Console Output
+ diff --exclude=.git --exclude=version_clubb_core.txt --exclude=version_silhs.txt -r clubb clubb_release
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/input/stats/all_stats.in clubb_release/input/stats/all_stats.in
16c16
< 'thlm', 'thvm', 'rtm', 'rcm', 'rvm', 'um', 'vm', 'um_ref', 'vm_ref', 'wm_zt',
---
> 'thlm', 'thvm', 'rtm', 'rcm', 'rvm', 'um', 'vm', 'um_ref','vm_ref','wm_zt',
19,20c19,20
< 'rel_humidity', 'wp3', 'thlp3', 'rtp3', 'wpthlp2', 'wp2thlp', 'wprtp2', 'wp2rtp', 'Skw_zt',
< 'Skthl_zt', 'Skrt_zt',
---
> 'rel_humidity', 'wp3','thlp3','rtp3', 'wpthlp2', 'wp2thlp', 'wprtp2', 'wp2rtp', 'Skw_zt',
> 'Skthl_zt','Skrt_zt',
32c32
< 'rtm_forcing', 'rtm_sdmp', 'rtm_mc', 'rtm_pd', 'rvm_mc', 'rcm_mc', 'rcm_sd_mg_morr',
---
> 'rtm_forcing', 'rtm_sdmp','rtm_mc', 'rtm_pd', 'rvm_mc', 'rcm_mc', 'rcm_sd_mg_morr',
34c34
< 'thlm_forcing', 'thlm_sdmp', 'thlm_mc',
---
> 'thlm_forcing', 'thlm_sdmp','thlm_mc',
136c136
< 'up2', 'vp2', 'Skw_zm', 'Skthl_zm', 'Skrt_zm',
---
> 'up2', 'vp2', 'Skw_zm', 'Skthl_zm','Skrt_zm',
142c142
< 'gamma_Skw_fnc', 'coef_wp4_implicit', 'C6rt_Skw_fnc', 'C6thl_Skw_fnc', 'C6_term',
---
> 'gamma_Skw_fnc', 'coef_wp4_implicit', 'C6rt_Skw_fnc', 'C6thl_Skw_fnc',
179c179
< 'wpsclrprtp', 'wpsclrpthlp', 'wpedsclrp', 'stability_correction',
---
> 'wpsclrprtp', 'wpsclrpthlp', 'wpedsclrp', 'stability_correction', 'Richardson_num',
181,182c181
< 'bv_freq_sqd', 'bv_freq_sqd_splat', 'bv_freq_sqd_mixed', 'bv_freq_sqd_moist', 'bv_freq_sqd_dry',
< 'shear_sqd', 'invrs_tau_zm', 'invrs_tau_xp2_zm', 'invrs_tau_wp2_zm',
---
> 'bv_freq_sqd', 'bv_freq_sqd_splat', 'shear_sqd', 'invrs_tau_zm', 'invrs_tau_xp2_zm', 'invrs_tau_wp2_zm',
184c183
< 'invrs_tau_sfc', 'invrs_tau_shear', 'Ri_zm',
---
> 'invrs_tau_sfc', 'invrs_tau_shear', 'sqrt_Ri_zm',
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/input/stats/standard_stats.in clubb_release/input/stats/standard_stats.in
16c16
< 'thlm', 'thvm', 'rtm', 'rcm', 'rvm', 'um', 'vm', 'um_ref', 'vm_ref', 'wm_zt',
---
> 'thlm', 'thvm', 'rtm', 'rcm', 'rvm', 'um', 'vm', 'um_ref','vm_ref','wm_zt',
18,24c18,23
< 'rcm_in_cloud', 'p_in_Pa', 'exner', 'rho_ds_zt', 'thv_ds_zt', 'Lscale', 'T_in_K',
< 'rel_humidity', 'wp3', 'thlp3', 'rtp3', 'wpthlp2', 'wp2thlp', 'wprtp2', 'wp2rtp', 'Skw_zt',
< 'Skthl_zt', 'Skrt_zt',
< 'Lscale_up', 'Lscale_down', 'tau_zt', 'Kh_zt', 'wp2thvp', 'wp2rcp', 'wp2hmp',
<
< 'wprtpthlp', 'wpup2', 'wpvp2', 'rc_coef', 'Lscale_pert_1', 'Lscale_pert_2',
< 'sigma_sqd_w_zt', 'rho', 'radht', 'radht_LW', 'radht_SW',
---
> 'rcm_in_cloud', 'p_in_Pa', 'exner', 'rho_ds_zt', 'thv_ds_zt', 'Lscale',
> 'Lscale_pert_1', 'Lscale_pert_2', 'T_in_K', 'rel_humidity', 'wp3','thlp3','rtp3','wpthlp2',
> 'wp2thlp', 'wprtp2', 'wp2rtp', 'Skw_zt', 'Skthl_zt','Skrt_zt', 'Lscale_up', 'Lscale_down', 'tau_zt',
> 'Kh_zt', 'wp2thvp', 'wp2rcp', 'wp2hmp', 'wprtpthlp', 'wpup2', 'wpvp2', 'rc_coef',
> 'sigma_sqd_w_zt', 'rho',
> 'radht', 'radht_LW', 'radht_SW',
29c28,29
< 'precip_rate_zt', 'rsm', 'rim', 'rgm',
---
> 'precip_rate_zt',
> 'rsm', 'rim', 'rgm',
32c32
< 'rtm_forcing', 'rtm_sdmp', 'rtm_mc', 'rtm_pd', 'rvm_mc', 'rcm_mc', 'rcm_sd_mg_morr',
---
> 'rtm_forcing', 'rtm_sdmp','rtm_mc', 'rtm_pd', 'rvm_mc', 'rcm_mc', 'rcm_sd_mg_morr',
34c34
< 'thlm_forcing', 'thlm_sdmp', 'thlm_mc',
---
> 'thlm_forcing', 'thlm_sdmp','thlm_mc',
83,84c83
< 'mu_hm_i', 'mu_Ncn_i',
< 'sigma_hm_i', 'sigma_Ncn_i',
---
> 'mu_hm_i', 'mu_Ncn_i', 'sigma_hm_i', 'sigma_Ncn_i',
89,90d87
<
<
94d90
<
103c99
< 'NIACRS', 'NGRACS', 'NSMLTS', 'NSAGG', 'NPRCI', 'NSCNG', 'NSUBS', 'PRC', 'PRA',
---
> 'NIACRS', 'NGRACS', 'NSMLTS', 'NSAGG', 'NPRCI', 'NSCNG','NSUBS', 'PRC', 'PRA',
124,126c120
< 'lh_Nrm_src_adj', 'lh_Nrm_evap_adj', 'lh_precip_frac', 'lh_mixt_frac',
<
< 'lh_m_vol_rad_rain',
---
> 'lh_Nrm_src_adj', 'lh_Nrm_evap_adj', 'lh_precip_frac', 'lh_mixt_frac', 'lh_m_vol_rad_rain',
136c130
< 'up2', 'vp2', 'Skw_zm', 'Skthl_zm', 'Skrt_zm',
---
> 'up2', 'vp2', 'Skw_zm', 'Skthl_zm','Skrt_zm',
142c136
< 'gamma_Skw_fnc', 'coef_wp4_implicit', 'C6rt_Skw_fnc', 'C6thl_Skw_fnc', 'C6_term',
---
> 'gamma_Skw_fnc', 'coef_wp4_implicit', 'C6rt_Skw_fnc', 'C6thl_Skw_fnc',
145c139
< 'em', 'mean_w_up', 'mean_w_down', 'shear', 'wp3_zm', 'thlp3_zm', 'rtp3_zm',
---
> 'em', 'mean_w_up', 'mean_w_down', 'shear', 'wp3_zm','thlp3_zm','rtp3_zm',
153,155c147,148
< 'wp2_pr3', 'wp2_pr_dfsn', 'wp2_dp1', 'wp2_dp2', 'wp2_sdmp',
< 'wp2_cl', 'wp2_pd', 'wp2_sf', 'wp2_splat',
< 'vp2_bt', 'vp2_ma', 'vp2_ta', 'vp2_tp', 'vp2_dp1', 'vp2_dp2',
---
> 'wp2_pr3', 'wp2_dp1', 'wp2_dp2', 'wp2_sdmp', 'wp2_cl', 'wp2_pd', 'wp2_sf', 'wp2_splat',
> 'wp2_pr_dfsn', 'vp2_bt', 'vp2_ma', 'vp2_ta', 'vp2_tp', 'vp2_dp1', 'vp2_dp2',
163,165c156,158
< 'wprtp_bt', 'wprtp_ma', 'wprtp_ta', 'wprtp_tp', 'wprtp_ac',
< 'wprtp_bp', 'wprtp_pr1', 'wprtp_pr2', 'wprtp_pr3', 'wprtp_dp1',
< 'wprtp_mfl', 'wprtp_cl', 'wprtp_sicl', 'wprtp_pd', 'wprtp_forcing', 'wprtp_mc',
---
> 'wprtp_bt', 'wprtp_ma', 'wprtp_ta', 'wprtp_tp', 'wprtp_ac', 'wprtp_bp',
> 'wprtp_pr1', 'wprtp_pr2', 'wprtp_pr3', 'wprtp_dp1', 'wprtp_mfl',
> 'wprtp_cl', 'wprtp_sicl', 'wprtp_pd', 'wprtp_forcing', 'wprtp_mc',
169,174c162,167
< 'rtp2_bt', 'rtp2_ma', 'rtp2_ta', 'rtp2_tp',
< 'rtp2_dp1', 'rtp2_dp2', 'rtp2_cl', 'rtp2_pd', 'rtp2_sf', 'rtp2_forcing', 'rtp2_mc',
< 'thlp2_bt', 'thlp2_ma', 'thlp2_ta', 'thlp2_tp',
< 'thlp2_dp1', 'thlp2_dp2', 'thlp2_cl', 'thlp2_pd', 'thlp2_sf', 'thlp2_forcing', 'thlp2_mc',
< 'rtpthlp_bt', 'rtpthlp_ma', 'rtpthlp_ta', 'rtpthlp_tp1', 'rtpthlp_tp2',
< 'rtpthlp_dp1', 'rtpthlp_dp2', 'rtpthlp_cl', 'rtpthlp_sf', 'rtpthlp_forcing', 'rtpthlp_mc',
---
> 'rtp2_bt', 'rtp2_ma', 'rtp2_ta', 'rtp2_tp', 'rtp2_dp1', 'rtp2_dp2',
> 'rtp2_cl', 'rtp2_pd', 'rtp2_sf', 'rtp2_forcing', 'rtp2_mc',
> 'thlp2_bt', 'thlp2_ma', 'thlp2_ta', 'thlp2_tp', 'thlp2_dp1', 'thlp2_dp2',
> 'thlp2_cl', 'thlp2_pd', 'thlp2_sf', 'thlp2_forcing', 'thlp2_mc',
> 'rtpthlp_bt', 'rtpthlp_ma', 'rtpthlp_ta', 'rtpthlp_tp1', 'rtpthlp_tp2', 'rtpthlp_dp1',
> 'rtpthlp_dp2', 'rtpthlp_cl', 'rtpthlp_sf', 'rtpthlp_forcing', 'rtpthlp_mc',
176,182c169,170
< 'wpthlp_mfl_min', 'wpthlp_mfl_max', 'wprtp_mfl_min', 'wprtp_mfl_max',
<
<
< 'stability_correction',
<
< 'bv_freq_sqd', 'bv_freq_sqd_splat', 'bv_freq_sqd_mixed', 'bv_freq_sqd_moist', 'bv_freq_sqd_dry',
< 'shear_sqd', 'invrs_tau_zm', 'invrs_tau_xp2_zm', 'invrs_tau_wp2_zm',
---
> 'wpthlp_mfl_min', 'wpthlp_mfl_max', 'wprtp_mfl_min', 'wprtp_mfl_max', 'stability_correction',
> 'bv_freq_sqd', 'bv_freq_sqd_splat', 'shear_sqd', 'invrs_tau_zm', 'invrs_tau_xp2_zm', 'invrs_tau_wp2_zm',
184c172
< 'invrs_tau_sfc', 'invrs_tau_shear', 'Ri_zm',
---
> 'invrs_tau_sfc', 'invrs_tau_shear', 'Richardson_num',
209,212c197
< 'rtm_spur_src', 'thlm_spur_src',
<
<
< 'rs_sd_morr_int',
---
> 'rtm_spur_src', 'thlm_spur_src', 'rs_sd_morr_int',
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/input/tunable_parameters_Lscale/configurable_model_flags.in clubb_release/input/tunable_parameters_Lscale/configurable_model_flags.in
3,5c3
< ipdf_call_placement = 2
< penta_solve_method = 1
< tridiag_solve_method = 1
---
> ipdf_call_placement = 1
7,55c5,47
<
< l_godunov_upwind_wpxp_ta = .false.,
< l_upwind_xpyp_ta = .true.,
< l_godunov_upwind_xpyp_ta = .false.,
< l_upwind_xm_ma = .true.,
< l_quintic_poly_interp = .false.,
< l_tke_aniso = .true.,
< l_vert_avg_closure = .false.,
< l_standard_term_ta = .false.,
< l_partial_upwind_wp3 = .false.,
< l_use_cloud_cover = .false.,
< l_rcm_supersat_adj = .true.,
< l_damp_wp3_Skw_squared = .true.,
< l_min_wp2_from_corr_wx = .true.,
< l_min_xp2_from_corr_wx = .true.,
< l_C2_cloud_frac = .false.,
< l_predict_upwp_vpwp = .true.,
< l_diag_Lscale_from_tau = .false.,
< l_stability_correct_tau_zm = .false.,
< l_damp_wp2_using_em = .true.,
< l_use_C7_Richardson = .true.,
< l_use_precip_frac = .true.,
< l_do_expldiff_rtm_thlm = .false.,
< l_use_C11_Richardson = .false.,
< l_use_shear_Richardson = .false.,
< l_prescribed_avg_deltaz = .false.,
< l_diffuse_rtm_and_thlm = .false.,
< l_stability_correct_Kh_N2_zm = .false.,
< l_trapezoidal_rule_zt = .false.,
< l_trapezoidal_rule_zm = .false.,
< l_call_pdf_closure_twice = .false.,
< l_Lscale_plume_centered = .false.,
< l_brunt_vaisala_freq_moist = .false.,
< l_use_thvm_in_bv_freq = .false.,
< l_lmm_stepping = .false.,
< l_e3sm_config = .false.,
< l_vary_convect_depth = .false.,
< l_use_tke_in_wp3_pr_turb_term = .true.,
< l_use_tke_in_wp2_wp3_K_dfsn = .false.,
< l_use_wp3_lim_with_smth_Heaviside = .false.,
< l_smooth_Heaviside_tau_wpxp = .false.,
< l_modify_limiters_for_cnvg_test = .false.,
< l_enable_relaxed_clipping = .false.,
< l_linearize_pbl_winds = .false.,
< l_mono_flux_lim_thlm = .true.,
< l_mono_flux_lim_rtm = .true.,
< l_mono_flux_lim_um = .true.,
< l_mono_flux_lim_vm = .true.,
< l_mono_flux_lim_spikefix = .true.
---
> l_godunov_upwind_wpxp_ta = .false.,
> l_upwind_xpyp_ta = .true.,
> l_godunov_upwind_xpyp_ta = .false.,
> l_upwind_xm_ma = .true.,
> l_quintic_poly_interp = .false.,
> l_tke_aniso = .true.,
> l_vert_avg_closure = .false.,
> l_standard_term_ta = .false.,
> l_partial_upwind_wp3 = .false.,
> l_use_cloud_cover = .false.,
> l_rcm_supersat_adj = .true.,
> l_damp_wp3_Skw_squared = .true.,
> l_min_wp2_from_corr_wx = .true.,
> l_min_xp2_from_corr_wx = .true.,
> l_C2_cloud_frac = .false.,
> l_predict_upwp_vpwp = .true.,
> l_diag_Lscale_from_tau = .false.,
> l_stability_correct_tau_zm = .false.,
> l_damp_wp2_using_em = .true.,
> l_use_C7_Richardson = .true.,
> l_use_precip_frac = .true.,
> l_do_expldiff_rtm_thlm = .false.,
> l_use_C11_Richardson = .false.,
> l_use_shear_Richardson = .false.,
> l_prescribed_avg_deltaz = .false.,
> l_diffuse_rtm_and_thlm = .false.,
> l_stability_correct_Kh_N2_zm = .false.,
> l_trapezoidal_rule_zt = .false.,
> l_trapezoidal_rule_zm = .false.,
> l_call_pdf_closure_twice = .false.,
> l_Lscale_plume_centered = .false.,
> l_brunt_vaisala_freq_moist = .false.,
> l_use_thvm_in_bv_freq = .false.,
> l_lmm_stepping = .false.,
> l_e3sm_config = .false.,
> l_vary_convect_depth = .false.,
> l_use_tke_in_wp3_pr_turb_term = .false.,
> l_use_tke_in_wp2_wp3_K_dfsn = .false.
> l_mono_flux_lim_thlm = .true.,
> l_mono_flux_lim_rtm = .true.,
> l_mono_flux_lim_um = .true.,
> l_mono_flux_lim_vm = .true.,
> l_mono_flux_lim_spikefix = .true.
72,75d63
< /
<
< &configurable_multi_column_nl
< num_standalone_columns = 1
Only in clubb/postprocessing/pyplotgen/config: VariableGroupBaseBudgetsLumpedBuoy.py
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupBaseBudgets.py clubb_release/postprocessing/pyplotgen/config/VariableGroupBaseBudgets.py
18c18
< priority_vars=False, background_rcm=False, background_rcm_folder=None):
---
> priority_vars=False):
538,539c538
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupBaseBudgetsSamStyle.py clubb_release/postprocessing/pyplotgen/config/VariableGroupBaseBudgetsSamStyle.py
18c18
< priority_vars=False, background_rcm=False, background_rcm_folder=None):
---
> priority_vars=False):
376,377c376
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupBase.py clubb_release/postprocessing/pyplotgen/config/VariableGroupBase.py
22,23c22
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
685,686c684
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupCorrelations.py clubb_release/postprocessing/pyplotgen/config/VariableGroupCorrelations.py
16c16
< priority_vars=False, background_rcm=False, background_rcm_folder=None):
---
> priority_vars=False):
161,162c161
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupIceMP.py clubb_release/postprocessing/pyplotgen/config/VariableGroupIceMP.py
14,15c14,15
< def __init__(self, case, clubb_datasets=None, sam_benchmark_dataset=None, sam_datasets=None,
< coamps_benchmark_dataset=None, wrf_benchmark_dataset=None,
---
> def __init__(self, case, clubb_datasets=None, sam_benchmark_dataset=None, sam_datasets=None, coamps_benchmark_dataset=None,
> wrf_benchmark_dataset=None,
17,18c17
< e3sm_datasets=None, wrf_datasets=None, priority_vars=False, background_rcm=False,
< background_rcm_folder=None):
---
> e3sm_datasets=None, wrf_datasets=None, priority_vars=False):
151,152c150
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupKKMP.py clubb_release/postprocessing/pyplotgen/config/VariableGroupKKMP.py
13,14c13,14
< def __init__(self, case, clubb_datasets=None, sam_benchmark_dataset=None,
< coamps_benchmark_dataset=None, r408_dataset=None, wrf_benchmark_dataset=None,
---
> def __init__(self, case, clubb_datasets=None, sam_benchmark_dataset=None, coamps_benchmark_dataset=None, r408_dataset=None,
> wrf_benchmark_dataset=None,
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
69,70c68
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupLiquidMP.py clubb_release/postprocessing/pyplotgen/config/VariableGroupLiquidMP.py
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None,priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None,priority_vars=False):
151,152c150
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupNondimMoments.py clubb_release/postprocessing/pyplotgen/config/VariableGroupNondimMoments.py
21,22c21
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
215,216c214
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupNormalizedVariations.py clubb_release/postprocessing/pyplotgen/config/VariableGroupNormalizedVariations.py
17,18c17
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
76,77c75
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupSamBudgets.py clubb_release/postprocessing/pyplotgen/config/VariableGroupSamBudgets.py
18c18
< priority_vars=False, background_rcm=False, background_rcm_folder=None):
---
> priority_vars=False):
984,985c984
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupSamMultilineProfiles.py clubb_release/postprocessing/pyplotgen/config/VariableGroupSamMultilineProfiles.py
18,19c18
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
451,452c450
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupSamProfiles.py clubb_release/postprocessing/pyplotgen/config/VariableGroupSamProfiles.py
18,19c18
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
1204,1205c1203
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupScalars.py clubb_release/postprocessing/pyplotgen/config/VariableGroupScalars.py
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
81,82c80
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupSubcolumns.py clubb_release/postprocessing/pyplotgen/config/VariableGroupSubcolumns.py
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None,
< background_rcm=False, background_rcm_folder=False):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None):
186,187c185
< cam_datasets=cam_datasets, sam_datasets=sam_datasets, wrf_datasets=wrf_datasets,
< background_rcm=background_rcm, background_rcm_folder=background_rcm_folder)
---
> cam_datasets=cam_datasets, sam_datasets=sam_datasets, wrf_datasets=wrf_datasets)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupTaus.py clubb_release/postprocessing/pyplotgen/config/VariableGroupTaus.py
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
37,81d35
< 'clubb': ['bv_freq_sqd_mixed'],
< 'sam': [],
< 'coamps': [],
< 'r408': ['bv_freq_sqd_mixed'],
< 'hoc': ['bv_freq_sqd_mixed'],
< 'e3sm': ['bv_freq_sqd_mixed'],
< 'cam': ['bv_freq_sqd_mixed'],
< 'wrf': ['bv_freq_sqd_mixed'],
< },
< 'title': 'Mixed Brunt-Vaisala frequency squared',
< 'axis_title': 'bv_freq_sqd_mixed [$\mathrm{1/s^2}$]',
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['bv_freq_sqd_moist'],
< 'sam': [],
< 'coamps': [],
< 'r408': ['bv_freq_sqd_moist'],
< 'hoc': ['bv_freq_sqd_moist'],
< 'e3sm': ['bv_freq_sqd_moist'],
< 'cam': ['bv_freq_sqd_moist'],
< 'wrf': ['bv_freq_sqd_moist'],
< },
< 'title': 'Brunt-Vaisala frequency squared in moist air',
< 'axis_title': 'bv_freq_sqd_moist [$\mathrm{1/s^2}$]',
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['bv_freq_sqd_dry'],
< 'sam': [],
< 'coamps': [],
< 'r408': ['bv_freq_sqd_dry'],
< 'hoc': ['bv_freq_sqd_dry'],
< 'e3sm': ['bv_freq_sqd_dry'],
< 'cam': ['bv_freq_sqd_dry'],
< 'wrf': ['bv_freq_sqd_dry'],
< },
< 'title': 'Brunt-Vaisala frequency squared in dry air',
< 'axis_title': 'bv_freq_sqd_dry [$\mathrm{1/s^2}$]',
< 'sci_scale': 0,
< },
< {'var_names':
< {
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< {'var_names':
< {
< 'clubb': ['C6_term'],
< 'sam': ['C6_TERM'], # Dummy
< 'coamps': ['C6_term'], # Dummy
< 'r408': ['C6_term'], # Dummy
< 'hoc': ['C6_term'], # Dummy
< 'e3sm': ['C6_term'], # Dummy
< 'cam': ['C6_term'], # Dummy
< 'wrf': ['C6_term'], # Dummy
< },
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['C1_Skw_fnc'],
< 'sam': ['C1_SKW_FNC'], # Dummy
< 'coamps': ['C1_Skw_fnc'], # Dummy
< 'r408': ['C1_Skw_fnc'], # Dummy
< 'hoc': ['C1_Skw_fnc'], # Dummy
< 'e3sm': ['C1_Skw_fnc'], # Dummy
< 'cam': ['C1_Skw_fnc'], # Dummy
< 'wrf': ['C1_Skw_fnc'], # Dummy
< },
< 'title': 'C1_Skw_fnc',
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['C6rt_Skw_fnc'],
< 'sam': ['C6RT_SKW_FNC'], # Dummy
< 'coamps': ['C6rt_Skw_fnc'], # Dummy
< 'r408': ['C6rt_Skw_fnc'], # Dummy
< 'hoc': ['C6rt_Skw_fnc'], # Dummy
< 'e3sm': ['C6rt_Skw_fnc'], # Dummy
< 'cam': ['C6rt_Skw_fnc'], # Dummy
< 'wrf': ['C6rt_Skw_fnc'], # Dummy
< },
< 'title': 'C6rt_Skw_fnc',
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['C6thl_Skw_fnc'],
< 'sam': ['C6THL_SKW_FNC'], # Dummy
< 'coamps': ['C6thl_Skw_fnc'], # Dummy
< 'r408': ['C6thl_Skw_fnc'], # Dummy
< 'hoc': ['C6thl_Skw_fnc'], # Dummy
< 'e3sm': ['C6thl_Skw_fnc'], # Dummy
< 'cam': ['C6thl_Skw_fnc'], # Dummy
< 'wrf': ['C6thl_Skw_fnc'], # Dummy
< },
< 'title': 'C6thl_Skw_fnc',
< 'sci_scale': 0,
< },
< {'var_names':
< {
< 'clubb': ['C7_Skw_fnc'],
< 'sam': ['C7_SKW_FNC'], # Dummy
< 'coamps': ['C7_Skw_fnc'], # Dummy
< 'r408': ['C7_Skw_fnc'], # Dummy
< 'hoc': ['C7_Skw_fnc'], # Dummy
< 'e3sm': ['C7_Skw_fnc'], # Dummy
< 'cam': ['C7_Skw_fnc'], # Dummy
< 'wrf': ['C7_Skw_fnc'], # Dummy
< },
< 'title': 'C7_Skw_fnc',
< 'sci_scale': 0,
< },
248,249c133
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/config/VariableGroupWs.py clubb_release/postprocessing/pyplotgen/config/VariableGroupWs.py
16,17c16
< e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False,
< background_rcm=False, background_rcm_folder=None):
---
> e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False):
211,212c210
< priority_vars=priority_vars, background_rcm=background_rcm,
< background_rcm_folder=background_rcm_folder)
---
> priority_vars=priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/pyplotgen.py clubb_release/postprocessing/pyplotgen/pyplotgen.py
48,50c48,50
< plot_budgets=False, bu_morr=False, lumped_buoy_budgets=False, background_rcm=False, diff=None,
< show_alphabetic_id=False, time_height=False, animation=None, samstyle=False, disable_multithreading=False,
< pdf=False, pdf_filesize_limit=None, plot_subcolumns=False, image_extension=".png"):
---
> plot_budgets=False, bu_morr=False, diff=None, show_alphabetic_id=False,
> time_height=False, animation=None, samstyle=False, disable_multithreading=False, pdf=False,
> pdf_filesize_limit=None, plot_subcolumns=False, image_extension=".png"):
88,89d87
< :param lumped_buoy_budgets: Lump together wpxp_bp and wpxp_pr3 terms in CLUBB's budgets
< :param background_rcm: Show a height-based "contour" plot of time-averaged rcm behind CLUBB profiles.
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< self.lumped_buoy_budgets = lumped_buoy_budgets
< self.background_rcm = background_rcm
422,424c418,420
< plot_subcolumns=self.plot_subcolumns, lumped_buoy_budgets=self.lumped_buoy_budgets,
< background_rcm=self.background_rcm, image_extension=self.image_extension,
< total_panels_to_plot=0, priority_vars=self.priority_vars)
---
> plot_subcolumns=self.plot_subcolumns,
> image_extension=self.image_extension, total_panels_to_plot=0,
> priority_vars=self.priority_vars)
646,649d641
< parser.add_argument("--lumped-buoy-budgets", help="Lump together wpxp_bp and wpxp_pr3 terms in CLUBB's budgets.",
< action="store_true")
< parser.add_argument("--background-rcm", help="Show a height-based 'contour' plot of time-averaged rcm behind CLUBB profiles.",
< action="store_true")
803,806c795,798
< no_legends=args.no_legends, plot_budgets=args.plot_budgets, bu_morr=args.bu_morr,
< lumped_buoy_budgets=args.lumped_buoy_budgets, background_rcm=args.background_rcm, diff=args.diff,
< show_alphabetic_id=args.show_alphabetic_id, time_height=args.time_height_plots, animation=args.movies,
< samstyle=args.sam_style_budgets, disable_multithreading=args.disable_multithreading, pdf=args.pdf,
---
> no_legends=args.no_legends, plot_budgets=args.plot_budgets,
> bu_morr=args.bu_morr, diff=args.diff, show_alphabetic_id=args.show_alphabetic_id,
> time_height=args.time_height_plots, animation=args.movies, samstyle=args.sam_style_budgets,
> disable_multithreading=args.disable_multithreading, pdf=args.pdf,
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/README.md clubb_release/postprocessing/pyplotgen/README.md
52,53d51
< | --lumped-buoy-budgets | Lump together wpxp_bp and wpxp_pr3 terms in CLUBB's budgets |
< | --background_rcm | Show a height-based "contour" plot of time-averaged rcm behind CLUBB profiles |
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/src/CaseGallerySetup.py clubb_release/postprocessing/pyplotgen/src/CaseGallerySetup.py
11d10
< from config.VariableGroupBaseBudgetsLumpedBuoy import VariableGroupBaseBudgetsLumpedBuoy
33,35c32,34
< plot_les=False, plot_budgets=False, lumped_buoy_budgets=False, background_rcm=False, plot_r408=False,
< plot_hoc=False, e3sm_folders=[], cam_folders=[], time_height=False, animation=None, samstyle=False,
< plot_subcolumns=False, image_extension=".png", total_panels_to_plot=0, priority_vars=False):
---
> plot_les=False, plot_budgets=False, plot_r408=False, plot_hoc=False, e3sm_folders=[], cam_folders=[],
> time_height=False, animation=None, samstyle=False, plot_subcolumns=False, image_extension=".png",
> total_panels_to_plot=0, priority_vars=False):
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< :param lumped_buoy_budgets: If True and if plot_budgets in true, wpxp_bp and wpxp_pr3 will be lumped into one budget term
< :param background_rcm: Show a height-based "contour" plot of time-averaged rcm behind CLUBB profiles.
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< self.lumped_buoy_budgets = lumped_buoy_budgets
< self.background_rcm = background_rcm
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< self.lumped_buoy_budgets = False
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< # Preserve the CLUBB folder listed first to use for background rcm plots.
< if len(clubb_folders) != 0:
< self.background_rcm_folder = clubb_folders[0]
< else:
< self.background_rcm_folder = None
<
172,177c160,161
< if not self.lumped_buoy_budgets:
< budget_variables = VariableGroupBaseBudgets(self, priority_vars=self.priority_vars,
< clubb_datasets={folder_name:self.clubb_datasets[input_folder]})
< else:
< budget_variables = VariableGroupBaseBudgetsLumpedBuoy(self, priority_vars=self.priority_vars,
< clubb_datasets={folder_name:self.clubb_datasets[input_folder]})
---
> budget_variables = VariableGroupBaseBudgets(self, priority_vars=self.priority_vars,
> clubb_datasets={folder_name:self.clubb_datasets[input_folder]})
250,251c234
< e3sm_datasets=self.e3sm_datasets, cam_datasets=self.cam_file, priority_vars=self.priority_vars,
< background_rcm=self.background_rcm, background_rcm_folder=self.background_rcm_folder)
---
> e3sm_datasets=self.e3sm_datasets, cam_datasets=self.cam_file, priority_vars=self.priority_vars)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/src/Panel.py clubb_release/postprocessing/pyplotgen/src/Panel.py
34,36c34,35
< def __init__(self, plots, bkgrnd_rcm_tavg, altitude_bkgrnd_rcm, start_alt_idx, end_alt_idx,
< panel_type="profile", title="Unnamed panel", dependent_title="dependent variable", sci_scale = None,
< centered = False, background_rcm = False):
---
> def __init__(self, plots, panel_type="profile", title="Unnamed panel", dependent_title="dependent variable",
> sci_scale = None, centered = False):
41,44d39
< :param bkgrnd_rcm_tavg: Time-average vertical profile of rcm that can be displayed in the background of plots.
< :param altitude_bkgrnd_rcm: Heights corresponding with the background rcm profile.
< :param start_alt_idx: Index of bkgrnd_rcm_tavg that corresponds with bottom of the plot.
< :param end_alt_idx: Index of bkgrnd_rcm_tavg that corresponds with top of the plot.
52d46
< :param background_rcm: Show a height-based "contour" plot of time-averaged rcm behind CLUBB profiles.
56,59d49
< self.bkgrnd_rcm_tavg = bkgrnd_rcm_tavg
< self.altitude_bkgrnd_rcm = altitude_bkgrnd_rcm
< self.start_alt_idx = start_alt_idx
< self.end_alt_idx = end_alt_idx
67d56
< self.background_rcm = background_rcm
236,239c225
< if not self.background_rcm:
< ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
< else:
< ax.legend(loc='upper right', bbox_to_anchor=(1, 1))
---
> ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
253,270d238
<
< # Background rcm contour plot
< if self.background_rcm:
< num_points = self.end_alt_idx - self.start_alt_idx + 1
< bkgrnd_rcm_tavg_contours = np.eye( num_points )
< for i in range(num_points):
< for j in range(num_points):
< bkgrnd_rcm_tavg_contours[i,j] = self.bkgrnd_rcm_tavg[self.start_alt_idx+i]
< min_value = min( self.bkgrnd_rcm_tavg )
< max_value = max( self.bkgrnd_rcm_tavg )
< x_vector_contour = np.zeros( num_points )
< x_diff = xlim[1] - xlim[0]
< x_interval = x_diff / ( num_points - 1 )
< for k in range(num_points):
< x_vector_contour[k] = xlim[0] + float(k) * x_interval
< plt.contourf( x_vector_contour, self.altitude_bkgrnd_rcm[self.start_alt_idx:self.end_alt_idx+1], bkgrnd_rcm_tavg_contours,
< vmin=min_value, vmax=2.0*max_value, cmap=plt.set_cmap("gist_yarg") )
< plt.colorbar( label="rcm [kg/kg]", orientation="vertical" )
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/postprocessing/pyplotgen/src/VariableGroup.py clubb_release/postprocessing/pyplotgen/src/VariableGroup.py
39c39
< priority_vars=False, background_rcm=False, background_rcm_folder=None):
---
> priority_vars=False):
55,56d54
< :param background_rcm: Show a height-based "contour" plot of time-averaged rcm behind CLUBB profiles.
< :param background_rcm_folder: Folder of CLUBB output data from which to pull the background rcm (if background_rcm is enabled).
80,81d77
< self.background_rcm = background_rcm
< self.background_rcm_folder = background_rcm_folder
98,137d93
< if self.background_rcm:
< if self.clubb_datasets is not None and len(self.clubb_datasets) != 0:
< # Extract rcm from the zt NetCDF file. Also extract the time and height values to which the
< # rcm data points correspond.
< bkgrnd_rcm = np.squeeze( self.clubb_datasets[self.background_rcm_folder]['zt'].variables['rcm'] )
< self.altitude_bkgrnd_rcm = np.squeeze( self.clubb_datasets[self.background_rcm_folder]['zt'].variables['altitude'] )
< time_bkgrnd_rcm = np.squeeze( self.clubb_datasets[self.background_rcm_folder]['zt'].variables['time'] )
< # Find the indices in the rcm data that correspond to the start time and end time requested as the
< # time-averaging interval for the case, as well as the minimum height and maximum height requested
< # for the plots.
< start_time_seconds = 60.0 * self.start_time # self.start_time is in minutes, while time_bkgrnd_rcm is in seconds.
< end_time_seconds = 60.0 * self.end_time # self.end_time is in minutes, while time_bkgrnd_rcm is in seconds.
< start_time_idx, end_time_idx = DataReader.__getStartEndIndex__(time_bkgrnd_rcm, start_time_seconds, end_time_seconds)
< self.start_alt_idx, self.end_alt_idx = DataReader.__getStartEndIndex__(self.altitude_bkgrnd_rcm, self.height_min_value, self.height_max_value)
< # Calculate the time-averaged vertical profile of rcm for use as contours in the background of plots
< # of CLUBB time-averaged vertical profiles of various model fields.
< nzt = np.shape(bkgrnd_rcm)[1]
< self.bkgrnd_rcm_tavg = np.zeros(nzt)
< for z_indx in range(nzt):
< lev_sum = 0
< count = 0
< for t_indx in range(start_time_idx, end_time_idx):
< lev_sum = lev_sum + bkgrnd_rcm[t_indx,z_indx]
< count = count + 1
< self.bkgrnd_rcm_tavg[z_indx] = lev_sum / float(count)
< else:
< # Set the relevant "output" variables to a value just to have them set.
< # They will be flagged out of interacting with the code.
< self.bkgrnd_rcm_tavg = 0
< self.altitude_bkgrnd_rcm = 0
< self.start_alt_idx = 0
< self.end_alt_idx = 0
< else:
< # Set the relevant "output" variables to a value just to have them set.
< # They will be flagged out of interacting with the code.
< self.bkgrnd_rcm_tavg = 0
< self.altitude_bkgrnd_rcm = 0
< self.start_alt_idx = 0
< self.end_alt_idx = 0
<
438,440c394,395
< panel = Panel(plotset, self.bkgrnd_rcm_tavg, self.altitude_bkgrnd_rcm, self.start_alt_idx, self.end_alt_idx,
< title=title, dependent_title=axis_label, panel_type=panel_type, sci_scale=sci_scale,
< centered=centered, background_rcm=self.background_rcm)
---
> panel = Panel(plotset, title=title, dependent_title=axis_label, panel_type=panel_type,
> sci_scale=sci_scale, centered=centered)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/run_scripts/run_bindiff_all.py clubb_release/run_scripts/run_bindiff_all.py
11c11,12
< DEBUG = False
---
> scriptPath = os.path.realpath(__file__)[0:-18]
> #print(scriptPath)
13,19d13
< scriptPath = os.path.dirname(os.path.realpath(__file__))+'/'
< if DEBUG:
< print(scriptPath)
< outFilePath = os.path.normpath(scriptPath+"../output/bindiffs/")
< filePostFix = "_diff{}.log"
<
< ## CONSTANTS ##
23,32d16
< # NetCDF file postfixes we want to diff
< nc_data_formats = ["_zm.nc", "_zt.nc", "_sfc.nc"]
<
< # File containing all the case names
< case_file = "RUN_CASES"
<
< # Define output messages:
< # 1. Sum of absolute differences below threshold for all variables in all files for a single case.
< no_value_diff_out = ">>>Differences in linux diff but no differences in common variables detected for case {}.<<<"
<
35,48c19,23
< # Set up and parse command line arguments
< parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
< parser.add_argument("-f", "--fileout", action="store", choices=["skip", "replace", "enumerate"], default=None,
< help="Output a table of diffs into a text file, with one output file per each pair of diffed netcdf files.\n"+
< "The text files will be named '<input_file>-diff_out' and will be stored in a subfolder of CLUBB's output folder.\n"+
< "The choices for this option are:"+
< "\n1. Flag omitted: No files will be created."+
< "\n2. 'enumerate': If a diff log file for a case already exists\n\t\tthe new file will be created with an increasing numerical postfix."+
< "\n3. 'skip': If a diff log file for a case already exists the creation of the log file will be skipped."+
< "\n4. 'replace': If a diff log file for a case already exists the old file will be replaced with a new log file.")
< parser.add_argument("-v", "--verbose", action="store_true", help="Print to console a table of numerical differences found in common variables.")
< parser.add_argument("-t", "--threshold", dest="threshold", type=float, action="store", help="(float) Define the maximum absolute difference for an individual variable to be treated as different.")
< parser.add_argument("dirs", nargs=2, help="Need 2 clubb output directories containing netCDF files with the same name to diff. Usage: python run_bindiff_all.py dir_path1 dir_path2")
< args = parser.parse_args()
---
> parser = argparse.ArgumentParser()
> parser.add_argument("-o", "--output", action="store_true", help="add this option if you want to get output for each file compared put into a file made in the cwd.")
> parser.add_argument("-d", "--diffs", action="store_true", help="print the differences found in variables")
> parser.add_argument("-t", "--threshold", dest="threshold", type=float, action="store", help="print the differences found in variables")
> parser.add_argument("dirs", nargs=2, help="need 2 clubb output directories to diff. Usage: python run_bindiff_all.py dir_path1 dir_path2")
50,51c25
< if DEBUG:
< print(args)
---
> args = parser.parse_args()
53d26
< # Check if folders exist
55c28
<
---
>
61c34,40
< # Define difference detection threshold
---
> dir1_files = os.listdir(args.dirs[0])
> dir2_files = os.listdir(args.dirs[1])
>
> #print(dir1_files, dir2_files)
>
> cases = get_cases(dir1_files, dir2_files)
>
69,85c48,55
< # Create folder in CLUBB output folder
< if args.fileout:
< if DEBUG:
< print("Creating output folder")
< if not os.path.exists(outFilePath):
< os.makedirs(outFilePath)
<
< linux_diff, diff_in_files, file_skipped = find_diffs_in_all_files(args.dirs[0], args.dirs[1], args.fileout, args.verbose, tot_abs_diff_thresh)
<
< if DEBUG:
< print(linux_diff, diff_in_files, file_skipped)
< print("SUMMARY:")
< if linux_diff:
< if not diff_in_files:
< print("Differences detected by linux diff but no differences in the common variables of the netCDF files compared.")
< else:
< print("There were differences detected in the common variables in netCDF (*.nc) files.")
---
> diff_in_files = run_diff(cases, args.dirs[0], args.dirs[1], args.output, args.diffs, tot_abs_diff_thresh)
>
> if( args.output and diff_in_files ):
> print("\nOutput files made and placed in " + os.getcwd())
>
> if( diff_in_files ):
> print("\nThere were differences detected in netCDF (*.nc) files.")
> sys.exit(1)
87,93c57,58
< print("Linux diff did not detect any differences in the compared files.")
< if args.fileout:
< if diff_in_files:
< print("Output files made and placed in " + outFilePath)
< if file_skipped:
< print("The creation of some or all of the output files was skipped because files with the same name already existed. "+
< "Use output options 'replace' or 'enumerate' to avoid this issue.")
---
> if( args.output ):
> print("\nNo differences detected, so no files were made!")
95,98c60
< print("No output files were created in " + outFilePath)
< if diff_in_files:
< sys.exit(1)
<
---
> print("\nNo differences detected!")
101c63,64
< sys.exit(2)
---
> sys.exit(1)
>
102a66
> def get_cases(dir1_files, dir2_files):
104,133c68,70
< def get_cases(dir1, dir2, l_verbose):
< # Generate and return a dict of all cases that have files present in the diff folders
< # This dict will have the following structure:
< # cases = {<case> : <folder_dict>, ...}
< # where folder_dict is another dict with two entries and the following structure:
< # folder_dict = {<dir>: [list, of, nc_files], ...}
< # where dir is either of the passed arguments and
< # [list, to, nc_files] contains the names of the existing netCDF files for <case> in the same order as nc_data_formats.
< # The entry will be None if a specific file does not exist.
< # Example: cases = {'arm': { <dir1>: ['arm_zm.nc', None, None], <dir2>: ['arm_zm.nc', None, None]},
< # 'bomex': { <dir1>: ['bomex_zm.nc', 'bomex_zt.nc', 'bomex_sfc.nc']}, <dir2>: ['bomex_zm.nc', 'bomex_zt.nc', 'bomex_sfc.nc']},
< # 'wangara': { <dir1>: [None, 'wangara_zt.nc',None]}, <dir2>: ['wangara_zm.nc', None, None]}
< #
< # Warnings will be printed for each missing file both on the console and in the output files.
< #####################################################################################
<
< # Get lists of files in each folder
< dir1_files = os.listdir(dir1)
< dir2_files = os.listdir(dir2)
<
< if DEBUG:
< print(dir1_files, dir2_files)
<
< # Create dict structure with case names as keys and a list for each folder of file names (or None if nonexistent) as values
< # This dict is returned from this function and will later be used as a basis for the comparison loop in 'find_diffs_in_all_files'
< cases = {}
<
< # Loop through all the case names listed in the RUN_CASES file and check if comparison is possible (i.e. at least one pair of case files is found in the folders)
< # First, open the RUN_CASES file
< with open(os.path.join(scriptPath, case_file)) as file:
---
> cases = []
>
> with open(scriptPath + "RUN_CASES") as file:
135,138d71
< if DEBUG:
< print(line)
< # Remove white space and the leading "!" in commented out lines
< # since we even want to compare commented out cases!
141,210c74,82
< if DEBUG:
< print(case)
< # Skip line if empty
< if case == "" or case == "!":
< if DEBUG:
< print("continue")
< continue
< # "Uncomment case name"
< if case[0] == "!":
< if DEBUG:
< print("Commented")
< case = case[1:].strip()
< if DEBUG:
< print(case)
< # Skip line if it contains multiple words
< if " " in case or "$" in case:
< if DEBUG:
< print("continue")
< continue
< if DEBUG:
< print("Create entry")
< cases[case] = {dir1: [], dir2: []}
<
< # We want to compare all cases that have at least one common file in the folders, i.e. add that case to dict cases with a list of comparable files.
< # Note ANY missing files in a warning (also in log file)
< if DEBUG:
< print("iterating")
< for postfix in nc_data_formats:
< if DEBUG:
< print(postfix)
< ncfname = case+postfix
< if DEBUG:
< print(ncfname)
< # Check if files exist in either folder
< # Check dir1
< if ncfname in dir1_files:
< if DEBUG:
< print("Exists in " + dir1)
< cases[case][dir1].append(ncfname)
< else:
< if DEBUG:
< print("Not in " + dir1)
< cases[case][dir1].append(None)
< # Check dir2
< if ncfname in dir2_files:
< if DEBUG:
< print("Exists in " + dir2)
< cases[case][dir2].append(ncfname)
< else:
< if DEBUG:
< print("Not in " + dir2)
< cases[case][dir2].append(None)
<
< # Remove case from list if no FILES exist
< if not any(cases[case][dir1]) or not any(cases[case][dir2]):
< if DEBUG:
< print("Removing case because no files exist.")
< print(cases[case])
< del cases[case]
< if l_verbose:
< print("Warning! No files found for case {}. It will be skipped.".format(case))
< # Remove case from list if no file PAIRS exist
< elif not find_comparable_files(cases[case][dir1], cases[case][dir2]):
< if DEBUG:
< print("Removing case because there are no pairs.")
< print(cases[case])
< del cases[case]
< if l_verbose:
< print("Warning! There are files for case {} but no pairs for comparison were found. It will be skipped.".format(case))
< print("\nThe following cases will be compared: {}\n".format(list(cases.keys())))
---
>
> #If statement goes through every case in the RUN_CASES file and checks if the case is able to be run,
> # and if that case has the files in the directories to diff it. If it passes all these conditions,
> # the case is added to the runnable cases.
> if(line[0] != "!" and len(line) > 1 and case + "_zt.nc" in dir1_files and case + "_zt.nc" in dir2_files
> and case + "_zm.nc" in dir1_files and case + "_zm.nc" in dir2_files
> and case + "_sfc.nc" in dir1_files and case + "_sfc.nc" in dir2_files):
> cases.append(case)
> #print(cases)
213,214d84
< def find_comparable_files(l1, l2):
< return any([p1 and p2 for (p1,p2) in zip(l1,l2)])
216,234c86,88
< def find_diffs_in_all_files(dir1, dir2, save_to_file, l_verbose, thresh):
< # For each case with existing netCDF files in the diff folders:
< # 1. Create an output file if those are requested
< # 2. Loop through the netCDF files and call `find_diffs_in_common_vars` on each pair
< # 3. The cumulative boolean `diff_in_all_files` stores whether or not any differences have been found
< #
< # Parameters:
< # dir1, dir2: Paths to folders containing netCDF files we want to compare
< # save_to_file: Value of the --outfile command line argument
< # l_verbose: Value of the --verbose command line argument
< # thresh: Detection threshold for differences in a variable
< #####################################################################################
< file_missing_msg = "Warning! The file {} does not exist in {}. No comparison possible.\n"
<
< # Find all cases that need to be compared (all files present in both folders)
< cases = get_cases(dir1, dir2, l_verbose)
< diff_in_all_files = False
< linux_diff = False
< file_skipped = False
---
> def run_diff(cases, dir1_path, dir2_path, l_output, l_diffs, thresh):
>
> diff_in_files = False
236,237c90,93
< # This for loop runs through all the cases you have the files to check,
< # and each netcdf format. It runs the linux diff on the binary netcdf files
---
> nc_data_formats = ["_zm.nc", "_zt.nc", "_sfc.nc"]
>
> # This for loop runs through all the cases you have the files to check,
> # and each netcdf format. It runs the linux diff on the binary netcdf files
240,267c96,99
< print("###DIFFING " + case + " netCDF (*.nc) files###")
< linux_diff_in_case = False
< diff_in_case = False
<
< # Write any output that should be written to file to string 'content'
< if save_to_file:
< content = "Output for 'run_bindiff_all.py' of the folders:\n"
< content += " - " + dir1
< content += "\n - " + dir2
< content += "\nDiffed case: " + case + "\n"
< content += "********************************************************************************************\n"
<
< # Looping over all files associated to <case>
< for i, postfix in enumerate(nc_data_formats):
< ncfname = case+postfix
< if not cases[case][dir1][i]:
< if l_verbose:
< print(file_missing_msg.format(ncfname, dir1))
< if save_to_file:
< content += file_missing_msg.format(ncfname, dir1)
< if not cases[case][dir2][i]:
< if l_verbose:
< print(file_missing_msg.format(ncfname, dir2))
< if save_to_file:
< content += file_missing_msg.format(ncfname, dir2)
< if not cases[case][dir1][i] or not cases[case][dir2][i]:
< print(">File {} can not be compared since it does not exist both folders.<".format(ncfname))
< continue
---
>
> print("DIFFING " + case + " netCDF (*.nc) files")
>
> for out_form in nc_data_formats:
270,283c102,103
< if( len(subprocess.getoutput("diff " + dir1 + "/" + ncfname + " " + dir2 + "/" + ncfname)) > 0 ):
< linux_diff_in_case = True
< print(">The linux diff detected differences in " + ncfname + "<")
< if save_to_file:
< content += ">The linux diff detected differences in " + ncfname + "<\n"
< # Update diff_in_all_files: if either it or the output of find_diffs_in_common_vars is true, we conclude that there is a difference. The printed messages will elaborate on the specific differences.
< diff_in_case, new_content = find_diffs_in_common_vars(ncfname, dir1, dir2, save_to_file, l_verbose, thresh) or diff_in_case
< if l_verbose:
< print('')
< if save_to_file:
< content += new_content
< content += '\n'
< else:
< print(">No differences detected by the linux diff in " + ncfname + "<")
---
> if( len(subprocess.getoutput("diff " + dir1_path + "/" + case + out_form + " " + dir2_path + "/" + case + out_form)) > 0 ):
> diff_in_files = run_py_diff(case+out_form, dir1_path, dir2_path, l_output, l_diffs, thresh) or diff_in_files
285,313c105
< if diff_in_case:
< print(">>>Differences in common variables detected for case {}<<<".format(case))
< if save_to_file:
< # Create file to save diff log for <case> into
< diff_file_name = outFilePath + case + filePostFix
< if DEBUG:
< print(diff_file_name)
< # Check if we need to create a file
< if not os.path.exists(diff_file_name.format('')) or save_to_file=="replace":
< # If file does not exist or should be overwritten, open file with default name and write content
< with open(diff_file_name.format(''), "w") as caseLogFile:
< caseLogFile.write(content)
< elif save_to_file == "enumerate":
< # (File exists already) First find which file index is next and create the file with that index in its name
< i = 1
< diff_file_name = diff_file_name.format('{:02d}')
< while os.path.exists(diff_file_name.format(i)):
< i = i+1
< with open(diff_file_name.format(i), "w") as caseLogFile:
< caseLogFile.write(content)
< elif save_to_file == "skip":
< file_skipped = True
< if l_verbose:
< print("Warning! Despite detected differences no log file was created for case {} since the file {} already exists and the 'skip' output option was used ".format(case, diff_file_name.format('')))
< elif linux_diff_in_case:
< print(no_value_diff_out.format(case))
< else:
< print(">>>The linux diff could not detect any differences in the file pairs for case {}.<<<".format(case))
< print('**********************************************************************************************************')
---
> return diff_in_files
315,339d106
< # Update diff_in_all_files: Set to True if diff_in_case==True (differences in case found)
< linux_diff = linux_diff_in_case or linux_diff
< diff_in_all_files = diff_in_case or diff_in_all_files
<
< return (linux_diff, diff_in_all_files, file_skipped)
<
< def find_diffs_in_common_vars( test_file, dir1, dir2, save_to_file, l_verbose, tot_abs_diff_thresh ):
< # This is the integral function of this script!
< # Compare content of one specific pair of files with the same name in each folder:
< # 1. Find the variables that are present in only one of the files
< # 2. Find common variables and iterate through them
< # 3. Compare values between the files and detect differences
< # 4. Print findings
< # 5. Return TRUE if significant differences were detected and FALSE otherwise
< #####################################################################################
<
< # Assume no differences until proven otherwise
< diff_in_common_vars = False
< # Declare string containing all the output for <test_file>
< new_content = ""
<
< if DEBUG:
< print("DIFFING " + test_file + "netCDF (*.nc) files")
< print(os.path.join(dir1, test_file))
< print(os.path.join(dir2, test_file))
341,343c108
< # Create datasets from nc files
< dset1 = netCDF4.Dataset(os.path.join(dir1, test_file))
< dset2 = netCDF4.Dataset(os.path.join(dir2, test_file))
---
> def run_py_diff( test_file, dir1_path, dir2_path, l_save_output, l_print_diffs, tot_abs_diff_thresh ):
345,365c110,119
< # Find variables that are only present in ONE of the files
< diff1 = set(dset1.variables.keys()).difference(dset2.variables.keys())
< diff2 = set(dset2.variables.keys()).difference(dset1.variables.keys())
< # Print those variables
< if diff1:
< var_set_diff_out = "{} contains the following extra variables:".format(os.path.join(dir1, test_file))
< var_set_diff_out = var_set_diff_out + "\n" + "\n".join(diff1)
< print(var_set_diff_out)
< if save_to_file:
< new_content += var_set_diff_out + "\n"
<
< if diff2:
< var_set_diff_out = "{} contains the following extra variables:".format(os.path.join(dir2, test_file))
< var_set_diff_out = var_set_diff_out + "\n" + "\n".join(diff2)
< print(var_set_diff_out)
< if save_to_file:
< new_content += var_set_diff_out + "\n"
< if diff1 or diff2:
< print('')
< if save_to_file:
< new_content += "\n"
---
> diff_in_files = False
> any_diff_above_thresh = False
>
> #print("DIFFING " + test_file + "netCDF (*.nc) files")
> #print(dir1_path + test_file)
> #print(dir2_path + test_file)
>
> # Create datasets from nc files
> dset1 = netCDF4.Dataset(dir1_path+"/"+test_file)
> dset2 = netCDF4.Dataset(dir2_path+"/"+test_file)
368,371c122,125
< table = [[ 'Var',
< 'Max Abs Diff',
< 'Max % Diff',
< 'Total Abs Diff',
---
> table = [[ 'Var',
> 'Max Abs Diff',
> 'Max % Diff',
> 'Total Abs Diff',
374,378c128,130
< # Create set of variables that are common to both files
< # These are the variables we can and want to compare the other variables are printed above
< vars_in_common = set(dset1.variables.keys()).intersection(dset2.variables.keys())
< for var in sorted(vars_in_common):
< # The CLUBB netcdf variables we are interested in are all
---
> for var in dset1.variables:
>
> # The CLUBB netcdf variables we are interested in are all
382c134
< # dimensions (unlikely), then the variables of interested would be 2D. So
---
> # dimensions (unlikely), then the variables of interested would be 2D. So
384c136,138
< if( dset1[var].ndim > 1 and dset2[var].ndim > 1 ):
---
> if( dset1[var].ndim > 1 ):
>
> diff_in_files = True
389c143
< if ( np.sum(abs_diff) <= tot_abs_diff_thresh ):
---
> if ( np.sum(abs_diff) < tot_abs_diff_thresh ):
392c146
< diff_in_common_vars = True
---
> any_diff_above_thresh = True
401,406c155,160
<
< # Append the table array with the values we want to print
< table.append( [ var,
< np.max(abs_diff),
< np.max(percent_diff),
< np.sum(abs_diff),
---
>
> # Append the table array with the values we want to print
> table.append( [ var,
> np.max(abs_diff),
> np.max(percent_diff),
> np.sum(abs_diff),
408,410d161
< else:
< if l_verbose:
< print("The variable {} is not comparable because its dimension is not fit for comparison: {}, {}".format(var, dset1[var].ndim, dset2[var].ndim))
411a163,165
> # Print results
> header = "************** Differences detected in " + test_file + "! **************"
> print( header )
413c167
< if diff_in_common_vars:
---
> if( any_diff_above_thresh ):
418c172
< if l_verbose:
---
> if( l_print_diffs ):
422c176
< if save_to_file:
---
> if( l_save_output ):
424c178,181
< new_content += output + "\n"
---
> f = open(test_file + "-diff_out", "w")
> f.write(header)
> f.write(output)
> f.close()
426,430c183
< if not diff_in_common_vars:
< print(">>No differences detected in the common fields in file " + test_file + "<<")
< else:
< print(">>Differences above threshold were detected in the common fields in file " + test_file + "<<")
< return (diff_in_common_vars, new_content)
---
> return diff_in_files
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/run_scripts/RUN_CASES clubb_release/run_scripts/RUN_CASES
2,4c2,4
< ! Declare any cases that should be run with run_scm_all.bash and run_bindiff_all.bash.
< ! Any blank lines and lines starting with '!' will be ignored in run_scm_all
< ! while run_bindiff_all runs all cases regardless if commented or not.
---
> ! Declare any cases that should be run with run_scm_all.bash and
> ! run_bindiff_all.bash. Any blank lines and lines starting with '!' will
> ! be ignored.
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/run_scripts/run_scm.bash clubb_release/run_scripts/run_scm.bash
539,540c539
< mv ../output/*.nc $CUSTOM_OUTPUT_DIR
< mv ../output/*_setup.txt $CUSTOM_OUTPUT_DIR
---
> mv ../output/* $CUSTOM_OUTPUT_DIR
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/advance_clubb_core_module.F90 clubb_release/src/CLUBB_core/advance_clubb_core_module.F90
365,368c365
< ibrunt_vaisala_freq_sqd_mixed, &
< ibrunt_vaisala_freq_sqd_moist, &
< ibrunt_vaisala_freq_sqd_dry, &
< iRi_zm
---
> isqrt_Ri_zm
817,820c814,817
< brunt_vaisala_freq_sqd_mixed, & ! A mixture of dry and moist N^2 [s^-2]
< brunt_vaisala_freq_sqd_dry, & ! dry N^2 [s^-2]
< brunt_vaisala_freq_sqd_moist, & ! moist N^2 [s^-2]
< brunt_vaisala_freq_sqd_splat, & ! [s^-2]
---
> brunt_vaisala_freq_sqd_mixed, & ! A mixture of dry and moist N^2
> brunt_vaisala_freq_sqd_dry, & ! dry N^2
> brunt_vaisala_freq_sqd_moist, & ! moist N^2
> brunt_vaisala_freq_sqd_splat, &
822c819
< Ri_zm ! Richardson number [-]
---
> sqrt_Ri_zm ! square root of Richardson number
989,990c986,987
< !$acc brunt_vaisala_freq_sqd_splat, &
< !$acc brunt_vaisala_freq_sqd_zt, Ri_zm, Lscale_max, &
---
> !$acc brunt_vaisala_freq_sqd_plus, brunt_vaisala_freq_sqd_splat, &
> !$acc brunt_vaisala_freq_sqd_zt, sqrt_Ri_zm, Lscale_max, &
1555d1551
< invrs_tau_wp3_zm(i,k) = invrs_tau_zm(i,k)
1594c1590
< Ri_zm, & ! Out
---
> sqrt_Ri_zm, & ! Out
1833c1829
< !$acc Ri_zm, invrs_tau_wp3_zm, invrs_tau_no_N2_zm, invrs_tau_bkgnd, &
---
> !$acc sqrt_Ri_zm, invrs_tau_wp3_zm, invrs_tau_no_N2_zm, invrs_tau_bkgnd, &
1835,1836c1831
< !$acc brunt_vaisala_freq_sqd_splat, brunt_vaisala_freq_sqd_mixed, &
< !$acc brunt_vaisala_freq_sqd_moist, brunt_vaisala_freq_sqd_dry )
---
> !$acc brunt_vaisala_freq_sqd_splat )
1848c1843
< call stat_update_var(iRi_zm, Ri_zm(i,:), & ! intent(in)
---
> call stat_update_var(isqrt_Ri_zm, sqrt_Ri_zm(i,:), & ! intent(in)
1861a1857,1858
> call stat_update_var(ibrunt_vaisala_freq_sqd, brunt_vaisala_freq_sqd(i,:), & ! intent(in)
> stats_zm(i)) ! intent(inout)
1863,1864d1859
< call stat_update_var(ibrunt_vaisala_freq_sqd, brunt_vaisala_freq_sqd(i,:), & ! intent(in)
< stats_zm(i))
1866,1872c1861
< stats_zm(i)) ! intent(inout)
< call stat_update_var(ibrunt_vaisala_freq_sqd_mixed, brunt_vaisala_freq_sqd_mixed(i,:), & ! intent(in)
< stats_zm(i)) ! intent(inout)
< call stat_update_var(ibrunt_vaisala_freq_sqd_moist, brunt_vaisala_freq_sqd_moist(i,:), & ! intent(in)
< stats_zm(i)) ! intent(inout)
< call stat_update_var(ibrunt_vaisala_freq_sqd_dry, brunt_vaisala_freq_sqd_dry(i,:), & ! intent(in)
< stats_zm(i)) ! intent(inout)
---
> stats_zm(i))
2913,2914c2902,2903
< !$acc brunt_vaisala_freq_sqd_splat, &
< !$acc brunt_vaisala_freq_sqd_zt, Ri_zm, Lscale_max, &
---
> !$acc brunt_vaisala_freq_sqd_plus, brunt_vaisala_freq_sqd_splat, &
> !$acc brunt_vaisala_freq_sqd_zt, sqrt_Ri_zm, Lscale_max, &
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/advance_helper_module.F90 clubb_release/src/CLUBB_core/advance_helper_module.F90
335c335
< !$acc lambda0_stability, Lscale_zm )
---
> !$acc brunt_vaisala_freq_sqd_plus, lambda0_stability, Lscale_zm )
601c601,602
< ishear_sqd, & ! Variable(s)
---
> iRichardson_num, & ! Variable(s)
> ishear_sqd, &
617c618
< l_Cx_fnc_Richardson_vert_avg = .false. ! Vertically average Cx_fnc_Richardson over a
---
> l_Cx_fnc_Richardson_vert_avg = .false., & ! Vertically average Cx_fnc_Richardson over a
618a620,623
> l_Richardson_vert_avg = .false., & ! Vertically average Richardson_num over a
> ! distance of Lscale
> l_use_shear_turb_freq_sqd = .false. ! Use turb_freq_sqd and shear_sqd in denominator of
> ! Richardson_num
676a682
> Richardson_num, &
683a690
> turb_freq_sqd, &
685a693
> Richardson_num_clipped, &
706,708c714,716
< !$acc Cx_fnc_interp, &
< !$acc Ri_zm, ddzt_um, ddzt_vm, shear_sqd, Lscale_zm, &
< !$acc Cx_fnc_Richardson_avg, fnc_Richardson, &
---
> !$acc Richardson_num, Cx_fnc_interp, &
> !$acc Ri_zm, ddzt_um, ddzt_vm, shear_sqd, turb_freq_sqd, Lscale_zm, &
> !$acc Richardson_num_clipped, Cx_fnc_Richardson_avg, fnc_Richardson, &
710a719,723
> if ( l_modify_limiters_for_cnvg_test .and. l_use_shear_turb_freq_sqd ) then
> error stop "ERROR: l_modify_limiters_for_cnvg_test .and. l_use_shear_turb_freq_sqd "// &
> "both true in compute_Cx_fnc_Richardson"
> end if
>
744,752c757
< if ( l_stats_samp ) then
< !$acc update host(shear_sqd)
< do i = 1, ngrdcol
< call stat_update_var( ishear_sqd, shear_sqd(i,:), & ! intent(in)
< stats_zm(i) ) ! intent(inout)
< end do
< end if
<
< if ( l_use_shear_Richardson ) then
---
> if ( l_use_shear_turb_freq_sqd ) then
757,758c762,764
< Ri_zm(i,k) = max( 1.0e-7_core_rknd, brunt_vaisala_freq_sqd_mixed(i,k) ) &
< / max( shear_sqd(i,k), 1.0e-7_core_rknd )
---
> turb_freq_sqd(i,k) = em(i,k) / Lscale_zm(i,k)**2
> Richardson_num(i,k) = brunt_vaisala_freq_sqd(i,k) / max( shear_sqd(i,k), turb_freq_sqd(i,k), &
> Richardson_num_divisor_threshold )
762a769,776
> if ( l_stats_samp ) then
> !$acc update host(shear_sqd)
> do i = 1, ngrdcol
> call stat_update_var( ishear_sqd, shear_sqd(i,:), & ! intent(in)
> stats_zm(i) ) ! intent(inout)
> end do
> end if
>
763a778,807
>
> if ( l_use_shear_Richardson ) then
>
> !$acc parallel loop gang vector collapse(2) default(present)
> do k = 1, nz
> do i = 1, ngrdcol
> Richardson_num(i,k) = brunt_vaisala_freq_sqd_mixed(i,k) * invrs_num_div_thresh
>
> Ri_zm(i,k) = max( 1.0e-7_core_rknd, brunt_vaisala_freq_sqd_mixed(i,k) ) &
> / max( shear_sqd(i,k), 1.0e-7_core_rknd )
> end do
> end do
> !$acc end parallel loop
>
> else
> !$acc parallel loop gang vector collapse(2) default(present)
> do k = 1, nz
> do i = 1, ngrdcol
> Richardson_num(i,k) = brunt_vaisala_freq_sqd(i,k) * invrs_num_div_thresh
> Ri_zm(i,k) = Richardson_num(i,k)
> end do
> end do
> !$acc end parallel loop
> endif
>
> end if
>
> if ( l_Richardson_vert_avg ) then
>
> ! Clip below-min values of Richardson_num
767c811
< Ri_zm(i,k) = brunt_vaisala_freq_sqd(i,k) * invrs_num_div_thresh
---
> Richardson_num_clipped(i,k) = max( Richardson_num(i,k), Richardson_num_min )
770a815,818
>
> Richardson_num = Lscale_width_vert_avg( nz, ngrdcol, gr, smth_type, &
> Richardson_num, Lscale_zm, rho_ds_zm, &
> Richardson_num_max )
840a889,897
> ! Stats sampling
> if ( l_stats_samp ) then
> !$acc update host(Richardson_num)
> do i = 1, ngrdcol
> call stat_update_var( iRichardson_num, Richardson_num(i,:), & ! intent(in)
> stats_zm(i) ) ! intent(inout)
> end do
> end if
>
843,845c900,902
< !$acc Cx_fnc_interp, Ri_zm, &
< !$acc ddzt_um, ddzt_vm, shear_sqd, Lscale_zm, &
< !$acc Cx_fnc_Richardson_avg, fnc_Richardson, &
---
> !$acc brunt_vaisala_freq_sqd_plus, Richardson_num, Cx_fnc_interp, &
> !$acc Ri_zm, ddzt_um, ddzt_vm, shear_sqd, turb_freq_sqd, Lscale_zm, &
> !$acc Richardson_num_clipped, Cx_fnc_Richardson_avg, fnc_Richardson, &
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/mixing_length.F90 clubb_release/src/CLUBB_core/mixing_length.F90
1258c1258
< Ri_zm, & ! intent out
---
> sqrt_Ri_zm, & ! intent out
1369c1369
< ! (reduce threshold on limiters for Ri_zm in mixing_length.F90)
---
> ! (reduce threshold on limiters for sqrt_Ri_zm in mixing_length.F90)
1379c1379
< Ri_zm, &
---
> sqrt_Ri_zm, &
1444,1445c1444,1445
< Ri_zm_clipped, &
< Ri_zm_smooth, &
---
> sqrt_Ri_zm_clipped, &
> sqrt_Ri_zm_smooth, &
1465,1466c1465,1466
< !$acc create( Ri_zm_clipped, ddzt_umvm_clipped, &
< !$acc tau_zm_unclipped, tau_zt_unclipped, Ri_zm_smooth, em_clipped, &
---
> !$acc create( sqrt_Ri_zm_clipped, ddzt_umvm_clipped, &
> !$acc tau_zm_unclipped, tau_zt_unclipped, sqrt_Ri_zm_smooth, em_clipped, &
1586c1586
< brunt_vaisala_freq_sqd_smth = zm2zt2zm( nz,ngrdcol,gr, brunt_vaisala_freq_sqd_mixed )
---
> brunt_vaisala_freq_sqd_smth = zm2zt2zm( nz,ngrdcol,gr, brunt_vaisala_freq_sqd )
1595c1595
< tmp_calc(i,k) = 1.e8_core_rknd * abs(brunt_vaisala_freq_sqd_mixed(i,k))**3
---
> tmp_calc(i,k) = 1.e8_core_rknd * abs(brunt_vaisala_freq_sqd(i,k))**3
1601c1601
< brunt_vaisala_freq_sqd_mixed, &
---
> brunt_vaisala_freq_sqd, &
1612,1613c1612,1613
< brunt_vaisala_freq_clipped(i,k) = min( brunt_vaisala_freq_sqd_mixed(i,k), &
< 1.e8_core_rknd * abs(brunt_vaisala_freq_sqd_mixed(i,k))**3)
---
> brunt_vaisala_freq_clipped(i,k) = min( brunt_vaisala_freq_sqd(i,k), &
> 1.e8_core_rknd * abs(brunt_vaisala_freq_sqd(i,k))**3)
1629,1630c1629,1630
< Ri_zm_clipped(i,k) = max( 0.0_core_rknd, brunt_vaisala_freq_sqd_smth(i,k) ) &
< / max( ddzt_umvm(i,k), 1.0e-12_core_rknd )
---
> sqrt_Ri_zm_clipped(i,k) = sqrt( max( 0.0_core_rknd, brunt_vaisala_freq_sqd_smth(i,k) ) &
> / max( ddzt_umvm(i,k), 1.0e-12_core_rknd) )
1635c1635
< Ri_zm = zm2zt2zm( nz, ngrdcol, gr, Ri_zm_clipped )
---
> sqrt_Ri_zm = zm2zt2zm( nz, ngrdcol, gr, sqrt_Ri_zm_clipped )
1650c1650
< Ri_zm(i,k) = brunt_vaisala_freq_clipped(i,k) / ddzt_umvm_clipped(i,k)
---
> sqrt_Ri_zm(i,k) = sqrt( brunt_vaisala_freq_clipped(i,k) / ddzt_umvm_clipped(i,k) )
1660,1661c1660,1661
< Ri_zm(i,k) = max( 1.0e-7_core_rknd, brunt_vaisala_freq_sqd_smth(i,k) ) &
< / max( ddzt_umvm(i,k), 1.0e-7_core_rknd )
---
> sqrt_Ri_zm(i,k) = sqrt( max( 1.0e-7_core_rknd, brunt_vaisala_freq_sqd_smth(i,k) ) &
> / max( ddzt_umvm(i,k), 1.0e-7_core_rknd) )
1844c1844
< invrs_tau_xp2_zm(i,k) = invrs_tau_bkgnd(i,k) + invrs_tau_sfc(i,k) &
---
> invrs_tau_xp2_zm(i,k) = 0.1_core_rknd * invrs_tau_bkgnd(i,k) + invrs_tau_sfc(i,k) &
1852,1861c1852,1861
< ! !$acc parallel loop gang vector collapse(2) default(present)
< ! do k = 1, nz
< ! do i = 1, ngrdcol
< ! if ( ice_supersat_frac_zm(i,k) <= 0.01_core_rknd &
< ! .and. invrs_tau_xp2_zm(i,k) >= 0.003_core_rknd ) then
< ! invrs_tau_xp2_zm(i,k) = 0.003_core_rknd
< ! end if
< ! end do
< ! end do
< ! !$acc end parallel loop
---
> !$acc parallel loop gang vector collapse(2) default(present)
> do k = 1, nz
> do i = 1, ngrdcol
> if ( ice_supersat_frac_zm(i,k) <= 0.01_core_rknd &
> .and. invrs_tau_xp2_zm(i,k) >= 0.003_core_rknd ) then
> invrs_tau_xp2_zm(i,k) = 0.003_core_rknd
> end if
> end do
> end do
> !$acc end parallel loop
1903,1904c1903,1904
< Ri_zm_clipped = smooth_max( nz, ngrdcol, Ri_zm, zero, &
< 12.0_core_rknd * min_max_smth_mag )
---
> sqrt_Ri_zm_clipped = smooth_max( nz, ngrdcol, sqrt_Ri_zm, zero, &
> 12.0_core_rknd * min_max_smth_mag )
1906,1907c1906,1907
< Ri_zm_smooth = smooth_min( nz, ngrdcol, Ri_zm_clipped, 12.0_core_rknd, &
< 12.0_core_rknd * min_max_smth_mag )
---
> sqrt_Ri_zm_smooth = smooth_min( nz, ngrdcol, sqrt_Ri_zm_clipped, 12.0_core_rknd, &
> 12.0_core_rknd * min_max_smth_mag )
1916c1916
< * C_invrs_tau_wpxp_Ri * sqrt ( Ri_zm_smooth(i,k) ))
---
> * C_invrs_tau_wpxp_Ri * sqrt_Ri_zm_smooth(i,k) )
1932c1932
< * min( max( sqrt( Ri_zm(i,k) ), zero), 12.0_core_rknd ) )
---
> * min( max( sqrt_Ri_zm(i,k), zero), 12.0_core_rknd ) )
2042,2043c2042,2043
< !$acc delete( Ri_zm_clipped, ddzt_umvm_clipped, &
< !$acc tau_zm_unclipped, tau_zt_unclipped, Ri_zm_smooth, em_clipped, &
---
> !$acc delete( sqrt_Ri_zm_clipped, ddzt_umvm_clipped, &
> !$acc tau_zm_unclipped, tau_zt_unclipped, sqrt_Ri_zm_smooth, em_clipped, &
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/model_flags.F90 clubb_release/src/CLUBB_core/model_flags.F90
604c604
< l_use_tke_in_wp3_pr_turb_term = .true.
---
> l_use_tke_in_wp3_pr_turb_term = .false.
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/stats_variables.F90 clubb_release/src/CLUBB_core/stats_variables.F90
1089,1092c1089,1090
< ibrunt_vaisala_freq_sqd_mixed = 0, &
< ibrunt_vaisala_freq_sqd_moist = 0, &
< ibrunt_vaisala_freq_sqd_dry = 0, &
< iRi_zm = 0, &
---
> isqrt_Ri_zm = 0, &
> iRichardson_num = 0, &
1097c1095
< !$omp threadprivate(iC7_Skw_fnc, iC1_Skw_fnc, iRi_zm)
---
> !$omp threadprivate(iC7_Skw_fnc, iC1_Skw_fnc, isqrt_Ri_zm)
1099,1100c1097
< !$omp threadprivate(ibrunt_vaisala_freq_sqd_mixed, ibrunt_vaisala_freq_sqd_moist)
< !$omp threadprivate(ibrunt_vaisala_freq_sqd_dry, ishear_sqd)
---
> !$omp threadprivate(iRichardson_num, ishear_sqd)
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/CLUBB_core/stats_zm_module.F90 clubb_release/src/CLUBB_core/stats_zm_module.F90
52c52
< iRi_zm, &
---
> isqrt_Ri_zm, &
323,325c323
< ibrunt_vaisala_freq_sqd_mixed, &
< ibrunt_vaisala_freq_sqd_moist, &
< ibrunt_vaisala_freq_sqd_dry, &
---
> iRichardson_num, &
2514,2534c2512,2515
< case ( 'bv_freq_sqd_mixed' )
< ibrunt_vaisala_freq_sqd_mixed = k
< call stat_assign( var_index=ibrunt_vaisala_freq_sqd_mixed, var_name="bv_freq_sqd_mixed", &
< var_description="Interpolated Brunt-Vaisala freq. squared between moist and dry air", &
< var_units="1/s^2", &
< l_silhs=.false., grid_kind=stats_zm )
< k = k + 1
<
< case ( 'bv_freq_sqd_moist' )
< ibrunt_vaisala_freq_sqd_moist = k
< call stat_assign( var_index=ibrunt_vaisala_freq_sqd_moist, var_name="bv_freq_sqd_moist", &
< var_description="Brunt-Vaisala freq. squared in moist air", &
< var_units="1/s^2", &
< l_silhs=.false., grid_kind=stats_zm )
< k = k + 1
<
< case ( 'bv_freq_sqd_dry' )
< ibrunt_vaisala_freq_sqd_dry = k
< call stat_assign( var_index=ibrunt_vaisala_freq_sqd_dry, var_name="bv_freq_sqd_dry", &
< var_description="Brunt-Vaisala freq. squared in dry air", &
< var_units="1/s^2", &
---
> case ( 'sqrt_Ri_zm' )
> isqrt_Ri_zm = k
> call stat_assign( var_index=isqrt_Ri_zm,var_name="sqrt_Ri_zm", &
> var_description="Richardson number [-]", var_units="-", &
2538,2541c2519,2522
< case ( 'Ri_zm' )
< iRi_zm = k
< call stat_assign( var_index=iRi_zm,var_name="Ri_zm", &
< var_description="Richardson number [-]", var_units="-", &
---
> case ( 'Richardson_num' )
> iRichardson_num = k
> call stat_assign( var_index=iRichardson_num, var_name="Richardson_num", &
> var_description="Richardson_num, Richardson number", var_units="-", &
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/src/error.F90 clubb_release/src/error.F90
111c111
< character(len=20), dimension(:), allocatable, private :: &
---
> character(len=10), dimension(:), allocatable, private :: &
290c290
< character(len=20), dimension(max_variables) :: &
---
> character(len=10), dimension(max_variables) :: &
376c376
< t_variables(1:max_variables) = " "
---
> t_variables(1:max_variables) = " "
423c423
< if (t_variables(i) == " ") exit
---
> if (t_variables(i) == " ") exit
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/utilities/sens_matrix/sens_matrix_dashboard.py clubb_release/utilities/sens_matrix/sens_matrix_dashboard.py
11,14c11,14
< from dash import dcc
< #import dash_core_components as dcc
< from dash import html
< #import dash_html_components as html
---
> #from dash import dcc
> import dash_core_components as dcc
> #from dash import html
> import dash_html_components as html
71,72d70
< #print("metricsNames=", metricsNames)
< #print("obsMetricValsCol=", obsMetricValsCol)
112,113c110
< # defaultBiasesCol + prescribedBiasesCol = -fwdFnc_tuned_params (see objFnc).
< # This lumps the prescribed-parameter adjustment into defaultBiasesCol
---
> # defaultBiasesCol + prescribedBiasesCol = -fwdFnc_tuned_params (see objFnc)
143,144d139
< normlzdWeightedLinplusSensMatrixPoly = np.diag(np.transpose(metricsWeights)[0]) @ normlzdLinplusSensMatrixPoly
<
231c226
< # For use in figures such as paramsErrorBarsFig.
---
> # For use in figures such as paramsBarChart.
244a240,250
>
> # Set up a column vector of metric values from the default simulation
> defaultMetricValsCol = setupDefaultMetricValsCol(metricsNames, defaultNcFilename)
>
> # Set up a column vector of metric values from the global simulation based on optimized
> # parameter values.
> linSolnMetricValsCol = setupDefaultMetricValsCol(metricsNames, linSolnNcFilename)
>
> # Store biases in default simulation, ( global_model - default )
> linSolnBiasesCol = np.subtract(linSolnMetricValsCol, defaultMetricValsCol)
>
248d253
< normlzdWeightedDefaultBiasesCol = metricsWeights * normlzdDefaultBiasesCol
273,299d277
< # Set up a column vector of metric values from the global simulation based on optimized
< # parameter values.
< linSolnMetricValsCol = setupDefaultMetricValsCol(metricsNames, linSolnNcFilename)
<
< # Store biases in default simulation, ( global_model - obs )
< linSolnBiasesCol = np.subtract(linSolnMetricValsCol, obsMetricValsCol)
< #linSolnBiasesCol = linSolnBiasesCol + prescribedBiasesCol
<
< # Check whether the minimizer actually reduces chisqd
< # Initial value of chisqd, which assumes parameter perturbations are zero
< normlzdLinSolnBiasesCol = linSolnBiasesCol/np.abs(normMetricValsCol)
< chisqdLinSolnMin = objFnc(np.zeros_like(defaultParamValsOrigRow), \
< normlzdSensMatrixPoly, normlzdLinSolnBiasesCol, metricsWeights, \
< normlzdCurvMatrix, reglrCoef, numMetrics)
<
< #print("chisqdZero =", chisqdZero)
< #print("chisqdMin =", chisqdMin)
< print("chisqdLinSolnMinRatio =", chisqdLinSolnMin/chisqdZero)
<
< chisqdUnweightedLinSolnMin = objFnc(np.zeros_like(defaultParamValsOrigRow), \
< normlzdSensMatrixPoly, normlzdLinSolnBiasesCol, np.ones_like(metricsWeights), \
< normlzdCurvMatrix, reglrCoef, numMetrics)
<
< #print("chisqdUnweightedZero =", chisqdUnweightedZero)
< #print("chisqdUnweightedMin =", chisqdUnweightedMin)
< print("chisqdUnweightedLinSolnMinRatio =", chisqdUnweightedLinSolnMin/chisqdUnweightedZero)
< print("-----------------------------------------------------")
344c322
< #linSolnBias = ( linSolnBiasesCol + defaultBiasesCol )
---
> linSolnBias = ( linSolnBiasesCol + defaultBiasesCol )
346,347c324,325
< #linSolnBiasMagRatio = np.linalg.norm(linSolnBias/np.abs(normMetricValsCol))**2 / \
< # np.linalg.norm(defaultBiasesCol/np.abs(normMetricValsCol))**2
---
> linSolnBiasMagRatio = np.linalg.norm(linSolnBias/np.abs(normMetricValsCol))**2 / \
> np.linalg.norm(defaultBiasesCol/np.abs(normMetricValsCol))**2
394,395c372,373
< #weightedBiasLinSoln = metricsWeights * ( linSolnBiasesCol + defaultBiasesCol ) / np.abs(normMetricValsCol)
< #weightedBiasLinSolnMagRatio = np.linalg.norm(weightedBiasLinSoln)**2 / np.linalg.norm(normlzdMDeltaB)**2
---
> weightedBiasLinSoln = metricsWeights * ( linSolnBiasesCol + defaultBiasesCol ) / np.abs(normMetricValsCol)
> weightedBiasLinSolnMagRatio = np.linalg.norm(weightedBiasLinSoln)**2 / np.linalg.norm(normlzdMDeltaB)**2
531,532c509
< #y= (-linSolnBiasesCol-defaultBiasesCol)[metricsSensOrdered[i],0]/np.abs(normMetricValsCol[metricsSensOrdered[i],0]),
< y= (-linSolnBiasesCol)[metricsSensOrdered[i],0]/np.abs(normMetricValsCol[metricsSensOrdered[i],0]),
---
> y= (-linSolnBiasesCol-defaultBiasesCol)[metricsSensOrdered[i],0]/np.abs(normMetricValsCol[metricsSensOrdered[i],0]),
638,642d614
<
< #wide_df = px.data.medals_wide()
< #paramsTotContrbBarFig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], base=[0,0,0])
< #pdb.set_trace()
<
645,646d616
< # barBase = np.zeros(numMetrics),
< #barBase = -defaultBiasesCol[metricsSensOrdered]/np.abs(normMetricValsCol[metricsSensOrdered]) @ np.ones((1,len(paramsNames))),
652,659c622
< # paramsTotContrbBarFig = \
< # createBarChart( minusNonlinMatrixOrdered.T, index=paramsNames, columns=metricsNamesOrdered,
< # orientation = 'v',
< # title="""Linear + nonlinear contributions of parameters to actual removal of regional biases""",
< # xlabel="Parameter", ylabel="Contribution to bias removal",
< # width=800, height=500 )
<
< sensMatrixBarFig = \
---
> paramsTotContrbBarFig = \
661,662d623
< #createBarChart( normlzdSensMatrixOrdered.T, index=paramsNames, columns=metricsNamesOrdered,
< # barBase = np.zeros_like(paramsScales),
664,672c625
< title="""Linear+nonlinear contributions of parameters to actual removal of regional biases""",
< xlabel="Parameter", ylabel="Contribution to bias removal",
< width=800, height=500 )
<
< linplusSensMatrixBarFig = \
< createBarChart( normlzdLinplusSensMatrixPoly[metricsSensOrdered,:].T, index=paramsNames, columns=metricsNamesOrdered,
< # barBase = np.zeros_like(paramsScales),
< orientation = 'v',
< title="""Linplus contributions of parameters to actual removal of regional biases""",
---
> title="""Linear + nonlinear contributions of parameters to actual removal of regional biases""",
676,683d628
< metricsCorrArrayFig = createCorrArrayFig( normlzdLinplusSensMatrixPoly, metricsNames,
< title='cos(angle) among metrics (i.e., rows of sens matrix)' )
<
< minusNormlzdDefaultBiasesCol = \
< -defaultBiasesCol[metricsSensOrdered,0]/np.abs(normMetricValsCol[metricsSensOrdered,0])
< metricsBarChart = createMetricsBarChart(metricsNames[metricsSensOrdered],paramsNames,
< minusNormlzdDefaultBiasesCol,minusNonlinMatrixOrdered)
<
1066c1011
< paramsErrorBarsFig = px.scatter(df, x=df.index, y=df.columns,
---
> paramsBarChart = px.scatter(df, x=df.index, y=df.columns,
1069c1014
< paramsErrorBarsFig.update_traces( go.Scatter(
---
> paramsBarChart.update_traces( go.Scatter(
1074c1019
< #paramsErrorBarsFig.add_trace(go.Scatter(x=paramsNames, y=paramsLowValsPCBound[:,0]*paramsScales,
---
> #paramsBarChart.add_trace(go.Scatter(x=paramsNames, y=paramsLowValsPCBound[:,0]*paramsScales,
1077c1022
< #paramsErrorBarsFig.add_trace(go.Scatter(x=paramsNames, y=paramsHiValsPCBound[:,0]*paramsScales, fill='tonexty',
---
> #paramsBarChart.add_trace(go.Scatter(x=paramsNames, y=paramsHiValsPCBound[:,0]*paramsScales, fill='tonexty',
1080c1025
< #paramsErrorBarsFig.add_trace(go.Scatter(x=paramsNames, y=defaultParamValsOrigRow[0,:]*paramsScales,
---
> #paramsBarChart.add_trace(go.Scatter(x=paramsNames, y=defaultParamValsOrigRow[0,:]*paramsScales,
1082c1027
< paramsErrorBarsFig.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnLin[:,0]*paramsScales,
---
> paramsBarChart.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnLin[:,0]*paramsScales,
1087c1032
< paramsErrorBarsFig.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnNonlin[:,0]*paramsScales,
---
> paramsBarChart.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnNonlin[:,0]*paramsScales,
1093c1038
< paramsErrorBarsFig.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnElastic[:,0]*paramsScales,
---
> paramsBarChart.add_trace(go.Scatter(x=paramsAbbrv, y=paramsSolnElastic[:,0]*paramsScales,
1103,1106c1048,1051
< paramsErrorBarsFig.update_yaxes(title="User-scaled parameter value")
< paramsErrorBarsFig.update_xaxes(title="Parameter Name")
< paramsErrorBarsFig.update_layout(hovermode="x")
< paramsErrorBarsFig.update_layout( width=1000, height=500 )
---
> paramsBarChart.update_yaxes(title="User-scaled parameter value")
> paramsBarChart.update_xaxes(title="Parameter Name")
> paramsBarChart.update_layout(hovermode="x")
> paramsBarChart.update_layout( width=1000, height=500 )
1139,1174c1084,1152
< # Create figure that shows the sensitivity matrix and bias column, both color coded.
< sensMatrixBiasFig = createMatrixPlusColFig( matrix = normlzdLinplusSensMatrixPoly,
< matIndexLabel = metricsNames,
< matColLabel = paramsNames,
< colVector = -np.around(defaultBiasesCol/np.abs(normMetricValsCol), decimals=2),
< colVectIndexLabel = metricsNames,
< colVectColLabel = ['-Normalized Biases'],
< plotTitle='Color-coded normalized sensitivity matrix',
< reversedYAxis = 'reversed' )
<
<
< # Create figure that plots color-coded parameter correlation matrix plus parameter-bias correlation column.
< XT_dot_X_Linplus = normlzdLinplusSensMatrixPoly.T @ normlzdLinplusSensMatrixPoly
< (XT_dot_X_Linplus_corr, stdMatrixInv ) = covMatrix2corrMatrix( XT_dot_X_Linplus, returnStd=True )
< normlzdStdDefaultBiasesCol = stdMatrixInv @ normlzdLinplusSensMatrixPoly.T @ normlzdDefaultBiasesCol
< paramsCorrArrayBiasFig = createMatrixPlusColFig( matrix = XT_dot_X_Linplus_corr,
< matIndexLabel = paramsNames,
< matColLabel = paramsNames,
< colVector = -np.around(normlzdStdDefaultBiasesCol, decimals=2),
< colVectIndexLabel = paramsNames,
< colVectColLabel = ['-Normalized, Standardized Biases'],
< plotTitle='XTdotX and bias column, converted to correlation',
< reversedYAxis = 'reversed' )
<
< # Create figure that plots color-coded projection matrix plus bias column.
< XT_dot_X_Linplus_inv = np.linalg.inv( XT_dot_X_Linplus )
< projMatrix = normlzdLinplusSensMatrixPoly @ XT_dot_X_Linplus_inv @ normlzdLinplusSensMatrixPoly.T
< print("projMatrix rows=", np.linalg.norm( projMatrix, axis=1))
< projectionMatrixFig = createMatrixPlusColFig( matrix = projMatrix,
< matIndexLabel = metricsNames,
< matColLabel = metricsNames,
< colVector = -np.around(normlzdDefaultBiasesCol, decimals=2),
< colVectIndexLabel = metricsNames,
< colVectColLabel = ['-Normalized Biases'],
< plotTitle='Projection matrix',
< reversedYAxis = 'reversed' )
---
> roundedNormlzdSensMatrix = np.around( normlzdLinplusSensMatrixPoly, decimals=2)
> df_sensmat = pd.DataFrame(roundedNormlzdSensMatrix,
> index=metricsNames,
> columns=paramsNames)
> matMaxAbs = np.max(np.abs(roundedNormlzdSensMatrix))
> normlzdSensMatrixFig = ff.create_annotated_heatmap(
> z=df_sensmat.to_numpy(),
> x=df_sensmat.columns.tolist(),
> y=df_sensmat.index.tolist(),
> #coloraxis="coloraxis",
> #colorscale=px.colors.diverging.balance,
> colorscale='balance',
> zmin=-matMaxAbs, zmax=matMaxAbs,
> showscale=False, ygap=1, xgap=1
> )
> normlzdSensMatrixFig.update_xaxes(side="bottom")
> normlzdSensMatrixFig.update_layout(
> title_text='Color-coded normalized sensitivity matrix',
> title_x=0.5,
> #width=800,
> #height=1400,
> xaxis_showgrid=False,
> yaxis_showgrid=False,
> xaxis_zeroline=False,
> yaxis_zeroline=False,
> #yaxis_autorange='reversed',
> template='plotly_white'
> )
> df_biasArray = pd.DataFrame( -np.around(defaultBiasesCol/np.abs(normMetricValsCol), decimals=2),
> index=metricsNames,
> columns= ['-Normalized Biases'])
> normlzdBiasArrayFig = ff.create_annotated_heatmap(
> z=df_biasArray.to_numpy(),
> x=df_biasArray.columns.tolist(),
> y=df_biasArray.index.tolist(),
> #colorscale=normlzdSensMatrixFig.colorscale,
> #colorscale=px.colors.diverging.balance,
> #coloraxis="coloraxis",
> colorscale='balance',
> zmin=-matMaxAbs, zmax=matMaxAbs,
> showscale=True, ygap=1, xgap=1
> )
> normlzdBiasArrayFig.update_layout(
> title_text='',
> title_x=0.5,
> #width=10,
> #height=1400,
> xaxis_showgrid=False,
> yaxis_showgrid=False,
> xaxis_zeroline=False,
> yaxis_zeroline=False,
> #yaxis_autorange='reversed',
> #template='plotly_white'
> )
> sensMatrixBiasFig = make_subplots(
> rows=1, cols=2,
> column_widths=[0.9, 0.1],
> horizontal_spacing=0.2,
> )
> sensMatrixBiasFig.add_trace(normlzdSensMatrixFig.data[0], row=1, col=1)
> #sensMatrixBiasFig.add_trace(normlzdBiasArrayFig.data[0], 1, 1)
> sensMatrixBiasFig.add_trace(normlzdBiasArrayFig.data[0], row=1, col=2)
> sensMatrixBiasFig.update_layout(
> title_text='Color-coded normalized sensitivity matrix',
> height=700,
> template='plotly_white')
> sensMatrixBiasFig.update_layout(coloraxis=dict(colorscale='RdBu',cmin=-1,cmax=1), showlegend=False)
>
> #pdb.set_trace()
1175a1154,1200
> cosAnglesMatrix = calcMatrixAngles( normlzdLinplusSensMatrixPoly )
> roundedCosAnglesMatrix = np.around(cosAnglesMatrix, decimals=2)
> df = pd.DataFrame(roundedCosAnglesMatrix,
> index=metricsNames,
> columns=metricsNames)
> upTriMask = np.logical_not( np.tril(np.ones_like(roundedCosAnglesMatrix, dtype=bool)) )
> df_mask = df.mask(upTriMask)
> #maskedRoundedCosAnglesMatrix = np.ma.masked_array(roundedCosAnglesMatrix, mask=upTriMask)
> #maskedRoundedCosAnglesMatrix.filled(np.nan)
> #print("maskedAngles =", maskedRoundedCosAnglesMatrix)
> #print("cosAnglesMatrix =", cosAnglesMatrix)
> #print("upTriMask =", upTriMask)
> metricsCorrArrayFig = ff.create_annotated_heatmap(
> z=df_mask.to_numpy(),
> x=df_mask.columns.tolist(),
> y=df_mask.columns.tolist(),
> colorscale=px.colors.diverging.balance,
> showscale=True, ygap=1, xgap=1
> )
> #metricsCorrArrayFig = go.Figure(data=go.Heatmap(
> # z=roundedCosAnglesMatrix,
> ## labels=dict(x="Metrics", y="Metrics")x=['SWCF_GLB', 'SWCF_DYCOMS', 'SWCF_HAWAII', 'SWCF_VOCAL', 'SWCF_VOCAL_near', 'SWCF_LBA', 'SWCF_WP', 'SWCF_EP', 'SWCF_NP', 'SWCF_SP', 'SWCF_CAF', 'SWCF_Nambian', 'SWCF_Nambian_near', 'LWCF_GLB', 'PRECT_GLB'])
> ## labels=dict(x="hullo")
> # x=metricsNames.tolist(),
> # y=metricsNames.tolist() )
> ## text_auto=True )
> # )
> # metricsCorrArrayFig = px.imshow(
> # img=roundedCosAnglesMatrix,
> # x=metricsNames.tolist(),
> # y=metricsNames.tolist(),
> # color_continuous_scale=px.colors.diverging.balance
> # )
> # metricsCorrArrayFig.update_traces(text=roundedCosAnglesMatrix)
> metricsCorrArrayFig.update_xaxes(side="bottom")
> metricsCorrArrayFig.update_layout(
> title_text='cos(angle) among metrics (i.e., rows of sens matrix)',
> title_x=0.5,
> width=800,
> height=700,
> xaxis_showgrid=False,
> yaxis_showgrid=False,
> xaxis_zeroline=False,
> yaxis_zeroline=False,
> yaxis_autorange='reversed',
> template='plotly_white'
> )
1179d1203
< #normlzdSensMatrixConcatBiases = np.hstack((normlzdWeightedLinplusSensMatrixPoly, -1*normlzdWeightedDefaultBiasesCol))
1210c1234
< invrsCosFactorPlusMatrix = np.power( np.maximum( np.finfo(float).eps, 2. * ( 1. + cosAnglesMatrix ) ), -0.5 )
---
> invrsCosFactorPlusMatrix = np.power( 2. * ( 1. + cosAnglesMatrix ) , -0.5 )
1253d1276
<
1259c1282
< dcc.Graph( id='paramsErrorBarsFig', figure=paramsErrorBarsFig ),
---
> dcc.Graph( id='paramsBarChart', figure=paramsBarChart ),
1261d1283
< dcc.Graph( id='metricsBarChart', figure=metricsBarChart ),
1263,1265c1285
< # dcc.Graph( id='paramsTotContrbBarFig', figure=paramsTotContrbBarFig ),
< dcc.Graph( id='sensMatrixBarFig', figure=sensMatrixBarFig ),
< #dcc.Graph( id='linplusSensMatrixBarFig', figure=linplusSensMatrixBarFig ),
---
> dcc.Graph( id='paramsTotContrbBarFig', figure=paramsTotContrbBarFig ),
1271d1290
< dcc.Graph( id='paramsCorrArrayBiasFig', figure=paramsCorrArrayBiasFig ),
1274d1292
< dcc.Graph( id='projectionMatrixFig', figure=projectionMatrixFig ),
1301,1314d1318
< def covMatrix2corrMatrix( covMatrix, returnStd=False ):
<
< # https://gist.github.com/wiso/ce2a9919ded228838703c1c7c7dad13b
<
< import numpy as np
<
< stdVector = np.sqrt( np.diag( covMatrix ) )
< stdMatrixInv = np.diag( 1.0 / stdVector )
< corrMatrix = stdMatrixInv @ covMatrix @ stdMatrixInv
< if returnStd:
< return ( corrMatrix, stdMatrixInv )
< else:
< return corrMatrix
<
1370,1371d1373
< #pdb.set_trace()
<
1375c1377
< dnormlzdParamsSolnNonlin = minimize(objFnc,x0=np.zeros_like(np.transpose(defaultParamValsOrigRow[0])), \
---
> dnormlzdParamsSolnNonlin = minimize(objFnc,x0=np.zeros_like(np.transpose(defaultParamValsOrigRow)), \
1419c1421
< dnormlzdParamsSolnLin = minimize(objFnc,x0=np.zeros_like(np.transpose(defaultParamValsOrigRow[0])), \
---
> dnormlzdParamsSolnLin = minimize(objFnc,x0=np.zeros_like(np.transpose(defaultParamValsOrigRow)), \
1606,1739d1607
<
<
<
< def createMatrixPlusColFig( matrix, matIndexLabel, matColLabel,
< colVector, colVectIndexLabel, colVectColLabel,
< plotTitle, reversedYAxis=None ):
< '''Creates a figure that displays a color-coded matrix and an accompanying column vector.'''
<
< import numpy as np
< import pandas as pd
< import plotly.figure_factory as ff
< import plotly.express as px
< from plotly.subplots import make_subplots
< import pdb
<
< # First create a sub-figure that displays color-coded matrix
< roundedNormlzdSensMatrix = np.around( matrix, decimals=2)
< df_sensmat = pd.DataFrame(roundedNormlzdSensMatrix,
< index=matIndexLabel,
< columns=matColLabel)
< matMaxAbs = np.max(np.abs(roundedNormlzdSensMatrix))
< matSubfig = px.imshow(
< df_sensmat.to_numpy(),
< x=df_sensmat.columns.tolist(),
< y=df_sensmat.index.tolist(),
< text_auto=True
< )
< matSubfig.update_xaxes(side="bottom")
< matSubfig.update_layout(
< title_text=plotTitle,
< title_x=0.5,
< #width=800,
< #height=1400,
< xaxis_showgrid=False,
< yaxis_showgrid=False,
< xaxis_zeroline=False,
< yaxis_zeroline=False,
< )
<
< # Now create a sub-figure showing a color-coded column matrix
< df_biasArray = pd.DataFrame( colVector,
< index=colVectIndexLabel,
< columns= colVectColLabel)
< colVectSubfig = px.imshow(
< df_biasArray.to_numpy(),
< x=df_biasArray.columns.tolist(),
< y=df_biasArray.index.tolist(),
< text_auto=True
< )
< colVectSubfig.update_layout(
< title_text='',
< title_x=0.5,
< #width=10,
< #height=1400,
< xaxis_showgrid=False,
< yaxis_showgrid=False,
< xaxis_zeroline=False,
< yaxis_zeroline=False,
< )
<
< # Now combine the matrix and column sub-figures into one figure
< matrixPlusColFig = make_subplots(
< rows=1, cols=2,
< column_widths=[0.9, 0.1],
< horizontal_spacing=0.3,
< )
< matrixPlusColFig.add_trace(matSubfig.data[0], row=1, col=1)
< matrixPlusColFig.add_trace(colVectSubfig.data[0], row=1, col=2)
< matrixPlusColFig.update_layout(
< title_text=plotTitle,
< height=700,
< width= 1000,
< template='plotly_white')
< matrixPlusColFig.update_layout(coloraxis=dict(colorscale='RdBu_r',cmin=-matMaxAbs,cmax=matMaxAbs), showlegend=False)
< matrixPlusColFig.update_yaxes( autorange=reversedYAxis, row=1, col=2 )
< matrixPlusColFig.update_yaxes( autorange=reversedYAxis, row=1, col=1 )
<
< #pdb.set_trace()
<
< return ( matrixPlusColFig )
<
<
< def createMetricsBarChart( metricsNames, paramsNames, biases, sensMatrix ):
<
<
< import plotly.graph_objects as go
< import numpy as np
< import pdb
<
< biases = np.reshape(biases, (-1,1))
< barBase = np.copy(biases) # np.copy prevents biases variable from changing
< rightEnd = np.copy(biases)
< leftEnd = np.copy(biases)
< barsData = []
< for col in range(len(paramsNames)):
< #print("paramsNames[col]=", paramsNames[col])
< sensCol = sensMatrix[:,[col]]
< #print("sensCol=", sensCol )
< #print("rightEnd=", rightEnd )
< for row in range( len(sensCol) ):
< if ( np.sign(sensCol[row]) > 0 ):
< barBase[row] = rightEnd[row]
< else:
< barBase[row] = leftEnd[row]
<
< #print("barBase=", barBase)
< #print("biases during=", biases)
< barsData.append( go.Bar(name=paramsNames[col], y=metricsNames, x=sensCol[:,0],
< base=barBase[:,0], orientation="h" ) )
< rightEnd = rightEnd + np.maximum( np.zeros_like(sensCol), sensCol )
< leftEnd = leftEnd + np.minimum( np.zeros_like(sensCol), sensCol )
<
< #print("biases after=", biases)
< # Insert a black line in each bar to denote default biases that we want to remove
< barsData.append( go.Bar(name='default bias',
< y=metricsNames, x=np.zeros(len(metricsNames)), base=biases[:,0],
< orientation="h",
< marker_line_color = 'black', marker_color='black', marker_line_width = 4
< )
< )
<
< metricsBarChart = go.Figure(data=barsData)
<
< # Change the bar mode
< metricsBarChart.update_layout(barmode='stack')
< metricsBarChart.update_xaxes(visible=True,zeroline=True,zerolinewidth=4,zerolinecolor='gray') # Plot y axis
< metricsBarChart.update_layout( width=800, height=50*len(metricsNames) )
< metricsBarChart.update_xaxes(title="-Normalized biases")
<
< #pdb.set_trace()
<
< return metricsBarChart
<
<
1741d1608
< # barBase,
1748d1614
< import plotly.graph_objects as go
1754,1757c1620
< barChart = px.bar(df, x=df.index, y=df.columns,
< #base=barBase,
< #offset=1,
< orientation=orientation,
---
> barChart = px.bar(df, x=df.index, y=df.columns, orientation=orientation,
1765d1627
< #barChart.update_layout(barmode='relative')
1915,1976d1776
< def createCorrArrayFig( matrix, indexLabels, title ):
<
< import numpy as np
< import pandas as pd
< import plotly.figure_factory as ff
< import plotly.express as px
< import pdb
<
< cosAnglesMatrix = calcMatrixAngles( matrix )
< #cosAnglesMatrix = np.copy( matrix )
< roundedCosAnglesMatrix = np.around(cosAnglesMatrix, decimals=2)
<
< df = pd.DataFrame(roundedCosAnglesMatrix,
< index=indexLabels,
< columns=indexLabels)
< # Display only the lower-triangular elements of the matrix
< upTriMask = np.logical_not( np.tril(np.ones_like(roundedCosAnglesMatrix, dtype=bool)) )
< df_mask = df.mask(upTriMask)
< #maskedRoundedCosAnglesMatrix = np.ma.masked_array(roundedCosAnglesMatrix, mask=upTriMask)
< #maskedRoundedCosAnglesMatrix.filled(np.nan)
< #print("maskedAngles =", maskedRoundedCosAnglesMatrix)
< #print("cosAnglesMatrix =", cosAnglesMatrix)
< #print("upTriMask =", upTriMask)
< corrArrayFig = ff.create_annotated_heatmap(
< z=df_mask.to_numpy(),
< x=df_mask.columns.tolist(),
< y=df_mask.columns.tolist(),
< colorscale=px.colors.diverging.balance,
< showscale=True, ygap=1, xgap=1
< )
< #metricsCorrArrayFig = go.Figure(data=go.Heatmap(
< # z=roundedCosAnglesMatrix,
< ## labels=dict(x="Metrics", y="Metrics")x=['SWCF_GLB', 'SWCF_DYCOMS', 'SWCF_HAWAII', 'SWCF_VOCAL', 'SWCF_VOCAL_near', 'SWCF_LBA', 'SWCF_WP', 'SWCF_EP', 'SWCF_NP', 'SWCF_SP', 'SWCF_CAF', 'SWCF_Nambian', 'SWCF_Nambian_near', 'LWCF_GLB', 'PRECT_GLB'])
< ## labels=dict(x="hullo")
< # x=metricsNames.tolist(),
< # y=metricsNames.tolist() )
< ## text_auto=True )
< # )
< # metricsCorrArrayFig = px.imshow(
< # img=roundedCosAnglesMatrix,
< # x=metricsNames.tolist(),
< # y=metricsNames.tolist(),
< # color_continuous_scale=px.colors.diverging.balance
< # )
< # metricsCorrArrayFig.update_traces(text=roundedCosAnglesMatrix)
< corrArrayFig.update_xaxes(side="bottom")
< corrArrayFig.update_layout(
< title_text=title,
< title_x=0.5,
< width=800,
< height=700,
< xaxis_showgrid=False,
< yaxis_showgrid=False,
< xaxis_zeroline=False,
< yaxis_zeroline=False,
< yaxis_autorange='reversed',
< template='plotly_white'
< )
<
< #pdb.set_trace()
<
< return ( corrArrayFig )
diff '--exclude=.git' '--exclude=version_clubb_core.txt' '--exclude=version_silhs.txt' -r clubb/utilities/sens_matrix/set_up_dashboard_inputs.py clubb_release/utilities/sens_matrix/set_up_dashboard_inputs.py
56,58c56
< # ['SWCF_RACC', 0.01, 0.2], \
< ['SWCF_RMSEP', 4.01, 15.], \
< # ['SWCF_RMSE', 0.01, 15.], \
---
> ['SWCF_RMSE', 4.00, 15.], \
61,62c59,60
< ['SWCF_HAWAII', 1.00, -999], \
< ['SWCF_VOCAL', 1.00, -999], \
---
> ['SWCF_HAWAII', 2.00, -999], \
> ['SWCF_VOCAL', 2.00, -999], \
69c67
< ## ['SWCF_PA', 1.01, -999], \
---
> # ['SWCF_PA', 1.01, -999], \
71c69
< ['SWCF_Namibia', 4.00, -999], \
---
> ['SWCF_Namibia', 1.00, -999], \
78c76
< ## ['LWCF_WP', 1.00, -999], \
---
> # ['LWCF_WP', 1.00, -999], \
80,81c78,79
< ## ['LWCF_NP', 1.01, -999], \
< ## ['LWCF_SP', 1.01, -999], \
---
> # ['LWCF_NP', 1.01, -999], \
> # ['LWCF_SP', 1.01, -999], \
85,87d82
< # ['PRECT_RACC', 0.01, 1.0], \
< # ['PRECT_RMSEP', 0.01, 1.0], \
< # ['PRECT_RMSE', 0.01, 1.0], \
129c124
< folder_name = 'Regional_files/20230910/' # folder where regional netcdf files are stored.
---
> folder_name = 'Regional_files/20230804/' # folder where regional netcdf files are stored.
153,155c148,150
< # ['clubb_c_invrs_tau_sfc', 1.0, \
< # folder_name + 'sens0707_6_Regional.nc',
< # folder_name + 'sens0707_7_Regional.nc'], \
---
> ['clubb_c_invrs_tau_sfc', 1.0, \
> folder_name + 'sens0707_6_Regional.nc',
> folder_name + 'sens0707_7_Regional.nc'], \
185,187d179
< # ['cldfrc_dp2', 1.0, \
< # folder_name + 'sens0707_38_Regional.nc',
< # folder_name + 'sens0707_39_Regional.nc'], \
211,231d202
< # ['clubb_c8', 1.0, 0.7, \
< # folder_name + 'sens0707_14_Regional.nc', \
< # folder_name + 'sens0707_15_Regional.nc'], \
< # ['clubb_c_k10', 1.0, 0.3, \
< # folder_name + 'sens0707_12_Regional.nc', \
< # folder_name + 'sens0707_13_Regional.nc'], \
< # ['clubb_c_invrs_tau_n2', 1.0, 0.4, \
< # folder_name + 'sens0707_10_Regional.nc',
< # folder_name + 'sens0707_11_Regional.nc'], \
< ['clubb_c_invrs_tau_sfc', 1.0, 0.05, \
< folder_name + 'sens0707_6_Regional.nc',
< folder_name + 'sens0707_7_Regional.nc'], \
< # ['clubb_c_invrs_tau_wpxp_n2_thresh', 1.e3, 0.00045, \
< # folder_name + 'sens0707_8_Regional.nc', \
< # folder_name + 'sens0707_9_Regional.nc'], \
< # ['clubb_c_invrs_tau_shear', 1.0, 0.22, \
< # folder_name + 'sens0707_2_Regional.nc', \
< # folder_name + 'sens0707_3_Regional.nc'], \
< # ['clubb_c_invrs_tau_bkgnd', 1.0, 1.1, \
< # folder_name + 'sens0707_16_Regional.nc',
< # folder_name + 'sens0707_17_Regional.nc'], \
234a206,208
> # ['clubb_c_invrs_tau_bkgnd', 1.0, 1.50686559, \
> # folder_name + 'sens0707_16_Regional.nc', \
> # folder_name + 'sens0707_17_Regional.nc'], \
264c238
< folder_name + 'sens0707_25_Regional.nc'
---
> folder_name + 'sens0707_1_Regional.nc'
273,274d246
< 'SWCF_RACC': 0, \
< 'SWCF_RMSEP': 0, \
290,292d261
< 'PRECT_RACC': 0, \
< 'PRECT_RMSEP': 0, \
< 'PRECT_RMSE': 0, \
394,395d362
<
< #print("defaultMetricValsCol=", defaultMetricValsCol)
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