', 'sstStd_hov__', 'sstStd_NDJ_lon__', 'sstStd_MAM_lon__', + "sstStd_NDJ_map__", "sstStd_MAM_map__"], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO Seasonality', + 'varpattern': 'diagnostic', + 'yname': 'SSTA std (NDJ/MAM)', + }, + 'dive_down01': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSTA standard deviation', + 'varpattern': 'sstStd_monthly__', + 'xname': 'months', + 'yname': 'SSTA std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude'], + 'title': ['SSTA standard deviation', 'SSTA standard deviation'], + 'varpattern': 'sstStd_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSTA std', + }, + 'dive_down03': { + 'plot_type': "curve", + 'nbr_panel': 1, + 'title': "SSTA standard deviation", + 'varpattern': ['sstStd_NDJ_lon__', 'sstStd_MAM_lon__'], + 'colors': {"model": ["red", "blue"], "reference": ["red", "blue"]}, + 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, + 'legend': ['NDJ', 'MAM'], + 'xname': 'longitude', + 'yname': 'SSTA std', + }, + 'dive_down04': { + 'plot_type': 'map', + 'nbr_panel': 4, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['NDJ', 'MAM'], #['SSTA std NDJ', 'SSTA std MAM'], + 'varpattern': ["sstStd_NDJ_map__", "sstStd_MAM_map__"], + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'monthly SSTA std', + }, + }, + "EnsoSstDiversity": { + 'netcdf_variables': ["Enso_lon_pos_maxSSTA__", "Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO diversity', + 'varpattern': 'diagnostic', + 'yname': 'IQR of min/max SSTA', + }, + 'dive_down01': { + 'plot_type': 'boxplot', + 'nbr_panel': 3, + 'title': ['ENSO diversity', 'La Nina diversity', 'El Nino diversity'], + 'varpattern': ["Enso_lon_pos_maxSSTA__", "Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], + 'yname': ['longitude of min/max SSTA', 'longitude of min SSTA', 'longitude of max SSTA'], + "custom_label": "longitude", + }, + }, + 'EnsoSstSkew': { + 'netcdf_variables': ['sstSke_lon__', 'sstSke_map__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'ENSO skewness', + 'varpattern': 'diagnostic', + 'yname': 'SSTA skewness', + }, + 'dive_down01': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSTA skewness', + 'varpattern': "sstSke_lon__", + 'xname': 'longitude', + 'yname': 'SSTA skew', + }, + 'dive_down02': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": True, + 'title': ['SSTA skew', 'SSTA skew'], + 'varpattern': "sstSke_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA skew', + }, + }, + 'NinaPrMap': { + 'netcdf_variables': ['prComp_map__', 'prComp_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG2'], + "maskland": False, + 'title': ['La Nina composite', 'La Nina composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'prComp_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + }, + 'NinaSlpMap': { + 'netcdf_variables': ['slp_map__', 'slp_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + 'title': ['La Nina composite', 'La Nina composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'slp_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + }, + 'NinaSstMap': { + 'netcdf_variables': ['ts_map__', 'ts_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + 'title': ['La Nina composite', 'La Nina composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'ts_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA', + }, + }, + 'NinaSstDur': { + 'netcdf_variables': ['Nina_duration__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'La Nina duration', + 'varpattern': 'diagnostic', + 'yname': 'duration (SSTA<-0.5)', + }, + 'dive_down01': { + 'plot_type': 'boxplot', + 'nbr_panel': 1, + 'title': 'La Nina duration', + 'varpattern': 'Nina_duration__', + 'yname': 'duration (SSTA<-0.5)', + }, + }, + 'NinaSstLonRmse': { + 'netcdf_variables': ['sst_lon__', 'sst_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "La Nina pattern", + 'varpattern': 'sst_lon__', + 'xname': 'longitude', + 'yname': 'SSTA', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['dSST'], + "maskland": True, + 'title': 'La Nina SSTA', + 'varpattern': 'sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA', + }, + }, + 'NinaSstTsRmse': { + 'netcdf_variables': ['sst_ts__', 'sst_hov__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "La Nina life-cycle", + 'varpattern': 'sst_ts__', + 'xname': 'months', + 'yname': 'SSTA', + }, + 'dive_down01': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + 'title': 'La Nina SSTA', + 'varpattern': 'sst_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSTA', + }, + }, + 'NinoPrMap': { + 'netcdf_variables': ['pr_map__', 'pr_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + 'title': ['El Nino composite', 'El Nino composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'pr_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PRA', + }, + }, + 'NinoSlpMap': { + 'netcdf_variables': ['slp_map__', 'slp_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG3'], + "maskland": False, + 'title': ['El Nino composite', 'El Nino composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'slp_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SLPA', + }, + }, + 'NinoSstMap': { + 'netcdf_variables': ['ts_map__', 'ts_map__'], + 'diagnostic': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + "maskland": False, + 'title': ['El Nino composite', 'El Nino composite'], + #'varpattern': 'sst_over_sst_map__', + 'varpattern': 'ts_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA', + }, + }, + "NinoSstDiversity": { + 'netcdf_variables': ['Nina_lon_pos_minSSTA__', "Nino_lon_pos_maxSSTA__"], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'El Nino diversity', + 'varpattern': 'diagnostic', + 'yname': 'IQR of max SSTA', + }, + 'dive_down01': { + 'plot_type': 'boxplot', + 'nbr_panel': 2, + 'title': ['La Nina diversity', 'El Nino diversity'], + 'varpattern': ['Nina_lon_pos_minSSTA__', 'Nino_lon_pos_maxSSTA__'], + 'yname': ['longitude of min SSTA', 'longitude of max SSTA'], + "custom_label": "longitude", + }, + }, + 'NinoSstDur': { + 'netcdf_variables': ['Nino_duration__'], + 'diagnostic': { + 'plot_type': 'dot', + 'nbr_panel': 1, + 'title': 'El Nino duration', + 'varpattern': 'diagnostic', + 'yname': 'duration (SSTA>0.5)', + }, + 'dive_down01': { + 'plot_type': 'boxplot', + 'nbr_panel': 1, + 'title': 'El Nino duration', + 'varpattern': 'Nino_duration__', + 'yname': 'duration (SSTA>0.5)', + }, + }, + 'NinoSstLonRmse': { + 'netcdf_variables': ['sst_lon__', 'sst_map__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "El Nino pattern", + 'varpattern': 'sst_lon__', + 'xname': 'longitude', + 'yname': 'SSTA', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['dSST'], + "maskland": True, + 'title': 'El Nino SSTA', + 'varpattern': 'sst_map__', + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSTA', + }, + }, + 'NinoSstTsRmse': { + 'netcdf_variables': ['sst_ts__', 'sst_hov__'], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': "El Nino life-cycle", + 'varpattern': 'sst_ts__', + 'xname': 'months', + 'yname': 'SSTA', + }, + 'dive_down01': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['SKEW'], + 'title': 'El Nino SSTA', + 'varpattern': 'sst_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSTA', + }, + }, + 'SeasonalPrLatRmse': { + 'netcdf_variables': ["pr_lat__", "pr_map__", "prMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'PR seasonal cycle std', + 'varpattern': 'pr_lat__', + 'xname': 'latitude', + 'yname': 'PR std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude5'], + "maskland": True, + 'title': ['PR seasonal cycle std'], + 'varpattern': "pr_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PR std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['PR'], + 'label': dict_label['amplitude15'], + 'title': ['PR seasonal cycle', 'PR seasonal cycle'], + 'varpattern': 'prMac_hov__', + 'xname': 'latitude', + 'yname': 'months', + 'zname': 'PR', + }, + }, + 'SeasonalPrLonRmse': { + 'netcdf_variables': ["pr_lon__", "pr_map__", "prMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'PR seasonal cycle std', + 'varpattern': 'pr_lon__', + 'xname': 'longitude', + 'yname': 'PR std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude5'], + "maskland": True, + 'title': ['PR seasonal cycle std'], + 'varpattern': "pr_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'PR std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['PR'], + 'label': dict_label['amplitude10'], + 'title': ['PR seasonal cycle', 'PR seasonal cycle'], + 'varpattern': 'prMac_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'PR', + }, + }, + 'SeasonalSshLatRmse': { + 'netcdf_variables': ["ssh_lat__", "ssh_map__", "sshMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSH seasonal cycle std', + 'varpattern': 'ssh_lat__', + 'xname': 'latitude', + 'yname': 'SSH std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['SSH seasonal cycle std'], + 'varpattern': "ssh_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSH std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['SST'], + 'title': ['SSH seasonal cycle', 'SSH seasonal cycle'], + 'varpattern': 'sshMac_hov__', + 'xname': 'latitude', + 'yname': 'months', + 'zname': 'SSH', + }, + }, + 'SeasonalSshLonRmse': { + 'netcdf_variables': ["ssh_lon__", "ssh_map__", "sshMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SSH seasonal cycle std', + 'varpattern': 'ssh_lon__', + 'xname': 'longitude', + 'yname': 'SSH std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['SSH seasonal cycle std'], + 'varpattern': "ssh_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SSH std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed + 'label': dict_label['SST'], + 'title': ['SSH seasonal cycle', 'SSH seasonal cycle'], + 'varpattern': 'sshMac_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SSH', + }, + }, + 'SeasonalSstLatRmse': { + 'netcdf_variables': ["sst_lat__", "sst_map__", "sstMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SST seasonal cycle std', + 'varpattern': 'sst_lat__', + 'xname': 'latitude', + 'yname': 'SST std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['SST seasonal cycle std'], + 'varpattern': "sst_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SST std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], + 'label': dict_label['SST'], + 'title': ['SST seasonal cycle', 'SST seasonal cycle'], + 'varpattern': 'sstMac_hov__', + 'xname': 'latitude', + 'yname': 'months', + 'zname': 'SST', + }, + }, + 'SeasonalSstLonRmse': { + 'netcdf_variables': ["sst_lon__", "sst_map__", "sstMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'SST seasonal cycle std', + 'varpattern': 'sst_lon__', + 'xname': 'longitude', + 'yname': 'SST std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude'], + "maskland": True, + 'title': ['SST seasonal cycle std'], + 'varpattern': "sst_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'SST std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['SST'], + 'label': dict_label['SST'], + 'title': ['SST seasonal cycle', 'SST seasonal cycle'], + 'varpattern': 'sstMac_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'SST', + }, + }, + 'SeasonalTauxLatRmse': { + 'netcdf_variables': ["taux_lat__", "taux_map__", "tauxMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'TAUX seasonal cycle std', + 'varpattern': 'taux_lat__', + 'xname': 'latitude', + 'yname': 'TAUX std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude60'], + "maskland": True, + 'title': ['TAUX seasonal cycle std'], + 'varpattern': "taux_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TAUX std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['TAUX'], + 'title': ['TAUX seasonal cycle', 'TAUX seasonal cycle'], + 'varpattern': 'tauxMac_hov__', + 'xname': 'latitude', + 'yname': 'months', + 'zname': 'TAUX', + }, + }, + 'SeasonalTauxLonRmse': { + 'netcdf_variables': ["taux_lon__", "taux_map__", "tauxMac_hov__"], + 'diagnostic': { + 'plot_type': 'curve', + 'nbr_panel': 1, + 'title': 'TAUX seasonal cycle std', + 'varpattern': 'taux_lon__', + 'xname': 'longitude', + 'yname': 'TAUX std', + }, + 'dive_down01': { + 'plot_type': 'map', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['amplitude'], + 'label': dict_label['amplitude60'], + "maskland": True, + 'title': ['TAUX seasonal cycle std'], + 'varpattern': "taux_map__", + 'xname': 'longitude', + 'yname': 'latitude', + 'zname': 'TAUX std', + }, + 'dive_down02': { + 'plot_type': 'hovmoeller', + 'nbr_panel': 2, + 'colorbar': dict_colorbar['anomalies'], + 'label': dict_label['REG80'], + 'title': ['TAUX seasonal cycle', 'TAUX seasonal cycle'], + 'varpattern': 'tauxMac_hov__', + 'xname': 'longitude', + 'yname': 'months', + 'zname': 'TAUX', + }, + }, + +} + +# reference_observations = { +# 'ssh': 'AVISO', 'pr': 'GPCPv2.3', 'sst': 'HadISST', 'lhf': 'Tropflux', 'lwr': 'Tropflux', 'shf': 'Tropflux', +# 'swr': 'Tropflux', 'taux': 'Tropflux', 'thf': 'Tropflux' +# } +reference_observations = { + 'ssh': 'AVISO', 'pr': 'GPCPv2.3', 'sst': 'Tropflux', 'lhf': 'Tropflux', 'lwr': 'Tropflux', 'shf': 'Tropflux', + 'slp': 'ERA-Interim', 'swr': 'Tropflux', 'taux': 'Tropflux', 'thf': 'Tropflux' +} + + +def plot_param(metric_collection, metric): + dict_MC = defCollection(metric_collection) + dict_MCm = dict_MC['metrics_list'][metric] + # get plot parameters + dict_out = plot_parameters[metric.replace("_1", "").replace("_2", "").replace("_3", "")] + # get metric computation + if metric_collection in ["ENSO_tel", "test_tel"] and "Map" in metric: + computation = ["CORR", "RMSE"] + elif "rmse" in metric.lower(): + computation = "RMSE" + elif "corr" in metric.lower(): + computation = "CORR" + else: + try: + computation = dict_MCm['metric_computation'] + except: + try: + computation = dict_MC['common_collection_parameters']['metric_computation'] + except: + computation = default_arg_values('metric_computation') + dict_out['metric_computation'] = computation + # get metric variables + variables = dict_MCm['variables'] + dict_out['metric_variables'] = variables + # get metric reference + references = dict((var, reference_observations[var]) for var in variables) + if 'sst' in variables and len(variables) > 1: + references['sst'] = 'Tropflux' + refname = references[variables[0]] + for var in variables[1:]: + refname = refname + "_" + references[var] + # if metric_collection == "ENSO_tel" and metric in ["EnsoPrMap", "EnsoSlpMap"]: + # refname = "ERA-Interim_ERA-Interim" + # elif metric_collection == "ENSO_tel" and metric in ["EnsoSstMap", "NinaSstMap", "NinoSstMap"]: + # refname = "ERA-Interim" + if metric_collection in ["ENSO_tel", "test_tel"] and metric in\ + ["EnsoSstMap", "NinaSstMap", "NinoSstMap", "EnsoSstMapDjf", "NinoSstMapDjf", "NinaSstMapDjf", + "EnsoSstMapJja", "NinoSstMapJja", "NinaSstMapJja"]: + refname = "ERA-Interim" + dict_out['metric_reference'] = refname + # get variable regions + dict_out['metric_regions'] = dict_MCm['regions'] + return dict_out diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py new file mode 100644 index 000000000..9b10b4143 --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py @@ -0,0 +1,24 @@ +# -*- coding:UTF-8 -*- +from inspect import stack as INSPECTstack + +# ENSO_metrics package functions: +from .EnsoErrorsWarnings import unknown_key_arg + + +# ---------------------------------------------------------------------------------------------------------------------# +# +# Library to ENSO metrics arguments (arg parser) +# These functions analyses given arguments and sets some arguments to their default value +# +def default_arg_values(arg): + default = { + 'detrending': False, 'frequency': None, 'metric_computation': 'difference', 'min_time_steps': None, + 'normalization': False, 'project_interpreter': 'CMIP', 'regridding': False, 'smoothing': False, + 'treshold_ep_ev': -140, 'time_bounds': None, 'time_bounds_mod': None, 'time_bounds_obs': None, + } + try: + default[arg] + except: + unknown_key_arg(arg, INSPECTstack()) + return default[arg] +# ---------------------------------------------------------------------------------------------------------------------# diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/__init__.py b/pcmdi_metrics/enso/lib/summary_plot_lib/__init__.py new file mode 100644 index 000000000..e69de29bb From 6023427f2c43ee68b8de77fa9732d36a109cc72d Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 28 Jan 2025 12:07:53 -0800 Subject: [PATCH 02/10] add function to download from github repo --- pcmdi_metrics/utils/__init__.py | 1 + pcmdi_metrics/utils/download.py | 84 +++++++++++++++++++++++++++++++++ 2 files changed, 85 insertions(+) create mode 100644 pcmdi_metrics/utils/download.py diff --git a/pcmdi_metrics/utils/__init__.py b/pcmdi_metrics/utils/__init__.py index f47d51950..53dd05758 100644 --- a/pcmdi_metrics/utils/__init__.py +++ b/pcmdi_metrics/utils/__init__.py @@ -12,6 +12,7 @@ regenerate_time_axis, replace_date_pattern, ) +from .download import download_files_from_github_directory from .grid import ( calculate_area_weights, calculate_grid_area, diff --git a/pcmdi_metrics/utils/download.py b/pcmdi_metrics/utils/download.py new file mode 100644 index 000000000..67dd83db9 --- /dev/null +++ b/pcmdi_metrics/utils/download.py @@ -0,0 +1,84 @@ +import os +import re + +import requests + + +def download_files_from_github_directory(url, output_dir): + """ + Download all files from a GitHub directory. + + Parameters + ---------- + url : str + The URL of the GitHub directory. + output_dir : str + The local directory to save the downloaded files. + + Raises + ------ + Exception + If unable to fetch or parse the directory. + + Notes + ----- + This function downloads all files from the specified GitHub directory + without using BeautifulSoup. It constructs the raw file URLs based on + the provided directory URL and saves the files to the specified output + directory. + + Example + ------- + >>> from pcmdi_metrics.utils import download_files_from_github_directory + >>> github_directory_url = "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf" + >>> output_directory = "downloaded_files" + >>> download_files_from_github_directory(github_directory_url, output_directory) + """ + # Ensure the output directory exists + os.makedirs(output_dir, exist_ok=True) + + # GitHub raw content base URL + base_raw_url = "https://raw.githubusercontent.com" + repo_path = "/".join(url.split("github.com/")[1].split("/tree/")) + + print("repo_path:", repo_path) + + # Get the HTML content of the directory page + response = requests.get(url) + if response.status_code != 200: + raise Exception( + f"Failed to fetch URL: {url} (Status Code: {response.status_code})" + ) + + # Extract file links using a more flexible regex pattern + html_content = response.text + file_pattern = re.compile(r'href="(/[^/]+/[^/]+/blob/[^"]+)"') + print("file_pattern:", file_pattern) + matches = file_pattern.findall(html_content) + + if not matches: + print("No files found in the directory.") + return + + # Remove duplicates + matches = list(set(matches)) + + # Download each file + for match in matches: + # Extract the file name and create the raw file URL + file_name = match.split("/")[-1] + raw_file_url = base_raw_url + match.replace("/blob/", "/") + + save_path = os.path.join(output_dir, file_name) + + # Download the file + print(f"Downloading {file_name}...") + file_response = requests.get(raw_file_url) + if file_response.status_code == 200: + with open(save_path, "wb") as file: + file.write(file_response.content) + print(f"Saved {file_name} to {save_path}") + else: + print( + f"Failed to download {file_name} (Status Code: {file_response.status_code})" + ) From 3d3a036c6935818ae914fe64ff857fec1291c1ec Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 09:23:38 -0800 Subject: [PATCH 03/10] update --- pcmdi_metrics/enso/lib/__init__.py | 3 +- .../summary_plot_lib/EnsoCollectionsLib.py | 2433 +++++--- .../summary_plot_lib/EnsoErrorsWarnings.py | 322 +- .../enso/lib/summary_plot_lib/EnsoPlotLib.py | 5401 +++++++++-------- .../enso/lib/summary_plot_lib/KeyArgLib.py | 19 +- .../enso/lib/summary_plot_lib/plot.py | 1197 ++++ pcmdi_metrics/utils/__init__.py | 2 +- pcmdi_metrics/utils/download.py | 100 +- 8 files changed, 5928 insertions(+), 3549 deletions(-) create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/plot.py diff --git a/pcmdi_metrics/enso/lib/__init__.py b/pcmdi_metrics/enso/lib/__init__.py index 450206c89..147a6ca01 100644 --- a/pcmdi_metrics/enso/lib/__init__.py +++ b/pcmdi_metrics/enso/lib/__init__.py @@ -9,4 +9,5 @@ tree, ) -from .summary_plot_lib.EnsoPlotLib import plot_param # noqa +from .summary_plot_lib.EnsoPlotLib import plot_param +from .summary_plot_lib.plot import enso_portrait_plot diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py index c34e264d7..2935f1176 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoCollectionsLib.py @@ -10,188 +10,314 @@ def defCollection(mc=True): # Name, list of metrics metrics_collection = { - 'ENSO_perf': { - 'long_name': 'Metrics Collection for ENSO performance', - 'metrics_list': { - 'BiasPrLatRmse': { - 'variables': ['pr'], - 'regions': {'pr': 'nino3_LatExt'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasPrLonRmse': { - 'variables': ['pr'], - 'regions': {'pr': 'equatorial_pacific'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasSshLatRmse': { - 'variables': ['ssh'], - 'regions': {'ssh': 'nino3_LatExt'}, - 'obs_name': {'ssh': ['AVISO']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasSshLonRmse': { - 'variables': ['ssh'], - 'regions': {'ssh': 'equatorial_pacific'}, - 'obs_name': {'ssh': ['AVISO']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasSstLatRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3_LatExt'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasTauxLatRmse': { - 'variables': ['taux'], - 'regions': {'taux': 'equatorial_pacific_LatExt'}, - 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasTauxLonRmse': { - 'variables': ['taux'], - 'regions': {'taux': 'equatorial_pacific'}, - 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalPrLatRmse': { - 'variables': ['pr'], - 'regions': {'pr': 'nino3_LatExt'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalPrLonRmse': { - 'variables': ['pr'], - 'regions': {'pr': 'equatorial_pacific'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalSshLatRmse': { - 'variables': ['ssh'], - 'regions': {'ssh': 'nino3_LatExt'}, - 'obs_name': {'ssh': ['AVISO']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalSshLonRmse': { - 'variables': ['ssh'], - 'regions': {'ssh': 'equatorial_pacific'}, - 'obs_name': {'ssh': ['AVISO']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalSstLatRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3_LatExt'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalTauxLatRmse': { - 'variables': ['taux'], - 'regions': {'taux': 'equatorial_pacific_LatExt'}, - 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'SeasonalTauxLonRmse': { - 'variables': ['taux'], - 'regions': {'taux': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoAmpl': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoDuration': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'nbr_years_window': 6, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSeasonality': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSstDiversity_1': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoSstDiversity_2': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoSstSkew': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, + "ENSO_perf": { + "long_name": "Metrics Collection for ENSO performance", + "metrics_list": { + "BiasPrLatRmse": { + "variables": ["pr"], + "regions": {"pr": "nino3_LatExt"}, + "obs_name": {"pr": ["ERA-Interim", "GPCPv2.3"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasPrLonRmse": { + "variables": ["pr"], + "regions": {"pr": "equatorial_pacific"}, + "obs_name": {"pr": ["ERA-Interim", "GPCPv2.3"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasSshLatRmse": { + "variables": ["ssh"], + "regions": {"ssh": "nino3_LatExt"}, + "obs_name": {"ssh": ["AVISO"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasSshLonRmse": { + "variables": ["ssh"], + "regions": {"ssh": "equatorial_pacific"}, + "obs_name": {"ssh": ["AVISO"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasSstLatRmse": { + "variables": ["sst"], + "regions": {"sst": "nino3_LatExt"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasTauxLatRmse": { + "variables": ["taux"], + "regions": {"taux": "equatorial_pacific_LatExt"}, + "obs_name": {"taux": ["ERA-Interim", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasTauxLonRmse": { + "variables": ["taux"], + "regions": {"taux": "equatorial_pacific"}, + "obs_name": {"taux": ["ERA-Interim", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalPrLatRmse": { + "variables": ["pr"], + "regions": {"pr": "nino3_LatExt"}, + "obs_name": {"pr": ["ERA-Interim", "GPCPv2.3"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalPrLonRmse": { + "variables": ["pr"], + "regions": {"pr": "equatorial_pacific"}, + "obs_name": {"pr": ["ERA-Interim", "GPCPv2.3"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalSshLatRmse": { + "variables": ["ssh"], + "regions": {"ssh": "nino3_LatExt"}, + "obs_name": {"ssh": ["AVISO"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalSshLonRmse": { + "variables": ["ssh"], + "regions": {"ssh": "equatorial_pacific"}, + "obs_name": {"ssh": ["AVISO"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalSstLatRmse": { + "variables": ["sst"], + "regions": {"sst": "nino3_LatExt"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalTauxLatRmse": { + "variables": ["taux"], + "regions": {"taux": "equatorial_pacific_LatExt"}, + "obs_name": {"taux": ["ERA-Interim", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "SeasonalTauxLonRmse": { + "variables": ["taux"], + "regions": {"taux": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoAmpl": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoDuration": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "nbr_years_window": 6, + "smoothing": {"window": 5, "method": "triangle"}, + "metric_computation": "abs_relative_difference", + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSeasonality": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSstDiversity_1": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoSstDiversity_2": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoSstSkew": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, }, # 'EnsoPrTsRmse': { # 'variables': ['sst', 'pr'], @@ -204,16 +330,25 @@ def defCollection(mc=True): # 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', # 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, # }, - 'EnsoSstTsRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'nbr_years_window': 6, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, + "EnsoSstTsRmse": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "nbr_years_window": 6, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, }, # 'EnsoTauxTsRmse': { # 'variables': ['sst', 'taux'], @@ -226,371 +361,674 @@ def defCollection(mc=True): # 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', # 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, # }, - 'NinoSstDiversity_1': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'NinoSstDiversity_2': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - }, - 'common_collection_parameters': { - 'detrending': {'method': 'linear'}, - 'frequency': 'monthly', - 'min_time_steps': 204, - 'normalization': False, - 'project_interpreter': 'CMIP', - 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), - 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), - }, - 'plot_order': ['BiasPrLatRmse', 'BiasPrLonRmse', 'BiasSstLonRmse', 'BiasTauxLonRmse', - 'SeasonalPrLatRmse', 'SeasonalPrLonRmse', 'SeasonalSstLonRmse', 'SeasonalTauxLonRmse', - 'EnsoSstLonRmse', 'EnsoSstTsRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoSstSkew', - 'EnsodDuration', 'EnsoSstDiversity_2'], - 'description': 'Describe which science question this collection is about', - }, - 'ENSO_tel': { - 'long_name': 'Metrics Collection for ENSO teleconnections', - 'metrics_list': { - 'EnsoAmpl': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoSeasonality': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoPrMapDjf': { - 'variables': ['sst', 'pr'], - 'regions': {'pr': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoPrMapJja': { - 'variables': ['sst', 'pr'], - 'regions': {'pr': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSlpMapDjf': { - 'variables': ['sst', 'slp'], - 'regions': {'slp': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSlpMapJja': { - 'variables': ['sst', 'slp'], - 'regions': {'slp': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSstMapDjf': { - 'variables': ['sst'], - 'regions': {'sst': 'global'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSstMapJja': { - 'variables': ['sst'], - 'regions': {'sst': 'global'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - }, - 'common_collection_parameters': { - 'detrending': {'method': 'linear'}, - 'frequency': 'monthly', - 'min_time_steps': 204, - 'normalization': False, - 'project_interpreter': 'CMIP', - 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), - 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), - }, - 'plot_order': ['EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoPrMapDjfRmse', 'EnsoPrMapJjaRmse', - 'EnsoSstMapDjfRmse', 'EnsoSstMapJjaRmse'], - 'description': 'Describe which science question this collection is about', - }, - 'ENSO_proc': { - 'long_name': 'Metrics Collection for ENSO processes', - 'metrics_list': { - 'BiasSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'BiasTauxLonRmse': { - 'variables': ['taux'], - 'regions': {'taux': 'equatorial_pacific'}, - 'obs_name': {'taux': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoAmpl': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoSstLonRmse': { - 'variables': ['sst'], - 'regions': {'sst': 'equatorial_pacific'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSeasonality': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsoSstSkew': { - 'variables': ['sst'], - 'regions': {'sst': 'nino3.4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - 'metric_computation': 'abs_relative_difference', - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - }, - 'EnsodSstOce_1': { - 'variables': ['sst', 'thf'], - 'regions': {'sst': 'nino3', 'thf': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsodSstOce_2': { - 'variables': ['sst', 'thf'], - 'regions': {'sst': 'nino3', 'thf': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': True}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSshSst': { - 'variables': ['sst', 'ssh'], - 'regions': {'sst': 'nino3', 'ssh': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'ssh': ['AVISO']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstLhf': { - 'variables': ['sst', 'lhf'], - 'regions': {'sst': 'nino3', 'lhf': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'lhf': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstLwr': { - 'variables': ['sst', 'lwr'], - 'regions': {'sst': 'nino3', 'lwr': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'lwr': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstShf': { - 'variables': ['sst', 'shf'], - 'regions': {'sst': 'nino3', 'shf': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'shf': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstSwr': { - 'variables': ['sst', 'swr'], - 'regions': {'sst': 'nino3', 'swr': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'swr': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstTaux': { - 'variables': ['sst', 'taux'], - 'regions': {'sst': 'nino3', 'taux': 'nino4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'taux': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbSstThf': { - 'variables': ['sst', 'thf'], - 'regions': {'sst': 'nino3', 'thf': 'nino3'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'thf': ['ERA-Interim', 'Tropflux']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - 'EnsoFbTauxSsh': { - 'variables': ['taux', 'ssh'], - 'regions': {'ssh': 'nino3', 'taux': 'nino4'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux'], 'ssh': ['AVISO']}, - 'regridding': {'regridder': 'cdms', 'regridTool': 'esmf', 'regridMethod': 'linear', - 'newgrid_name': 'generic_1x1deg'}, - 'metric_computation': 'abs_relative_difference', - }, - }, - 'common_collection_parameters': { - 'detrending': {'method': 'linear'}, - 'frequency': 'monthly', - 'min_time_steps': 204, - 'normalization': False, - 'project_interpreter': 'CMIP', - 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), - 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), - }, - 'plot_order': ['BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', - 'EnsoSstSkew', 'EnsodSstOce_2', 'EnsoFbSstThf', 'EnsoFbSstTaux', 'EnsoFbTauxSsh', - 'EnsoFbSshSst'], - 'description': 'Describe which science question this collection is about', - }, - 'test_tel': { - 'long_name': 'Metrics Collection for ENSO teleconnections', - 'metrics_list': { - 'EnsoPrMapDjf': { - 'variables': ['sst', 'pr'], - 'regions': {'pr': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoPrMapJja': { - 'variables': ['sst', 'pr'], - 'regions': {'pr': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'pr': ['ERA-Interim', 'GPCPv2.3'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSlpMapDjf': { - 'variables': ['sst', 'slp'], - 'regions': {'slp': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSlpMapJja': { - 'variables': ['sst', 'slp'], - 'regions': {'slp': 'global', 'sst': 'nino3.4'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'slp': ['AVISO'], 'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSstMapDjf': { - 'variables': ['sst'], - 'regions': {'sst': 'global'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - 'EnsoSstMapJja': { - 'variables': ['sst'], - 'regions': {'sst': 'global'}, - 'event_definition': {'region_ev': 'nino3.4', 'season_ev': 'DEC', 'threshold': 0.75, - 'normalization': False}, - 'smoothing': {'window': 5, 'method': 'triangle'}, - 'regridding': {'model_orand_obs': 2, 'regridder': 'cdms', 'regridTool': 'esmf', - 'regridMethod': 'linear', 'newgrid_name': 'generic_1x1deg'}, - 'obs_name': {'sst': ['ERA-Interim', 'HadISST', 'Tropflux']}, - }, - }, - 'common_collection_parameters': { - 'detrending': {'method': 'linear'}, - 'frequency': 'monthly', - 'min_time_steps': 204, - 'normalization': False, - 'project_interpreter': 'CMIP', - 'observed_period': ('1850-01-01 00:00:00', '2018-12-31 23:59:60.0'), - 'modeled_period': ('1850-01-01 00:00:00', '2015-12-31 23:59:60.0'), - }, - 'plot_order': ['EnsoSstLonRmse', 'EnsoAmpl', 'EnsoSeasonality', 'EnsoPrMapDjfRmse', 'EnsoPrMapJjaRmse', - 'EnsoSstMapDjfRmse', 'EnsoSstMapJjaRmse'], - 'description': 'Describe which science question this collection is about', + "NinoSstDiversity_1": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "NinoSstDiversity_2": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + }, + "common_collection_parameters": { + "detrending": {"method": "linear"}, + "frequency": "monthly", + "min_time_steps": 204, + "normalization": False, + "project_interpreter": "CMIP", + "observed_period": ("1850-01-01 00:00:00", "2018-12-31 23:59:60.0"), + "modeled_period": ("1850-01-01 00:00:00", "2015-12-31 23:59:60.0"), + }, + "plot_order": [ + "BiasPrLatRmse", + "BiasPrLonRmse", + "BiasSstLonRmse", + "BiasTauxLonRmse", + "SeasonalPrLatRmse", + "SeasonalPrLonRmse", + "SeasonalSstLonRmse", + "SeasonalTauxLonRmse", + "EnsoSstLonRmse", + "EnsoSstTsRmse", + "EnsoAmpl", + "EnsoSeasonality", + "EnsoSstSkew", + "EnsodDuration", + "EnsoSstDiversity_2", + ], + "description": "Describe which science question this collection is about", + }, + "ENSO_tel": { + "long_name": "Metrics Collection for ENSO teleconnections", + "metrics_list": { + "EnsoAmpl": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + }, + "EnsoSeasonality": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoPrMapDjf": { + "variables": ["sst", "pr"], + "regions": {"pr": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "pr": ["ERA-Interim", "GPCPv2.3"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoPrMapJja": { + "variables": ["sst", "pr"], + "regions": {"pr": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "pr": ["ERA-Interim", "GPCPv2.3"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSlpMapDjf": { + "variables": ["sst", "slp"], + "regions": {"slp": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "slp": ["AVISO"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSlpMapJja": { + "variables": ["sst", "slp"], + "regions": {"slp": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "slp": ["AVISO"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSstMapDjf": { + "variables": ["sst"], + "regions": {"sst": "global"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + }, + "EnsoSstMapJja": { + "variables": ["sst"], + "regions": {"sst": "global"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + }, + }, + "common_collection_parameters": { + "detrending": {"method": "linear"}, + "frequency": "monthly", + "min_time_steps": 204, + "normalization": False, + "project_interpreter": "CMIP", + "observed_period": ("1850-01-01 00:00:00", "2018-12-31 23:59:60.0"), + "modeled_period": ("1850-01-01 00:00:00", "2015-12-31 23:59:60.0"), + }, + "plot_order": [ + "EnsoSstLonRmse", + "EnsoAmpl", + "EnsoSeasonality", + "EnsoPrMapDjfRmse", + "EnsoPrMapJjaRmse", + "EnsoSstMapDjfRmse", + "EnsoSstMapJjaRmse", + ], + "description": "Describe which science question this collection is about", + }, + "ENSO_proc": { + "long_name": "Metrics Collection for ENSO processes", + "metrics_list": { + "BiasSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "BiasTauxLonRmse": { + "variables": ["taux"], + "regions": {"taux": "equatorial_pacific"}, + "obs_name": {"taux": ["ERA-Interim", "Tropflux"]}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoAmpl": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + }, + "EnsoSstLonRmse": { + "variables": ["sst"], + "regions": {"sst": "equatorial_pacific"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSeasonality": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsoSstSkew": { + "variables": ["sst"], + "regions": {"sst": "nino3.4"}, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + "metric_computation": "abs_relative_difference", + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + }, + "EnsodSstOce_1": { + "variables": ["sst", "thf"], + "regions": {"sst": "nino3", "thf": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "thf": ["ERA-Interim", "Tropflux"], + }, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsodSstOce_2": { + "variables": ["sst", "thf"], + "regions": {"sst": "nino3", "thf": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "thf": ["ERA-Interim", "Tropflux"], + }, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": True, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSshSst": { + "variables": ["sst", "ssh"], + "regions": {"sst": "nino3", "ssh": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "ssh": ["AVISO"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstLhf": { + "variables": ["sst", "lhf"], + "regions": {"sst": "nino3", "lhf": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "lhf": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstLwr": { + "variables": ["sst", "lwr"], + "regions": {"sst": "nino3", "lwr": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "lwr": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstShf": { + "variables": ["sst", "shf"], + "regions": {"sst": "nino3", "shf": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "shf": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstSwr": { + "variables": ["sst", "swr"], + "regions": {"sst": "nino3", "swr": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "swr": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstTaux": { + "variables": ["sst", "taux"], + "regions": {"sst": "nino3", "taux": "nino4"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "taux": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbSstThf": { + "variables": ["sst", "thf"], + "regions": {"sst": "nino3", "thf": "nino3"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "thf": ["ERA-Interim", "Tropflux"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + "EnsoFbTauxSsh": { + "variables": ["taux", "ssh"], + "regions": {"ssh": "nino3", "taux": "nino4"}, + "obs_name": { + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + "ssh": ["AVISO"], + }, + "regridding": { + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "metric_computation": "abs_relative_difference", + }, + }, + "common_collection_parameters": { + "detrending": {"method": "linear"}, + "frequency": "monthly", + "min_time_steps": 204, + "normalization": False, + "project_interpreter": "CMIP", + "observed_period": ("1850-01-01 00:00:00", "2018-12-31 23:59:60.0"), + "modeled_period": ("1850-01-01 00:00:00", "2015-12-31 23:59:60.0"), + }, + "plot_order": [ + "BiasSstLonRmse", + "BiasTauxLonRmse", + "EnsoSstLonRmse", + "EnsoAmpl", + "EnsoSeasonality", + "EnsoSstSkew", + "EnsodSstOce_2", + "EnsoFbSstThf", + "EnsoFbSstTaux", + "EnsoFbTauxSsh", + "EnsoFbSshSst", + ], + "description": "Describe which science question this collection is about", + }, + "test_tel": { + "long_name": "Metrics Collection for ENSO teleconnections", + "metrics_list": { + "EnsoPrMapDjf": { + "variables": ["sst", "pr"], + "regions": {"pr": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "pr": ["ERA-Interim", "GPCPv2.3"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoPrMapJja": { + "variables": ["sst", "pr"], + "regions": {"pr": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "pr": ["ERA-Interim", "GPCPv2.3"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSlpMapDjf": { + "variables": ["sst", "slp"], + "regions": {"slp": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "slp": ["AVISO"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSlpMapJja": { + "variables": ["sst", "slp"], + "regions": {"slp": "global", "sst": "nino3.4"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": { + "slp": ["AVISO"], + "sst": ["ERA-Interim", "HadISST", "Tropflux"], + }, + }, + "EnsoSstMapDjf": { + "variables": ["sst"], + "regions": {"sst": "global"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + }, + "EnsoSstMapJja": { + "variables": ["sst"], + "regions": {"sst": "global"}, + "event_definition": { + "region_ev": "nino3.4", + "season_ev": "DEC", + "threshold": 0.75, + "normalization": False, + }, + "smoothing": {"window": 5, "method": "triangle"}, + "regridding": { + "model_orand_obs": 2, + "regridder": "cdms", + "regridTool": "esmf", + "regridMethod": "linear", + "newgrid_name": "generic_1x1deg", + }, + "obs_name": {"sst": ["ERA-Interim", "HadISST", "Tropflux"]}, + }, + }, + "common_collection_parameters": { + "detrending": {"method": "linear"}, + "frequency": "monthly", + "min_time_steps": 204, + "normalization": False, + "project_interpreter": "CMIP", + "observed_period": ("1850-01-01 00:00:00", "2018-12-31 23:59:60.0"), + "modeled_period": ("1850-01-01 00:00:00", "2015-12-31 23:59:60.0"), + }, + "plot_order": [ + "EnsoSstLonRmse", + "EnsoAmpl", + "EnsoSeasonality", + "EnsoPrMapDjfRmse", + "EnsoPrMapJjaRmse", + "EnsoSstMapDjfRmse", + "EnsoSstMapJjaRmse", + ], + "description": "Describe which science question this collection is about", }, } if mc is True: @@ -602,219 +1040,275 @@ def defCollection(mc=True): # List of reference observations for each variables def ReferenceObservations(dataset=True): dict_ref_obs = { - '20CRv2': { - 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.monolevel.mm.html', - 'file_name': '' + '*.mon.mean.nc', - 'variable_name_in_file': { - 'landmask': {'var_name': 'land'}, - 'lhf': {'var_name': 'lhtfl'}, + "20CRv2": { + "website": "https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.monolevel.mm.html", + "file_name": "" + "*.mon.mean.nc", + "variable_name_in_file": { + "landmask": {"var_name": "land"}, + "lhf": {"var_name": "lhtfl"}, # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # lwr = dlwrf - ulwrf - 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, - 'pr': {'var_name': 'prate'}, - 'slp': {'var_name': 'press'}, - 'shf': {'var_name': 'shtfl'}, - 'sst': {'var_name': 'air'}, + "lwr": { + "var_name": ["dlwrf", "ulwrf"], + "algebric_calculation": ["plus", "minus"], + }, + "pr": {"var_name": "prate"}, + "slp": {"var_name": "press"}, + "shf": {"var_name": "shtfl"}, + "sst": {"var_name": "air"}, # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # swr = dswrf - uswrf - 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, - 'taux': {'var_name': 'uflx'}, - 'tauy': {'var_name': 'vflx'}, + "swr": { + "var_name": ["dswrf", "uswrf"], + "algebric_calculation": ["plus", "minus"], + }, + "taux": {"var_name": "uflx"}, + "tauy": {"var_name": "vflx"}, # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # tfh = lhtfl + shtfl + dlwrf - ulwrf + dswrf - uswrf - 'thf': { - 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], - 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + "thf": { + "var_name": ["lhtfl", "shtfl", "dlwrf", "ulwrf", "dswrf", "uswrf"], + "algebric_calculation": [ + "plus", + "plus", + "plus", + "minus", + "plus", + "minus", + ], }, }, }, - '20CRv3': { - 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV3.monolevel.html', - 'file_name': '' + '*.mon.mean.nc', - 'variable_name_in_file': { - 'landmask': {'var_name': 'land'}, - 'lhf': {'var_name': 'lhtfl'}, + "20CRv3": { + "website": "https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV3.monolevel.html", + "file_name": "" + "*.mon.mean.nc", + "variable_name_in_file": { + "landmask": {"var_name": "land"}, + "lhf": {"var_name": "lhtfl"}, # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # lwr = dlwrf - ulwrf - 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, - 'pr': {'var_name': 'prate'}, - 'slp': {'var_name': 'prmsl'}, - 'shf': {'var_name': 'shtfl'}, - 'sst': {'var_name': 'skt'}, + "lwr": { + "var_name": ["dlwrf", "ulwrf"], + "algebric_calculation": ["plus", "minus"], + }, + "pr": {"var_name": "prate"}, + "slp": {"var_name": "prmsl"}, + "shf": {"var_name": "shtfl"}, + "sst": {"var_name": "skt"}, # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # swr = dswrf - uswrf - 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, + "swr": { + "var_name": ["dswrf", "uswrf"], + "algebric_calculation": ["plus", "minus"], + }, # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # tfh = lhtfl + shtfl + dlwrf - ulwrf + dswrf - uswrf - 'thf': { - 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], - 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], - }, - }, - }, - 'AVISO': { - 'website': 'https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global.html', - 'file_name': 'dt_global_allsat_msla_h_y????_m??.nc', - 'variable_name_in_file': {'ssh': {'var_name': 'sla'}, }, - }, - 'CFSR': { - 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', - 'file_name': '' + '_Omon_reanalysis_CFSR_*.nc', - 'variable_name_in_file': { - 'ssh': {'var_name': 'zos'}, - 'so': {'var_name': 'so'}, - 'thetao': {'var_name': 'thetao'}, - 'thf': {'var_name': 'hfds'}, # I'm not sure yet if it is the total heat flux - 'uo': {'var_name': 'uo'}, - 'vo': {'var_name': 'vo'}, - }, - }, - 'CMAP': { - 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html', - 'file_name': '' + '.mon.mean.nc', - 'variable_name_in_file': { - 'pr': {'var_name': 'precip'}, - }, - }, - 'ERA-Interim': { - 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', - 'file_name': '' + '_Amon_reanalysis_IFS-Cy31r2_*.nc', - 'variable_name_in_file': { - 'landmask': {'var_name': 'lsmask'}, - 'lhf': {'var_name': 'hfls'}, + "thf": { + "var_name": ["lhtfl", "shtfl", "dlwrf", "ulwrf", "dswrf", "uswrf"], + "algebric_calculation": [ + "plus", + "plus", + "plus", + "minus", + "plus", + "minus", + ], + }, + }, + }, + "AVISO": { + "website": "https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global.html", + "file_name": "dt_global_allsat_msla_h_y????_m??.nc", + "variable_name_in_file": { + "ssh": {"var_name": "sla"}, + }, + }, + "CFSR": { + "website": "see https://esgf.nccs.nasa.gov/search/create-ip/", + "file_name": "" + "_Omon_reanalysis_CFSR_*.nc", + "variable_name_in_file": { + "ssh": {"var_name": "zos"}, + "so": {"var_name": "so"}, + "thetao": {"var_name": "thetao"}, + "thf": { + "var_name": "hfds" + }, # I'm not sure yet if it is the total heat flux + "uo": {"var_name": "uo"}, + "vo": {"var_name": "vo"}, + }, + }, + "CMAP": { + "website": "https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html", + "file_name": "" + ".mon.mean.nc", + "variable_name_in_file": { + "pr": {"var_name": "precip"}, + }, + }, + "ERA-Interim": { + "website": "see https://esgf.nccs.nasa.gov/search/create-ip/", + "file_name": "" + "_Amon_reanalysis_IFS-Cy31r2_*.nc", + "variable_name_in_file": { + "landmask": {"var_name": "lsmask"}, + "lhf": {"var_name": "hfls"}, # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # lwr = rlds - rlus # sometimes lwr is included in the datasets in a variable called 'rls' - 'lwr': {'var_name': ['rlds', 'rlus'], 'algebric_calculation': ['plus', 'minus']}, - 'pr': {'var_name': 'pr'}, - 'slp': {'var_name': 'psl'}, - 'shf': {'var_name': 'hfss'}, - 'sst': {'var_name': 'ts'}, + "lwr": { + "var_name": ["rlds", "rlus"], + "algebric_calculation": ["plus", "minus"], + }, + "pr": {"var_name": "pr"}, + "slp": {"var_name": "psl"}, + "shf": {"var_name": "hfss"}, + "sst": {"var_name": "ts"}, # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # swr = rsds - rsus # sometimes swr is included in the datasets in a variable called 'rss' - 'swr': {'var_name': ['rsds', 'rsus'], 'algebric_calculation': ['plus', 'minus']}, - 'taux': {'var_name': 'tauu'}, - 'tauy': {'var_name': 'tauv'}, + "swr": { + "var_name": ["rsds", "rsus"], + "algebric_calculation": ["plus", "minus"], + }, + "taux": {"var_name": "tauu"}, + "tauy": {"var_name": "tauv"}, # total heat flux computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # tfh = hfls + hfss + rlds - rlus + rsds - rsus # sometimes rls = rlds - rlus and rss = rsds - rsus # sometimes thf is included in the datasets in a variable called 'hfds', 'netflux', 'thflx',... - 'thf': { - 'var_name': ['hfls', 'hfss', 'rlds', 'rlus', 'rsds', 'rsus'], - 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], + "thf": { + "var_name": ["hfls", "hfss", "rlds", "rlus", "rsds", "rsus"], + "algebric_calculation": [ + "plus", + "plus", + "plus", + "minus", + "plus", + "minus", + ], }, - 'uas': {'var_name': 'uas'}, - 'vas': {'var_name': 'vas'}, + "uas": {"var_name": "uas"}, + "vas": {"var_name": "vas"}, }, }, - 'ERSSTv5': { - 'website': 'see https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/netcdf/', - 'file_name': 'ersst.v5.' + '' + '.nc', - 'variable_name_in_file': { - 'sst': {'var_name': 'sst'}, + "ERSSTv5": { + "website": "see https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/netcdf/", + "file_name": "ersst.v5." + "" + ".nc", + "variable_name_in_file": { + "sst": {"var_name": "sst"}, }, }, - 'GODAS': { - 'website': 'https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html', - 'file_name': '' + '_YYYY.nc', - 'variable_name_in_file': { - 'ssh': {'var_name': 'sshg'}, - 'taux': {'var_name': 'uflx'}, - 'tauy': {'var_name': 'vflx'}, - 'thf': {'var_name': 'thflx'}, + "GODAS": { + "website": "https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html", + "file_name": "" + "_YYYY.nc", + "variable_name_in_file": { + "ssh": {"var_name": "sshg"}, + "taux": {"var_name": "uflx"}, + "tauy": {"var_name": "vflx"}, + "thf": {"var_name": "thflx"}, }, }, - 'GPCPv2.3': { - 'website': 'see https://www.esrl.noaa.gov/psd/cgi-bin/db_search/DBSearch.pl?Dataset=GPCP+Version+2.3+' + - 'Combined+Precipitation+Dataset&group=0&submit=Search', - 'file_name': 'precip.mon.mean.nc', - 'variable_name_in_file': { - 'landmask': {'var_name': 'lsmask'}, - 'pr': {'var_name': 'precip'}, + "GPCPv2.3": { + "website": "see https://www.esrl.noaa.gov/psd/cgi-bin/db_search/DBSearch.pl?Dataset=GPCP+Version+2.3+" + + "Combined+Precipitation+Dataset&group=0&submit=Search", + "file_name": "precip.mon.mean.nc", + "variable_name_in_file": { + "landmask": {"var_name": "lsmask"}, + "pr": {"var_name": "precip"}, }, }, - 'HadISST': { - 'website': 'see https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html', - 'file_name': 'HadISST_' + '' + '.nc', - 'variable_name_in_file': { - 'sst': {'var_name': 'sst'}, + "HadISST": { + "website": "see https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html", + "file_name": "HadISST_" + "" + ".nc", + "variable_name_in_file": { + "sst": {"var_name": "sst"}, }, }, - 'NCEP2': { - 'website': 'see https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.gaussian.html', - 'file_name': '' + '.sfc.mon.mean.nc', - 'variable_name_in_file': { - 'landmask': {'var_name': 'land'}, - 'lhf': {'var_name': 'lhtfl'}, + "NCEP2": { + "website": "see https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.gaussian.html", + "file_name": "" + ".sfc.mon.mean.nc", + "variable_name_in_file": { + "landmask": {"var_name": "land"}, + "lhf": {"var_name": "lhtfl"}, # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # lwr = rlds - rlus - 'lwr': {'var_name': ['dlwrf', 'ulwrf'], 'algebric_calculation': ['plus', 'minus']}, - 'pr': {'var_name': 'prate'}, - 'shf': {'var_name': 'shtfl'}, - 'slp': {'var_name': 'pres'}, - 'sst': {'var_name': 'skt'}, + "lwr": { + "var_name": ["dlwrf", "ulwrf"], + "algebric_calculation": ["plus", "minus"], + }, + "pr": {"var_name": "prate"}, + "shf": {"var_name": "shtfl"}, + "slp": {"var_name": "pres"}, + "sst": {"var_name": "skt"}, # shortwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # swr = rsds - rsus - 'swr': {'var_name': ['dswrf', 'uswrf'], 'algebric_calculation': ['plus', 'minus']}, - 'taux': {'var_name': 'uflx'}, - 'tauy': {'var_name': 'vflx'}, - 'thf': { - 'var_name': ['lhtfl', 'shtfl', 'dlwrf', 'ulwrf', 'dswrf', 'uswrf'], - 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'], - }, - }, - }, - 'OAFlux': { - 'website': 'see ftp://ftp.whoi.edu/pub/science/oaflux/data_v3/monthly/turbulence/', - 'file_name': '' + '_oaflux_*.nc', - 'variable_name_in_file': { - 'lhf': {'var_name': 'lhtfl'}, - 'shf': {'var_name': 'shtfl'}, - 'sst': {'var_name': 'tmpsf'}, - }, - }, - 'OISST': { - 'website': 'see https://www.earthsystemcog.org/search/obs4mips/?template=obs4mips&limit=200', - 'file_name': '' + '_OISST_L4_AVHRR-only-v2_*-*.nc', - 'variable_name_in_file': { - 'sst': {'var_name': 'sst'}, - }, - }, - 'ORAS4': { - 'website': 'see https://esgf.nccs.nasa.gov/search/create-ip/', - 'file_name': '' + '_Omon_ORAreanalysis_ORAS4_*.nc', - 'variable_name_in_file': { - 'so': {'var_name': 'so'}, - 'thetao': {'var_name': 'thetao'}, - 'uo': {'var_name': 'uo'}, - 'vo': {'var_name': 'vo'}, - }, - }, - 'SODA3.4.2': { - 'website': 'see https://www.atmos.umd.edu/~ocean/index_files/soda3.4.2_mn_download_b.htm', - 'file_name': 'soda3.4.2_mn_ocean_reg_????.nc', - 'variable_name_in_file': { - 'so': {'var_name': 'salt'}, - 'ssh': {'var_name': 'ssh'}, - 'taux': {'var_name': 'taux'}, - 'thetao': {'var_name': 'temp'}, - 'thf': {'var_name': 'net_heating'}, - 'uo': {'var_name': 'u'}, - 'vo': {'var_name': 'v'}, - }, - }, - 'Tropflux': { - 'website': 'see https://incois.gov.in/tropflux/tf_products.jsp', - 'file_name': '' + '_tropflux_1m_*.nc', - 'variable_name_in_file': { - 'lhf': {'var_name': 'lhf'}, - 'lwr': {'var_name': 'lwr'}, - 'shf': {'var_name': 'shf'}, - 'sst': {'var_name': 'sst'}, - 'swr': {'var_name': 'swr'}, - 'taux': {'var_name': 'taux'}, - 'thf': {'var_name': 'netflux'}, + "swr": { + "var_name": ["dswrf", "uswrf"], + "algebric_calculation": ["plus", "minus"], + }, + "taux": {"var_name": "uflx"}, + "tauy": {"var_name": "vflx"}, + "thf": { + "var_name": ["lhtfl", "shtfl", "dlwrf", "ulwrf", "dswrf", "uswrf"], + "algebric_calculation": [ + "plus", + "plus", + "plus", + "minus", + "plus", + "minus", + ], + }, + }, + }, + "OAFlux": { + "website": "see ftp://ftp.whoi.edu/pub/science/oaflux/data_v3/monthly/turbulence/", + "file_name": "" + "_oaflux_*.nc", + "variable_name_in_file": { + "lhf": {"var_name": "lhtfl"}, + "shf": {"var_name": "shtfl"}, + "sst": {"var_name": "tmpsf"}, + }, + }, + "OISST": { + "website": "see https://www.earthsystemcog.org/search/obs4mips/?template=obs4mips&limit=200", + "file_name": "" + "_OISST_L4_AVHRR-only-v2_*-*.nc", + "variable_name_in_file": { + "sst": {"var_name": "sst"}, + }, + }, + "ORAS4": { + "website": "see https://esgf.nccs.nasa.gov/search/create-ip/", + "file_name": "" + "_Omon_ORAreanalysis_ORAS4_*.nc", + "variable_name_in_file": { + "so": {"var_name": "so"}, + "thetao": {"var_name": "thetao"}, + "uo": {"var_name": "uo"}, + "vo": {"var_name": "vo"}, + }, + }, + "SODA3.4.2": { + "website": "see https://www.atmos.umd.edu/~ocean/index_files/soda3.4.2_mn_download_b.htm", + "file_name": "soda3.4.2_mn_ocean_reg_????.nc", + "variable_name_in_file": { + "so": {"var_name": "salt"}, + "ssh": {"var_name": "ssh"}, + "taux": {"var_name": "taux"}, + "thetao": {"var_name": "temp"}, + "thf": {"var_name": "net_heating"}, + "uo": {"var_name": "u"}, + "vo": {"var_name": "v"}, + }, + }, + "Tropflux": { + "website": "see https://incois.gov.in/tropflux/tf_products.jsp", + "file_name": "" + "_tropflux_1m_*.nc", + "variable_name_in_file": { + "lhf": {"var_name": "lhf"}, + "lwr": {"var_name": "lwr"}, + "shf": {"var_name": "shf"}, + "sst": {"var_name": "sst"}, + "swr": {"var_name": "swr"}, + "taux": {"var_name": "taux"}, + "thf": {"var_name": "netflux"}, }, }, } @@ -826,112 +1320,317 @@ def ReferenceObservations(dataset=True): def ReferenceRegions(region=True): dict_reference_regions = { - 'global': {'long_name': 'Global 60S-60N', 'latitude': (-60., 60.), 'longitude': (0., 360.)}, - 'global2': {'long_name': 'Global', 'latitude': (-90., 90.), 'longitude': (0., 360.)}, - 'tropical_pacific': { - 'long_name': 'Tropical Pacific (TP)', 'latitude': (-30., 30.), 'longitude': (120., 280.), + "global": { + "long_name": "Global 60S-60N", + "latitude": (-60.0, 60.0), + "longitude": (0.0, 360.0), + }, + "global2": { + "long_name": "Global", + "latitude": (-90.0, 90.0), + "longitude": (0.0, 360.0), + }, + "tropical_pacific": { + "long_name": "Tropical Pacific (TP)", + "latitude": (-30.0, 30.0), + "longitude": (120.0, 280.0), + }, + "equatorial_pacific": { + "long_name": "Equatorial Pacific (EP)", + "latitude": (-5.0, 5.0), + "longitude": (150.0, 270.0), + }, + "equatorial_pacific_LatExt": { + "long_name": "Equatorial Pacific (EP)", + "latitude": (-15.0, 15.0), + "longitude": (150.0, 270.0), + }, + "equatorial_pacific_LatExt2": { + "long_name": "Equatorial Pacific extended in latitude", + "latitude": (-15.0, 15.0), + "longitude": (120.0, 285.0), }, - 'equatorial_pacific': { - 'long_name': 'Equatorial Pacific (EP)', 'latitude': (-5., 5.), 'longitude': (150., 270.), + "eastern_equatorial_pacific": { + "long_name": "Western Equatorial Pacific (WEP)", + "latitude": (-5.0, 5.0), + "longitude": (205.0, 280.0), }, - 'equatorial_pacific_LatExt': { - 'long_name': 'Equatorial Pacific (EP)', 'latitude': (-15., 15.), 'longitude': (150., 270.), + "western_equatorial_pacific": { + "long_name": "Eastern Equatorial Pacific (EEP)", + "latitude": (-5.0, 5.0), + "longitude": (120.0, 205.0), }, - 'equatorial_pacific_LatExt2': { - 'long_name': 'Equatorial Pacific extended in latitude', 'latitude': (-15., 15.), 'longitude': (120., 285.), + "nino1+2": { + "long_name": "Niño 1+2", + "latitude": (-10.0, 0.0), + "longitude": (270.0, 280.0), }, - 'eastern_equatorial_pacific': { - 'long_name': 'Western Equatorial Pacific (WEP)', 'latitude': (-5., 5.), 'longitude': (205., 280.), + "nino3": { + "long_name": "Niño 3", + "latitude": (-5.0, 5.0), + "longitude": (210.0, 270.0), }, - 'western_equatorial_pacific': { - 'long_name': 'Eastern Equatorial Pacific (EEP)', 'latitude': (-5., 5.), 'longitude': (120., 205.), + "nino3_LatExt": { + "long_name": "Niño 3 extended in latitude", + "latitude": (-15.0, 15.0), + "longitude": (210.0, 270.0), }, - 'nino1+2': {'long_name': 'Niño 1+2', 'latitude': (-10., 0.), 'longitude': (270., 280.)}, - 'nino3': {'long_name': 'Niño 3', 'latitude': (-5., 5.), 'longitude': (210., 270.)}, - 'nino3_LatExt': { - 'long_name': 'Niño 3 extended in latitude', 'latitude': (-15., 15.), 'longitude': (210., 270.) + "nino3.4": { + "long_name": "Niño 3.4", + "latitude": (-5.0, 5.0), + "longitude": (190.0, 240.0), + }, + "nino4": { + "long_name": "Niño 4", + "latitude": (-5.0, 5.0), + "longitude": (160.0, 210.0), }, - 'nino3.4': {'long_name': 'Niño 3.4', 'latitude': (-5., 5.), 'longitude': (190., 240.)}, - 'nino4': {'long_name': 'Niño 4', 'latitude': (-5., 5.), 'longitude': (160., 210.)}, # AR5 reference regions - 'ALA': {'long_name': 'Alaska/N.W. Canada', 'latitude': (60., 72.6), 'longitude': (192., 255.), - 'maskland': False, 'maskocean': True}, + "ALA": { + "long_name": "Alaska/N.W. Canada", + "latitude": (60.0, 72.6), + "longitude": (192.0, 255.0), + "maskland": False, + "maskocean": True, + }, # 'AMZ': {'long_name': 'Amazon', 'polygon shaped region, I do not know how to select it'}, # 'CAM': {'long_name': 'Central America/Mexico', 'polygon shaped region, I do not know how to select it'}, - 'CAS': {'long_name': 'Central Asia', 'latitude': (30., 50.), 'longitude': (60., 75.), 'maskland': False, - 'maskocean': True}, + "CAS": { + "long_name": "Central Asia", + "latitude": (30.0, 50.0), + "longitude": (60.0, 75.0), + "maskland": False, + "maskocean": True, + }, # 'CEU': {'long_name': 'Central Europe', 'polygon shaped region, I do not know how to select it'}, - 'CGI': {'long_name': 'Canada/Greenland/Iceland', 'latitude': (50., 85.), 'longitude': (255., 350.), - 'maskland': False, 'maskocean': True}, - 'CNA': {'long_name': 'Central North America', 'latitude': (28.6, 50.), 'longitude': (255., 275.), - 'maskland': False, 'maskocean': True}, - 'EAF': {'long_name': 'East Africa', 'latitude': (-11.4, 15.), 'longitude': (25., 52.), 'maskland': False, - 'maskocean': True}, - 'EAS': {'long_name': 'East Asia', 'latitude': (20., 50.), 'longitude': (100., 145.), 'maskland': False, - 'maskocean': True}, - 'ENA': {'long_name': 'East North America', 'latitude': (25., 50.), 'longitude': (275., 300.), 'maskland': False, - 'maskocean': True}, - 'MED': {'long_name': 'South Europe/Mediterranean', 'latitude': (30., 45.), 'longitude': (350., 400.), - 'maskland': False, 'maskocean': True}, - 'NAS': {'long_name': 'North Asia', 'latitude': (50., 70.), 'longitude': (40., 180.), 'maskland': False, - 'maskocean': True}, - 'NAU': {'long_name': 'North Australia', 'latitude': (-30., -10.), 'longitude': (110., 155.), 'maskland': False, - 'maskocean': True}, - 'NEB': {'long_name': 'North-East Brazil', 'latitude': (-20., 0.), 'longitude': (310., 326.), 'maskland': False, - 'maskocean': True}, + "CGI": { + "long_name": "Canada/Greenland/Iceland", + "latitude": (50.0, 85.0), + "longitude": (255.0, 350.0), + "maskland": False, + "maskocean": True, + }, + "CNA": { + "long_name": "Central North America", + "latitude": (28.6, 50.0), + "longitude": (255.0, 275.0), + "maskland": False, + "maskocean": True, + }, + "EAF": { + "long_name": "East Africa", + "latitude": (-11.4, 15.0), + "longitude": (25.0, 52.0), + "maskland": False, + "maskocean": True, + }, + "EAS": { + "long_name": "East Asia", + "latitude": (20.0, 50.0), + "longitude": (100.0, 145.0), + "maskland": False, + "maskocean": True, + }, + "ENA": { + "long_name": "East North America", + "latitude": (25.0, 50.0), + "longitude": (275.0, 300.0), + "maskland": False, + "maskocean": True, + }, + "MED": { + "long_name": "South Europe/Mediterranean", + "latitude": (30.0, 45.0), + "longitude": (350.0, 400.0), + "maskland": False, + "maskocean": True, + }, + "NAS": { + "long_name": "North Asia", + "latitude": (50.0, 70.0), + "longitude": (40.0, 180.0), + "maskland": False, + "maskocean": True, + }, + "NAU": { + "long_name": "North Australia", + "latitude": (-30.0, -10.0), + "longitude": (110.0, 155.0), + "maskland": False, + "maskocean": True, + }, + "NEB": { + "long_name": "North-East Brazil", + "latitude": (-20.0, 0.0), + "longitude": (310.0, 326.0), + "maskland": False, + "maskocean": True, + }, # 'NEU': {'long_name': 'North Europe', 'polygon shaped region, I do not know how to select it'}, - 'SAF': {'long_name': 'Southern Africa', 'latitude': (-35., -11.4), 'longitude': (350., 412.), 'maskland': False, - 'maskocean': True}, - 'SAH': {'long_name': 'Sahara', 'latitude': (15., 30.), 'longitude': (340., 400.), 'maskland': False, - 'maskocean': True}, + "SAF": { + "long_name": "Southern Africa", + "latitude": (-35.0, -11.4), + "longitude": (350.0, 412.0), + "maskland": False, + "maskocean": True, + }, + "SAH": { + "long_name": "Sahara", + "latitude": (15.0, 30.0), + "longitude": (340.0, 400.0), + "maskland": False, + "maskocean": True, + }, # 'SAS': {'long_name': 'South Asia', 'polygon shaped region, I do not know how to select it'}, - 'SAU': {'long_name': 'South Australia/New Zealand', 'latitude': (-50., -30.), 'longitude': (110., 180.), - 'maskland': False, 'maskocean': True}, - 'SEA': {'long_name': 'Southeast Asia', 'latitude': (-10., 20.), 'longitude': (95., 155.), 'maskland': False, - 'maskocean': False}, + "SAU": { + "long_name": "South Australia/New Zealand", + "latitude": (-50.0, -30.0), + "longitude": (110.0, 180.0), + "maskland": False, + "maskocean": True, + }, + "SEA": { + "long_name": "Southeast Asia", + "latitude": (-10.0, 20.0), + "longitude": (95.0, 155.0), + "maskland": False, + "maskocean": False, + }, # 'SSA': {'long_name': 'Southeastern South America', 'polygon shaped region, I do not know how to select it'}, - 'TIB': {'long_name': 'Tibetan Plateau', 'latitude': (30., 50.), 'longitude': (75., 100.), 'maskland': False, - 'maskocean': True}, - 'WAF': {'long_name': 'West Africa', 'latitude': (-11.4, 15.), 'longitude': (340., 385.), 'maskland': False, - 'maskocean': True}, - 'WAS': {'long_name': 'West Asia', 'latitude': (15., 50.), 'longitude': (40., 60.), 'maskland': False, - 'maskocean': True}, - 'WNA': {'long_name': 'West North America', 'latitude': (28.6, 60.), 'longitude': (230., 255.), - 'maskland': False, 'maskocean': True}, + "TIB": { + "long_name": "Tibetan Plateau", + "latitude": (30.0, 50.0), + "longitude": (75.0, 100.0), + "maskland": False, + "maskocean": True, + }, + "WAF": { + "long_name": "West Africa", + "latitude": (-11.4, 15.0), + "longitude": (340.0, 385.0), + "maskland": False, + "maskocean": True, + }, + "WAS": { + "long_name": "West Asia", + "latitude": (15.0, 50.0), + "longitude": (40.0, 60.0), + "maskland": False, + "maskocean": True, + }, + "WNA": { + "long_name": "West North America", + "latitude": (28.6, 60.0), + "longitude": (230.0, 255.0), + "maskland": False, + "maskocean": True, + }, # 'WSA': {'long_name': 'West Coast South America', 'polygon shaped region, I do not know how to select it'}, # non-SREX reference regions - 'ANT': {'long_name': 'Antarctica', 'latitude': (-90., -50.), 'longitude': (0., 360.), 'maskland': False, - 'maskocean': False}, - 'ARC': {'long_name': 'Arctic', 'latitude': (67.5, 90.), 'longitude': (0., 360.), 'maskland': False, - 'maskocean': False}, + "ANT": { + "long_name": "Antarctica", + "latitude": (-90.0, -50.0), + "longitude": (0.0, 360.0), + "maskland": False, + "maskocean": False, + }, + "ARC": { + "long_name": "Arctic", + "latitude": (67.5, 90.0), + "longitude": (0.0, 360.0), + "maskland": False, + "maskocean": False, + }, # 'CAR': { # 'long_name': 'Caribbean', 'polygon shaped region, I do not know how to select it' # }, - 'NTP': {'long_name': 'Northern Tropical Pacific', 'latitude': (5., 25.), 'longitude': (155., 210.)}, - 'STP': {'long_name': 'Southern Topical Pacific', 'latitude': (-25., -5.), 'longitude': (155., 230.)}, - 'ETP': {'long_name': 'Equatorial Tropical Pacific', 'latitude': (-5., 5.), 'longitude': (155., 210.)}, - 'WIO': {'long_name': 'West Indian Ocean', 'latitude': (-25., 5.), 'longitude': (52., 75.), 'maskland': False, - 'maskocean': False}, + "NTP": { + "long_name": "Northern Tropical Pacific", + "latitude": (5.0, 25.0), + "longitude": (155.0, 210.0), + }, + "STP": { + "long_name": "Southern Topical Pacific", + "latitude": (-25.0, -5.0), + "longitude": (155.0, 230.0), + }, + "ETP": { + "long_name": "Equatorial Tropical Pacific", + "latitude": (-5.0, 5.0), + "longitude": (155.0, 210.0), + }, + "WIO": { + "long_name": "West Indian Ocean", + "latitude": (-25.0, 5.0), + "longitude": (52.0, 75.0), + "maskland": False, + "maskocean": False, + }, # Power and Delage's (2018) oceanic regions - 'CEP': {'long_name': 'Central Equatorial Pacific', 'latitude': (-5., 5.), 'longitude': (180., 220.), - 'maskland': True, 'maskocean': False}, - 'CNP': {'long_name': 'Central Northern Tropical Pacific', 'latitude': (5., 15.), 'longitude': (180., 220.), - 'maskland': True, 'maskocean': False}, - 'CSP': {'long_name': 'Central Southern Tropical Pacific', 'latitude': (-15., -5.), 'longitude': (180., 220.), - 'maskland': True, 'maskocean': False}, - 'INO': {'long_name': 'Indian Ocean', 'latitude': (-25., 0.), 'longitude': (55., 95.), 'maskland': True, - 'maskocean': False}, + "CEP": { + "long_name": "Central Equatorial Pacific", + "latitude": (-5.0, 5.0), + "longitude": (180.0, 220.0), + "maskland": True, + "maskocean": False, + }, + "CNP": { + "long_name": "Central Northern Tropical Pacific", + "latitude": (5.0, 15.0), + "longitude": (180.0, 220.0), + "maskland": True, + "maskocean": False, + }, + "CSP": { + "long_name": "Central Southern Tropical Pacific", + "latitude": (-15.0, -5.0), + "longitude": (180.0, 220.0), + "maskland": True, + "maskocean": False, + }, + "INO": { + "long_name": "Indian Ocean", + "latitude": (-25.0, 0.0), + "longitude": (55.0, 95.0), + "maskland": True, + "maskocean": False, + }, # YYP regions - 'africaSE': {'long_name': 'South and East Africa', 'latitude': (-40, 15.), 'longitude': (0., 55.), - 'maskland': False, 'maskocean': True}, - 'americaN': {'long_name': 'North America', 'latitude': (10., 60.), 'longitude': (235., 300.), - 'maskland': False, 'maskocean': True}, - 'americaS': {'long_name': 'South America', 'latitude': (-60., 15.), 'longitude': (275., 330.), - 'maskland': False, 'maskocean': True}, - 'asiaS': {'long_name': 'South Asia', 'latitude': (-10., 30.), 'longitude': (65., 130.), - 'maskland': False, 'maskocean': True}, - 'oceania': {'long_name': 'oceania', 'latitude': (-50., 0.), 'longitude': (110., 180.), 'maskland': False, - 'maskocean': True}, + "africaSE": { + "long_name": "South and East Africa", + "latitude": (-40, 15.0), + "longitude": (0.0, 55.0), + "maskland": False, + "maskocean": True, + }, + "americaN": { + "long_name": "North America", + "latitude": (10.0, 60.0), + "longitude": (235.0, 300.0), + "maskland": False, + "maskocean": True, + }, + "americaS": { + "long_name": "South America", + "latitude": (-60.0, 15.0), + "longitude": (275.0, 330.0), + "maskland": False, + "maskocean": True, + }, + "asiaS": { + "long_name": "South Asia", + "latitude": (-10.0, 30.0), + "longitude": (65.0, 130.0), + "maskland": False, + "maskocean": True, + }, + "oceania": { + "long_name": "oceania", + "latitude": (-50.0, 0.0), + "longitude": (110.0, 180.0), + "maskland": False, + "maskocean": True, + }, } if region is True: return dict_reference_regions @@ -941,54 +1640,108 @@ def ReferenceRegions(region=True): def CmipVariables(): dict_cmip_variables = { - 'reference': 'http://cfconventions.org/Data/cf-standard-names/46/build/cf-standard-name-table.html', - 'variable_name_in_file': { + "reference": "http://cfconventions.org/Data/cf-standard-names/46/build/cf-standard-name-table.html", + "variable_name_in_file": { # line keys: # '':{'var_name':'','cf_name':, # 'cf_unit':''} # areacell - 'areacell': {'var_name': 'areacella', 'cf_name': 'cell_area', 'cf_units': 'm2'}, + "areacell": { + "var_name": "areacella", + "cf_name": "cell_area", + "cf_units": "m2", + }, # landmask - 'landmask': {'var_name': 'sftlf', 'cf_name': 'cell_area', 'cf_units': '1'}, + "landmask": {"var_name": "sftlf", "cf_name": "cell_area", "cf_units": "1"}, # latent heat flux (on ocean grid or ocean points only) - 'lhf': {'var_name': 'hfls', 'cf_name': 'surface_upward_latent_heat_flux', 'cf_units': 'W m-2'}, + "lhf": { + "var_name": "hfls", + "cf_name": "surface_upward_latent_heat_flux", + "cf_units": "W m-2", + }, # longwave radiation computed from these variables IN THAT ORDER (on ocean grid or ocean points only) # lwr = rlds - rlus # sometimes lwr is included in the datasets in a variable called 'rls' - 'lwr': { - 'var_name': ['rlds', 'rlus'], - 'cf_name': ['surface_downwelling_longwave_flux_in_air', 'surface_upwelling_longwave_flux_in_air'], - 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'minus']}, + "lwr": { + "var_name": ["rlds", "rlus"], + "cf_name": [ + "surface_downwelling_longwave_flux_in_air", + "surface_upwelling_longwave_flux_in_air", + ], + "cf_units": "W m-2", + "algebric_calculation": ["plus", "minus"], + }, # Rainfall Flux - 'pr': {'var_name': 'pr', 'cf_name': 'rainfall_flux', 'cf_units': 'kg m-2 s-1'}, + "pr": { + "var_name": "pr", + "cf_name": "rainfall_flux", + "cf_units": "kg m-2 s-1", + }, # Sea Level Pressure - 'slp': {'var_name': 'psl', 'cf_name': 'air_pressure_at_mean_sea_level', 'cf_units': 'Pa'}, + "slp": { + "var_name": "psl", + "cf_name": "air_pressure_at_mean_sea_level", + "cf_units": "Pa", + }, # sensible heat flux (on ocean grid or ocean points only) - 'shf': {'var_name': 'hfss', 'cf_name': 'surface_upward_sensible_heat_flux', 'cf_units': 'W m-2'}, + "shf": { + "var_name": "hfss", + "cf_name": "surface_upward_sensible_heat_flux", + "cf_units": "W m-2", + }, # sea surface height - 'ssh': {'var_name': 'zos', 'cf_name': 'sea_surface_height_above_geoid', 'cf_units': 'm'}, + "ssh": { + "var_name": "zos", + "cf_name": "sea_surface_height_above_geoid", + "cf_units": "m", + }, # sea surface temperature - 'sst': {'var_name': 'ts', 'cf_name': 'sea_surface_temperature', 'cf_units': 'K'}, + "sst": { + "var_name": "ts", + "cf_name": "sea_surface_temperature", + "cf_units": "K", + }, # shortwave radiation computed from these variables IN THAT ORDER # swr = rsds - rsus # sometimes swr is included in the datasets in a variable called 'rss' - 'swr': { - 'var_name': ['rsds', 'rsus'], - 'cf_name': ['surface_downwelling_shortwave_flux_in_air', 'surface_upwelling_shortwave_flux_in_air'], - 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'minus'] + "swr": { + "var_name": ["rsds", "rsus"], + "cf_name": [ + "surface_downwelling_shortwave_flux_in_air", + "surface_upwelling_shortwave_flux_in_air", + ], + "cf_units": "W m-2", + "algebric_calculation": ["plus", "minus"], }, # zonal surface wind stress - 'taux': {'var_name': 'tauu', 'cf_name': 'surface_downward_eastward_stress', 'cf_units': 'Pa'}, + "taux": { + "var_name": "tauu", + "cf_name": "surface_downward_eastward_stress", + "cf_units": "Pa", + }, # total heat flux computed from these variables IN THAT ORDER # tfh = hfls + hfss + rlds - rlus + rsds - rsus # sometimes rls = rlds - rlus and rss = rsds - rsus # sometimes thf is included in the datasets in a variable called 'hfds', 'netflux', 'thflx',... - 'thf': { - 'var_name': ['hfls', 'hfss', 'rlds', 'rlus', 'rsds', 'rsus'], - 'cf_name': ['surface_upward_latent_heat_flux', 'surface_upward_sensible_heat_flux', - 'surface_downwelling_longwave_flux_in_air', 'surface_upwelling_longwave_flux_in_air', - 'surface_downwelling_shortwave_flux_in_air', 'surface_upwelling_shortwave_flux_in_air'], - 'cf_units': 'W m-2', 'algebric_calculation': ['plus', 'plus', 'plus', 'minus', 'plus', 'minus'] + "thf": { + "var_name": ["hfls", "hfss", "rlds", "rlus", "rsds", "rsus"], + "cf_name": [ + "surface_upward_latent_heat_flux", + "surface_upward_sensible_heat_flux", + "surface_downwelling_longwave_flux_in_air", + "surface_upwelling_longwave_flux_in_air", + "surface_downwelling_shortwave_flux_in_air", + "surface_upwelling_shortwave_flux_in_air", + ], + "cf_units": "W m-2", + "algebric_calculation": [ + "plus", + "plus", + "plus", + "minus", + "plus", + "minus", + ], }, }, } diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py index 97262ca66..c7d7847a9 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoErrorsWarnings.py @@ -1,19 +1,20 @@ # -*- coding:UTF-8 -*- from __future__ import print_function + from sys import exit as sys_exit # ---------------------------------------------------# # colors for printing class bcolors: - HEADER = '\033[95m' - OKBLUE = '\033[94m' - OKGREEN = '\033[92m' - WARNING = '\033[93m' - FAIL = '\033[91m' - ENDC = '\033[0m' - BOLD = '\033[1m' - UNDERLINE = '\033[4m' + HEADER = "\033[95m" + OKBLUE = "\033[94m" + OKGREEN = "\033[92m" + WARNING = "\033[93m" + FAIL = "\033[91m" + ENDC = "\033[0m" + BOLD = "\033[1m" + UNDERLINE = "\033[4m" # ---------------------------------------------------------------------------------------------------------------------# @@ -52,7 +53,7 @@ def plus_comma_space(string): elif not string: return string else: - return string+', ' + return string + ", " def message_formating(inspect_stack): @@ -74,15 +75,15 @@ def message_formating(inspect_stack): print string ' File filename, line n, in module' """ - string = ' ' + string = " " # adds file's name - if inspect_stack[0][1] != '': - string = plus_comma_space(string) + 'File ' + str(inspect_stack[0][1]) + if inspect_stack[0][1] != "": + string = plus_comma_space(string) + "File " + str(inspect_stack[0][1]) # adds line number - string = plus_comma_space(string) + 'line ' + str(inspect_stack[0][2]) + string = plus_comma_space(string) + "line " + str(inspect_stack[0][2]) # adds module's name - if inspect_stack[0][3] != '': - string = plus_comma_space(string) + 'in ' + str(inspect_stack[0][3]) + if inspect_stack[0][3] != "": + string = plus_comma_space(string) + "in " + str(inspect_stack[0][3]) return string @@ -149,18 +150,33 @@ def mismatch_shapes_error(tab1, tab2, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - try: name1 = tab1.name - except: name1 = 'no_name' - try: name2 = tab2.name - except: name2 = 'no_name' - list_strings = ["ERROR " + message_formating(inspect_stack) + ": array shape", - str().ljust(5) + "arrays shapes mismatch: " + str(name1) + " = " + str(tab1.shape) + "', and " - + str(name2) + " = " + str(tab2.shape)] + try: + name1 = tab1.name + except AttributeError: + name1 = "no_name" + try: + name2 = tab2.name + except AttributeError: + name2 = "no_name" + list_strings = [ + "ERROR " + message_formating(inspect_stack) + ": array shape", + str().ljust(5) + + "arrays shapes mismatch: " + + str(name1) + + " = " + + str(tab1.shape) + + "', and " + + str(name2) + + " = " + + str(tab2.shape), + ] my_warning(list_strings) return -def object_type_error(parameter_name, type_parameter, type_parameter_should_be, inspect_stack): +def object_type_error( + parameter_name, type_parameter, type_parameter_should_be, inspect_stack +): """ ################################################################################# Description: @@ -178,9 +194,16 @@ def object_type_error(parameter_name, type_parameter, type_parameter_should_be, list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR " + message_formating(inspect_stack) + ": object type", - str().ljust(5) + str(parameter_name) + ": should be '" + str(type_parameter_should_be) + "', not '" - + str(type_parameter) + "'"] + list_strings = [ + "ERROR " + message_formating(inspect_stack) + ": object type", + str().ljust(5) + + str(parameter_name) + + ": should be '" + + str(type_parameter_should_be) + + "', not '" + + str(type_parameter) + + "'", + ] my_warning(list_strings) return @@ -203,9 +226,16 @@ def too_short_time_period(metric_name, length, minimum_length, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR " + message_formating(inspect_stack) + ": too short time-period", - str().ljust(5) + str(metric_name) + ": the time-period is too short: " + str(length) - + " (minimum time-period: " + str(minimum_length) + ")"] + list_strings = [ + "ERROR " + message_formating(inspect_stack) + ": too short time-period", + str().ljust(5) + + str(metric_name) + + ": the time-period is too short: " + + str(length) + + " (minimum time-period: " + + str(minimum_length) + + ")", + ] my_warning(list_strings) return @@ -230,9 +260,19 @@ def unlikely_units(var_name, name_in_file, units, min_max, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR " + message_formating(inspect_stack) + ": units", - str().ljust(5) + "the file says that " + str(var_name) + " (" + str(name_in_file) - + ") is in " + str(units) + " but it seems unlikely (" + str(min_max) + ")"] + list_strings = [ + "ERROR " + message_formating(inspect_stack) + ": units", + str().ljust(5) + + "the file says that " + + str(var_name) + + " (" + + str(name_in_file) + + ") is in " + + str(units) + + " but it seems unlikely (" + + str(min_max) + + ")", + ] my_warning(list_strings) return @@ -253,9 +293,11 @@ def unknown_averaging(average, known_average, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR" + message_formating(inspect_stack) + ": averaging method", - str().ljust(5) + "unkwown averaging method (axis): " + str(average), - str().ljust(10) + "known averaging method: " + str(sorted(known_average))] + list_strings = [ + "ERROR" + message_formating(inspect_stack) + ": averaging method", + str().ljust(5) + "unkwown averaging method (axis): " + str(average), + str().ljust(10) + "known averaging method: " + str(sorted(known_average)), + ] my_error(list_strings) return @@ -274,8 +316,10 @@ def unknown_frequency(frequency, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR" + message_formating(inspect_stack) + ": frequency", - str().ljust(5) + "unknown frequency: " + str(frequency)] + list_strings = [ + "ERROR" + message_formating(inspect_stack) + ": frequency", + str().ljust(5) + "unknown frequency: " + str(frequency), + ] my_error(list_strings) return @@ -294,8 +338,10 @@ def unknown_key_arg(arg, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR" + message_formating(inspect_stack) + ": argument", - str().ljust(5) + "unknown argument(s): " + str(arg)] + list_strings = [ + "ERROR" + message_formating(inspect_stack) + ": argument", + str().ljust(5) + "unknown argument(s): " + str(arg), + ] my_warning(list_strings) return @@ -318,20 +364,62 @@ def unknown_units(var_name, name_in_file, units, inspect_stack): list of information about the program/module/line,... created using inspect.stack() :return: """ - list_strings = ["ERROR" + message_formating(inspect_stack) + ": units", - str().ljust(5) + "unknown units: " + str(var_name) + " (" + str(name_in_file) - + ") is in " + str(units)] + list_strings = [ + "ERROR" + message_formating(inspect_stack) + ": units", + str().ljust(5) + + "unknown units: " + + str(var_name) + + " (" + + str(name_in_file) + + ") is in " + + str(units), + ] my_warning(list_strings) return + + # ---------------------------------------------------------------------------------------------------------------------# # ---------------------------------------------------------------------------------------------------------------------# # Just prints -def debug_mode(color, title, nbr_spaces, axes1='', axes2='', axes3='', axes4='', file1='', file2='', file3='', file4='', - line1='', line2='', line3='', line4='', nina1='', nina2='', nina3='', nina4='', nino1='', nino2='', - nino3='', nino4='', shape1='', shape2='', shape3='', shape4='', time1='', time2='', time3='', time4='', - var1='', var2='', var3='', var4=''): +def debug_mode( + color, + title, + nbr_spaces, + axes1="", + axes2="", + axes3="", + axes4="", + file1="", + file2="", + file3="", + file4="", + line1="", + line2="", + line3="", + line4="", + nina1="", + nina2="", + nina3="", + nina4="", + nino1="", + nino2="", + nino3="", + nino4="", + shape1="", + shape2="", + shape3="", + shape4="", + time1="", + time2="", + time3="", + time4="", + var1="", + var2="", + var3="", + var4="", +): """ ################################################################################# Description: @@ -413,71 +501,153 @@ def debug_mode(color, title, nbr_spaces, axes1='', axes2='', axes3='', axes4='', # first variable print(color + str().ljust(nbr_spaces) + title + bcolors.ENDC) if file1: - print(color + str().ljust(nbr_spaces+5) + 'file name 1: ' + file1 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "file name 1: " + file1 + bcolors.ENDC + ) if var1: - print(color + str().ljust(nbr_spaces+5) + 'variable name 1: ' + var1 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "variable name 1: " + + var1 + + bcolors.ENDC + ) if axes1: - print(color + str().ljust(nbr_spaces+5) + 'axes list 1: ' + axes1 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "axes list 1: " + axes1 + bcolors.ENDC + ) if time1: - print(color + str().ljust(nbr_spaces+5) + 'time bounds 1: ' + time1 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "time bounds 1: " + + time1 + + bcolors.ENDC + ) if shape1: - print(color + str().ljust(nbr_spaces+5) + 'shape 1: ' + shape1 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + "shape 1: " + shape1 + bcolors.ENDC) if nina1: - print(color + str().ljust(nbr_spaces+5) + 'nina year 1: ' + nina1 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nina year 1: " + nina1 + bcolors.ENDC + ) if nino1: - print(color + str().ljust(nbr_spaces+5) + 'nino year 1: ' + nino1 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nino year 1: " + nino1 + bcolors.ENDC + ) if line1: - print(color + str().ljust(nbr_spaces+5) + line1 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + line1 + bcolors.ENDC) # second variable if file2: - print(color + str().ljust(nbr_spaces+5) + 'file name 2: ' + file2 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "file name 2: " + file2 + bcolors.ENDC + ) if var2: - print(color + str().ljust(nbr_spaces+5) + 'variable name 2: ' + var2 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "variable name 2: " + + var2 + + bcolors.ENDC + ) if axes2: - print(color + str().ljust(nbr_spaces+5) + 'axes list 2: ' + axes2 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "axes list 2: " + axes2 + bcolors.ENDC + ) if time2: - print(color + str().ljust(nbr_spaces+5) + 'time bounds 2: ' + time2 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "time bounds 2: " + + time2 + + bcolors.ENDC + ) if shape2: - print(color + str().ljust(nbr_spaces+5) + 'shape 2: ' + shape2 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + "shape 2: " + shape2 + bcolors.ENDC) if nina2: - print(color + str().ljust(nbr_spaces+5) + 'nina year 2: ' + nina2 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nina year 2: " + nina2 + bcolors.ENDC + ) if nino2: - print(color + str().ljust(nbr_spaces+5) + 'nino year 2: ' + nino2 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nino year 2: " + nino2 + bcolors.ENDC + ) if line2: - print(color + str().ljust(nbr_spaces+5) + line2 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + line2 + bcolors.ENDC) # third variable if file3: - print(color + str().ljust(nbr_spaces + 5) + 'file name 3: ' + file3 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "file name 3: " + file3 + bcolors.ENDC + ) if var3: - print(color + str().ljust(nbr_spaces + 5) + 'variable name 3: ' + var3 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "variable name 3: " + + var3 + + bcolors.ENDC + ) if axes3: - print(color + str().ljust(nbr_spaces + 5) + 'axes list 3: ' + axes3 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "axes list 3: " + axes3 + bcolors.ENDC + ) if time3: - print(color + str().ljust(nbr_spaces + 5) + 'time bounds 3: ' + time3 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "time bounds 3: " + + time3 + + bcolors.ENDC + ) if shape3: - print(color + str().ljust(nbr_spaces + 5) + 'shape 3: ' + shape3 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + "shape 3: " + shape3 + bcolors.ENDC) if nina3: - print(color + str().ljust(nbr_spaces + 5) + 'nina year 3: ' + nina3 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nina year 3: " + nina3 + bcolors.ENDC + ) if nino3: - print(color + str().ljust(nbr_spaces + 5) + 'nino year 3: ' + nino3 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nino year 3: " + nino3 + bcolors.ENDC + ) if line3: print(color + str().ljust(nbr_spaces + 5) + line3 + bcolors.ENDC) # fourth variable if file4: - print(color + str().ljust(nbr_spaces + 5) + 'file name 4: ' + file4 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "file name 4: " + file4 + bcolors.ENDC + ) if var4: - print(color + str().ljust(nbr_spaces + 5) + 'variable name 4: ' + var4 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "variable name 4: " + + var4 + + bcolors.ENDC + ) if axes4: - print(color + str().ljust(nbr_spaces + 5) + 'axes list 4: ' + axes4 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "axes list 4: " + axes4 + bcolors.ENDC + ) if time4: - print(color + str().ljust(nbr_spaces + 5) + 'time bounds 4: ' + time4 + bcolors.ENDC) + print( + color + + str().ljust(nbr_spaces + 5) + + "time bounds 4: " + + time4 + + bcolors.ENDC + ) if shape4: - print(color + str().ljust(nbr_spaces + 5) + 'shape 4: ' + shape4 + bcolors.ENDC) + print(color + str().ljust(nbr_spaces + 5) + "shape 4: " + shape4 + bcolors.ENDC) if nina4: - print(color + str().ljust(nbr_spaces + 5) + 'nina year 4: ' + nina4 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nina year 4: " + nina4 + bcolors.ENDC + ) if nino4: - print(color + str().ljust(nbr_spaces + 5) + 'nino year 4: ' + nino4 + bcolors.ENDC) + print( + color + str().ljust(nbr_spaces + 5) + "nino year 4: " + nino4 + bcolors.ENDC + ) if line4: print(color + str().ljust(nbr_spaces + 5) + line4 + bcolors.ENDC) return + + # ---------------------------------------------------------------------------------------------------------------------# diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py index 155d188a5..ab952abb5 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py @@ -2,1192 +2,1347 @@ # # Define ENSO metrics plots # -from copy import deepcopy + from numpy import arange as NUMPYarange + # ENSO_metrics functions from .EnsoCollectionsLib import defCollection from .KeyArgLib import default_arg_values - dict_colorbar = { - 'amplitude': 'amp', - 'anomalies': 'balance', - 'PR': 'rain', - 'SST': 'thermal', + "amplitude": "amp", + "anomalies": "balance", + "PR": "rain", + "SST": "thermal", } dict_label = { - 'amplitude': [round(ii, 1) for ii in NUMPYarange(0, 2.1, 0.5)], - 'amplitude5': list(range(0, 6, 1)), - 'amplitude10': [round(ii, 1) for ii in NUMPYarange(0, 10.1, 2.5)], - 'amplitude15': list(range(0, 16, 5)), - 'amplitude60': list(range(0, 61, 20)), - 'PR': list(range(0, 13, 4)), - 'PRA': [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], - 'REG03': [round(ii, 1) for ii in NUMPYarange(-0.3, 0.35, 0.1)], - 'REG05': [round(ii, 2) for ii in NUMPYarange(-0.5, 0.55, 0.25)], - 'REG12': [round(ii, 1) for ii in NUMPYarange(-1.2, 1.4, 0.6)], - 'REG2': list(range(-2, 3, 1)), - 'REG25': [round(ii, 1) for ii in NUMPYarange(-2.5, 2.6, 1.0)], - 'REG3': list(range(-3, 4, 1)), - 'REG4': list(range(-4, 5, 1)), - 'REG5': [round(ii, 1) for ii in NUMPYarange(-5, 6, 2.5)], - 'REG20': list(range(-20, 25, 10)), - 'REG30': list(range(-30, 35, 15)), - 'REG50': list(range(-50, 55, 25)), - 'REG60': list(range(-60, 65, 30)), - 'REG80': list(range(-80, 85, 40)), - 'SKEW': [round(ii, 1) for ii in NUMPYarange(-1.5, 1.6, 0.5)], - 'dSST': list(range(-2, 3, 1)), - 'SST': list(range(21, 31, 3)), - 'SSTA': [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], - 'TAUX': list(range(-100, 110, 50)), + "amplitude": [round(ii, 1) for ii in NUMPYarange(0, 2.1, 0.5)], + "amplitude5": list(range(0, 6, 1)), + "amplitude10": [round(ii, 1) for ii in NUMPYarange(0, 10.1, 2.5)], + "amplitude15": list(range(0, 16, 5)), + "amplitude60": list(range(0, 61, 20)), + "PR": list(range(0, 13, 4)), + "PRA": [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], + "REG03": [round(ii, 1) for ii in NUMPYarange(-0.3, 0.35, 0.1)], + "REG05": [round(ii, 2) for ii in NUMPYarange(-0.5, 0.55, 0.25)], + "REG12": [round(ii, 1) for ii in NUMPYarange(-1.2, 1.4, 0.6)], + "REG2": list(range(-2, 3, 1)), + "REG25": [round(ii, 1) for ii in NUMPYarange(-2.5, 2.6, 1.0)], + "REG3": list(range(-3, 4, 1)), + "REG4": list(range(-4, 5, 1)), + "REG5": [round(ii, 1) for ii in NUMPYarange(-5, 6, 2.5)], + "REG20": list(range(-20, 25, 10)), + "REG30": list(range(-30, 35, 15)), + "REG50": list(range(-50, 55, 25)), + "REG60": list(range(-60, 65, 30)), + "REG80": list(range(-80, 85, 40)), + "SKEW": [round(ii, 1) for ii in NUMPYarange(-1.5, 1.6, 0.5)], + "dSST": list(range(-2, 3, 1)), + "SST": list(range(21, 31, 3)), + "SSTA": [round(ii, 1) for ii in NUMPYarange(-1, 1.1, 0.5)], + "TAUX": list(range(-100, 110, 50)), } plot_parameters = { - 'BiasPrLatRmse': { - 'netcdf_variables': ['pr_lat__', 'pr_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean PR', # 'a) Mean meridional PR', # - 'varpattern': 'pr_lat__', - 'xname': 'latitude', - 'yname': 'PR', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['PR'], - 'label': dict_label['PR'], + "BiasPrLatRmse": { + "netcdf_variables": ["pr_lat__", "pr_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean PR", # 'a) Mean meridional PR', # + "varpattern": "pr_lat__", + "xname": "latitude", + "yname": "PR", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["PR"], + "label": dict_label["PR"], "maskland": True, - 'title': ['Mean PR', 'Mean PR'], - 'varpattern': 'pr_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PR', + "title": ["Mean PR", "Mean PR"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PR", }, }, - 'BiasPrLonRmse': { - 'netcdf_variables': ['pr_lon__', 'pr_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean PR', - 'varpattern': 'pr_lon__', - 'xname': 'longitude', - 'yname': 'PR', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['PR'], - 'label': dict_label['PR'], + "BiasPrLonRmse": { + "netcdf_variables": ["pr_lon__", "pr_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean PR", + "varpattern": "pr_lon__", + "xname": "longitude", + "yname": "PR", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["PR"], + "label": dict_label["PR"], "maskland": True, - 'title': ['Mean PR', 'Mean PR'], - 'varpattern': 'pr_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PR', + "title": ["Mean PR", "Mean PR"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PR", }, }, - 'BiasPrRmse': { - 'netcdf_variables': ['pr_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['PR'], - 'label': dict_label['PR'], + "BiasPrRmse": { + "netcdf_variables": ["pr_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["PR"], + "label": dict_label["PR"], "maskland": True, - 'title': ['Mean PR', 'Mean PR'], - 'varpattern': 'pr_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PR', + "title": ["Mean PR", "Mean PR"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PR", }, }, - 'BiasSshLatRmse': { - 'netcdf_variables': ['ssh_lat__', 'ssh_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean SSH', - 'varpattern': 'ssh_lat__', - 'xname': 'latitude', - 'yname': 'SSH', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['SST'], + "BiasSshLatRmse": { + "netcdf_variables": ["ssh_lat__", "ssh_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean SSH", + "varpattern": "ssh_lat__", + "xname": "latitude", + "yname": "SSH", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "SST" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SSH', 'Mean SSH'], - 'varpattern': 'ssh_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSH', + "title": ["Mean SSH", "Mean SSH"], + "varpattern": "ssh_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSH", }, }, - 'BiasSshLonRmse': { - 'netcdf_variables': ['ssh_lon__', 'ssh_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean SSH', - 'varpattern': 'ssh_lon__', - 'xname': 'longitude', - 'yname': 'SSH', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['SST'], + "BiasSshLonRmse": { + "netcdf_variables": ["ssh_lon__", "ssh_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean SSH", + "varpattern": "ssh_lon__", + "xname": "longitude", + "yname": "SSH", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "SST" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SSH', 'Mean SSH'], - 'varpattern': 'ssh_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSH', + "title": ["Mean SSH", "Mean SSH"], + "varpattern": "ssh_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSH", }, }, - 'BiasSshRmse': { - 'netcdf_variables': ['ssh_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['SST'], + "BiasSshRmse": { + "netcdf_variables": ["ssh_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "SST" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SSH', 'Mean SSH'], - 'varpattern': 'ssh_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSH', + "title": ["Mean SSH", "Mean SSH"], + "varpattern": "ssh_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSH", }, }, - 'BiasSstLatRmse': { - 'netcdf_variables': ['sst_lat__', 'sst_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean SST', - 'varpattern': 'sst_lat__', - 'xname': 'latitude', - 'yname': 'SST', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], - 'label': dict_label['SST'], + "BiasSstLatRmse": { + "netcdf_variables": ["sst_lat__", "sst_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean SST", + "varpattern": "sst_lat__", + "xname": "latitude", + "yname": "SST", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["SST"], + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SST', 'Mean SST'], - 'varpattern': 'sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SST', + "title": ["Mean SST", "Mean SST"], + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SST", }, }, - 'BiasSstLonRmse': { - 'netcdf_variables': ['sst_lon__', 'sst_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean SST', - 'varpattern': 'sst_lon__', - 'xname': 'longitude', - 'yname': 'SST', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], - 'label': dict_label['SST'], + "BiasSstLonRmse": { + "netcdf_variables": ["sst_lon__", "sst_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean SST", + "varpattern": "sst_lon__", + "xname": "longitude", + "yname": "SST", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["SST"], + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SST', 'Mean SST'], - 'varpattern': 'sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SST', + "title": ["Mean SST", "Mean SST"], + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SST", }, }, - 'BiasSstRmse': { - 'netcdf_variables': ['sst_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], - 'label': dict_label['SST'], + "BiasSstRmse": { + "netcdf_variables": ["sst_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["SST"], + "label": dict_label["SST"], "maskland": True, - 'title': ['Mean SST', 'Mean SST'], - 'varpattern': 'sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SST', + "title": ["Mean SST", "Mean SST"], + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SST", }, }, - 'BiasTauxLatRmse': { - 'netcdf_variables': ['taux_lat__', 'taux_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean TAUX', - 'varpattern': 'taux_lat__', - 'xname': 'latitude', - 'yname': 'TAUX', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['TAUX'], + "BiasTauxLatRmse": { + "netcdf_variables": ["taux_lat__", "taux_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean TAUX", + "varpattern": "taux_lat__", + "xname": "latitude", + "yname": "TAUX", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["TAUX"], "maskland": True, - 'title': ['Mean TAUX', 'Mean TAUX'], - 'varpattern': 'taux_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TAUX', + "title": ["Mean TAUX", "Mean TAUX"], + "varpattern": "taux_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "TAUX", }, }, - 'BiasTauxLonRmse': { - 'netcdf_variables': ['taux_lon__', 'taux_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Mean TAUX', - 'varpattern': 'taux_lon__', - 'xname': 'longitude', - 'yname': 'TAUX', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['TAUX'], + "BiasTauxLonRmse": { + "netcdf_variables": ["taux_lon__", "taux_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Mean TAUX", + "varpattern": "taux_lon__", + "xname": "longitude", + "yname": "TAUX", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["TAUX"], "maskland": True, - 'title': ['Mean TAUX', 'Mean TAUX'], - 'varpattern': 'taux_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TAUX', + "title": ["Mean TAUX", "Mean TAUX"], + "varpattern": "taux_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "TAUX", }, }, - 'BiasTauxRmse': { - 'netcdf_variables': ['taux_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['TAUX'], + "BiasTauxRmse": { + "netcdf_variables": ["taux_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["TAUX"], "maskland": True, - 'title': ['Mean TAUX', 'Mean TAUX'], - 'varpattern': 'taux_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TAUX', + "title": ["Mean TAUX", "Mean TAUX"], + "varpattern": "taux_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "TAUX", }, }, - 'EnsoAmpl': { - 'netcdf_variables': ['sstStd_lon__', 'sstStd_map__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO amplitude', - 'varpattern': 'diagnostic', - 'yname': 'SSTA std', - }, - 'dive_down01': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSTA standard deviation', - 'varpattern': 'sstStd_lon__', - 'xname': 'longitude', - 'yname': 'SSTA std', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude'], + "EnsoAmpl": { + "netcdf_variables": ["sstStd_lon__", "sstStd_map__"], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO amplitude", + "varpattern": "diagnostic", + "yname": "SSTA std", + }, + "dive_down01": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSTA standard deviation", + "varpattern": "sstStd_lon__", + "xname": "longitude", + "yname": "SSTA std", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude"], "maskland": True, - 'title': ['SSTA standard deviation', 'SSTA standard deviation'], - 'varpattern': 'sstStd_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA std', + "title": ["SSTA standard deviation", "SSTA standard deviation"], + "varpattern": "sstStd_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA std", }, }, - 'EnsodSstOce': { - 'netcdf_variables': ['dSST_ts__', 'dSSTthf_ts__', 'dSSToce_ts__', 'dSSTthf_lon__', 'dSSToce_lon__', - 'dSST_hov__', 'dSSTthf_hov__', 'dSSToce_hov__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO ocean-driven SST change', - 'varpattern': 'diagnostic', - 'yname': 'normalized dSSToce', - }, - 'dive_down01': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'ENSO SST change', - 'varpattern': ['dSST_ts__', 'dSSTthf_ts__', 'dSSToce_ts__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['dSST', 'dSSTthf', 'dSSToce'], - 'xname': 'months', - 'yname': 'normalized dSST', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'ENSO SST change', - 'varpattern': ['dSSTthf_lon__', 'dSSToce_lon__'], - 'colors': {"model": ["red", "blue"], "reference": ["red", "blue"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['dSSTthf', 'dSSToce'], - 'xname': 'longitude', - 'yname': 'normalized dSST', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['dSST'], - 'title': ['ENSO dSST', 'ENSO heat flux dSST', 'ENSO ocean dSST'], - 'varpattern': ['dSST_hov__', 'dSSTthf_hov__', 'dSSToce_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'normalized dSST', + "EnsodSstOce": { + "netcdf_variables": [ + "dSST_ts__", + "dSSTthf_ts__", + "dSSToce_ts__", + "dSSTthf_lon__", + "dSSToce_lon__", + "dSST_hov__", + "dSSTthf_hov__", + "dSSToce_hov__", + ], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO ocean-driven SST change", + "varpattern": "diagnostic", + "yname": "normalized dSSToce", + }, + "dive_down01": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO SST change", + "varpattern": ["dSST_ts__", "dSSTthf_ts__", "dSSToce_ts__"], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["dSST", "dSSTthf", "dSSToce"], + "xname": "months", + "yname": "normalized dSST", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO SST change", + "varpattern": ["dSSTthf_lon__", "dSSToce_lon__"], + "colors": {"model": ["red", "blue"], "reference": ["red", "blue"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["dSSTthf", "dSSToce"], + "xname": "longitude", + "yname": "normalized dSST", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["dSST"], + "title": ["ENSO dSST", "ENSO heat flux dSST", "ENSO ocean dSST"], + "varpattern": ["dSST_hov__", "dSSTthf_hov__", "dSSToce_hov__"], + "xname": "longitude", + "yname": "months", + "zname": "normalized dSST", }, - }, - 'EnsoDuration': { - 'netcdf_variables': ["sst_against_sst_ts__", 'Nina_duration__', 'Nino_duration__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO duration', - 'varpattern': 'diagnostic', - 'yname': 'duration (reg>0.25)', - }, - 'dive_down01': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'ENSO life-cycle', - 'varpattern': "sst_against_sst_ts__", #'sst_over_sst_ts__', - 'xname': 'months', - 'yname': 'reg(SSTA, SSTA)', - }, - 'dive_down02': { - 'plot_type': 'boxplot', - 'nbr_panel': 2, - 'title': ['La Nina duration', 'El Nino duration'], - 'varpattern': ['Nina_duration__', 'Nino_duration__'], - 'yname': ['duration (SSTA<-0.5)', 'duration (SSTA>0.5)'], + "EnsoDuration": { + "netcdf_variables": [ + "sst_against_sst_ts__", + "Nina_duration__", + "Nino_duration__", + ], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO duration", + "varpattern": "diagnostic", + "yname": "duration (reg>0.25)", + }, + "dive_down01": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO life-cycle", + "varpattern": "sst_against_sst_ts__", + "xname": "months", + "yname": "reg(SSTA, SSTA)", + }, + "dive_down02": { + "plot_type": "boxplot", + "nbr_panel": 2, + "title": ["La Nina duration", "El Nino duration"], + "varpattern": ["Nina_duration__", "Nino_duration__"], + "yname": ["duration (SSTA<-0.5)", "duration (SSTA>0.5)"], }, }, - 'EnsoFbSshSst': { - 'netcdf_variables': ['ssh__', 'sst__', 'ssh_over_sst_lon__', 'sshPOS_over_sst_lon__', 'sshNEG_over_sst_lon__', - 'ssh_over_sst_hov__', 'sshPOS_over_sst_hov__', 'sshNEG_over_sst_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'SSH-to-SST coupling', - 'varpattern': ['ssh__', 'sst__'], - 'xname': 'SSHA', - 'yname': 'SSTA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['ssh__', 'sst__'], - 'xname': 'SSHA', - 'yname': 'SSTA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Thermocline feedback', - #'varpattern': ['ssh_over_sst_lon__', 'sshPOS_over_sst_lon__', 'sshNEG_over_sst_lon__'], - 'varpattern': ['reg_sst_over_ssh_lon__', 'reg_sst_over_POSssh_lon__', 'reg_sst_over_NEGssh_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSHA>0', 'SSHA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSHA, SSTA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG03'], - 'title': ['reg(SSHA, SSTA)', 'reg(SSHA>0, SSTA)', 'reg(SSHA<0, SSTA)'], - #'varpattern': ['ssh_over_sst_hov__', 'sshPOS_over_sst_hov__', 'sshNEG_over_sst_hov__'], - 'varpattern': ['reg_sst_over_ssh_hov__', 'reg_sst_over_POSssh_hov__', 'reg_sst_over_NEGssh_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSshSst": { + "netcdf_variables": [ + "ssh__", + "sst__", + "ssh_over_sst_lon__", + "sshPOS_over_sst_lon__", + "sshNEG_over_sst_lon__", + "ssh_over_sst_hov__", + "sshPOS_over_sst_hov__", + "sshNEG_over_sst_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "SSH-to-SST coupling", + "varpattern": ["ssh__", "sst__"], + "xname": "SSHA", + "yname": "SSTA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["ssh__", "sst__"], + "xname": "SSHA", + "yname": "SSTA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Thermocline feedback", + "varpattern": [ + "reg_sst_over_ssh_lon__", + "reg_sst_over_POSssh_lon__", + "reg_sst_over_NEGssh_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSHA>0", "SSHA<0"], + "xname": "longitude", + "yname": "reg(SSHA, SSTA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG03"], + "title": ["reg(SSHA, SSTA)", "reg(SSHA>0, SSTA)", "reg(SSHA<0, SSTA)"], + "varpattern": [ + "reg_sst_over_ssh_hov__", + "reg_sst_over_POSssh_hov__", + "reg_sst_over_NEGssh_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstLhf': { - 'netcdf_variables': ['sst__', 'lhf__', 'sst_over_lhf_lon__', 'sstPOS_over_lhf_lon__', 'sstNEG_over_lhf_lon__', - 'sst_over_lhf_hov__', 'sstPOS_over_lhf_hov__', 'sstNEG_over_lhf_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Latent heat feedback', - 'varpattern': ['sst__', 'lhf__'], - 'xname': 'SSTA', - 'yname': 'LHFA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'lhf__'], - 'xname': 'SSTA', - 'yname': 'LHFA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Latent heat feedback', - #'varpattern': ['sst_over_lhf_lon__', 'sstPOS_over_lhf_lon__', 'sstNEG_over_lhf_lon__'], - 'varpattern': ['reg_lhf_over_sst_lon__', 'reg_lhf_over_POSsst_lon__', 'reg_lhf_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, LHFA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG20'], - 'title': ['reg(SSTA, LHFA)', 'reg(SSTA>0, LHFA)', 'reg(SSTA<0, LHFA)'], - #'varpattern': ['sst_over_lhf_hov__', 'sstPOS_over_lhf_hov__', 'sstNEG_over_lhf_hov__'], - 'varpattern': ['reg_lhf_over_sst_hov__', 'reg_lhf_over_POSsst_hov__', 'reg_lhf_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstLhf": { + "netcdf_variables": [ + "sst__", + "lhf__", + "sst_over_lhf_lon__", + "sstPOS_over_lhf_lon__", + "sstNEG_over_lhf_lon__", + "sst_over_lhf_hov__", + "sstPOS_over_lhf_hov__", + "sstNEG_over_lhf_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Latent heat feedback", + "varpattern": ["sst__", "lhf__"], + "xname": "SSTA", + "yname": "LHFA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "lhf__"], + "xname": "SSTA", + "yname": "LHFA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Latent heat feedback", + "varpattern": [ + "reg_lhf_over_sst_lon__", + "reg_lhf_over_POSsst_lon__", + "reg_lhf_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, LHFA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG20"], + "title": ["reg(SSTA, LHFA)", "reg(SSTA>0, LHFA)", "reg(SSTA<0, LHFA)"], + "varpattern": [ + "reg_lhf_over_sst_hov__", + "reg_lhf_over_POSsst_hov__", + "reg_lhf_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstLwr': { - 'netcdf_variables': ['sst__', 'lwr__', 'sst_over_lwr_lon__', 'sstPOS_over_lwr_lon__', 'sstNEG_over_lwr_lon__', - 'sst_over_lwr_hov__', 'sstPOS_over_lwr_hov__', 'sstNEG_over_lwr_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Longwave feedback', - 'varpattern': ['sst__', 'lwr__'], - 'xname': 'SSTA', - 'yname': 'LWRA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'lwr__'], - 'xname': 'SSTA', - 'yname': 'LWRA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Longwave feedback', - #'varpattern': ['sst_over_lwr_lon__', 'sstPOS_over_lwr_lon__', 'sstNEG_over_lwr_lon__'], - 'varpattern': ['reg_lwr_over_sst_lon__', 'reg_lwr_over_POSsst_lon__', 'reg_lwr_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, LWRA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG20'], - 'title': ['reg(SSTA, LWRA)', 'reg(SSTA>0, LWRA)', 'reg(SSTA<0, LWRA)'], - #'varpattern': ['sst_over_lwr_hov__', 'sstPOS_over_lwr_hov__', 'sstNEG_over_lwr_hov__'], - 'varpattern': ['reg_lwr_over_sst_hov__', 'reg_lwr_over_POSsst_hov__', 'reg_lwr_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstLwr": { + "netcdf_variables": [ + "sst__", + "lwr__", + "sst_over_lwr_lon__", + "sstPOS_over_lwr_lon__", + "sstNEG_over_lwr_lon__", + "sst_over_lwr_hov__", + "sstPOS_over_lwr_hov__", + "sstNEG_over_lwr_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Longwave feedback", + "varpattern": ["sst__", "lwr__"], + "xname": "SSTA", + "yname": "LWRA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "lwr__"], + "xname": "SSTA", + "yname": "LWRA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Longwave feedback", + "varpattern": [ + "reg_lwr_over_sst_lon__", + "reg_lwr_over_POSsst_lon__", + "reg_lwr_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, LWRA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG20"], + "title": ["reg(SSTA, LWRA)", "reg(SSTA>0, LWRA)", "reg(SSTA<0, LWRA)"], + "varpattern": [ + "reg_lwr_over_sst_hov__", + "reg_lwr_over_POSsst_hov__", + "reg_lwr_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstShf': { - 'netcdf_variables': ['sst__', 'shf__', 'sst_over_shf_lon__', 'sstPOS_over_shf_lon__', 'sstNEG_over_shf_lon__', - 'sst_over_shf_hov__', 'sstPOS_over_shf_hov__', 'sstNEG_over_shf_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Sensible heat feedback', - 'varpattern': ['sst__', 'shf__'], - 'xname': 'SSTA', - 'yname': 'SHFA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'shf__'], - 'xname': 'SSTA', - 'yname': 'SHFA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Sensible heat feedback', - #'varpattern': ['sst_over_shf_lon__', 'sstPOS_over_shf_lon__', 'sstNEG_over_shf_lon__'], - 'varpattern': ['reg_shf_over_sst_lon__', 'reg_shf_over_POSsst_lon__', 'reg_shf_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, SHFA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG5'], - 'title': ['reg(SSTA, SHFA)', 'reg(SSTA>0, SHFA)', 'reg(SSTA<0, SHFA)'], - #'varpattern': ['sst_over_shf_hov__', 'sstPOS_over_shf_hov__', 'sstNEG_over_shf_hov__'], - 'varpattern': ['reg_shf_over_sst_hov__', 'reg_shf_over_POSsst_hov__', 'reg_shf_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstShf": { + "netcdf_variables": [ + "sst__", + "shf__", + "sst_over_shf_lon__", + "sstPOS_over_shf_lon__", + "sstNEG_over_shf_lon__", + "sst_over_shf_hov__", + "sstPOS_over_shf_hov__", + "sstNEG_over_shf_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Sensible heat feedback", + "varpattern": ["sst__", "shf__"], + "xname": "SSTA", + "yname": "SHFA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "shf__"], + "xname": "SSTA", + "yname": "SHFA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Sensible heat feedback", + "varpattern": [ + "reg_shf_over_sst_lon__", + "reg_shf_over_POSsst_lon__", + "reg_shf_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, SHFA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG5"], + "title": ["reg(SSTA, SHFA)", "reg(SSTA>0, SHFA)", "reg(SSTA<0, SHFA)"], + "varpattern": [ + "reg_shf_over_sst_hov__", + "reg_shf_over_POSsst_hov__", + "reg_shf_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstSwr': { - 'netcdf_variables': ['sst__', 'swr__', 'sst_over_swr_lon__', 'sstPOS_over_swr_lon__', 'sstNEG_over_swr_lon__', - 'sst_over_swr_hov__', 'sstPOS_over_swr_hov__', 'sstNEG_over_swr_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Shortwave feedback', - 'varpattern': ['sst__', 'swr__'], - 'xname': 'SSTA', - 'yname': 'SWRA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'swr__'], - 'xname': 'SSTA', - 'yname': 'SWRA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Shortwave feedback', - #'varpattern': ['sst_over_swr_lon__', 'sstPOS_over_swr_lon__', 'sstNEG_over_swr_lon__'], - 'varpattern': ['reg_swr_over_sst_lon__', 'reg_swr_over_POSsst_lon__', 'reg_swr_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, SWRA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG50'], - 'title': ['reg(SSTA, SWRA)', 'reg(SSTA>0, SWRA)', 'reg(SSTA<0, SWRA)'], - #'varpattern': ['sst_over_swr_hov__', 'sstPOS_over_swr_hov__', 'sstNEG_over_swr_hov__'], - 'varpattern': ['reg_swr_over_sst_hov__', 'reg_swr_over_POSsst_hov__', 'reg_swr_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstSwr": { + "netcdf_variables": [ + "sst__", + "swr__", + "sst_over_swr_lon__", + "sstPOS_over_swr_lon__", + "sstNEG_over_swr_lon__", + "sst_over_swr_hov__", + "sstPOS_over_swr_hov__", + "sstNEG_over_swr_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Shortwave feedback", + "varpattern": ["sst__", "swr__"], + "xname": "SSTA", + "yname": "SWRA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "swr__"], + "xname": "SSTA", + "yname": "SWRA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Shortwave feedback", + "varpattern": [ + "reg_swr_over_sst_lon__", + "reg_swr_over_POSsst_lon__", + "reg_swr_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, SWRA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG50"], + "title": ["reg(SSTA, SWRA)", "reg(SSTA>0, SWRA)", "reg(SSTA<0, SWRA)"], + "varpattern": [ + "reg_swr_over_sst_hov__", + "reg_swr_over_POSsst_hov__", + "reg_swr_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstTaux': { - 'netcdf_variables': [ - 'sst__', 'taux__', 'sst_over_taux_lon__', 'sstPOS_over_taux_lon__', 'sstNEG_over_taux_lon__', - 'sst_over_taux_hov__', 'sstPOS_over_taux_hov__', 'sstNEG_over_taux_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'SST-to-Taux coupling', - 'varpattern': ['sst__', 'taux__'], - 'xname': 'SSTA', - 'yname': 'TAUXA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'taux__'], - 'xname': 'SSTA', - 'yname': 'TAUXA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Wind-SST feedback', - #'varpattern': ['sst_over_taux_lon__', 'sstPOS_over_taux_lon__', 'sstNEG_over_taux_lon__'], - 'varpattern': ['reg_taux_over_sst_lon__', 'reg_taux_over_POSsst_lon__', 'reg_taux_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, TAUXA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG20'], - 'title': ['reg(SSTA, TAUXA)', 'reg(SSTA>0, TAUXA)', 'reg(SSTA<0, TAUXA)'], - #'varpattern': ['sst_over_taux_hov__', 'sstPOS_over_taux_hov__', 'sstNEG_over_taux_hov__'], - 'varpattern': ['reg_taux_over_sst_hov__', 'reg_taux_over_POSsst_hov__', 'reg_taux_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstTaux": { + "netcdf_variables": [ + "sst__", + "taux__", + "sst_over_taux_lon__", + "sstPOS_over_taux_lon__", + "sstNEG_over_taux_lon__", + "sst_over_taux_hov__", + "sstPOS_over_taux_hov__", + "sstNEG_over_taux_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "SST-to-Taux coupling", + "varpattern": ["sst__", "taux__"], + "xname": "SSTA", + "yname": "TAUXA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "taux__"], + "xname": "SSTA", + "yname": "TAUXA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Wind-SST feedback", + "varpattern": [ + "reg_taux_over_sst_lon__", + "reg_taux_over_POSsst_lon__", + "reg_taux_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, TAUXA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG20"], + "title": ["reg(SSTA, TAUXA)", "reg(SSTA>0, TAUXA)", "reg(SSTA<0, TAUXA)"], + "varpattern": [ + "reg_taux_over_sst_hov__", + "reg_taux_over_POSsst_hov__", + "reg_taux_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbSstThf': { - 'netcdf_variables': ['sst__', 'thf__', 'sst_over_thf_lon__', 'sstPOS_over_thf_lon__', 'sstNEG_over_thf_lon__', - 'sst_over_thf_hov__', 'sstPOS_over_thf_hov__', 'sstNEG_over_thf_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Total heat feedback', - 'varpattern': ['sst__', 'thf__'], - 'xname': 'SSTA', - 'yname': 'THFA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['sst__', 'thf__'], - 'xname': 'SSTA', - 'yname': 'THFA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'Total heat feedback', - #'varpattern': ['sst_over_thf_lon__', 'sstPOS_over_thf_lon__', 'sstNEG_over_thf_lon__'], - 'varpattern': ['reg_thf_over_sst_lon__', 'reg_thf_over_POSsst_lon__', 'reg_thf_over_NEGsst_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'SSTA>0', 'SSTA<0'], - 'xname': 'longitude', - 'yname': 'reg(SSTA, THFA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG50'], - 'title': ['reg(SSTA, THFA)', 'reg(SSTA>0, THFA)', 'reg(SSTA<0, THFA)'], - #'varpattern': ['sst_over_thf_hov__', 'sstPOS_over_thf_hov__', 'sstNEG_over_thf_hov__'], - 'varpattern': ['reg_thf_over_sst_hov__', 'reg_thf_over_POSsst_hov__', 'reg_thf_over_NEGsst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbSstThf": { + "netcdf_variables": [ + "sst__", + "thf__", + "sst_over_thf_lon__", + "sstPOS_over_thf_lon__", + "sstNEG_over_thf_lon__", + "sst_over_thf_hov__", + "sstPOS_over_thf_hov__", + "sstNEG_over_thf_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Total heat feedback", + "varpattern": ["sst__", "thf__"], + "xname": "SSTA", + "yname": "THFA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["sst__", "thf__"], + "xname": "SSTA", + "yname": "THFA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "Total heat feedback", + "varpattern": [ + "reg_thf_over_sst_lon__", + "reg_thf_over_POSsst_lon__", + "reg_thf_over_NEGsst_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "SSTA>0", "SSTA<0"], + "xname": "longitude", + "yname": "reg(SSTA, THFA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG50"], + "title": ["reg(SSTA, THFA)", "reg(SSTA>0, THFA)", "reg(SSTA<0, THFA)"], + "varpattern": [ + "reg_thf_over_sst_hov__", + "reg_thf_over_POSsst_hov__", + "reg_thf_over_NEGsst_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoFbTauxSsh': { - 'netcdf_variables': [ - 'taux__', 'ssh__', 'taux_over_ssh_lon__', 'tauxPOS_over_ssh_lon__', 'tauxNEG_over_ssh_lon__', - 'taux_over_ssh_hov__', 'tauxPOS_over_ssh_hov__', 'tauxNEG_over_ssh_hov__'], - 'diagnostic': { - 'plot_type': 'scatterplot', - 'nbr_panel': 1, - 'title': 'Taux-to-SSH coupling', - 'varpattern': ['taux__', 'ssh__'], - 'xname': 'TAUXA', - 'yname': 'SSHA', - }, - 'dive_down01': { - 'plot_type': 'scatterplot', - 'nbr_panel': 2, - 'title': 'nonlinarity', - 'varpattern': ['taux__', 'ssh__'], - 'xname': 'TAUXA', - 'yname': 'SSHA', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSH-Wind feedback', - #'varpattern': ['taux_over_ssh_lon__', 'tauxPOS_over_ssh_lon__', 'tauxNEG_over_ssh_lon__'], - 'varpattern': ['reg_ssh_over_taux_lon__', 'reg_ssh_over_POStaux_lon__', 'reg_ssh_over_NEGtaux_lon__'], - 'colors': {"model": ["black", "red", "blue"], "reference": ["black", "red", "blue"]}, - 'linestyles': {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, - 'legend': ['All', 'TAUXA>0', 'TAUXA<0'], - 'xname': 'longitude', - 'yname': 'reg(TAUXA, SSHA)', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 6, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG05'], - 'title': ['reg(TAUXA, SSHA)', 'reg(TAUXA>0, SSHA)', 'reg(TAUXA<0, SSHA)'], - #'varpattern': ['taux_over_ssh_hov__', 'tauxPOS_over_ssh_hov__', 'tauxNEG_over_ssh_hov__'], - 'varpattern': ['reg_ssh_over_taux_hov__', 'reg_ssh_over_POStaux_hov__', 'reg_ssh_over_NEGtaux_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', + "EnsoFbTauxSsh": { + "netcdf_variables": [ + "taux__", + "ssh__", + "taux_over_ssh_lon__", + "tauxPOS_over_ssh_lon__", + "tauxNEG_over_ssh_lon__", + "taux_over_ssh_hov__", + "tauxPOS_over_ssh_hov__", + "tauxNEG_over_ssh_hov__", + ], + "diagnostic": { + "plot_type": "scatterplot", + "nbr_panel": 1, + "title": "Taux-to-SSH coupling", + "varpattern": ["taux__", "ssh__"], + "xname": "TAUXA", + "yname": "SSHA", + }, + "dive_down01": { + "plot_type": "scatterplot", + "nbr_panel": 2, + "title": "nonlinarity", + "varpattern": ["taux__", "ssh__"], + "xname": "TAUXA", + "yname": "SSHA", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSH-Wind feedback", + "varpattern": [ + "reg_ssh_over_taux_lon__", + "reg_ssh_over_POStaux_lon__", + "reg_ssh_over_NEGtaux_lon__", + ], + "colors": { + "model": ["black", "red", "blue"], + "reference": ["black", "red", "blue"], + }, + "linestyles": {"model": ["-", "-", "-"], "reference": ["-.", "-.", "-."]}, + "legend": ["All", "TAUXA>0", "TAUXA<0"], + "xname": "longitude", + "yname": "reg(TAUXA, SSHA)", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 6, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG05"], + "title": ["reg(TAUXA, SSHA)", "reg(TAUXA>0, SSHA)", "reg(TAUXA<0, SSHA)"], + "varpattern": [ + "reg_ssh_over_taux_hov__", + "reg_ssh_over_POStaux_hov__", + "reg_ssh_over_NEGtaux_hov__", + ], + "xname": "longitude", + "yname": "months", + "zname": "regression", }, }, - 'EnsoPrMap': { - 'netcdf_variables': ['reg_pr_over_sst_map__', 'reg_pr_over_sst_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_pr_over_sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', + "EnsoPrMap": { + "netcdf_variables": ["reg_pr_over_sst_map__", "reg_pr_over_sst_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - 'varpattern': 'reg_pr_over_sst_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - 'varpattern': 'reg_pr_over_sst_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - 'varpattern': 'reg_pr_over_sst_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - 'varpattern': 'reg_pr_over_sst_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - 'varpattern': 'reg_pr_over_sst_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "reg_pr_over_sst_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", }, }, - 'EnsoPrMapDjf': { - 'netcdf_variables': ['reg_pr_over_sst_djf_map__', 'reg_pr_over_sst_djf_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_pr_over_sst_djf_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map__", "pr_nino_djf_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "EnsoPrMapDjf": { + "netcdf_variables": ["reg_pr_over_sst_djf_map__", "reg_pr_over_sst_djf_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map__", "pr_nino_djf_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - 'varpattern': 'reg_pr_over_sst_djf_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - 'varpattern': 'reg_pr_over_sst_djf_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - 'varpattern': 'reg_pr_over_sst_djf_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - 'varpattern': 'reg_pr_over_sst_djf_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) DJF', 'reg(ENSO SSTA, PRA) DJF'], - 'varpattern': 'reg_pr_over_sst_djf_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map_africaSE__", "pr_nino_djf_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map_americaN__", "pr_nino_djf_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map_americaS__", "pr_nino_djf_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map_asiaS__", "pr_nino_djf_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA DJF', 'El Nino PRA DJF'], - 'varpattern': ["pr_nina_djf_map_oceania__", "pr_nino_djf_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) DJF", "reg(ENSO SSTA, PRA) DJF"], + "varpattern": "reg_pr_over_sst_djf_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map_africaSE__", "pr_nino_djf_map_africaSE__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map_americaN__", "pr_nino_djf_map_americaN__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map_americaS__", "pr_nino_djf_map_americaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map_asiaS__", "pr_nino_djf_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA DJF", "El Nino PRA DJF"], + "varpattern": ["pr_nina_djf_map_oceania__", "pr_nino_djf_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, }, - 'EnsoPrMapJja': { - 'netcdf_variables': ['reg_pr_over_sst_jja_map__', 'reg_pr_over_sst_jja_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_pr_over_sst_jja_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map__", "pr_nino_jja_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "EnsoPrMapJja": { + "netcdf_variables": ["reg_pr_over_sst_jja_map__", "reg_pr_over_sst_jja_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map__", "pr_nino_jja_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - 'varpattern': 'reg_pr_over_sst_jja_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - 'varpattern': 'reg_pr_over_sst_jja_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - 'varpattern': 'reg_pr_over_sst_jja_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - 'varpattern': 'reg_pr_over_sst_jja_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, PRA) JJA', 'reg(ENSO SSTA, PRA) JJA'], - 'varpattern': 'reg_pr_over_sst_jja_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map_africaSE__", "pr_nino_jja_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map_americaN__", "pr_nino_jja_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map_americaS__", "pr_nino_jja_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map_asiaS__", "pr_nino_jja_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina PRA JJA', 'El Nino PRA JJA'], - 'varpattern': ["pr_nina_jja_map_oceania__", "pr_nino_jja_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, PRA) JJA", "reg(ENSO SSTA, PRA) JJA"], + "varpattern": "reg_pr_over_sst_jja_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map_africaSE__", "pr_nino_jja_map_africaSE__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map_americaN__", "pr_nino_jja_map_americaN__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map_americaS__", "pr_nino_jja_map_americaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map_asiaS__", "pr_nino_jja_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "maskocean": True, + "title": ["La Nina PRA JJA", "El Nino PRA JJA"], + "varpattern": ["pr_nina_jja_map_oceania__", "pr_nino_jja_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, }, - 'EnsoPrTsRmse': { - 'netcdf_variables': ['pr_over_sst_ts__', 'pr_over_sst_hov__', 'Nina_pr_ts__', 'Nino_pr_ts__', 'Nina_pr_hov__', - 'Nino_pr_hov__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO's PRA life-cycle", - #'varpattern': 'pr_over_sst_ts__', - 'varpattern': 'sst_against_pr_ts__', - 'xname': 'months', - 'yname': 'reg(ENSO SSTA, PRA)', - }, - 'dive_down01': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - 'title': ['reg(ENSO SSTA, PRA)', 'reg(ENSO SSTA, PRA)'], - #'varpattern': 'pr_over_sst_hov__', - 'varpattern': 'sst_against_pr_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO's PRA life-cycle", - 'varpattern': ['Nina_pr_ts__', 'Nino_pr_ts__'], - 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['La Nina', 'El Nino'], - 'xname': 'months', - 'yname': 'ENSO PRA', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG5'], - 'title': ['La Nina PRA', 'El Nino PRA'], - 'varpattern': ['Nina_pr_hov__', 'Nino_pr_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'PRA', + "EnsoPrTsRmse": { + "netcdf_variables": [ + "pr_over_sst_ts__", + "pr_over_sst_hov__", + "Nina_pr_ts__", + "Nino_pr_ts__", + "Nina_pr_hov__", + "Nino_pr_hov__", + ], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO's PRA life-cycle", + "varpattern": "sst_against_pr_ts__", + "xname": "months", + "yname": "reg(ENSO SSTA, PRA)", + }, + "dive_down01": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "title": ["reg(ENSO SSTA, PRA)", "reg(ENSO SSTA, PRA)"], + "varpattern": "sst_against_pr_hov__", + "xname": "longitude", + "yname": "months", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO's PRA life-cycle", + "varpattern": ["Nina_pr_ts__", "Nino_pr_ts__"], + "colors": {"model": ["blue", "red"], "reference": ["blue", "red"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["La Nina", "El Nino"], + "xname": "months", + "yname": "ENSO PRA", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG5"], + "title": ["La Nina PRA", "El Nino PRA"], + "varpattern": ["Nina_pr_hov__", "Nino_pr_hov__"], + "xname": "longitude", + "yname": "months", + "zname": "PRA", }, }, # 'EnsoSlpMap': { @@ -1206,451 +1361,477 @@ # 'zname': 'regression', # }, # }, - 'EnsoSlpMap': { - 'netcdf_variables': ['reg_slp_over_sst_map__', 'reg_slp_over_sst_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA)', 'reg(ENSO SSTA, SLPA)'], - 'varpattern': 'reg_slp_over_sst_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', + "EnsoSlpMap": { + "netcdf_variables": ["reg_slp_over_sst_map__", "reg_slp_over_sst_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA)", "reg(ENSO SSTA, SLPA)"], + "varpattern": "reg_slp_over_sst_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", }, }, - 'EnsoSlpMapDjf': { - 'netcdf_variables': ['reg_slp_over_sst_djf_map__', 'reg_slp_over_sst_djf_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - # 'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_slp_over_sst_djf_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map__", "slp_nino_djf_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "EnsoSlpMapDjf": { + "netcdf_variables": [ + "reg_slp_over_sst_djf_map__", + "reg_slp_over_sst_djf_map__", + ], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": ["slp_nina_djf_map__", "slp_nino_djf_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - 'varpattern': 'reg_slp_over_sst_djf_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - 'varpattern': 'reg_slp_over_sst_djf_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - 'varpattern': 'reg_slp_over_sst_djf_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - 'varpattern': 'reg_slp_over_sst_djf_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) DJF', 'reg(ENSO SSTA, SLPA) DJF'], - 'varpattern': 'reg_slp_over_sst_djf_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map_africaSE__", "slp_nino_djf_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map_americaN__", "slp_nino_djf_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map_americaS__", "slp_nino_djf_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map_asiaS__", "slp_nino_djf_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA DJF', 'El Nino SLPA DJF'], - 'varpattern': ["slp_nina_djf_map_oceania__", "slp_nino_djf_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) DJF", "reg(ENSO SSTA, SLPA) DJF"], + "varpattern": "reg_slp_over_sst_djf_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": [ + "slp_nina_djf_map_africaSE__", + "slp_nino_djf_map_africaSE__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": [ + "slp_nina_djf_map_americaN__", + "slp_nino_djf_map_americaN__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": [ + "slp_nina_djf_map_americaS__", + "slp_nino_djf_map_americaS__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": ["slp_nina_djf_map_asiaS__", "slp_nino_djf_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA DJF", "El Nino SLPA DJF"], + "varpattern": ["slp_nina_djf_map_oceania__", "slp_nino_djf_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, }, - 'EnsoSlpMapJja': { - 'netcdf_variables': ['reg_slp_over_sst_jja_map__', 'reg_slp_over_sst_jja_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - # 'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_slp_over_sst_jja_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map__", "slp_nino_jja_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "EnsoSlpMapJja": { + "netcdf_variables": [ + "reg_slp_over_sst_jja_map__", + "reg_slp_over_sst_jja_map__", + ], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": ["slp_nina_jja_map__", "slp_nino_jja_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - 'varpattern': 'reg_slp_over_sst_jja_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - 'varpattern': 'reg_slp_over_sst_jja_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - 'varpattern': 'reg_slp_over_sst_jja_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - 'varpattern': 'reg_slp_over_sst_jja_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SLPA) JJA', 'reg(ENSO SSTA, SLPA) JJA'], - 'varpattern': 'reg_slp_over_sst_jja_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map_africaSE__", "slp_nino_jja_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map_americaN__", "slp_nino_jja_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map_americaS__", "slp_nino_jja_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map_asiaS__", "slp_nino_jja_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina SLPA JJA', 'El Nino SLPA JJA'], - 'varpattern': ["slp_nina_jja_map_oceania__", "slp_nino_jja_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SLPA) JJA", "reg(ENSO SSTA, SLPA) JJA"], + "varpattern": "reg_slp_over_sst_jja_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": [ + "slp_nina_jja_map_africaSE__", + "slp_nino_jja_map_africaSE__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": [ + "slp_nina_jja_map_americaN__", + "slp_nino_jja_map_americaN__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": [ + "slp_nina_jja_map_americaS__", + "slp_nino_jja_map_americaS__", + ], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": ["slp_nina_jja_map_asiaS__", "slp_nino_jja_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina SLPA JJA", "El Nino SLPA JJA"], + "varpattern": ["slp_nina_jja_map_oceania__", "slp_nino_jja_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, }, - 'EnsoSstLonRmse': { - 'netcdf_variables': ['sst_over_sst_lon__', 'sst_over_sst_map__', 'Nina_sst_lon__', 'Nino_sst_lon__', - 'Nina_sst_map__', 'Nino_sst_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO pattern", - #'varpattern': 'sst_over_sst_lon__', - 'varpattern': 'sst_against_sst_lon__', - 'xname': 'longitude', - 'yname': 'reg(ENSO SSTA, SSTA)', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], + "EnsoSstLonRmse": { + "netcdf_variables": [ + "sst_over_sst_lon__", + "sst_over_sst_map__", + "Nina_sst_lon__", + "Nino_sst_lon__", + "Nina_sst_map__", + "Nino_sst_map__", + ], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO pattern", + "varpattern": "sst_against_sst_lon__", + "xname": "longitude", + "yname": "reg(ENSO SSTA, SSTA)", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], "maskland": True, - 'title': ['reg(ENSO SSTA, SSTA)', 'reg(ENSO SSTA, SSTA)'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'sst_against_sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO's SSTA pattern", - 'varpattern': ['Nina_sst_lon__', 'Nino_sst_lon__'], - 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['La Nina', 'El Nino'], - 'xname': 'longitude', - 'yname': 'ENSO SSTA', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG25'], + "title": ["reg(ENSO SSTA, SSTA)", "reg(ENSO SSTA, SSTA)"], + "varpattern": "sst_against_sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO's SSTA pattern", + "varpattern": ["Nina_sst_lon__", "Nino_sst_lon__"], + "colors": {"model": ["blue", "red"], "reference": ["blue", "red"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["La Nina", "El Nino"], + "xname": "longitude", + "yname": "ENSO SSTA", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG25"], "maskland": True, - 'title': ['La Nina SSTA', 'El Nino SSTA'], - 'varpattern': ['Nina_sst_map__', 'Nino_sst_map__'], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA', + "title": ["La Nina SSTA", "El Nino SSTA"], + "varpattern": ["Nina_sst_map__", "Nino_sst_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA", }, }, # 'EnsoSstMap': { @@ -1669,1109 +1850,1134 @@ # 'zname': 'regression', # }, # }, - 'EnsoSstMap': { - 'netcdf_variables': ['reg_ts_over_sst_map__', 'reg_ts_over_sst_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, TSA)', 'reg(ENSO SSTA, TSA)'], - 'varpattern': 'reg_ts_over_sst_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', + "EnsoSstMap": { + "netcdf_variables": ["reg_ts_over_sst_map__", "reg_ts_over_sst_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, TSA)", "reg(ENSO SSTA, TSA)"], + "varpattern": "reg_ts_over_sst_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", }, }, - 'EnsoSstMapDjf': { - 'netcdf_variables': ['reg_ts_over_sst_djf_map__', 'reg_ts_over_sst_djf_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - # 'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_ts_over_sst_djf_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map__", "ts_nino_djf_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', + "EnsoSstMapDjf": { + "netcdf_variables": ["reg_ts_over_sst_djf_map__", "reg_ts_over_sst_djf_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map__", "ts_nino_djf_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", }, # ["africaSE", "americaN", "americaS", "asiaS", "oceania"] - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - 'varpattern': 'reg_ts_over_sst_djf_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - 'varpattern': 'reg_ts_over_sst_djf_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - 'varpattern': 'reg_ts_over_sst_djf_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - 'varpattern': 'reg_ts_over_sst_djf_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) DJF', 'reg(ENSO SSTA, SSTA) DJF'], - 'varpattern': 'reg_ts_over_sst_djf_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map_africaSE__", "ts_nino_djf_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map_americaN__", "ts_nino_djf_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map_americaS__", "ts_nino_djf_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map_asiaS__", "ts_nino_djf_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA DJF', 'El Nino TSA DJF'], - 'varpattern': ["ts_nina_djf_map_oceania__", "ts_nino_djf_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) DJF", "reg(ENSO SSTA, SSTA) DJF"], + "varpattern": "reg_ts_over_sst_djf_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map_africaSE__", "ts_nino_djf_map_africaSE__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map_americaN__", "ts_nino_djf_map_americaN__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map_americaS__", "ts_nino_djf_map_americaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map_asiaS__", "ts_nino_djf_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA DJF", "El Nino TSA DJF"], + "varpattern": ["ts_nina_djf_map_oceania__", "ts_nino_djf_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", }, }, - 'EnsoSstMapJja': { - 'netcdf_variables': ['reg_ts_over_sst_jja_map__', 'reg_ts_over_sst_jja_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - # 'varpattern': 'sst_over_sst_map__', - 'varpattern': 'reg_ts_over_sst_jja_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map__", "ts_nino_jja_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - 'varpattern': 'reg_ts_over_sst_jja_map_africaSE__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down03': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - 'varpattern': 'reg_ts_over_sst_jja_map_americaN__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - 'varpattern': 'reg_ts_over_sst_jja_map_americaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down05': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - 'varpattern': 'reg_ts_over_sst_jja_map_asiaS__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down06': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['reg(ENSO SSTA, SSTA) JJA', 'reg(ENSO SSTA, SSTA) JJA'], - 'varpattern': 'reg_ts_over_sst_jja_map_oceania__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'regression', - }, - 'dive_down07': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map_africaSE__", "ts_nino_jja_map_africaSE__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down08': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map_americaN__", "ts_nino_jja_map_americaN__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down09': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map_americaS__", "ts_nino_jja_map_americaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down10': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map_asiaS__", "ts_nino_jja_map_asiaS__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', - }, - 'dive_down11': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['PRA'], - "maskland": False, - "maskocean": True, - 'title': ['La Nina TSA JJA', 'El Nino TSA JJA'], - 'varpattern': ["ts_nina_jja_map_oceania__", "ts_nino_jja_map_oceania__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TSA', + "EnsoSstMapJja": { + "netcdf_variables": ["reg_ts_over_sst_jja_map__", "reg_ts_over_sst_jja_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map__", "ts_nino_jja_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map_africaSE__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down03": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map_americaN__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map_americaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down05": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map_asiaS__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down06": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["reg(ENSO SSTA, SSTA) JJA", "reg(ENSO SSTA, SSTA) JJA"], + "varpattern": "reg_ts_over_sst_jja_map_oceania__", + "xname": "longitude", + "yname": "latitude", + "zname": "regression", + }, + "dive_down07": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map_africaSE__", "ts_nino_jja_map_africaSE__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down08": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map_americaN__", "ts_nino_jja_map_americaN__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down09": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map_americaS__", "ts_nino_jja_map_americaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down10": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map_asiaS__", "ts_nino_jja_map_asiaS__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", + }, + "dive_down11": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["PRA"], + "maskland": False, + "maskocean": True, + "title": ["La Nina TSA JJA", "El Nino TSA JJA"], + "varpattern": ["ts_nina_jja_map_oceania__", "ts_nino_jja_map_oceania__"], + "xname": "longitude", + "yname": "latitude", + "zname": "TSA", }, }, - 'EnsoSstTsRmse': { - 'netcdf_variables': ['sst_over_sst_ts__', 'sst_over_sst_hov__', 'Nina_sst_ts__', 'Nino_sst_ts__', - 'Nina_sst_hov__', 'Nino_sst_hov__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO life-cycle", - #'varpattern': 'sst_over_sst_ts__', - 'varpattern': 'sst_against_sst_ts__', - 'xname': 'months', - 'yname': 'reg(ENSO SSTA, SSTA)', - }, - 'dive_down01': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG12'], - 'title': ['reg(ENSO SSTA, SSTA)', 'reg(ENSO SSTA, SSTA)'], - #'varpattern': 'sst_over_sst_hov__', - 'varpattern': 'sst_against_sst_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO's SSTA life-cycle", - 'varpattern': ['Nina_sst_ts__', 'Nino_sst_ts__'], - 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['La Nina', 'El Nino'], - 'xname': 'months', - 'yname': 'ENSO SSTA', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - 'title': ['La Nina SSTA', 'El Nino SSTA'], - 'varpattern': ['Nina_sst_hov__', 'Nino_sst_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSTA', + "EnsoSstTsRmse": { + "netcdf_variables": [ + "sst_over_sst_ts__", + "sst_over_sst_hov__", + "Nina_sst_ts__", + "Nino_sst_ts__", + "Nina_sst_hov__", + "Nino_sst_hov__", + ], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO life-cycle", + "varpattern": "sst_against_sst_ts__", + "xname": "months", + "yname": "reg(ENSO SSTA, SSTA)", + }, + "dive_down01": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG12"], + "title": ["reg(ENSO SSTA, SSTA)", "reg(ENSO SSTA, SSTA)"], + "varpattern": "sst_against_sst_hov__", + "xname": "longitude", + "yname": "months", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO's SSTA life-cycle", + "varpattern": ["Nina_sst_ts__", "Nino_sst_ts__"], + "colors": {"model": ["blue", "red"], "reference": ["blue", "red"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["La Nina", "El Nino"], + "xname": "months", + "yname": "ENSO SSTA", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "title": ["La Nina SSTA", "El Nino SSTA"], + "varpattern": ["Nina_sst_hov__", "Nino_sst_hov__"], + "xname": "longitude", + "yname": "months", + "zname": "SSTA", }, }, - 'EnsoTauxTsRmse': { - 'netcdf_variables': ['taux_over_sst_ts__', 'taux_over_sst_hov__', 'Nina_taux_ts__', 'Nino_taux_ts__', - 'Nina_taux_hov__', 'Nino_taux_hov__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO life-cycle", - #'varpattern': 'taux_over_sst_ts__', - 'varpattern': 'sst_against_taux_ts__', - 'xname': 'months', - 'yname': 'reg(ENSO SSTA, TAUXA)', - }, - 'dive_down01': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG20'], - 'title': ['reg(ENSO SSTA, TAUXA)', 'reg(ENSO SSTA, TAUXA)'], - #'varpattern': 'taux_over_sst_hov__', - 'varpattern': 'sst_against_taux_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'regression', - }, - 'dive_down02': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "ENSO's TAUXA life-cycle", - 'varpattern': ['Nina_taux_ts__', 'Nino_taux_ts__'], - 'colors': {"model": ["blue", "red"], "reference": ["blue", "red"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['La Nina', 'El Nino'], - 'xname': 'months', - 'yname': 'ENSO TAUXA', - }, - 'dive_down03': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG30'], - 'title': ['La Nina TAUXA', 'El Nino TAUXA'], - 'varpattern': ['Nina_taux_hov__', 'Nino_taux_hov__'], - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSTA', + "EnsoTauxTsRmse": { + "netcdf_variables": [ + "taux_over_sst_ts__", + "taux_over_sst_hov__", + "Nina_taux_ts__", + "Nino_taux_ts__", + "Nina_taux_hov__", + "Nino_taux_hov__", + ], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO life-cycle", + "varpattern": "sst_against_taux_ts__", + "xname": "months", + "yname": "reg(ENSO SSTA, TAUXA)", + }, + "dive_down01": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG20"], + "title": ["reg(ENSO SSTA, TAUXA)", "reg(ENSO SSTA, TAUXA)"], + "varpattern": "sst_against_taux_hov__", + "xname": "longitude", + "yname": "months", + "zname": "regression", + }, + "dive_down02": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "ENSO's TAUXA life-cycle", + "varpattern": ["Nina_taux_ts__", "Nino_taux_ts__"], + "colors": {"model": ["blue", "red"], "reference": ["blue", "red"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["La Nina", "El Nino"], + "xname": "months", + "yname": "ENSO TAUXA", + }, + "dive_down03": { + "plot_type": "hovmoeller", + "nbr_panel": 4, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG30"], + "title": ["La Nina TAUXA", "El Nino TAUXA"], + "varpattern": ["Nina_taux_hov__", "Nino_taux_hov__"], + "xname": "longitude", + "yname": "months", + "zname": "SSTA", }, }, - 'EnsoSeasonality': { - 'netcdf_variables': ['sstStd_monthly__', 'sstStd_hov__', 'sstStd_NDJ_lon__', 'sstStd_MAM_lon__', - "sstStd_NDJ_map__", "sstStd_MAM_map__"], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO Seasonality', - 'varpattern': 'diagnostic', - 'yname': 'SSTA std (NDJ/MAM)', - }, - 'dive_down01': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSTA standard deviation', - 'varpattern': 'sstStd_monthly__', - 'xname': 'months', - 'yname': 'SSTA std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude'], - 'title': ['SSTA standard deviation', 'SSTA standard deviation'], - 'varpattern': 'sstStd_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSTA std', - }, - 'dive_down03': { - 'plot_type': "curve", - 'nbr_panel': 1, - 'title': "SSTA standard deviation", - 'varpattern': ['sstStd_NDJ_lon__', 'sstStd_MAM_lon__'], - 'colors': {"model": ["red", "blue"], "reference": ["red", "blue"]}, - 'linestyles': {"model": ["-", "-"], "reference": ["-.", "-."]}, - 'legend': ['NDJ', 'MAM'], - 'xname': 'longitude', - 'yname': 'SSTA std', - }, - 'dive_down04': { - 'plot_type': 'map', - 'nbr_panel': 4, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude'], + "EnsoSeasonality": { + "netcdf_variables": [ + "sstStd_monthly__", + "sstStd_hov__", + "sstStd_NDJ_lon__", + "sstStd_MAM_lon__", + "sstStd_NDJ_map__", + "sstStd_MAM_map__", + ], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO Seasonality", + "varpattern": "diagnostic", + "yname": "SSTA std (NDJ/MAM)", + }, + "dive_down01": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSTA standard deviation", + "varpattern": "sstStd_monthly__", + "xname": "months", + "yname": "SSTA std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude"], + "title": ["SSTA standard deviation", "SSTA standard deviation"], + "varpattern": "sstStd_hov__", + "xname": "longitude", + "yname": "months", + "zname": "SSTA std", + }, + "dive_down03": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSTA standard deviation", + "varpattern": ["sstStd_NDJ_lon__", "sstStd_MAM_lon__"], + "colors": {"model": ["red", "blue"], "reference": ["red", "blue"]}, + "linestyles": {"model": ["-", "-"], "reference": ["-.", "-."]}, + "legend": ["NDJ", "MAM"], + "xname": "longitude", + "yname": "SSTA std", + }, + "dive_down04": { + "plot_type": "map", + "nbr_panel": 4, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude"], "maskland": True, - 'title': ['NDJ', 'MAM'], #['SSTA std NDJ', 'SSTA std MAM'], - 'varpattern': ["sstStd_NDJ_map__", "sstStd_MAM_map__"], - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'monthly SSTA std', + "title": ["NDJ", "MAM"], # ['SSTA std NDJ', 'SSTA std MAM'], + "varpattern": ["sstStd_NDJ_map__", "sstStd_MAM_map__"], + "xname": "longitude", + "yname": "latitude", + "zname": "monthly SSTA std", }, }, "EnsoSstDiversity": { - 'netcdf_variables': ["Enso_lon_pos_maxSSTA__", "Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO diversity', - 'varpattern': 'diagnostic', - 'yname': 'IQR of min/max SSTA', - }, - 'dive_down01': { - 'plot_type': 'boxplot', - 'nbr_panel': 3, - 'title': ['ENSO diversity', 'La Nina diversity', 'El Nino diversity'], - 'varpattern': ["Enso_lon_pos_maxSSTA__", "Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], - 'yname': ['longitude of min/max SSTA', 'longitude of min SSTA', 'longitude of max SSTA'], + "netcdf_variables": [ + "Enso_lon_pos_maxSSTA__", + "Nina_lon_pos_minSSTA__", + "Nino_lon_pos_maxSSTA__", + ], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO diversity", + "varpattern": "diagnostic", + "yname": "IQR of min/max SSTA", + }, + "dive_down01": { + "plot_type": "boxplot", + "nbr_panel": 3, + "title": ["ENSO diversity", "La Nina diversity", "El Nino diversity"], + "varpattern": [ + "Enso_lon_pos_maxSSTA__", + "Nina_lon_pos_minSSTA__", + "Nino_lon_pos_maxSSTA__", + ], + "yname": [ + "longitude of min/max SSTA", + "longitude of min SSTA", + "longitude of max SSTA", + ], "custom_label": "longitude", }, }, - 'EnsoSstSkew': { - 'netcdf_variables': ['sstSke_lon__', 'sstSke_map__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'ENSO skewness', - 'varpattern': 'diagnostic', - 'yname': 'SSTA skewness', - }, - 'dive_down01': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSTA skewness', - 'varpattern': "sstSke_lon__", - 'xname': 'longitude', - 'yname': 'SSTA skew', - }, - 'dive_down02': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], + "EnsoSstSkew": { + "netcdf_variables": ["sstSke_lon__", "sstSke_map__"], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "ENSO skewness", + "varpattern": "diagnostic", + "yname": "SSTA skewness", + }, + "dive_down01": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSTA skewness", + "varpattern": "sstSke_lon__", + "xname": "longitude", + "yname": "SSTA skew", + }, + "dive_down02": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], "maskland": True, - 'title': ['SSTA skew', 'SSTA skew'], - 'varpattern': "sstSke_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA skew', + "title": ["SSTA skew", "SSTA skew"], + "varpattern": "sstSke_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA skew", }, }, - 'NinaPrMap': { - 'netcdf_variables': ['prComp_map__', 'prComp_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG2'], - "maskland": False, - 'title': ['La Nina composite', 'La Nina composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'prComp_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "NinaPrMap": { + "netcdf_variables": ["prComp_map__", "prComp_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG2"], + "maskland": False, + "title": ["La Nina composite", "La Nina composite"], + "varpattern": "prComp_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, }, - 'NinaSlpMap': { - 'netcdf_variables': ['slp_map__', 'slp_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - 'title': ['La Nina composite', 'La Nina composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'slp_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "NinaSlpMap": { + "netcdf_variables": ["slp_map__", "slp_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "title": ["La Nina composite", "La Nina composite"], + "varpattern": "slp_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, }, - 'NinaSstMap': { - 'netcdf_variables': ['ts_map__', 'ts_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - 'title': ['La Nina composite', 'La Nina composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'ts_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA', + "NinaSstMap": { + "netcdf_variables": ["ts_map__", "ts_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "title": ["La Nina composite", "La Nina composite"], + "varpattern": "ts_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA", }, }, - 'NinaSstDur': { - 'netcdf_variables': ['Nina_duration__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'La Nina duration', - 'varpattern': 'diagnostic', - 'yname': 'duration (SSTA<-0.5)', - }, - 'dive_down01': { - 'plot_type': 'boxplot', - 'nbr_panel': 1, - 'title': 'La Nina duration', - 'varpattern': 'Nina_duration__', - 'yname': 'duration (SSTA<-0.5)', + "NinaSstDur": { + "netcdf_variables": ["Nina_duration__"], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "La Nina duration", + "varpattern": "diagnostic", + "yname": "duration (SSTA<-0.5)", + }, + "dive_down01": { + "plot_type": "boxplot", + "nbr_panel": 1, + "title": "La Nina duration", + "varpattern": "Nina_duration__", + "yname": "duration (SSTA<-0.5)", }, }, - 'NinaSstLonRmse': { - 'netcdf_variables': ['sst_lon__', 'sst_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "La Nina pattern", - 'varpattern': 'sst_lon__', - 'xname': 'longitude', - 'yname': 'SSTA', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['dSST'], + "NinaSstLonRmse": { + "netcdf_variables": ["sst_lon__", "sst_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "La Nina pattern", + "varpattern": "sst_lon__", + "xname": "longitude", + "yname": "SSTA", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["dSST"], "maskland": True, - 'title': 'La Nina SSTA', - 'varpattern': 'sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA', + "title": "La Nina SSTA", + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA", }, }, - 'NinaSstTsRmse': { - 'netcdf_variables': ['sst_ts__', 'sst_hov__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "La Nina life-cycle", - 'varpattern': 'sst_ts__', - 'xname': 'months', - 'yname': 'SSTA', - }, - 'dive_down01': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - 'title': 'La Nina SSTA', - 'varpattern': 'sst_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSTA', + "NinaSstTsRmse": { + "netcdf_variables": ["sst_ts__", "sst_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "La Nina life-cycle", + "varpattern": "sst_ts__", + "xname": "months", + "yname": "SSTA", + }, + "dive_down01": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "title": "La Nina SSTA", + "varpattern": "sst_hov__", + "xname": "longitude", + "yname": "months", + "zname": "SSTA", }, }, - 'NinoPrMap': { - 'netcdf_variables': ['pr_map__', 'pr_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - 'title': ['El Nino composite', 'El Nino composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'pr_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PRA', + "NinoPrMap": { + "netcdf_variables": ["pr_map__", "pr_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "title": ["El Nino composite", "El Nino composite"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PRA", }, }, - 'NinoSlpMap': { - 'netcdf_variables': ['slp_map__', 'slp_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG3'], - "maskland": False, - 'title': ['El Nino composite', 'El Nino composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'slp_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SLPA', + "NinoSlpMap": { + "netcdf_variables": ["slp_map__", "slp_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG3"], + "maskland": False, + "title": ["El Nino composite", "El Nino composite"], + "varpattern": "slp_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SLPA", }, }, - 'NinoSstMap': { - 'netcdf_variables': ['ts_map__', 'ts_map__'], - 'diagnostic': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - "maskland": False, - 'title': ['El Nino composite', 'El Nino composite'], - #'varpattern': 'sst_over_sst_map__', - 'varpattern': 'ts_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA', + "NinoSstMap": { + "netcdf_variables": ["ts_map__", "ts_map__"], + "diagnostic": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "maskland": False, + "title": ["El Nino composite", "El Nino composite"], + "varpattern": "ts_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA", }, }, "NinoSstDiversity": { - 'netcdf_variables': ['Nina_lon_pos_minSSTA__', "Nino_lon_pos_maxSSTA__"], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'El Nino diversity', - 'varpattern': 'diagnostic', - 'yname': 'IQR of max SSTA', - }, - 'dive_down01': { - 'plot_type': 'boxplot', - 'nbr_panel': 2, - 'title': ['La Nina diversity', 'El Nino diversity'], - 'varpattern': ['Nina_lon_pos_minSSTA__', 'Nino_lon_pos_maxSSTA__'], - 'yname': ['longitude of min SSTA', 'longitude of max SSTA'], + "netcdf_variables": ["Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "El Nino diversity", + "varpattern": "diagnostic", + "yname": "IQR of max SSTA", + }, + "dive_down01": { + "plot_type": "boxplot", + "nbr_panel": 2, + "title": ["La Nina diversity", "El Nino diversity"], + "varpattern": ["Nina_lon_pos_minSSTA__", "Nino_lon_pos_maxSSTA__"], + "yname": ["longitude of min SSTA", "longitude of max SSTA"], "custom_label": "longitude", }, }, - 'NinoSstDur': { - 'netcdf_variables': ['Nino_duration__'], - 'diagnostic': { - 'plot_type': 'dot', - 'nbr_panel': 1, - 'title': 'El Nino duration', - 'varpattern': 'diagnostic', - 'yname': 'duration (SSTA>0.5)', - }, - 'dive_down01': { - 'plot_type': 'boxplot', - 'nbr_panel': 1, - 'title': 'El Nino duration', - 'varpattern': 'Nino_duration__', - 'yname': 'duration (SSTA>0.5)', + "NinoSstDur": { + "netcdf_variables": ["Nino_duration__"], + "diagnostic": { + "plot_type": "dot", + "nbr_panel": 1, + "title": "El Nino duration", + "varpattern": "diagnostic", + "yname": "duration (SSTA>0.5)", + }, + "dive_down01": { + "plot_type": "boxplot", + "nbr_panel": 1, + "title": "El Nino duration", + "varpattern": "Nino_duration__", + "yname": "duration (SSTA>0.5)", }, }, - 'NinoSstLonRmse': { - 'netcdf_variables': ['sst_lon__', 'sst_map__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "El Nino pattern", - 'varpattern': 'sst_lon__', - 'xname': 'longitude', - 'yname': 'SSTA', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['dSST'], + "NinoSstLonRmse": { + "netcdf_variables": ["sst_lon__", "sst_map__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "El Nino pattern", + "varpattern": "sst_lon__", + "xname": "longitude", + "yname": "SSTA", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["dSST"], "maskland": True, - 'title': 'El Nino SSTA', - 'varpattern': 'sst_map__', - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSTA', + "title": "El Nino SSTA", + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSTA", }, }, - 'NinoSstTsRmse': { - 'netcdf_variables': ['sst_ts__', 'sst_hov__'], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': "El Nino life-cycle", - 'varpattern': 'sst_ts__', - 'xname': 'months', - 'yname': 'SSTA', - }, - 'dive_down01': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['SKEW'], - 'title': 'El Nino SSTA', - 'varpattern': 'sst_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSTA', + "NinoSstTsRmse": { + "netcdf_variables": ["sst_ts__", "sst_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "El Nino life-cycle", + "varpattern": "sst_ts__", + "xname": "months", + "yname": "SSTA", + }, + "dive_down01": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["SKEW"], + "title": "El Nino SSTA", + "varpattern": "sst_hov__", + "xname": "longitude", + "yname": "months", + "zname": "SSTA", }, }, - 'SeasonalPrLatRmse': { - 'netcdf_variables': ["pr_lat__", "pr_map__", "prMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'PR seasonal cycle std', - 'varpattern': 'pr_lat__', - 'xname': 'latitude', - 'yname': 'PR std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude5'], + "SeasonalPrLatRmse": { + "netcdf_variables": ["pr_lat__", "pr_map__", "prMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "PR seasonal cycle std", + "varpattern": "pr_lat__", + "xname": "latitude", + "yname": "PR std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude5"], "maskland": True, - 'title': ['PR seasonal cycle std'], - 'varpattern': "pr_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PR std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['PR'], - 'label': dict_label['amplitude15'], - 'title': ['PR seasonal cycle', 'PR seasonal cycle'], - 'varpattern': 'prMac_hov__', - 'xname': 'latitude', - 'yname': 'months', - 'zname': 'PR', + "title": ["PR seasonal cycle std"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PR std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["PR"], + "label": dict_label["amplitude15"], + "title": ["PR seasonal cycle", "PR seasonal cycle"], + "varpattern": "prMac_hov__", + "xname": "latitude", + "yname": "months", + "zname": "PR", }, }, - 'SeasonalPrLonRmse': { - 'netcdf_variables': ["pr_lon__", "pr_map__", "prMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'PR seasonal cycle std', - 'varpattern': 'pr_lon__', - 'xname': 'longitude', - 'yname': 'PR std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude5'], + "SeasonalPrLonRmse": { + "netcdf_variables": ["pr_lon__", "pr_map__", "prMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "PR seasonal cycle std", + "varpattern": "pr_lon__", + "xname": "longitude", + "yname": "PR std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude5"], "maskland": True, - 'title': ['PR seasonal cycle std'], - 'varpattern': "pr_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'PR std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['PR'], - 'label': dict_label['amplitude10'], - 'title': ['PR seasonal cycle', 'PR seasonal cycle'], - 'varpattern': 'prMac_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'PR', + "title": ["PR seasonal cycle std"], + "varpattern": "pr_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "PR std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["PR"], + "label": dict_label["amplitude10"], + "title": ["PR seasonal cycle", "PR seasonal cycle"], + "varpattern": "prMac_hov__", + "xname": "longitude", + "yname": "months", + "zname": "PR", }, }, - 'SeasonalSshLatRmse': { - 'netcdf_variables': ["ssh_lat__", "ssh_map__", "sshMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSH seasonal cycle std', - 'varpattern': 'ssh_lat__', - 'xname': 'latitude', - 'yname': 'SSH std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['amplitude'], + "SeasonalSshLatRmse": { + "netcdf_variables": ["ssh_lat__", "ssh_map__", "sshMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSH seasonal cycle std", + "varpattern": "ssh_lat__", + "xname": "latitude", + "yname": "SSH std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "amplitude" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["amplitude"], "maskland": True, - 'title': ['SSH seasonal cycle std'], - 'varpattern': "ssh_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSH std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['SST'], - 'title': ['SSH seasonal cycle', 'SSH seasonal cycle'], - 'varpattern': 'sshMac_hov__', - 'xname': 'latitude', - 'yname': 'months', - 'zname': 'SSH', + "title": ["SSH seasonal cycle std"], + "varpattern": "ssh_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSH std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "SST" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["SST"], + "title": ["SSH seasonal cycle", "SSH seasonal cycle"], + "varpattern": "sshMac_hov__", + "xname": "latitude", + "yname": "months", + "zname": "SSH", }, }, - 'SeasonalSshLonRmse': { - 'netcdf_variables': ["ssh_lon__", "ssh_map__", "sshMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SSH seasonal cycle std', - 'varpattern': 'ssh_lon__', - 'xname': 'longitude', - 'yname': 'SSH std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['amplitude'], + "SeasonalSshLonRmse": { + "netcdf_variables": ["ssh_lon__", "ssh_map__", "sshMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SSH seasonal cycle std", + "varpattern": "ssh_lon__", + "xname": "longitude", + "yname": "SSH std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "amplitude" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["amplitude"], "maskland": True, - 'title': ['SSH seasonal cycle std'], - 'varpattern': "ssh_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SSH std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], # YYP: I do not know yet the colobar / label needed - 'label': dict_label['SST'], - 'title': ['SSH seasonal cycle', 'SSH seasonal cycle'], - 'varpattern': 'sshMac_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SSH', + "title": ["SSH seasonal cycle std"], + "varpattern": "ssh_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SSH std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar[ + "SST" + ], # YYP: I do not know yet the colobar / label needed + "label": dict_label["SST"], + "title": ["SSH seasonal cycle", "SSH seasonal cycle"], + "varpattern": "sshMac_hov__", + "xname": "longitude", + "yname": "months", + "zname": "SSH", }, }, - 'SeasonalSstLatRmse': { - 'netcdf_variables': ["sst_lat__", "sst_map__", "sstMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SST seasonal cycle std', - 'varpattern': 'sst_lat__', - 'xname': 'latitude', - 'yname': 'SST std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude'], + "SeasonalSstLatRmse": { + "netcdf_variables": ["sst_lat__", "sst_map__", "sstMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SST seasonal cycle std", + "varpattern": "sst_lat__", + "xname": "latitude", + "yname": "SST std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude"], "maskland": True, - 'title': ['SST seasonal cycle std'], - 'varpattern': "sst_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SST std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], - 'label': dict_label['SST'], - 'title': ['SST seasonal cycle', 'SST seasonal cycle'], - 'varpattern': 'sstMac_hov__', - 'xname': 'latitude', - 'yname': 'months', - 'zname': 'SST', + "title": ["SST seasonal cycle std"], + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SST std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["SST"], + "label": dict_label["SST"], + "title": ["SST seasonal cycle", "SST seasonal cycle"], + "varpattern": "sstMac_hov__", + "xname": "latitude", + "yname": "months", + "zname": "SST", }, }, - 'SeasonalSstLonRmse': { - 'netcdf_variables': ["sst_lon__", "sst_map__", "sstMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'SST seasonal cycle std', - 'varpattern': 'sst_lon__', - 'xname': 'longitude', - 'yname': 'SST std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude'], + "SeasonalSstLonRmse": { + "netcdf_variables": ["sst_lon__", "sst_map__", "sstMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "SST seasonal cycle std", + "varpattern": "sst_lon__", + "xname": "longitude", + "yname": "SST std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude"], "maskland": True, - 'title': ['SST seasonal cycle std'], - 'varpattern': "sst_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'SST std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['SST'], - 'label': dict_label['SST'], - 'title': ['SST seasonal cycle', 'SST seasonal cycle'], - 'varpattern': 'sstMac_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'SST', + "title": ["SST seasonal cycle std"], + "varpattern": "sst_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "SST std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["SST"], + "label": dict_label["SST"], + "title": ["SST seasonal cycle", "SST seasonal cycle"], + "varpattern": "sstMac_hov__", + "xname": "longitude", + "yname": "months", + "zname": "SST", }, }, - 'SeasonalTauxLatRmse': { - 'netcdf_variables': ["taux_lat__", "taux_map__", "tauxMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'TAUX seasonal cycle std', - 'varpattern': 'taux_lat__', - 'xname': 'latitude', - 'yname': 'TAUX std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude60'], + "SeasonalTauxLatRmse": { + "netcdf_variables": ["taux_lat__", "taux_map__", "tauxMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "TAUX seasonal cycle std", + "varpattern": "taux_lat__", + "xname": "latitude", + "yname": "TAUX std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude60"], "maskland": True, - 'title': ['TAUX seasonal cycle std'], - 'varpattern': "taux_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TAUX std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['TAUX'], - 'title': ['TAUX seasonal cycle', 'TAUX seasonal cycle'], - 'varpattern': 'tauxMac_hov__', - 'xname': 'latitude', - 'yname': 'months', - 'zname': 'TAUX', + "title": ["TAUX seasonal cycle std"], + "varpattern": "taux_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "TAUX std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["TAUX"], + "title": ["TAUX seasonal cycle", "TAUX seasonal cycle"], + "varpattern": "tauxMac_hov__", + "xname": "latitude", + "yname": "months", + "zname": "TAUX", }, }, - 'SeasonalTauxLonRmse': { - 'netcdf_variables': ["taux_lon__", "taux_map__", "tauxMac_hov__"], - 'diagnostic': { - 'plot_type': 'curve', - 'nbr_panel': 1, - 'title': 'TAUX seasonal cycle std', - 'varpattern': 'taux_lon__', - 'xname': 'longitude', - 'yname': 'TAUX std', - }, - 'dive_down01': { - 'plot_type': 'map', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['amplitude'], - 'label': dict_label['amplitude60'], + "SeasonalTauxLonRmse": { + "netcdf_variables": ["taux_lon__", "taux_map__", "tauxMac_hov__"], + "diagnostic": { + "plot_type": "curve", + "nbr_panel": 1, + "title": "TAUX seasonal cycle std", + "varpattern": "taux_lon__", + "xname": "longitude", + "yname": "TAUX std", + }, + "dive_down01": { + "plot_type": "map", + "nbr_panel": 2, + "colorbar": dict_colorbar["amplitude"], + "label": dict_label["amplitude60"], "maskland": True, - 'title': ['TAUX seasonal cycle std'], - 'varpattern': "taux_map__", - 'xname': 'longitude', - 'yname': 'latitude', - 'zname': 'TAUX std', - }, - 'dive_down02': { - 'plot_type': 'hovmoeller', - 'nbr_panel': 2, - 'colorbar': dict_colorbar['anomalies'], - 'label': dict_label['REG80'], - 'title': ['TAUX seasonal cycle', 'TAUX seasonal cycle'], - 'varpattern': 'tauxMac_hov__', - 'xname': 'longitude', - 'yname': 'months', - 'zname': 'TAUX', + "title": ["TAUX seasonal cycle std"], + "varpattern": "taux_map__", + "xname": "longitude", + "yname": "latitude", + "zname": "TAUX std", + }, + "dive_down02": { + "plot_type": "hovmoeller", + "nbr_panel": 2, + "colorbar": dict_colorbar["anomalies"], + "label": dict_label["REG80"], + "title": ["TAUX seasonal cycle", "TAUX seasonal cycle"], + "varpattern": "tauxMac_hov__", + "xname": "longitude", + "yname": "months", + "zname": "TAUX", }, }, - } # reference_observations = { @@ -2779,16 +2985,26 @@ # 'swr': 'Tropflux', 'taux': 'Tropflux', 'thf': 'Tropflux' # } reference_observations = { - 'ssh': 'AVISO', 'pr': 'GPCPv2.3', 'sst': 'Tropflux', 'lhf': 'Tropflux', 'lwr': 'Tropflux', 'shf': 'Tropflux', - 'slp': 'ERA-Interim', 'swr': 'Tropflux', 'taux': 'Tropflux', 'thf': 'Tropflux' + "ssh": "AVISO", + "pr": "GPCPv2.3", + "sst": "Tropflux", + "lhf": "Tropflux", + "lwr": "Tropflux", + "shf": "Tropflux", + "slp": "ERA-Interim", + "swr": "Tropflux", + "taux": "Tropflux", + "thf": "Tropflux", } def plot_param(metric_collection, metric): dict_MC = defCollection(metric_collection) - dict_MCm = dict_MC['metrics_list'][metric] + dict_MCm = dict_MC["metrics_list"][metric] # get plot parameters - dict_out = plot_parameters[metric.replace("_1", "").replace("_2", "").replace("_3", "")] + dict_out = plot_parameters[ + metric.replace("_1", "").replace("_2", "").replace("_3", "") + ] # get metric computation if metric_collection in ["ENSO_tel", "test_tel"] and "Map" in metric: computation = ["CORR", "RMSE"] @@ -2798,20 +3014,22 @@ def plot_param(metric_collection, metric): computation = "CORR" else: try: - computation = dict_MCm['metric_computation'] - except: + computation = dict_MCm["metric_computation"] + except KeyError: try: - computation = dict_MC['common_collection_parameters']['metric_computation'] - except: - computation = default_arg_values('metric_computation') - dict_out['metric_computation'] = computation + computation = dict_MC["common_collection_parameters"][ + "metric_computation" + ] + except KeyError: + computation = default_arg_values("metric_computation") + dict_out["metric_computation"] = computation # get metric variables - variables = dict_MCm['variables'] - dict_out['metric_variables'] = variables + variables = dict_MCm["variables"] + dict_out["metric_variables"] = variables # get metric reference references = dict((var, reference_observations[var]) for var in variables) - if 'sst' in variables and len(variables) > 1: - references['sst'] = 'Tropflux' + if "sst" in variables and len(variables) > 1: + references["sst"] = "Tropflux" refname = references[variables[0]] for var in variables[1:]: refname = refname + "_" + references[var] @@ -2819,11 +3037,30 @@ def plot_param(metric_collection, metric): # refname = "ERA-Interim_ERA-Interim" # elif metric_collection == "ENSO_tel" and metric in ["EnsoSstMap", "NinaSstMap", "NinoSstMap"]: # refname = "ERA-Interim" - if metric_collection in ["ENSO_tel", "test_tel"] and metric in\ - ["EnsoSstMap", "NinaSstMap", "NinoSstMap", "EnsoSstMapDjf", "NinoSstMapDjf", "NinaSstMapDjf", - "EnsoSstMapJja", "NinoSstMapJja", "NinaSstMapJja"]: + if metric_collection in ["ENSO_tel", "test_tel"] and metric in [ + "EnsoSstMap", + "NinaSstMap", + "NinoSstMap", + "EnsoSstMapDjf", + "NinoSstMapDjf", + "NinaSstMapDjf", + "EnsoSstMapJja", + "NinoSstMapJja", + "NinaSstMapJja", + ]: refname = "ERA-Interim" - dict_out['metric_reference'] = refname + + """ + # If the same reference is repeated, then remove repeated + if "_" in refname: + parts = refname.split("_") + # Use a set to remove duplicates and then join back to a string + unique_parts = list(dict.fromkeys(parts)) # Preserves order while removing duplicates + refname = "_".join(unique_parts) + """ + + # Assign refname to the output dict + dict_out["metric_reference"] = refname # get variable regions - dict_out['metric_regions'] = dict_MCm['regions'] + dict_out["metric_regions"] = dict_MCm["regions"] return dict_out diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py index 9b10b4143..f9f0a6562 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/KeyArgLib.py @@ -12,13 +12,24 @@ # def default_arg_values(arg): default = { - 'detrending': False, 'frequency': None, 'metric_computation': 'difference', 'min_time_steps': None, - 'normalization': False, 'project_interpreter': 'CMIP', 'regridding': False, 'smoothing': False, - 'treshold_ep_ev': -140, 'time_bounds': None, 'time_bounds_mod': None, 'time_bounds_obs': None, + "detrending": False, + "frequency": None, + "metric_computation": "difference", + "min_time_steps": None, + "normalization": False, + "project_interpreter": "CMIP", + "regridding": False, + "smoothing": False, + "treshold_ep_ev": -140, + "time_bounds": None, + "time_bounds_mod": None, + "time_bounds_obs": None, } try: default[arg] - except: + except KeyError: unknown_key_arg(arg, INSPECTstack()) return default[arg] + + # ---------------------------------------------------------------------------------------------------------------------# diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py new file mode 100644 index 000000000..04a0ad53c --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py @@ -0,0 +1,1197 @@ +# ---------------------------------------------------# +# Aim of the program: +# plot portraitplots based on json files outputs of the CLIVAR ENSO metrics package +# ---------------------------------------------------# + +# ---------------------------------------------------# +# Import the right packages +# ---------------------------------------------------# +from __future__ import print_function + +import json +import string +from copy import deepcopy +import difflib + +import matplotlib.pyplot as plt +from matplotlib.colors import BoundaryNorm, ListedColormap +from matplotlib.gridspec import GridSpec +from matplotlib.lines import Line2D +from matplotlib.ticker import MaxNLocator +from numpy import linspace as NUMPYlinspace +from numpy import mean as NUMPYmean +from numpy import std as NUMPYstd +from numpy.ma import array as NUMPYma__array +from numpy.ma import masked_invalid as NUMPYma__masked_invalid +from numpy.ma import masked_where as NUMPYmasked_where +from numpy.ma import zeros as NUMPYma__zeros + +# ENSO_metrics functions +from .EnsoPlotLib import plot_param + +# ---------------------------------------------------# +# Main +# ---------------------------------------------------# + + +def enso_portrait_plot( + metric_collections, + list_project, + list_obs, + dict_json_path, + figure_name="enso_portrait_plot.png", + reduced_set=False, + met_order=None, + mod_order=None, + sort_y_names=False, + show_proj_means=False, + show_ref_row=False, + show_alt_obs_rows=False, +): + """ + Generates a summary plot for ENSO metrics. + + Parameters + ---------- + metric_collections : list of str + List of metric collections to be plotted. + list_project : list of str + List of project names. + list_obs : list of str + List of observational datasets. + dict_json_path : dict + Dictionary containing paths to JSON files with metric data. + figure_name : str + Name of the output figure file. + If True, use a reduced set of metrics, by default False. + met_order : list of str, optional + Custom order for metrics, by default None. + mod_order : list of str, optional + Custom order for models, by default None. + sort_y_names : bool, optional + If True, sort y-axis names in alphabetical order across `list_project`, by default False. + + Returns + ------- + None + This function does not return any value. It generates and saves a plot. + """ + # name of metric collections for the plot and new metric names + metric_names_for_plot, met_names = load_met_names() + + # metric type and order + if met_order is None: + met_order, met_o1, met_o2, met_o3, met_o4 = load_met_order() + + # model order + if mod_order == "predefined": + mod_order = predefined_mod_order() + + # list of observations + if list_obs is None: + list_obs = list() + + # get data + tab_all, x_names, y_names = json_dict_to_numpy_array_list( + metric_collections, + list_project, + list_obs, + dict_json_path, + reduced_set, + met_order, + mod_order, + sort_y_names=sort_y_names, + show_proj_means=show_proj_means, + show_ref_row=show_ref_row, + show_alt_obs_rows=show_alt_obs_rows, + ) + + # all portraitplots in one plot + numbering = [ii + ") " for ii in list(string.ascii_lowercase)] + + # plot + title = [ + numbering[ii] + metric_names_for_plot[mc] + for ii, mc in enumerate(metric_collections) + ] + + if "CMIP6" in list_project and "CMIP5" in list_project: + text = "* = CMIP6\nmodel" + else: + text = None + + levels = list(range(-2, 3)) + multiportraitplot( + tab_all, + figure_name, + x_names, + y_names, + title=title, + my_text=text, + levels=levels, + highlight=True, + met_o1=met_o1, + met_o2=met_o2, + met_o3=met_o3, + met_o4=met_o4, + met_names=met_names, + ) + del levels, numbering, text, title + + +# ---------------------------------------------------# +# Support Functions +# ---------------------------------------------------# +def json_dict_to_numpy_array_list( + metric_collections, + list_project, + list_obs, + dict_json_path, + reduced_set, + met_order, + mod_order, + sort_y_names=False, + show_proj_means=False, + show_ref_row=False, + show_alt_obs_rows=False, + debug=False, +): + if debug: + print("metric_collections:", metric_collections) + print("list_project:", list_project) + print("list_obs:", list_obs) + # get members by model by project from json file + # only metrics from models/members chosen here will be used + # all metrics from models/members chosen here will be used (ensures that if a model/member is not available for one or + # several metric collections, the corresponding line will still be created in the portraitplot) + model_by_proj = dict() + dict_members = dict() + for proj in list_project: + list_models = list() + dict_members[proj] = dict() + for mc in metric_collections: + # read json files + tmp = read_data(dict_json_path[proj][mc]) + # list models + list_models += list(tmp.keys()) + # members + for mod in list(tmp.keys()): + try: + dict_members[proj][mod] + except: + dict_members[proj][mod] = list(tmp[mod].keys()) + else: + dict_members[proj][mod] += list(tmp[mod].keys()) + del tmp + list_models = sorted(list(set(list_models)), key=lambda v: v.upper()) + list_to_remove = None + list_to_remove = [ + "EC-EARTH", + "FIO-ESM", + "GFDL-CM2p1", + "HadGEM2-AO", + "CIESM", + "E3SM-1-1-ECA", + "FGOALS-g3", + "MCM-UA-1-0", + "AWI-CM-1-1-MR", + "AWI-ESM-1-1-LR", + ] + # EC-EARTH: incorrect time coordinate + # FIO-ESM, HadCM3: grid issues + # GFDL-CM2p1: hfls not published + # HadGEM2-AO: rlus and rsus not published + # E3SM-1-1-ECA: Experimental stage + # CIESM, FGOALS-g3: ??? + # MCM-UA-1-0: unit issue with pr + # AWI-CM-1-1-MR, AWI-ESM-1-1-LR: error at ENSO_proc calculation + if list_to_remove is not None: + for mod in list_to_remove: + while mod in list_models: + list_models.remove(mod) + for mod in list_models: + list_members = sorted( + list(set(dict_members[proj][mod])), key=lambda v: v.upper() + ) + mem = find_first_member(list_members, mod=mod) + try: + model_by_proj[proj] + except: + model_by_proj[proj] = {mod: mem} + else: + try: + model_by_proj[proj][mod] + except: + model_by_proj[proj][mod] = mem + else: + print("this model should not be here") + del list_members, mem + # del dict_members, list_models, list_to_remove + del list_models, list_to_remove + + # read json file + tab_all, tab_all_act, x_names = list(), list(), list() + different_ref_keys = list() + for mc in metric_collections: + if debug: + print("mc:", mc) + dict1 = dict() + list_models_all = list() + for proj in list_project: + # open and read json file + data_json = read_data(dict_json_path[proj][mc]) + # read metrics + list_models = sorted( + list(model_by_proj[proj].keys()), key=lambda v: v.upper() + ) + list_models_all.extend(list_models) + for mod in list_models: + data_mod = data_json[mod][model_by_proj[proj][mod]]["value"] + list_metrics = sorted(list(data_mod.keys()), key=lambda v: v.upper()) + if reduced_set is True: + list_metrics = remove_metrics(list_metrics, mc) + for met in list_metrics: + ref = get_reference(mc, met) # e.g., 'GPCPv2.3' + ref_key_list = list(data_mod[met]["metric"]) # e.g., ['GPCP-2-3', and others] + ref_key_act = most_similar_string(ref, ref_key_list) + if ref != ref_key_act: + if debug: + print(f"Note: For metrics collection '{mc}' metric '{met}', reference key in the JSON for the project '{proj}', '{ref_key_act}', is assumed to be same as the predefined reference, '{ref}'.") + different_ref_keys.append([ref, ref_key_act]) + + # val = data_mod[met]["metric"][ref]["value"] + # Below, if any part of the dictionary chain is missing, val will be set to None without raising a KeyError. + val = ( + data_mod.get(met, {}) + .get("metric", {}) + .get(ref_key_act, {}) + .get("value", None) + ) + if val is None: + val = 1e20 + try: + dict1[mod] + except: + dict1[mod] = {met: val} + else: + dict1[mod][met] = val + del ref, ref_key_act, val + del data_mod, list_metrics + del data_json, list_models + + if len(different_ref_keys) > 0: + print(f"Note: The following keys were considered to be the same for {proj}:") + unique_different_ref_keys = list(map(list, dict.fromkeys(map(tuple, different_ref_keys)))) + for diff_keys in unique_different_ref_keys: + print(f"Predefined reference: {diff_keys[0]}, reference key in the JSON: {diff_keys[1]}") + + # models and metrics + if sort_y_names: + tmp_models = sorted( + [str(mod) for mod in list(dict1.keys())], key=lambda v: v.upper() + ) + else: + tmp_models = list_models_all + + if debug: + print("tmp_models:", tmp_models) + + my_metrics = list() + for mod in tmp_models: + try: + list(dict1[mod].keys()) + except: + pass + else: + my_metrics += list(dict1[mod].keys()) + + my_metrics = sorted(list(set(my_metrics)), key=lambda v: v.upper()) + + if met_order is not None: + my_metrics = [met for met in met_order if met in my_metrics] + + if mod_order is not None: + my_models = [mod for mod in mod_order if mod in tmp_models] + else: + my_models = tmp_models + my_models += sorted( + list(set(tmp_models) - set(my_models)), key=lambda v: v.upper() + ) + my_models = list(reversed(my_models)) + del tmp_models + + if debug: + print("my_models:", my_models) + + # Additional rows (project multi-model means (e.g., CMIP mean), reference, and alternative observation datasets) + rows_to_add = list() + dict_ref_met = dict() + + if show_alt_obs_rows and list_obs is not None: + rows_to_add += list_obs + + if len(list_obs) > 0 and "obs2obs" in dict_json_path.keys(): + # read other observational datasets compared to the reference + dict_ref_met = read_obs(dict_json_path["obs2obs"][mc], list_obs, my_metrics, mc) + + if show_ref_row: + rows_to_add += ["reference"] + + if show_proj_means: + rows_to_add += list(reversed(list_project)) + + plus = len(rows_to_add) + + if debug: + print("rows_to_add:", rows_to_add) + print("plus:", plus) + + # fill array + tab = NUMPYma__zeros((len(my_models) + plus, len(my_metrics))) + if debug: + print("tab.shape:", tab.shape) + + for ii, mod in enumerate(my_models): + for jj, met in enumerate(my_metrics): + try: + dict1[mod][met] + except: + tab[ii + plus, jj] = 1e20 + else: + tab[ii + plus, jj] = dict1[mod][met] + tab = NUMPYma__masked_invalid(tab) + tab = NUMPYmasked_where(tab == 1e20, tab) + tab_act = deepcopy(tab) + + # add values to the array (CMIP mean, reference, other observational datasets,...) + for jj, met in enumerate(my_metrics): + tmp = tab[plus:, jj].compressed() + mea = float(NUMPYmean(tmp)) + std = float(NUMPYstd(tmp)) + del tmp + for ii, dd in enumerate(rows_to_add): + if dd in list_obs: + # Get value from dict_ref_met for alternative observations in list_obs + val = dict_ref_met[dd][met] + elif dd in list_project: + # Get average value of models for the project + tmp = [ + tab[ii + plus, jj] + for ii, mod in enumerate(my_models) + if mod in list(model_by_proj[dd].keys()) + ] + tmp = NUMPYma__masked_invalid(NUMPYma__array(tmp)) + tmp = NUMPYmasked_where(tmp == 1e20, tmp).compressed() + val = float(NUMPYmean(tmp)) + del tmp + else: + # For the default reference + val = 0 + tab[ii, jj] = val + tab_act[ii, jj] = val + del val + # normalize + tab[:, jj] = (tab[:, jj] - mea) / std + del mea, std + tab = NUMPYma__masked_invalid(tab) + tab = NUMPYmasked_where(tab > 1e3, tab) + tab_act = NUMPYma__masked_invalid(tab_act) + tab_act = NUMPYmasked_where(tab_act > 1e3, tab_act) + tab_all.append(tab) + tab_all_act.append(tab_act) + + if reduced_set is True: + x_names.append( + [met.replace("_1", "").replace("_2", "") for met in my_metrics] + ) + else: + x_names.append(my_metrics) + + if "CMIP6" in list_project and "CMIP5" in list_project: + my_models = [ + "* " + mod if mod in list(model_by_proj["CMIP6"].keys()) else mod + for mod in my_models + ] + + if mc == metric_collections[0]: + y_names = rows_to_add + my_models + y_names = [ + "(" + dd + ")" if dd in (list_obs + ["reference"]) else dd for dd in y_names + ] + del dict1, dict_ref_met, my_metrics, my_models, plus, tab, tab_act + + if debug: + print("len(tab_all):", len(tab_all)) + + return tab_all, x_names, y_names + + +def most_similar_string(target, string_list): + return max(string_list, key=lambda s: difflib.SequenceMatcher(None, target, s).ratio()) + + +def load_met_names(): + # name of metric collections for the plot + metric_names_for_plot = { + "ENSO_perf": "Performance", + "ENSO_proc": "Processes", + "ENSO_tel": "Telecon.", + } + + # new metric names + met_names = { + "BiasPrLatRmse": "double_ITCZ_bias", + "BiasPrLonRmse": "eq_PR_bias", + "BiasSstLonRmse": "eq_SST_bias", + "BiasTauxLonRmse": "eq_Taux_bias", + "SeasonalPrLatRmse": "double_ITCZ_sea_cycle", + "SeasonalPrLonRmse": "eq_PR_sea_cycle", + "SeasonalSstLonRmse": "eq_SST_sea_cycle", + "SeasonalTauxLonRmse": "eq_Taux_sea_cycle", + "EnsoSstLonRmse": "ENSO_pattern", + "EnsoSstTsRmse": "ENSO_lifecycle", + "EnsoAmpl": "ENSO_amplitude", + "EnsoSeasonality": "ENSO_seasonality", + "EnsoSstSkew": "ENSO_asymmetry", + "EnsoDuration": "ENSO_duration", + "EnsoSstDiversity": "ENSO_diversity", + "EnsoSstDiversity_1": "ENSO_diversity", + "EnsoSstDiversity_2": "ENSO_diversity", + "EnsoPrMapDjfRmse": "DJF_PR_teleconnection", + "EnsoPrMapJjaRmse": "JJA_PR_teleconnection", + "EnsoSstMapDjfRmse": "DJF_TS_teleconnection", + "EnsoSstMapJjaRmse": "JJA_TS_teleconnection", + "EnsoFbSstTaux": "SST-Taux_feedback", + "EnsoFbTauxSsh": "Taux-SSH_feedback", + "EnsoFbSshSst": "SSH-SST_feedback", + "EnsoFbSstThf": "SST-NHF_feedback", + "EnsodSstOce": "ocean_driven_SST", + "EnsodSstOce_1": "ocean_driven_SST", + "EnsodSstOce_2": "ocean_driven_SST", + } + + return metric_names_for_plot, met_names + + +def load_met_order(): + # metric type and order + met_o1 = [ + "BiasPrLatRmse", + "BiasPrLonRmse", + "BiasSshLatRmse", + "BiasSshLonRmse", + "BiasSstLatRmse", + "BiasSstLonRmse", + "BiasTauxLatRmse", + "BiasTauxLonRmse", + "SeasonalPrLatRmse", + "SeasonalPrLonRmse", + "SeasonalSshLatRmse", + "SeasonalSshLonRmse", + "SeasonalSstLatRmse", + "SeasonalSstLonRmse", + "SeasonalTauxLatRmse", + "SeasonalTauxLonRmse", + ] + met_o2 = [ + "EnsoSstLonRmse", + "EnsoPrTsRmse", + "EnsoSstTsRmse", + "EnsoTauxTsRmse", + "EnsoAmpl", + "EnsoSeasonality", + "EnsoSstSkew", + "EnsoDuration", + "EnsoSstDiversity", + "EnsoSstDiversity_1", + "EnsoSstDiversity_2", + "NinoSstDiversity", + "NinoSstDiversity_1", + "NinoSstDiversity_2", + ] + met_o3 = [ + "EnsoPrMapCorr", + "EnsoPrMapRmse", + "EnsoPrMapStd", + "EnsoPrMapDjfCorr", + "EnsoPrMapDjfRmse", + "EnsoPrMapDjfStd", + "EnsoPrMapJjaCorr", + "EnsoPrMapJjaRmse", + "EnsoPrMapJjaStd", + "EnsoSlpMapCorr", + "EnsoSlpMapRmse", + "EnsoSlpMapStd", + "EnsoSlpMapDjfCorr", + "EnsoSlpMapDjfRmse", + "EnsoSlpMapDjfStd", + "EnsoSlpMapJjaCorr", + "EnsoSlpMapJjaRmse", + "EnsoSlpMapJjaStd", + "EnsoSstMapCorr", + "EnsoSstMapRmse", + "EnsoSstMapStd", + "EnsoSstMapDjfCorr", + "EnsoSstMapDjfRmse", + "EnsoSstMapDjfStd", + "EnsoSstMapJjaCorr", + "EnsoSstMapJjaRmse", + "EnsoSstMapJjaStd", + ] + met_o4 = [ + "EnsoFbSstTaux", + "EnsoFbTauxSsh", + "EnsoFbSshSst", + "EnsoFbSstThf", + "EnsoFbSstSwr", + "EnsoFbSstLhf", + "EnsoFbSstLwr", + "EnsoFbSstShf", + "EnsodSstOce", + "EnsodSstOce_1", + "EnsodSstOce_2", + ] + met_order = met_o1 + met_o2 + met_o3 + met_o4 + + return met_order, met_o1, met_o2, met_o3, met_o4 + + +def predefined_mod_order(): + # model order + mod_order = [ + "ACCESS1-0", + "ACCESS1-3", + "ACCESS-CM2", + "ACCESS-ESM1-5", + "BCC-CSM1-1", + "BCC-CSM1-1-M", + "BCC-CSM2-MR", + "BCC-ESM1", + "BNU-ESM", + "CAMS-CSM1-0", + "CanCM4", + "CanESM2", + "CanESM5", + "CanESM5-CanOE", + "CCSM4", + "CESM1-BGC", + "CESM1-CAM5", + "CESM2", + "CESM2-FV2", + "CESM1-FASTCHEM", + "CESM1-WACCM", + "CESM2-WACCM", + "CESM2-WACCM-FV2", + "CMCC-CESM", + "CMCC-CM", + "CMCC-CMS", + "CNRM-CM5", + "CNRM-CM5-2", + "CNRM-CM6-1", + "CNRM-CM6-1-HR", + "CNRM-ESM2-1", + "CSIRO-Mk3-6-0", + "CSIRO-Mk3L-1-2", + "E3SM-1-0", + "E3SM-1-1", + "EC-EARTH", + "EC-Earth3", + "EC-Earth3-Veg", + "FGOALS-f3-L", + "FGOALS-g2", + "FGOALS-s2", + "FIO-ESM", + "GFDL-CM2p1", + "GFDL-CM3", + "GFDL-CM4", + "GFDL-ESM2G", + "GFDL-ESM2M", + "GFDL-ESM4", + "GISS-E2-1-G", + "GISS-E2-1-G-CC", + "GISS-E2-H", + "GISS-E2-H-CC", + "GISS-E2-1-H", + "GISS-E2-R", + "GISS-E2-R-CC", + "HadCM3", + "HadGEM2-AO", + "HadGEM2-CC", + "HadGEM2-ES", + "HadGEM3-GC31-LL", + "INMCM4", + "INM-CM4-8", + "INM-CM5-0", + "IPSL-CM5A-LR", + "IPSL-CM5A-MR", + "IPSL-CM5B-LR", + "IPSL-CM6A-LR", + "KACE-1-0-G", + "MIROC4h", + "MIROC5", + "MIROC6", + "MIROC-ESM", + "MIROC-ESM-CHEM", + "MIROC-ES2L", + "MPI-ESM-LR", + "MPI-ESM-MR", + "MPI-ESM-P", + "MPI-ESM-1-2-HAM", + "MPI-ESM1-2-HR", + "MPI-ESM1-2-LR", + "MRI-CGCM3", + "MRI-ESM1", + "MRI-ESM2-0", + "NESM3", + "NorESM1-M", + "NorESM1-ME", + "NorCPM1", + "NorESM2-LM", + "NorESM2-MM", + "SAM0-UNICON", + "TaiESM1", + "UKESM1-0-LL", + ] + + mod_order += [ + "CAS-ESM2-0", + "CMCC-CM2-HR4", + "CMCC-CM2-SR5", + "EC-Earth3-AerChem", + "EC-Earth3-Veg-LR", + "FIO-ESM-2-0", + "HadGEM3-GC31-MM", + "KIOST-ESM", + ] + + mod_order = sorted(mod_order, key=str.casefold) + + return mod_order + + +def find_first_member(members, mod=None): + """ + Finds the first member + + Inputs: + ------ + :param members: list of string + List of members. + + Output: + ------ + :return mem: string + First member of the given list. + """ + if "r1i1p1" in members: + mem = "r1i1p1" + elif "r1i1p1f1" in members: + mem = "r1i1p1f1" + elif "r1i1p1f2" in members: + mem = "r1i1p1f2" + else: + tmp = deepcopy(members) + members = list() + for mem in tmp: + for ii in range(1, 10): + if "r" + str(ii) + "i" in mem: + members.append( + mem.replace("r" + str(ii) + "i", "r" + str(ii).zfill(2) + "i") + ) + else: + members.append(mem) + del tmp + mem = sorted(list(set(members)), key=lambda v: v.upper())[0].replace("r0", "r") + # special case + if mod == "NorESM2-LM": + mem = "r2i1p1f1" + return mem + + +def get_reference(metric_collection, metric): + """ + Gets main reference for the given metric_collection / metric from EnsoPlotLib.plot_param + + Inputs: + ------ + :param metric_collection: string + Name of a metric collection. + :param metric: string + Name of a metric. + + Output: + ------ + :return reference: string + Name of the main reference for the given metric_collection / metric + """ + if metric_collection in ["ENSO_tel", "test_tel"] and "Map" in metric: + my_met = metric.replace("Corr", "").replace("Rmse", "").replace("Std", "") + else: + my_met = deepcopy(metric) + reference = plot_param(metric_collection, my_met)["metric_reference"] + return reference + + +def my_colorbar(mini=-1.0, maxi=1.0, nbins=20): + """ + Modifies cmo.balance colobar (removes the darkest blue and red) + + Inputs: + ------ + **Optional arguments:** + :param mini: float + Minimum value of the colorbar. + :param maxi: float + Maximum value of the colorbar. + :param nbins: integer + Number of interval in the colorbar. + + Outputs: + ------- + :return newcmp1: object + Colormap, baseclass for all scalar to RGBA mappings + :return norm: object + Normalize, a class which can normalize data into the [0.0, 1.0] interval. + """ + levels = MaxNLocator(nbins=nbins).tick_values(mini, maxi) + # cmap = plt.get_cmap("cmo.balance") + cmap = plt.get_cmap("RdBu_r") + newcmp1 = cmap(NUMPYlinspace(0.15, 0.85, 256)) + newcmp2 = cmap(NUMPYlinspace(0.0, 1.0, 256)) + newcmp1 = ListedColormap(newcmp1) + newcmp1.set_over(newcmp2[-30]) + newcmp1.set_under(newcmp2[29]) + newcmp1.set_bad(color="k") # missing values in black + norm = BoundaryNorm(levels, ncolors=newcmp1.N) + return newcmp1, norm + + +def multiportraitplot( + tab, + name_plot, + x_names, + y_names, + title=[], + write_metrics=False, + my_text="", + levels=None, + highlight=False, + nbr_space=2, + met_o1=None, + met_o2=None, + met_o3=None, + met_o4=None, + met_names=None, +): + """ + Plot the portraitplot (as in BAMS paper) + + Inputs: + ------ + :param tab: list of masked_array + List of masked_array containing metric collections values. + :param name_plot: string + Name of the output figure. + :param x_names: list of list of string + List of metric collection's metric names. + :param y_names: list of string + List of model/member names. + **Optional arguments:** + :param title: list of string, optional + List of metric collection's title. + :param my_text: string, optional + Text to add at the bottom right of the plot (I use it to indicate how CMIP6 models are marked in the plot). + :param levels: list of floats, optional + Levels of the colorbar, if None is given, colobar ranges from -1 to 1. + :param highlight: boolean, optional + If True metric names are highlighted and lines indicate metric types. + + Output: + ------ + """ + if levels is None: + levels = [-1.0, -0.5, 0.0, 0.5, 1.0] + fontdict = {"fontsize": 40, "fontweight": "bold"} + # nbr of columns of the portraitplot + nbrc = sum([len(tab[ii][0]) for ii in range(len(tab))]) + (len(tab) - 1) * nbr_space + # figure definition + fig = plt.figure(0, figsize=(0.5 * nbrc, 0.5 * len(tab[0]))) + gs = GridSpec(1, nbrc) + # adapt the colorbar + cmap, norm = my_colorbar(mini=min(levels), maxi=max(levels)) + # loop on metric collections + count = 0 + for kk, tmp in enumerate(tab): + ax = plt.subplot(gs[0, count : count + len(tmp[0])]) + # shading + cs = ax.pcolormesh(tmp, cmap=cmap, norm=norm) + # title + xx1, xx2 = ax.get_xlim() + dx = 0.5 / (xx2 - xx1) + yy1, yy2 = ax.get_ylim() + dy = 0.5 / (yy2 - yy1) + try: + ax.set_title(title[kk], fontdict=fontdict, y=1 + dy, loc="center") + except: + pass + # x axis + ticks = [ii + 0.5 for ii in range(len(x_names[kk]))] + ax.set_xticks(ticks) + ax.set_xticklabels([] * len(ticks)) + for ll, txt in enumerate(x_names[kk]): + if highlight is True: + # find the metric color + if txt in met_o1 or txt + "_1" in met_o1 or txt + "_2" in met_o1: + cc = "yellowgreen" + elif txt in met_o2 or txt + "_1" in met_o2 or txt + "_2" in met_o2: + cc = "plum" + elif txt in met_o3 or txt + "_1" in met_o3 or txt + "_2" in met_o3: + cc = "gold" + else: + cc = "turquoise" + # write highlighted metric name + ax.text( + ll + 0.5, + -0.2, + met_names[txt], + fontsize=15, + ha="right", + va="top", + rotation=45, + color="k", + bbox=dict(lw=0, facecolor=cc, pad=3, alpha=1), + ) + else: + # write metric name in black + ax.text( + ll + 0.5, + -0.2, + met_names[txt], + fontsize=20, + ha="right", + va="top", + rotation=45, + color="k", + ) + if highlight is True: + tmp1 = [met_o1, met_o2, met_o3, met_o4] + # draw vertical black lines to separate metric types + nn = 0 + lix = [[0, 0]] + for tt in tmp1: + tmp2 = [ + txt + for ll, txt in enumerate(x_names[kk]) + if txt in tt or txt + "_1" in tt or txt + "_2" in tt + ] + nn += len(tmp2) + if len(tmp2) > 0: + lix += [[nn, nn]] + del tmp2 + liy = [[0, len(tab[0])]] * len(lix) + lic, lis = ["k"] * len(lix), ["-"] * len(lix) + for lc, ls, lx, ly in zip(lic, lis, lix, liy): + line = Line2D(lx, ly, c=lc, lw=7, ls=ls, zorder=10) + line.set_clip_on(False) + ax.add_line(line) + # draw horizontal colored lines to indicate metric types + nn = 0 + lic, lix = list(), list() + for uu, tt in enumerate(tmp1): + tmp2 = [ + txt + for ll, txt in enumerate(x_names[kk]) + if txt in tt or txt + "_1" in tt or txt + "_2" in tt + ] + if len(tmp2) > 0: + if uu == 0: + cc = "yellowgreen" + elif uu == 1: + cc = "plum" + elif uu == 2: + cc = "gold" + else: + cc = "turquoise" + lic += [cc, cc] + if nn > 0: + lix += [[nn + 0.2, nn + len(tmp2)], [nn + 0.2, nn + len(tmp2)]] + else: + lix += [[nn, nn + len(tmp2)], [nn, nn + len(tmp2)]] + nn += len(tmp2) + del cc + del tmp2 + liy = [[len(tab[0]), len(tab[0])], [0, 0]] * int(float(len(lix)) / 2) + lis = ["-"] * len(lix) + for mm, (lc, ls, lx, ly) in enumerate(zip(lic, lis, lix, liy)): + if mm < 2: + line = Line2D( + [lx[0] + 0.05, lx[1]], ly, c=lc, lw=10, ls=ls, zorder=10 + ) + elif mm > len(lis) - 3: + line = Line2D( + [lx[0], lx[1] - 0.05], ly, c=lc, lw=10, ls=ls, zorder=10 + ) + else: + line = Line2D(lx, ly, c=lc, lw=10, ls=ls, zorder=10) + line.set_clip_on(False) + ax.add_line(line) + # y axis + ticks = [ii + 0.5 for ii in range(len(y_names))] + ax.set_yticks(ticks) + if kk != 0: + ax.set_yticklabels([""] * len(ticks)) + else: + ax.text( + -5 * dx, + -1 * dy, + my_text, + fontsize=25, + ha="right", + va="top", + transform=ax.transAxes, + ) + ax.tick_params(axis="y", labelsize=20) + ax.set_yticklabels(y_names) + ax.yaxis.set_label_coords(-20 * dx, 0.5) + # grid (create squares around metric values) + for ii in range(1, len(tmp)): + ax.axhline(ii, color="k", linestyle="-", linewidth=1) + for ii in range(1, len(tmp[0])): + ax.axvline(ii, color="k", linestyle="-", linewidth=1) + # write metric value in each square (standardized value!) + if write_metrics is True: + for jj in range(len(tmp[0])): + for ii in range(len(tmp)): + if tmp.mask[ii, jj] is False: + plt.text( + jj + 0.5, + ii + 0.5, + str(round(tmp[ii, jj], 1)), + fontsize=10, + ha="center", + va="center", + ) + if kk == len(tab) - 1: + x2 = ax.get_position().x1 + y1 = ax.get_position().y0 + y2 = ax.get_position().y1 + count += len(tmp[0]) + nbr_space + # color bar + cax = plt.axes([x2 + 0.03, y1, 0.02, y2 - y1]) + cbar = plt.colorbar( + cs, + cax=cax, + orientation="vertical", + ticks=levels, + pad=0.05, + extend="both", + aspect=40, + ) + cbar.ax.set_yticklabels( + ["-2 $\sigma$", "-1", "MMV", "1", "2 $\sigma$"], fontdict=fontdict # noqa + ) + dict_arrow = dict(facecolor="k", width=8, headwidth=40, headlength=40, shrink=0.0) + dict_txt = dict(fontsize=40, rotation="vertical", ha="center", weight="bold") + cax.annotate( + "", + xy=(3.7, 0.06), + xycoords="axes fraction", + xytext=(3.7, 0.45), + arrowprops=dict_arrow, + ) + cax.text(5.2, -0.55, "closer to reference", va="top", **dict_txt) + cax.annotate( + "", + xy=(3.7, 0.94), + xycoords="axes fraction", + xytext=(3.7, 0.55), + arrowprops=dict_arrow, + ) + cax.text(5.2, 0.55, "further from reference", va="bottom", **dict_txt) + plt.savefig(name_plot, bbox_inches="tight") + plt.savefig(name_plot + ".eps", bbox_inches="tight", format="eps") + # plt.close() + # return + return fig + + +def read_data(filename_json): + """ + Reads given json file (must have usual PMP's structure) + + Input: + ----- + :param filename_json: string + Path and name of a json file output of the CLIVAR ENSO metrics package. + + Output: + ------ + :return data: dictionary + Dictionary output of the CLIVAR ENSO metrics package, first level is models, second is members. + """ + with open(filename_json) as ff: + data = json.load(ff) + ff.close() + data = data["RESULTS"]["model"] + return data + + +def read_obs(filename_json, obsvation_names, list_met, metric_collection): + """ + Reads given json file (must have usual PMP's structure) and read given obs + + Input: + ----- + :param filename_json: string + Path and name of a json file output of the CLIVAR ENSO metrics package. + :param obsvation_names: list of string + Names of wanted additional observations for the portrait plot + :param list_met: list of string + List of metrics. + :param metric_collection: string + Name of a metric collection. + + Output: + ------ + :return data: list + Dictionary output of additional observations metric values. + """ + data_json = read_data(filename_json) + dict_out = dict() + for obs in obsvation_names: + for met in list_met: + ref = get_reference(metric_collection, met) + if obs == "20CRv2": + if "Ssh" not in met: + try: + tab = data_json["20CRv2"]["r1i1p1"]["value"][met]["metric"] + except: + tab = data_json["20CRv2_20CRv2"]["r1i1p1"]["value"][met][ + "metric" + ] + elif obs == "NCEP2": + if "TauxSsh" in met or "SshSst" in met: + tab = data_json["NCEP2_GODAS"]["r1i1p1"]["value"][met]["metric"] + elif "Ssh" in met: + tab = data_json["GODAS"]["r1i1p1"]["value"][met]["metric"] + else: + try: + tab = data_json["NCEP2"]["r1i1p1"]["value"][met]["metric"] + except: + tab = data_json["NCEP2_NCEP2"]["r1i1p1"]["value"][met]["metric"] + elif obs == "ERA-Interim": + if "SstMap" in met: + tab = {ref: {"value": 0}} + elif "TauxSsh" in met or "SshSst" in met: + tab = data_json["ERA-Interim_SODA3.4.2"]["r1i1p1"]["value"][met][ + "metric" + ] + elif "Ssh" in met: + tab = data_json["SODA3.4.2"]["r1i1p1"]["value"][met]["metric"] + else: + try: + tab = data_json["ERA-Interim"]["r1i1p1"]["value"][met]["metric"] + except: + tab = data_json["ERA-Interim_ERA-Interim"]["r1i1p1"]["value"][ + met + ]["metric"] + try: + val = tab[ref]["value"] + except: + val = 1e20 + try: + dict_out[obs] + except: + dict_out[obs] = {met: val} + else: + dict_out[obs][met] = val + try: + del tab + except: + pass + del ref, val + return dict_out + + +def remove_metrics(list_met, metric_collection): + """ + Removes some metrics from given list + + Inputs: + ------ + :param list_met: list of string + List of metrics. + :param metric_collection: string + Name of a metric collection. + + Output: + ------ + :return list_met_out: list of string + Input list of metrics minus some metrics depending on given metric collection. + """ + if metric_collection == "ENSO_perf": + to_remove = [ + "BiasSshLatRmse", + "BiasSshLonRmse", + "BiasSstLatRmse", + "BiasTauxLatRmse", + "EnsoPrTsRmse", + "EnsoSstDiversity_1", + "EnsoTauxTsRmse", + "NinaSstDur_1", + "NinaSstDur_2", + "NinaSstLonRmse_1", + "NinaSstLonRmse_2", + "NinaSstTsRmse_1", + "NinaSstTsRmse_2", + "NinoSstDiversity_1", + "NinoSstDiversity_2", + "NinoSstDur_1", + "NinoSstDur_2", + "NinoSstLonRmse_1", + "NinoSstLonRmse_2", + "NinoSstTsRmse_1", + "NinoSstTsRmse_2", + "SeasonalSshLatRmse", + "SeasonalSshLonRmse", + "SeasonalSstLatRmse", + "SeasonalTauxLatRmse", + ] + elif metric_collection == "ENSO_proc": + to_remove = [ + "BiasSshLonRmse", + "EnsodSstOce_1", + "EnsoFbSstLhf", + "EnsoFbSstLwr", + "EnsoFbSstShf", + "EnsoFbSstSwr", + ] + else: + to_remove = [ + "EnsoPrMapCorr", + "EnsoPrMapRmse", + "EnsoPrMapStd", + "EnsoPrMapDjfCorr", + "EnsoPrMapDjfStd", + "EnsoPrMapJjaCorr", + "EnsoPrMapJjaStd", + "EnsoSlpMapCorr", + "EnsoSlpMapRmse", + "EnsoSlpMapStd", + "EnsoSlpMapDjfCorr", + "EnsoSlpMapDjfRmse", + "EnsoSlpMapDjfStd", + "EnsoSlpMapJjaCorr", + "EnsoSlpMapJjaRmse", + "EnsoSlpMapJjaStd", + "EnsoSstMapCorr", + "EnsoSstMapRmse", + "EnsoSstMapStd", + "EnsoSstMapDjfCorr", + "EnsoSstMapDjfStd", + "EnsoSstMapJjaCorr", + "EnsoSstMapJjaStd", + ] + # remove given metrics + list_met_out = sorted(list(set(list_met) - set(to_remove)), key=lambda v: v.upper()) + return list_met_out diff --git a/pcmdi_metrics/utils/__init__.py b/pcmdi_metrics/utils/__init__.py index 53dd05758..14e70af3e 100644 --- a/pcmdi_metrics/utils/__init__.py +++ b/pcmdi_metrics/utils/__init__.py @@ -12,7 +12,7 @@ regenerate_time_axis, replace_date_pattern, ) -from .download import download_files_from_github_directory +from .download import download_files_from_github from .grid import ( calculate_area_weights, calculate_grid_area, diff --git a/pcmdi_metrics/utils/download.py b/pcmdi_metrics/utils/download.py index 67dd83db9..5ccdc809c 100644 --- a/pcmdi_metrics/utils/download.py +++ b/pcmdi_metrics/utils/download.py @@ -4,81 +4,91 @@ import requests -def download_files_from_github_directory(url, output_dir): +def download_files_from_github(url, output_dir): """ - Download all files from a GitHub directory. + Download files from a GitHub directory or a single file. Parameters ---------- url : str - The URL of the GitHub directory. + The URL of the GitHub directory or file. output_dir : str The local directory to save the downloaded files. Raises ------ Exception - If unable to fetch or parse the directory. + If unable to fetch or parse the directory or file. Notes ----- This function downloads all files from the specified GitHub directory - without using BeautifulSoup. It constructs the raw file URLs based on - the provided directory URL and saves the files to the specified output - directory. + or a single file if a file URL is provided. It constructs the raw file + URLs based on the provided URL and saves the files to the specified + output directory. Example ------- - >>> from pcmdi_metrics.utils import download_files_from_github_directory >>> github_directory_url = "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf" >>> output_directory = "downloaded_files" - >>> download_files_from_github_directory(github_directory_url, output_directory) + >>> download_files_from_github(github_directory_url, output_directory) + >>> github_file_url = "https://github.com/PCMDI/pcmdi_metrics_results_archive/blob/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json" + >>> download_files_from_github(github_file_url, output_directory) """ # Ensure the output directory exists os.makedirs(output_dir, exist_ok=True) # GitHub raw content base URL base_raw_url = "https://raw.githubusercontent.com" - repo_path = "/".join(url.split("github.com/")[1].split("/tree/")) - print("repo_path:", repo_path) - - # Get the HTML content of the directory page - response = requests.get(url) - if response.status_code != 200: - raise Exception( - f"Failed to fetch URL: {url} (Status Code: {response.status_code})" + # Check if the URL is for a file or a directory + if "blob" in url: + # It's a file URL + file_name = url.split("/")[-1] + raw_file_url = url.replace("github.com/", "raw.githubusercontent.com/").replace( + "/blob/", "/" ) + download_file(raw_file_url, file_name, output_dir) + + elif "tree" in url: + # It's a directory URL + response = requests.get(url) + if response.status_code != 200: + raise Exception( + f"Failed to fetch URL: {url} (Status Code: {response.status_code})" + ) - # Extract file links using a more flexible regex pattern - html_content = response.text - file_pattern = re.compile(r'href="(/[^/]+/[^/]+/blob/[^"]+)"') - print("file_pattern:", file_pattern) - matches = file_pattern.findall(html_content) - - if not matches: - print("No files found in the directory.") - return + # Extract file links using a regex pattern + html_content = response.text + file_pattern = re.compile(r'href="(/[^/]+/[^/]+/blob/[^"]+)"') + matches = file_pattern.findall(html_content) + + if matches: + # Remove duplicates and download each file + matches = list(set(matches)) + for match in matches: + file_name = match.split("/")[-1] + raw_file_url = base_raw_url + match.replace("/blob/", "/") + download_file(raw_file_url, file_name, output_dir) + else: + print("No files found in the directory.") - # Remove duplicates - matches = list(set(matches)) + else: + raise Exception( + "The provided URL is neither a valid GitHub directory nor a file." + ) - # Download each file - for match in matches: - # Extract the file name and create the raw file URL - file_name = match.split("/")[-1] - raw_file_url = base_raw_url + match.replace("/blob/", "/") +def download_file(raw_file_url, file_name, output_dir): + """Helper function to download a single file.""" + print(f"Downloading {file_name} from {raw_file_url}...") + file_response = requests.get(raw_file_url) + if file_response.status_code == 200: save_path = os.path.join(output_dir, file_name) - - # Download the file - print(f"Downloading {file_name}...") - file_response = requests.get(raw_file_url) - if file_response.status_code == 200: - with open(save_path, "wb") as file: - file.write(file_response.content) - print(f"Saved {file_name} to {save_path}") - else: - print( - f"Failed to download {file_name} (Status Code: {file_response.status_code})" - ) + with open(save_path, "wb") as file: + file.write(file_response.content) + print(f"Saved {file_name} to {save_path}") + else: + print( + f"Failed to download {file_name} (Status Code: {file_response.status_code})" + ) From 9d4d98527d1cce31cd4ebbbfbfa8b3b02cecd922 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 09:30:24 -0800 Subject: [PATCH 04/10] clean up --- pcmdi_metrics/enso/lib/summary_plot_lib/plot.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py index 04a0ad53c..7dd4d6205 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py @@ -1009,9 +1009,7 @@ def multiportraitplot( ) cax.text(5.2, 0.55, "further from reference", va="bottom", **dict_txt) plt.savefig(name_plot, bbox_inches="tight") - plt.savefig(name_plot + ".eps", bbox_inches="tight", format="eps") - # plt.close() - # return + plt.close() return fig From 53175cdef7f577db594242e2fe0e88a062d8ed62 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 09:30:54 -0800 Subject: [PATCH 05/10] pre-commit clean up --- .../enso/lib/summary_plot_lib/EnsoPlotLib.py | 4 +- .../enso/lib/summary_plot_lib/plot.py | 61 ++++++++++++------- 2 files changed, 40 insertions(+), 25 deletions(-) diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py index ab952abb5..5e63cb946 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/EnsoPlotLib.py @@ -3058,8 +3058,8 @@ def plot_param(metric_collection, metric): unique_parts = list(dict.fromkeys(parts)) # Preserves order while removing duplicates refname = "_".join(unique_parts) """ - - # Assign refname to the output dict + + # Assign refname to the output dict dict_out["metric_reference"] = refname # get variable regions dict_out["metric_regions"] = dict_MCm["regions"] diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py index 7dd4d6205..3e107af64 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py @@ -8,10 +8,10 @@ # ---------------------------------------------------# from __future__ import print_function +import difflib import json import string from copy import deepcopy -import difflib import matplotlib.pyplot as plt from matplotlib.colors import BoundaryNorm, ListedColormap @@ -228,7 +228,7 @@ def json_dict_to_numpy_array_list( del list_members, mem # del dict_members, list_models, list_to_remove del list_models, list_to_remove - + # read json file tab_all, tab_all_act, x_names = list(), list(), list() different_ref_keys = list() @@ -252,13 +252,17 @@ def json_dict_to_numpy_array_list( list_metrics = remove_metrics(list_metrics, mc) for met in list_metrics: ref = get_reference(mc, met) # e.g., 'GPCPv2.3' - ref_key_list = list(data_mod[met]["metric"]) # e.g., ['GPCP-2-3', and others] + ref_key_list = list( + data_mod[met]["metric"] + ) # e.g., ['GPCP-2-3', and others] ref_key_act = most_similar_string(ref, ref_key_list) if ref != ref_key_act: if debug: - print(f"Note: For metrics collection '{mc}' metric '{met}', reference key in the JSON for the project '{proj}', '{ref_key_act}', is assumed to be same as the predefined reference, '{ref}'.") + print( + f"Note: For metrics collection '{mc}' metric '{met}', reference key in the JSON for the project '{proj}', '{ref_key_act}', is assumed to be same as the predefined reference, '{ref}'." + ) different_ref_keys.append([ref, ref_key_act]) - + # val = data_mod[met]["metric"][ref]["value"] # Below, if any part of the dictionary chain is missing, val will be set to None without raising a KeyError. val = ( @@ -278,12 +282,18 @@ def json_dict_to_numpy_array_list( del ref, ref_key_act, val del data_mod, list_metrics del data_json, list_models - + if len(different_ref_keys) > 0: - print(f"Note: The following keys were considered to be the same for {proj}:") - unique_different_ref_keys = list(map(list, dict.fromkeys(map(tuple, different_ref_keys)))) + print( + f"Note: The following keys were considered to be the same for {proj}:" + ) + unique_different_ref_keys = list( + map(list, dict.fromkeys(map(tuple, different_ref_keys))) + ) for diff_keys in unique_different_ref_keys: - print(f"Predefined reference: {diff_keys[0]}, reference key in the JSON: {diff_keys[1]}") + print( + f"Predefined reference: {diff_keys[0]}, reference key in the JSON: {diff_keys[1]}" + ) # models and metrics if sort_y_names: @@ -292,10 +302,10 @@ def json_dict_to_numpy_array_list( ) else: tmp_models = list_models_all - + if debug: print("tmp_models:", tmp_models) - + my_metrics = list() for mod in tmp_models: try: @@ -306,7 +316,7 @@ def json_dict_to_numpy_array_list( my_metrics += list(dict1[mod].keys()) my_metrics = sorted(list(set(my_metrics)), key=lambda v: v.upper()) - + if met_order is not None: my_metrics = [met for met in met_order if met in my_metrics] @@ -320,37 +330,39 @@ def json_dict_to_numpy_array_list( my_models = list(reversed(my_models)) del tmp_models - if debug: + if debug: print("my_models:", my_models) # Additional rows (project multi-model means (e.g., CMIP mean), reference, and alternative observation datasets) rows_to_add = list() dict_ref_met = dict() - + if show_alt_obs_rows and list_obs is not None: rows_to_add += list_obs if len(list_obs) > 0 and "obs2obs" in dict_json_path.keys(): # read other observational datasets compared to the reference - dict_ref_met = read_obs(dict_json_path["obs2obs"][mc], list_obs, my_metrics, mc) - + dict_ref_met = read_obs( + dict_json_path["obs2obs"][mc], list_obs, my_metrics, mc + ) + if show_ref_row: rows_to_add += ["reference"] - + if show_proj_means: rows_to_add += list(reversed(list_project)) - + plus = len(rows_to_add) - + if debug: print("rows_to_add:", rows_to_add) print("plus:", plus) - + # fill array tab = NUMPYma__zeros((len(my_models) + plus, len(my_metrics))) if debug: print("tab.shape:", tab.shape) - + for ii, mod in enumerate(my_models): for jj, met in enumerate(my_metrics): try: @@ -416,7 +428,8 @@ def json_dict_to_numpy_array_list( if mc == metric_collections[0]: y_names = rows_to_add + my_models y_names = [ - "(" + dd + ")" if dd in (list_obs + ["reference"]) else dd for dd in y_names + "(" + dd + ")" if dd in (list_obs + ["reference"]) else dd + for dd in y_names ] del dict1, dict_ref_met, my_metrics, my_models, plus, tab, tab_act @@ -427,7 +440,9 @@ def json_dict_to_numpy_array_list( def most_similar_string(target, string_list): - return max(string_list, key=lambda s: difflib.SequenceMatcher(None, target, s).ratio()) + return max( + string_list, key=lambda s: difflib.SequenceMatcher(None, target, s).ratio() + ) def load_met_names(): From 4ff5d0a28a622996f06869400e2518866a645f27 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 09:31:17 -0800 Subject: [PATCH 06/10] initial commit --- .../summary_plot_lib/enso_portrait_plot.ipynb | 292 ++++++++++++++++++ 1 file changed, 292 insertions(+) create mode 100644 pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb new file mode 100644 index 000000000..d2c425495 --- /dev/null +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'enso_portrait_plot' from 'pcmdi_metrics.enso.lib' (/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/pcmdi_metrics/enso/lib/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mglob\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m glob\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpcmdi_metrics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menso\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m enso_portrait_plot\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpcmdi_metrics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m download_files_from_github\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'enso_portrait_plot' from 'pcmdi_metrics.enso.lib' (/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/pcmdi_metrics/enso/lib/__init__.py)" + ] + } + ], + "source": [ + "import os\n", + "from glob import glob\n", + "from pcmdi_metrics.enso.lib import enso_portrait_plot\n", + "from pcmdi_metrics.utils import download_files_from_github" + ] + }, + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "db_url_head = \"https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric\"\n", + "\n", + "dirs_to_downlaod = [\n", + " \"cmip5/historical/v20210104/ENSO_perf\",\n", + " \"cmip5/historical/v20210104/ENSO_tel\",\n", + " \"cmip5/historical/v20210104/ENSO_proc\",\n", + " \"cmip6/historical/v20210620/ENSO_perf\",\n", + " \"cmip6/historical/v20210620/ENSO_tel\",\n", + " \"cmip6/historical/v20210620/ENSO_proc\",\n", + " \"obs2obs\"\n", + "]\n", + "\n", + "path_json = \"json_files\"\n", + "\n", + "for directiry in dirs_to_downlaod:\n", + " github_directory_url = os.path.join(db_url_head, directiry)\n", + " print(github_directory_url)\n", + " download_files_from_github(github_directory_url, path_json)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "path_json = \"json_files\" # v20210620 for CMIP6 and v20210104 for CMIP5\n", + "#path_json = \"/Users/lee1043/Documents/Research/PMP/ENSO_metrics/PortraitPlot/Interactive_bokeh/script_v20201028/json_files\"\n", + "\n", + "list_project = [\"CMIP6\", \"CMIP5\"]\n", + "#list_project = [\"CMIP6\"]\n", + "#list_project = [\"CMIP5\"]\n", + "\n", + "list_obs = [\"20CRv2\", \"NCEP2\", \"ERA-Interim\"]\n", + "#list_obs = [\"20CRv2\"]\n", + "#list_obs = []\n", + "\n", + "figure_name = \"test.png\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json'},\n", + " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json'},\n", + " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", + " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json',\n", + " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json'}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics_collections = [\"ENSO_perf\", \"ENSO_tel\", \"ENSO_proc\"]\n", + "mips = [\"CMIP5\", \"CMIP6\", \"obs2obs\"]\n", + "\n", + "dict_json_path = dict()\n", + "for mip in mips:\n", + " dict_json_path[mip] = dict()\n", + " for metrics_collection in metrics_collections:\n", + " dict_json_path[mip][metrics_collection] = glob(os.path.join(path_json, f\"{mip.lower()}*{metrics_collection}_*.json\"))[0]\n", + " \n", + "dict_json_path" + ] + }, + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show additional rows for CMIP means and alternative observation datasets" + ] + }, + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True, \n", + " show_proj_means=True, show_ref_row=True, show_alt_obs_rows=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add my model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading my_model_ENSO_proc.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_proc.json...\n", + "Saved my_model_ENSO_proc.json to json_files/my_model/my_model_ENSO_proc.json\n", + "Downloading my_model_ENSO_tel.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_tel.json...\n", + "Saved my_model_ENSO_tel.json to json_files/my_model/my_model_ENSO_tel.json\n", + "Downloading my_model_ENSO_perf.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_perf.json...\n", + "Saved my_model_ENSO_perf.json to json_files/my_model/my_model_ENSO_perf.json\n" + ] + } + ], + "source": [ + "db_url = \"https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/test_case/enso\"\n", + "path_json_my_model = \"json_files/my_model\"\n", + "download_files_from_github(db_url, path_json_my_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dict_json_path[\"my_model\"] = dict()\n", + "\n", + "for metrics_collection in metrics_collections:\n", + " dict_json_path[\"my_model\"][metrics_collection] = glob(os.path.join(path_json_my_model, f\"*_{metrics_collection}.json\"))[0]\n", + " \n", + "#list_project = [\"CMIP6\", \"CMIP5\", \"my_model\"]\n", + "list_project = [\"CMIP5\", \"CMIP6\", \"my_model\"]\n", + "#list_project = [ \"my_model\", \"CMIP5\", \"CMIP6\"]\n", + "list_project = [\"CMIP6\", \"my_model\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json'},\n", + " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json'},\n", + " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", + " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json',\n", + " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json'},\n", + " 'my_model': {'ENSO_perf': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_perf.json',\n", + " 'ENSO_tel': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_tel.json',\n", + " 'ENSO_proc': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_proc.json'}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict_json_path" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: The following keys were considered to be the same:\n", + "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", + "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", + "Note: The following keys were considered to be the same:\n", + "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", + "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", + "Predefined reference: Tropflux_GPCPv2.3, reference key in the JSON: TropFlux-1-0_GPCP-2-3\n", + "Predefined reference: ERA-Interim, reference key in the JSON: ERA-INT\n", + "Note: The following keys were considered to be the same:\n", + "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", + "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", + "Predefined reference: Tropflux_GPCPv2.3, reference key in the JSON: TropFlux-1-0_GPCP-2-3\n", + "Predefined reference: ERA-Interim, reference key in the JSON: ERA-INT\n", + "Predefined reference: Tropflux_Tropflux, reference key in the JSON: Tropflux_ERA-Interim\n", + "Predefined reference: Tropflux_AVISO, reference key in the JSON: TropFlux-1-0_AVISO-1-0\n", + "Predefined reference: Tropflux_Tropflux, reference key in the JSON: TropFlux-1-0_TropFlux-1-0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8c3282735c0c002fd09595e11827130fba1b6bca Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 09:34:00 -0800 Subject: [PATCH 07/10] add demo notebook for enso portrait plot --- docs/demo-notebooks.rst | 1 + docs/examples/enso_portrait_plot.ipynb | 1 + 2 files changed, 2 insertions(+) create mode 120000 docs/examples/enso_portrait_plot.ipynb diff --git a/docs/demo-notebooks.rst b/docs/demo-notebooks.rst index 5376256f1..c2def1336 100644 --- a/docs/demo-notebooks.rst +++ b/docs/demo-notebooks.rst @@ -65,6 +65,7 @@ Practical Use Cases examples/parallel_coordinate_plot_mean_clim_multiMIPs examples/taylor_diagram_multiple_CMIPs examples/mean_clim_plots_test_model + examples/enso_portrait_plot examples/variability_modes_plots_all-stats examples/return_value_portrait_plot_demo diff --git a/docs/examples/enso_portrait_plot.ipynb b/docs/examples/enso_portrait_plot.ipynb new file mode 120000 index 000000000..43fa5a920 --- /dev/null +++ b/docs/examples/enso_portrait_plot.ipynb @@ -0,0 +1 @@ +../../pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb \ No newline at end of file From 2ac416a616e9ef089a37d5ef6ce9ff4260044933 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 10:56:38 -0800 Subject: [PATCH 08/10] clean up --- .../summary_plot_lib/enso_portrait_plot.ipynb | 355 ++++++++++++++---- .../enso/lib/summary_plot_lib/plot.py | 32 +- 2 files changed, 299 insertions(+), 88 deletions(-) diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb index d2c425495..43dd259ed 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb @@ -1,36 +1,88 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Portrait Plot: ENSO\n", + "\n", + "Generate a static image of Portrait plot for PMP ENSO metrics obtained from CMIP5 and CMIP6 models, compare multi-model averaged statistics from each group, and compare to the user's model.\n", + "\n", + "Jiwoo Lee (LLNL/PCMDI), 2025. 02\n", + "\n", + "#### References\n", + "\n", + "* Planton, Y., E. Guilyardi, A. T. Wittenberg, J. Lee, P. J. Gleckler, T. Bayr, S. McGregor, M. J. McPhaden, S. Power, R. Roehrig, J. Vialard, A. Voldoire, 2021: A New Way of Evaluating ENSO in Climate Models: The CLIVAR ENSO Metrics Package. Bulletin of the American Meteorological Society, 102, 1073-1080, [doi: 10.1175/BAMS-D-19-0337.A](https://doi.org/10.1175/BAMS-D-19-0337.A)\n", + "\n", + "* Lee, J., P. J. Gleckler, M.-S. Ahn, A. Ordonez, P. Ullrich, K. R. Sperber, K. E. Taylor, Y. Y. Planton, E. Guilyardi, P. Durack, C. Bonfils, M. D. Zelinka, L.-W. Chao, B. Dong, C. Doutriaux, C. Zhang, T. Vo, J. Boutte, M. F. Wehner, A. G. Pendergrass, D. Kim, Z. Xue, A. T. Wittenberg, and J. Krasting, 2024: Systematic and Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3. Geoscientific Model Development, 17, 3919–3948, [doi: 10.5194/gmd-17-3919-2024](https://doi.org/10.5194/gmd-17-3919-2024)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import functions" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'enso_portrait_plot' from 'pcmdi_metrics.enso.lib' (/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/pcmdi_metrics/enso/lib/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mglob\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m glob\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpcmdi_metrics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menso\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m enso_portrait_plot\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpcmdi_metrics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m download_files_from_github\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'enso_portrait_plot' from 'pcmdi_metrics.enso.lib' (/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/pcmdi_metrics/enso/lib/__init__.py)" - ] - } - ], + "outputs": [], "source": [ "import os\n", "from glob import glob\n", "from pcmdi_metrics.enso.lib import enso_portrait_plot\n", - "from pcmdi_metrics.utils import download_files_from_github" + "from pcmdi_metrics.utils import download_files_from_github\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-calculated PMP output\n", + "\n", + "Download PMP ENSO metric output for CMIP5 and CMIP6 from the [PMP output DB](https://github.com/PCMDI/pcmdi_metrics_results_archive)" ] }, { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf\n", + "Downloading cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json...\n", + "Saved cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json to json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_tel\n", + "Downloading cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_tel/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json...\n", + "Saved cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json to json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_proc\n", + "Downloading cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_proc/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json...\n", + "Saved cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json to json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf\n", + "Downloading cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json...\n", + "Saved cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json to json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_tel\n", + "Downloading cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_tel/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json...\n", + "Saved cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json to json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc\n", + "Downloading cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json...\n", + "Saved cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json to json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json\n", + "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/obs2obs\n", + "Downloading obs2obs_ENSO_proc_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_proc_v20200420.json...\n", + "Saved obs2obs_ENSO_proc_v20200420.json to json_files/obs2obs_ENSO_proc_v20200420.json\n", + "Downloading obs2obs_ENSO_perf_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_perf_v20200420.json...\n", + "Saved obs2obs_ENSO_perf_v20200420.json to json_files/obs2obs_ENSO_perf_v20200420.json\n", + "Downloading obs2obs_ENSO_tel_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_tel_v20200420.json...\n", + "Saved obs2obs_ENSO_tel_v20200420.json to json_files/obs2obs_ENSO_tel_v20200420.json\n" + ] } - }, + ], "source": [ "db_url_head = \"https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric\"\n", "\n", @@ -53,23 +105,10 @@ ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "path_json = \"json_files\" # v20210620 for CMIP6 and v20210104 for CMIP5\n", - "#path_json = \"/Users/lee1043/Documents/Research/PMP/ENSO_metrics/PortraitPlot/Interactive_bokeh/script_v20201028/json_files\"\n", - "\n", - "list_project = [\"CMIP6\", \"CMIP5\"]\n", - "#list_project = [\"CMIP6\"]\n", - "#list_project = [\"CMIP5\"]\n", - "\n", - "list_obs = [\"20CRv2\", \"NCEP2\", \"ERA-Interim\"]\n", - "#list_obs = [\"20CRv2\"]\n", - "#list_obs = []\n", - "\n", - "figure_name = \"test.png\"" + "Make a dictionary that includes path for the JSON files:" ] }, { @@ -110,40 +149,114 @@ ] }, { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate the Portrait Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "list_project = [\"CMIP5\", \"CMIP6\"]\n", + "#list_project = [\"CMIP6\"]\n", + "#list_project = [\"CMIP5\"]\n", + "\n", + "list_obs = [\"20CRv2\", \"NCEP2\", \"ERA-Interim\"]\n", + "#list_obs = [\"20CRv2\"] # To show one alternative reference dataset\n", + "#list_obs = [] # To not show any alternative reference dataset\n", + "\n", + "figure_name = \"enso_portrait_plot.png\" # output file path. To make it as a vector image, FILEPATH.pdf would also work. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: The following keys were considered to be the same for CMIP6:\n", + "Predefined reference: Tropflux_Tropflux, reference key in the JSON: Tropflux_ERA-Interim\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } - }, + ], "source": [ - "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" + "#ref_info_dict = enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)\n", + "fig, ref_info_dict = enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Show additional rows for CMIP means and alternative observation datasets" + "### Optional information: CMIP means and alternative observation datasets\n", + "\n", + "Attach additional rows at the bottom of the plot for CMIP means and alternative observation datasets. Also, sorting models in alpabetical order regardless of the CMIP generation." ] }, { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/numpy/ma/core.py:2846: UserWarning: Warning: converting a masked element to nan.\n", + " _data = np.array(data, dtype=dtype, copy=copy,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: The following keys were considered to be the same for CMIP6:\n", + "Predefined reference: Tropflux_Tropflux, reference key in the JSON: Tropflux_ERA-Interim\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } - }, + ], "source": [ - "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True, \n", - " show_proj_means=True, show_ref_row=True, show_alt_obs_rows=True)" + "fig, ref_info_dict = enso_portrait_plot(\n", + " metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True, \n", + " sort_y_names=True, show_proj_means=True, show_ref_row=True, show_alt_obs_rows=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Add my model" + "## Add my model\n", + "\n", + "Add my own model's PMP ENSO output to compare with CMIP models. \n", + "\n", + "Download an example set of JSON files as a user model:" ] }, { @@ -155,12 +268,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading my_model_ENSO_proc.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_proc.json...\n", - "Saved my_model_ENSO_proc.json to json_files/my_model/my_model_ENSO_proc.json\n", + "Downloading my_model_ENSO_perf.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_perf.json...\n", + "Saved my_model_ENSO_perf.json to json_files/my_model/my_model_ENSO_perf.json\n", "Downloading my_model_ENSO_tel.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_tel.json...\n", "Saved my_model_ENSO_tel.json to json_files/my_model/my_model_ENSO_tel.json\n", - "Downloading my_model_ENSO_perf.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_perf.json...\n", - "Saved my_model_ENSO_perf.json to json_files/my_model/my_model_ENSO_perf.json\n" + "Downloading my_model_ENSO_proc.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_proc.json...\n", + "Saved my_model_ENSO_proc.json to json_files/my_model/my_model_ENSO_proc.json\n" ] } ], @@ -171,25 +284,15 @@ ] }, { - "cell_type": "code", - "execution_count": 4, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dict_json_path[\"my_model\"] = dict()\n", - "\n", - "for metrics_collection in metrics_collections:\n", - " dict_json_path[\"my_model\"][metrics_collection] = glob(os.path.join(path_json_my_model, f\"*_{metrics_collection}.json\"))[0]\n", - " \n", - "#list_project = [\"CMIP6\", \"CMIP5\", \"my_model\"]\n", - "list_project = [\"CMIP5\", \"CMIP6\", \"my_model\"]\n", - "#list_project = [ \"my_model\", \"CMIP5\", \"CMIP6\"]\n", - "list_project = [\"CMIP6\", \"my_model\"]" + "Update the dictionary of JSON path to include my model:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -204,38 +307,50 @@ " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json',\n", " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json'},\n", - " 'my_model': {'ENSO_perf': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_perf.json',\n", - " 'ENSO_tel': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_tel.json',\n", - " 'ENSO_proc': '/Users/lee1043/Documents/Research/git/pcmdi_metrics_results_archive/test_case/enso/my_model_ENSO_proc.json'}}" + " 'my_model': {'ENSO_perf': 'json_files/my_model/my_model_ENSO_perf.json',\n", + " 'ENSO_tel': 'json_files/my_model/my_model_ENSO_tel.json',\n", + " 'ENSO_proc': 'json_files/my_model/my_model_ENSO_proc.json'}}" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "dict_json_path[\"my_model\"] = dict()\n", + "\n", + "for metrics_collection in metrics_collections:\n", + " dict_json_path[\"my_model\"][metrics_collection] = glob(os.path.join(path_json_my_model, f\"*_{metrics_collection}.json\"))[0]\n", + " \n", "dict_json_path" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate the plot:" + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Note: The following keys were considered to be the same:\n", + "Note: The following keys were considered to be the same for my_model:\n", "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", - "Note: The following keys were considered to be the same:\n", + "Note: The following keys were considered to be the same for my_model:\n", "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", "Predefined reference: Tropflux_GPCPv2.3, reference key in the JSON: TropFlux-1-0_GPCP-2-3\n", "Predefined reference: ERA-Interim, reference key in the JSON: ERA-INT\n", - "Note: The following keys were considered to be the same:\n", + "Note: The following keys were considered to be the same for my_model:\n", "Predefined reference: GPCPv2.3, reference key in the JSON: GPCP-2-3\n", "Predefined reference: Tropflux, reference key in the JSON: TropFlux-1-0\n", "Predefined reference: Tropflux_GPCPv2.3, reference key in the JSON: TropFlux-1-0_GPCP-2-3\n", @@ -257,15 +372,97 @@ } ], "source": [ - "enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" + "list_project = [\"CMIP6\", \"my_model\"]\n", + "\n", + "fig, ref_info_dict = enso_portrait_plot(metrics_collections, list_project, list_obs, dict_json_path, figure_name=figure_name, reduced_set=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reference dataset information" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'ENSO_perf': {'CMIP6': {'BiasPrLatRmse': 'GPCPv2.3',\n", + " 'BiasPrLonRmse': 'GPCPv2.3',\n", + " 'BiasSstLonRmse': 'Tropflux',\n", + " 'BiasTauxLonRmse': 'Tropflux',\n", + " 'EnsoAmpl': 'Tropflux',\n", + " 'EnsoDuration': 'Tropflux',\n", + " 'EnsoSeasonality': 'Tropflux',\n", + " 'EnsoSstDiversity_2': 'Tropflux',\n", + " 'EnsoSstLonRmse': 'Tropflux',\n", + " 'EnsoSstSkew': 'Tropflux',\n", + " 'EnsoSstTsRmse': 'Tropflux',\n", + " 'SeasonalPrLatRmse': 'GPCPv2.3',\n", + " 'SeasonalPrLonRmse': 'GPCPv2.3',\n", + " 'SeasonalSstLonRmse': 'Tropflux',\n", + " 'SeasonalTauxLonRmse': 'Tropflux'},\n", + " 'my_model': {'BiasPrLatRmse': 'GPCP-2-3',\n", + " 'BiasPrLonRmse': 'GPCP-2-3',\n", + " 'BiasSstLonRmse': 'TropFlux-1-0',\n", + " 'BiasTauxLonRmse': 'TropFlux-1-0',\n", + " 'EnsoAmpl': 'TropFlux-1-0',\n", + " 'EnsoDuration': 'TropFlux-1-0',\n", + " 'EnsoSeasonality': 'TropFlux-1-0',\n", + " 'EnsoSstDiversity_2': 'TropFlux-1-0',\n", + " 'EnsoSstLonRmse': 'TropFlux-1-0',\n", + " 'EnsoSstSkew': 'TropFlux-1-0',\n", + " 'EnsoSstTsRmse': 'TropFlux-1-0',\n", + " 'SeasonalPrLatRmse': 'GPCP-2-3',\n", + " 'SeasonalPrLonRmse': 'GPCP-2-3',\n", + " 'SeasonalSstLonRmse': 'TropFlux-1-0',\n", + " 'SeasonalTauxLonRmse': 'TropFlux-1-0'}},\n", + " 'ENSO_proc': {'CMIP6': {'BiasSstLonRmse': 'Tropflux',\n", + " 'BiasTauxLonRmse': 'Tropflux',\n", + " 'EnsoAmpl': 'Tropflux',\n", + " 'EnsoFbSshSst': 'Tropflux_AVISO',\n", + " 'EnsoFbSstTaux': 'Tropflux_Tropflux',\n", + " 'EnsoFbSstThf': 'Tropflux_ERA-Interim',\n", + " 'EnsoFbTauxSsh': 'Tropflux_AVISO',\n", + " 'EnsoSeasonality': 'Tropflux',\n", + " 'EnsoSstLonRmse': 'Tropflux',\n", + " 'EnsoSstSkew': 'Tropflux',\n", + " 'EnsodSstOce_2': 'Tropflux_ERA-Interim'},\n", + " 'my_model': {'BiasSstLonRmse': 'TropFlux-1-0',\n", + " 'BiasTauxLonRmse': 'TropFlux-1-0',\n", + " 'EnsoAmpl': 'TropFlux-1-0',\n", + " 'EnsoFbSshSst': 'TropFlux-1-0_AVISO-1-0',\n", + " 'EnsoFbSstTaux': 'TropFlux-1-0_TropFlux-1-0',\n", + " 'EnsoFbTauxSsh': 'TropFlux-1-0_AVISO-1-0',\n", + " 'EnsoSeasonality': 'TropFlux-1-0',\n", + " 'EnsoSstLonRmse': 'TropFlux-1-0',\n", + " 'EnsoSstSkew': 'TropFlux-1-0'}},\n", + " 'ENSO_tel': {'CMIP6': {'EnsoAmpl': 'Tropflux',\n", + " 'EnsoPrMapDjfRmse': 'Tropflux_GPCPv2.3',\n", + " 'EnsoPrMapJjaRmse': 'Tropflux_GPCPv2.3',\n", + " 'EnsoSeasonality': 'Tropflux',\n", + " 'EnsoSstLonRmse': 'Tropflux',\n", + " 'EnsoSstMapDjfRmse': 'ERA-Interim',\n", + " 'EnsoSstMapJjaRmse': 'ERA-Interim'},\n", + " 'my_model': {'EnsoAmpl': 'TropFlux-1-0',\n", + " 'EnsoPrMapDjfRmse': 'TropFlux-1-0_GPCP-2-3',\n", + " 'EnsoPrMapJjaRmse': 'TropFlux-1-0_GPCP-2-3',\n", + " 'EnsoSeasonality': 'TropFlux-1-0',\n", + " 'EnsoSstLonRmse': 'TropFlux-1-0',\n", + " 'EnsoSstMapDjfRmse': 'ERA-INT',\n", + " 'EnsoSstMapJjaRmse': 'ERA-INT'}}}\n" + ] + } + ], + "source": [ + "pprint(ref_info_dict)" + ] } ], "metadata": { diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py index 3e107af64..4f7a10752 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py @@ -61,20 +61,29 @@ def enso_portrait_plot( List of observational datasets. dict_json_path : dict Dictionary containing paths to JSON files with metric data. - figure_name : str - Name of the output figure file. + figure_name : str, optional + Name of the output figure file, by default "enso_portrait_plot.png". + reduced_set : bool, optional If True, use a reduced set of metrics, by default False. met_order : list of str, optional Custom order for metrics, by default None. mod_order : list of str, optional Custom order for models, by default None. sort_y_names : bool, optional - If True, sort y-axis names in alphabetical order across `list_project`, by default False. + If True, sort y-axis names in alphabetical order, by default False. + show_proj_means : bool, optional + If True, show project means, by default False. + show_ref_row : bool, optional + If True, show reference row, by default False. + show_alt_obs_rows : bool, optional + If True, show alternative observation rows, by default False. Returns ------- - None - This function does not return any value. It generates and saves a plot. + fig : matplotlib.figure.Figure + The generated figure. + ref_info_dict : dict + Dictionary containing reference information. """ # name of metric collections for the plot and new metric names metric_names_for_plot, met_names = load_met_names() @@ -92,7 +101,7 @@ def enso_portrait_plot( list_obs = list() # get data - tab_all, x_names, y_names = json_dict_to_numpy_array_list( + tab_all, x_names, y_names, ref_info_dict = json_dict_to_numpy_array_list( metric_collections, list_project, list_obs, @@ -121,7 +130,7 @@ def enso_portrait_plot( text = None levels = list(range(-2, 3)) - multiportraitplot( + fig = multiportraitplot( tab_all, figure_name, x_names, @@ -137,6 +146,7 @@ def enso_portrait_plot( met_names=met_names, ) del levels, numbering, text, title + return fig, ref_info_dict # ---------------------------------------------------# @@ -232,12 +242,16 @@ def json_dict_to_numpy_array_list( # read json file tab_all, tab_all_act, x_names = list(), list(), list() different_ref_keys = list() + ref_info_dict = dict() + for mc in metric_collections: if debug: print("mc:", mc) dict1 = dict() list_models_all = list() + ref_info_dict[mc] = dict() for proj in list_project: + ref_info_dict[mc][proj] = dict() # open and read json file data_json = read_data(dict_json_path[proj][mc]) # read metrics @@ -263,6 +277,7 @@ def json_dict_to_numpy_array_list( ) different_ref_keys.append([ref, ref_key_act]) + ref_info_dict[mc][proj][met] = ref_key_act # val = data_mod[met]["metric"][ref]["value"] # Below, if any part of the dictionary chain is missing, val will be set to None without raising a KeyError. val = ( @@ -436,7 +451,7 @@ def json_dict_to_numpy_array_list( if debug: print("len(tab_all):", len(tab_all)) - return tab_all, x_names, y_names + return tab_all, x_names, y_names, ref_info_dict def most_similar_string(target, string_list): @@ -1024,7 +1039,6 @@ def multiportraitplot( ) cax.text(5.2, 0.55, "further from reference", va="bottom", **dict_txt) plt.savefig(name_plot, bbox_inches="tight") - plt.close() return fig From 496517e80e56f9f35ed379919dd3603a436771d5 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 11:18:10 -0800 Subject: [PATCH 09/10] clean up --- .../summary_plot_lib/enso_portrait_plot.ipynb | 78 +++++++++---------- .../enso/lib/summary_plot_lib/plot.py | 33 ++++---- 2 files changed, 52 insertions(+), 59 deletions(-) diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb index 43dd259ed..cfb743350 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb @@ -10,7 +10,7 @@ "\n", "Jiwoo Lee (LLNL/PCMDI), 2025. 02\n", "\n", - "#### References\n", + "References\n", "\n", "* Planton, Y., E. Guilyardi, A. T. Wittenberg, J. Lee, P. J. Gleckler, T. Bayr, S. McGregor, M. J. McPhaden, S. Power, R. Roehrig, J. Vialard, A. Voldoire, 2021: A New Way of Evaluating ENSO in Climate Models: The CLIVAR ENSO Metrics Package. Bulletin of the American Meteorological Society, 102, 1073-1080, [doi: 10.1175/BAMS-D-19-0337.A](https://doi.org/10.1175/BAMS-D-19-0337.A)\n", "\n", @@ -74,12 +74,12 @@ "Downloading cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json...\n", "Saved cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json to json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json\n", "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/obs2obs\n", - "Downloading obs2obs_ENSO_proc_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_proc_v20200420.json...\n", - "Saved obs2obs_ENSO_proc_v20200420.json to json_files/obs2obs_ENSO_proc_v20200420.json\n", "Downloading obs2obs_ENSO_perf_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_perf_v20200420.json...\n", "Saved obs2obs_ENSO_perf_v20200420.json to json_files/obs2obs_ENSO_perf_v20200420.json\n", "Downloading obs2obs_ENSO_tel_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_tel_v20200420.json...\n", - "Saved obs2obs_ENSO_tel_v20200420.json to json_files/obs2obs_ENSO_tel_v20200420.json\n" + "Saved obs2obs_ENSO_tel_v20200420.json to json_files/obs2obs_ENSO_tel_v20200420.json\n", + "Downloading obs2obs_ENSO_proc_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_proc_v20200420.json...\n", + "Saved obs2obs_ENSO_proc_v20200420.json to json_files/obs2obs_ENSO_proc_v20200420.json\n" ] } ], @@ -117,22 +117,19 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", - " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json',\n", - " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json'},\n", - " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", - " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json',\n", - " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json'},\n", - " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", - " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json',\n", - " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json'}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json'},\n", + " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json'},\n", + " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", + " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json',\n", + " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json'}}\n" + ] } ], "source": [ @@ -145,7 +142,7 @@ " for metrics_collection in metrics_collections:\n", " dict_json_path[mip][metrics_collection] = glob(os.path.join(path_json, f\"{mip.lower()}*{metrics_collection}_*.json\"))[0]\n", " \n", - "dict_json_path" + "pprint(dict_json_path)" ] }, { @@ -296,25 +293,22 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", - " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json',\n", - " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json'},\n", - " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", - " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json',\n", - " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json'},\n", - " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", - " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json',\n", - " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json'},\n", - " 'my_model': {'ENSO_perf': 'json_files/my_model/my_model_ENSO_perf.json',\n", - " 'ENSO_tel': 'json_files/my_model/my_model_ENSO_tel.json',\n", - " 'ENSO_proc': 'json_files/my_model/my_model_ENSO_proc.json'}}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'CMIP5': {'ENSO_perf': 'json_files/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip5_historical_ENSO_proc_v20210104_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip5_historical_ENSO_tel_v20210104_allModels_allRuns.json'},\n", + " 'CMIP6': {'ENSO_perf': 'json_files/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", + " 'ENSO_proc': 'json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json',\n", + " 'ENSO_tel': 'json_files/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json'},\n", + " 'my_model': {'ENSO_perf': 'json_files/my_model/my_model_ENSO_perf.json',\n", + " 'ENSO_proc': 'json_files/my_model/my_model_ENSO_proc.json',\n", + " 'ENSO_tel': 'json_files/my_model/my_model_ENSO_tel.json'},\n", + " 'obs2obs': {'ENSO_perf': 'json_files/obs2obs_ENSO_perf_v20200420.json',\n", + " 'ENSO_proc': 'json_files/obs2obs_ENSO_proc_v20200420.json',\n", + " 'ENSO_tel': 'json_files/obs2obs_ENSO_tel_v20200420.json'}}\n" + ] } ], "source": [ @@ -323,7 +317,7 @@ "for metrics_collection in metrics_collections:\n", " dict_json_path[\"my_model\"][metrics_collection] = glob(os.path.join(path_json_my_model, f\"*_{metrics_collection}.json\"))[0]\n", " \n", - "dict_json_path" + "pprint(dict_json_path)" ] }, { @@ -381,7 +375,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reference dataset information" + "## Reference dataset information" ] }, { diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py index 4f7a10752..771b895f2 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/plot.py @@ -188,7 +188,7 @@ def json_dict_to_numpy_array_list( for mod in list(tmp.keys()): try: dict_members[proj][mod] - except: + except KeyError: dict_members[proj][mod] = list(tmp[mod].keys()) else: dict_members[proj][mod] += list(tmp[mod].keys()) @@ -226,12 +226,12 @@ def json_dict_to_numpy_array_list( mem = find_first_member(list_members, mod=mod) try: model_by_proj[proj] - except: + except KeyError: model_by_proj[proj] = {mod: mem} else: try: model_by_proj[proj][mod] - except: + except KeyError: model_by_proj[proj][mod] = mem else: print("this model should not be here") @@ -290,7 +290,7 @@ def json_dict_to_numpy_array_list( val = 1e20 try: dict1[mod] - except: + except KeyError: dict1[mod] = {met: val} else: dict1[mod][met] = val @@ -325,7 +325,7 @@ def json_dict_to_numpy_array_list( for mod in tmp_models: try: list(dict1[mod].keys()) - except: + except KeyError: pass else: my_metrics += list(dict1[mod].keys()) @@ -382,7 +382,7 @@ def json_dict_to_numpy_array_list( for jj, met in enumerate(my_metrics): try: dict1[mod][met] - except: + except KeyError: tab[ii + plus, jj] = 1e20 else: tab[ii + plus, jj] = dict1[mod][met] @@ -862,8 +862,8 @@ def multiportraitplot( dy = 0.5 / (yy2 - yy1) try: ax.set_title(title[kk], fontdict=fontdict, y=1 + dy, loc="center") - except: - pass + except Exception as e: + print(f"An error occurred: {e}") # x axis ticks = [ii + 0.5 for ii in range(len(x_names[kk]))] ax.set_xticks(ticks) @@ -1092,7 +1092,7 @@ def read_obs(filename_json, obsvation_names, list_met, metric_collection): if "Ssh" not in met: try: tab = data_json["20CRv2"]["r1i1p1"]["value"][met]["metric"] - except: + except KeyError: tab = data_json["20CRv2_20CRv2"]["r1i1p1"]["value"][met][ "metric" ] @@ -1104,7 +1104,7 @@ def read_obs(filename_json, obsvation_names, list_met, metric_collection): else: try: tab = data_json["NCEP2"]["r1i1p1"]["value"][met]["metric"] - except: + except KeyError: tab = data_json["NCEP2_NCEP2"]["r1i1p1"]["value"][met]["metric"] elif obs == "ERA-Interim": if "SstMap" in met: @@ -1118,24 +1118,23 @@ def read_obs(filename_json, obsvation_names, list_met, metric_collection): else: try: tab = data_json["ERA-Interim"]["r1i1p1"]["value"][met]["metric"] - except: + except KeyError: tab = data_json["ERA-Interim_ERA-Interim"]["r1i1p1"]["value"][ met ]["metric"] + try: val = tab[ref]["value"] - except: + except KeyError: val = 1e20 + try: dict_out[obs] - except: + except KeyError: dict_out[obs] = {met: val} else: dict_out[obs][met] = val - try: - del tab - except: - pass + del ref, val return dict_out From 54f44764aa257dbbc182845e61a359749b326580 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 13:07:01 -0800 Subject: [PATCH 10/10] clean up --- .../lib/summary_plot_lib/enso_portrait_plot.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb index cfb743350..6647a2423 100644 --- a/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb +++ b/pcmdi_metrics/enso/lib/summary_plot_lib/enso_portrait_plot.ipynb @@ -74,12 +74,12 @@ "Downloading cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json...\n", "Saved cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json to json_files/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json\n", "https://github.com/PCMDI/pcmdi_metrics_results_archive/tree/main/metrics_results/enso_metric/obs2obs\n", + "Downloading obs2obs_ENSO_proc_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_proc_v20200420.json...\n", + "Saved obs2obs_ENSO_proc_v20200420.json to json_files/obs2obs_ENSO_proc_v20200420.json\n", "Downloading obs2obs_ENSO_perf_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_perf_v20200420.json...\n", "Saved obs2obs_ENSO_perf_v20200420.json to json_files/obs2obs_ENSO_perf_v20200420.json\n", "Downloading obs2obs_ENSO_tel_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_tel_v20200420.json...\n", - "Saved obs2obs_ENSO_tel_v20200420.json to json_files/obs2obs_ENSO_tel_v20200420.json\n", - "Downloading obs2obs_ENSO_proc_v20200420.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/obs2obs/obs2obs_ENSO_proc_v20200420.json...\n", - "Saved obs2obs_ENSO_proc_v20200420.json to json_files/obs2obs_ENSO_proc_v20200420.json\n" + "Saved obs2obs_ENSO_tel_v20200420.json to json_files/obs2obs_ENSO_tel_v20200420.json\n" ] } ], @@ -265,12 +265,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading my_model_ENSO_perf.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_perf.json...\n", - "Saved my_model_ENSO_perf.json to json_files/my_model/my_model_ENSO_perf.json\n", + "Downloading my_model_ENSO_proc.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_proc.json...\n", + "Saved my_model_ENSO_proc.json to json_files/my_model/my_model_ENSO_proc.json\n", "Downloading my_model_ENSO_tel.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_tel.json...\n", "Saved my_model_ENSO_tel.json to json_files/my_model/my_model_ENSO_tel.json\n", - "Downloading my_model_ENSO_proc.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_proc.json...\n", - "Saved my_model_ENSO_proc.json to json_files/my_model/my_model_ENSO_proc.json\n" + "Downloading my_model_ENSO_perf.json from https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/test_case/enso/my_model_ENSO_perf.json...\n", + "Saved my_model_ENSO_perf.json to json_files/my_model/my_model_ENSO_perf.json\n" ] } ],