Dependencies
Dependency graphs for zppy tasks and subtasks:
Graphs generated by Claude based on the Mermaid code below.
Main connected component:
1%%{init: {'flowchart': {'htmlLabels': true, 'wrappingWidth': 260, 'nodeSpacing': 60, 'rankSpacing': 80}}}%%
2flowchart LR
3
4 %% Subtasks (vertices)
5 climo_month_lnd["climo: monthly, lnd"]
6 ts_month_atm["ts: monthly, atm"]
7 ts_month_lnd["ts: monthly, lnd"]
8 e3sm_to_cmip_month_atm["e3sm_to_cmip: monthly, atm"]
9 e3sm_to_cmip_month_lnd["e3sm_to_cmip: monthly, lnd"]
10 e3sm_diags_climo_month_lnd["e3sm_diags: lat_lon_land"]
11 e3sm_diags_ts_month_atm["e3sm_diags: enso_diags, qbo,<br/>area_mean_time_series, mp_partition"]
12 ilamb_lnd_atm["ilamb: lnd & atm"]
13 ilamb_lnd_only["ilamb: lnd only"]
14 livvkit["livvkit<br/>NOTE: Specify mapping files"]
15 pcmdi_diags_mean_climate["pcmdi_diags: mean_climate"]
16 pcmdi_diags_variability_modes_atm["pcmdi_diags: variability_modes_atm"]
17 pcmdi_diags_variability_modes_cpl["pcmdi_diags: variability_modes_cpl"]
18 pcmdi_diags_enso["pcmdi_diags: enso"]
19 pcmdi_diags_synthetic_plots["pcmdi_diags: synthetic_plots"]
20
21 %% Defined in e3sm_to_cmip.py:
22 ts_month_atm --> e3sm_to_cmip_month_atm
23 ts_month_lnd --> e3sm_to_cmip_month_lnd
24
25 %% Defined in e3sm_diags.py:
26 climo_month_lnd --> e3sm_diags_climo_month_lnd
27 ts_month_atm --> e3sm_diags_ts_month_atm
28
29 %% Defined in ilamb.py:
30 ts_month_atm --> ilamb_lnd_atm
31 ts_month_lnd --> ilamb_lnd_atm
32 ts_month_lnd --> ilamb_lnd_only
33 e3sm_to_cmip_month_atm --> ilamb_lnd_atm
34 e3sm_to_cmip_month_lnd --> ilamb_lnd_atm
35 e3sm_to_cmip_month_lnd --> ilamb_lnd_only
36
37 %% Defined in livvkit.py:
38 climo_month_lnd -->livvkit
39 ts_month_lnd -->livvkit
40
41 %% Defined in pcmdi_diags.py:
42 ts_month_atm --> pcmdi_diags_mean_climate
43 ts_month_atm --> pcmdi_diags_variability_modes_atm
44 ts_month_atm --> pcmdi_diags_variability_modes_cpl
45 ts_month_atm --> pcmdi_diags_enso
46 e3sm_to_cmip_month_atm --> pcmdi_diags_mean_climate
47 e3sm_to_cmip_month_atm --> pcmdi_diags_variability_modes_atm
48 e3sm_to_cmip_month_atm --> pcmdi_diags_variability_modes_cpl
49 e3sm_to_cmip_month_atm --> pcmdi_diags_enso
50 pcmdi_diags_mean_climate --> pcmdi_diags_synthetic_plots
51 pcmdi_diags_variability_modes_atm --> pcmdi_diags_synthetic_plots
52 pcmdi_diags_variability_modes_cpl --> pcmdi_diags_synthetic_plots
53 pcmdi_diags_enso --> pcmdi_diags_synthetic_plots
54
55 %% Define colors for each task
56 classDef climoStyle fill:#fde68a,stroke:#b45309,color:#000,padding:14px;
57 classDef tsStyle fill:#bfdbfe,stroke:#1d4ed8,color:#000,padding:14px;
58 classDef cmipStyle fill:#99f6e4,stroke:#0f766e,color:#000,padding:14px;
59 classDef e3smdiagsStyle fill:#c7f9cc,stroke:#15803d,color:#000,padding:14px;
60 classDef ilambStyle fill:#fed7aa,stroke:#c2410c,color:#000,padding:14px;
61 classDef livvkitStyle fill:#a5f3fc,stroke:#0e7490,color:#000,padding:14px;
62 classDef pcmdiStyle fill:#fecaca,stroke:#b91c1c,color:#000,padding:14px;
63
64 %% Group each subtask into a task for coloring
65 class climo_month_lnd climoStyle;
66 class ts_month_atm,ts_month_lnd tsStyle;
67 class e3sm_to_cmip_month_atm,e3sm_to_cmip_month_lnd cmipStyle;
68 class e3sm_diags_climo_month_lnd,e3sm_diags_ts_month_atm e3smdiagsStyle;
69 class ilamb_lnd_atm,ilamb_lnd_only ilambStyle;
70 class livvkit livvkitStyle;
71 class pcmdi_diags_mean_climate,pcmdi_diags_variability_modes_atm,pcmdi_diags_variability_modes_cpl,pcmdi_diags_enso,pcmdi_diags_synthetic_plots pcmdiStyle;
Trivial dependencies:
1%%{init: {'flowchart': {'htmlLabels': true, 'wrappingWidth': 260, 'nodeSpacing': 60, 'rankSpacing': 80}}}%%
2flowchart LR
3
4 %% Defined in e3sm_diags.py:
5 climo_month_atm["climo: monthly, atm"] --> e3sm_diags_climo_month_atm["e3sm_diags: lat_lon, zonal_mean_xy,<br/>zonal_mean_2d, polar, cosp_histogram,<br/>meridional_mean_2d, annual_cycle_zonal_mean,<br/>zonal_mean_2d_stratosphere,<br/>aerosol_aeronet, aerosol_budget"]
6 climo_diurnal_atm["climo: diurnal, atm"] --> e3sm_diags_diurnal["e3sm_diags: diurnal_cycle"]
7 tc_analysis["tc_analysis"] --> e3sm_diags_tc["e3sm_diags: tc_analysis"]
8 ts_month_rof["ts: monthly, rof"] --> e3sm_diags_streamflow["e3sm_diags: streamflow"]
9 ts_daily_atm["ts: daily, atm"] --> e3sm_diags_ts_daily_atm["e3sm_diags: tropical_subseasonal, precip_pdf"]
10
11 %% Defined in mpas_analysis.py:
12 mpas_analysis["mpas_analysis<br/>NOTE: Later year sets depend on earlier runs"] ---> mpas_analysis
13
14 %% Defined in global_time_series.py:
15 ts_month_atm_glb["ts: monthly, atm, glb"] --> global_time_series_classic_atm["global_time_series:<br/>The 5 atm classic plots"]
16 ts_month_atm_glb --> global_time_series_plots_atm["global_time_series:<br/>atm plots"]
17 ts_month_lnd_glb["ts: monthly, lnd, glb"] --> global_time_series_plots_lnd["global_time_series:<br/>lnd plots"]
18 mpas_analysis --> global_time_series_classic_ocn["global_time_series:<br/>The 3 ocn classic plots"]
19 mpas_analysis --> global_time_series_plots_ocn["global_time_series:<br/>ocn plots"]
20 global_time_series_plots_ice["global_time_series:<br/>ice plots (currently unused)"]
21
22 %% Define colors for each task
23 classDef climoStyle fill:#fde68a,stroke:#b45309,color:#000,padding:14px;
24 classDef tsStyle fill:#bfdbfe,stroke:#1d4ed8,color:#000,padding:14px;
25 classDef tcStyle fill:#e5e7eb,stroke:#4b5563,color:#000,padding:14px;
26 classDef e3smdiagsStyle fill:#c7f9cc,stroke:#15803d,color:#000,padding:14px;
27 classDef mpasStyle fill:#fbcfe8,stroke:#be185d,color:#000,padding:14px;
28 classDef gtsStyle fill:#ddd6fe,stroke:#6d28d9,color:#000,padding:14px;
29
30 %% Group each subtask into a task for coloring
31 class climo_month_atm,climo_diurnal_atm climoStyle;
32 class ts_month_rof,ts_daily_atm,ts_month_atm_glb,ts_month_lnd_glb tsStyle;
33 class tc_analysis tcStyle;
34 class e3sm_diags_climo_month_atm,e3sm_diags_diurnal,e3sm_diags_tc,e3sm_diags_streamflow,e3sm_diags_ts_daily_atm e3smdiagsStyle;
35 class mpas_analysis mpasStyle;
36 class global_time_series_classic_ocn,global_time_series_plots_ocn,global_time_series_classic_atm,global_time_series_plots_atm,global_time_series_plots_lnd,global_time_series_plots_ice gtsStyle;