gomd — Official Python Scientific, Oceanographic, Maritime Decarbonization & Econometric SDK (v2.1.2)

gomd (v2.1.2) is the official scientific computing, oceanographic modeling, maritime regulatory compliance, and climate econometric Python software development kit for the Global Ocean & Maritime Data Platform (GOMD — Piri Reis University · Directorate General of Environmental Management / COP31 Türkiye).

Unlike simple REST wrappers, gomd v2.1.2 includes 7 specialized scientific submodules with zero C/Fortran compilation requirements:

1. gomd.client — Authenticated REST & Bulk Client, Multi-Indicator Econometric Panel Builder (build_panel), Global Historical Blue & Marine Insurance Finance Atlas (get_blue_finance_atlas), Local SHA-256 Disk Cache (~/.gomd_cache), and Cryptographic Provenance Verification (verify_provenance).
2. gomd.ocean — Physical, Biogeochemical, Blue Carbon, CWON Natural Capital & Parametric Sustainability Insurance:
   • seawater_density_eos80() — UNESCO 1983 (EOS-80) equation of state ($\rho(S,T,p)$ & potential density anomaly $\sigma_0$).
   • teos10_gsw_stratification() — TEOS-10 Gibbs Seawater Absolute Salinity ($S_A$), Conservative Temperature ($\Theta$), and Sallée et al. (Nature, 2021) pycnocline stratification barrier ($N^2$).
   • oxygen_solubility() — Garcia & Gordon (1992) Dissolved Oxygen saturation ($\text{DO}_{\text{sat}}$), Apparent Oxygen Utilization ($\text{AOU}$), and Diaz & Rosenberg (2008) hypoxia staging.
   • carbonate_system() — Marine carbonate equilibrium solver (Dickson & Millero 1987; Lueker et al. 2000; Mucci 1983) for $p\text{CO}_2$, $\text{DIC}$, $[\text{HCO}3^-]$, $[\text{CO}3^{2-}]$, Aragonite saturation ($\Omega{\text{arag}}$), and Calcite saturation ($\Omega{\text{calc}}$).
   • marine_heatwave_detect() & compound_mhw_oax_event() — Hobday et al. (2016) MHW detector and Gruber et al. (Science, 2021) / Burger et al. (Nature, 2022) Triple Compound MHW+OAX+LOX & Clarke (2021) Aerobic Growth Index (AGI).
   • trophic_state_index() & mucilage_risk_index() — Carlson (1977) TSI, Vollenweider (1998) TRIX, and composite 0–100 marine mucilage hazard index.
   • blue_carbon_sequestration_valuation() — Bertram et al. (Nature Climate Change, 2021) & Rennert et al. (Nature, 2022, $$185/\text{tCO}_2$) coastal blue carbon NPV & Paris Agreement Article 6 VCM valuation.
   • sustainable_insurance_cat_var() — UNEP FI Principles for Sustainable Insurance (PSI, 2023), Reguero et al. (Nature Comms, 2020) & Beck et al. (Nature Sustainability, 2022) parametric coral reef, mangrove & seagrass wave-attenuation catastrophe risk-transfer and resilience Benefit-Cost Ratio (BCR) model.
   • cwon_wealth_accounting() — World Bank Changing Wealth of Nations (CWON 2021/2024) & Dasgupta Review (2021) Adjusted Net Savings (ANS / Genuine Savings) and Blue-Inclusive Natural Capital Depletion Accounting.
3. gomd.maritime — Maritime Decarbonization, Marine Hull/P&I Underwriting (IUMI / PPMI), Green Finance & Port State Control:
   • calculate_imo_cii() — IMO MEPC.336(76)–MEPC.354(78) Attained AER, Required CII reference line, annual reduction factor $Z%$, and A–E rating boundaries.
   • calculate_eu_ets_exposure() & calculate_fueleu_maritime() — Directive (EU) 2023/959 Maritime EU ETS and Regulation (EU) 2023/1805 FuelEU Maritime Well-to-Wake (WtW) GHG intensity & RFNBO $2\times$ multiplier.
   • imo_2023_strive_trajectory() & ais_speed_power_emissions() — IMO MEPC.377(80) (2023 Revised GHG Strategy) Stranded-Asset Year & AIS Admiralty-Cube propulsion elasticity.
   • poseidon_principles_alignment() & psc_detention_risk_score() — Poseidon Principles ship finance alignment ($\Delta_{\text{PP}}%$) and Paris/Med MoU PSC detention risk profile.
   • marine_insurance_actuarial_pricing() — IUMI Global Marine Insurance Report (2025), International Group of P&I Clubs (IG P&I) mutual pool call, Cargo CIF-FOB, War-Risk chokepoint surcharge, Poseidon Principles for Marine Insurance (PPMI, 2024) climate alignment delta ($\Delta_{\text{PPMI}}%$), and Solvency II 99.5% Marine Underwriting VaR.
4. gomd.stats — Non-Parametric Environmental, Causal & Panel Econometrics:
   • mann_kendall_sen(), pettitt_changepoint(), extreme_return_levels(), cointegration_eg_test(), panel_fixed_effects(), synthetic_did_ate() (Arkhangelsky et al., AER 2021), innovative_polygon_trend() (Şen, 2020/2021), and transfer_entropy_causal() (Runge et al., Nature Comms 2019 / Nat. Rev. 2023).
5. gomd.spatial — Geodesic & Chokepoint Spatial Analytics (haversine_distance, classify_sea_basin, chokepoint_proximity, to_geojson).
6. gomd.cli — Command-Line Interface (python -m gomd cite | cii | carbonate | basin).

Installation

Install directly from the GOMD PEP 503 Simple Repository Index:

pip install gomd --extra-index-url https://gomd.pirireis.edu.tr/sdk/dist/simple/
# Or direct wheel installation (Google Colab zero-cache recommended):
!pip install -q --upgrade gomd

Official Documentation & 10 Downloadable Real-Data Google Colab Case Studies (.ipynb & .py)

• Interactive Python SDK Documentation & Case Studies Hub: https://gomd.pirireis.edu.tr/python_sdk_docs.php
  1. Case Study 01 (cs01_multivariate_ocean_climate_data_mining.ipynb): Eastern Mediterranean Multivariate Ocean-Climate Regime Mining (8 Live GOMD Series, Spectral PCA, K-Means++, Walk-Forward Ridge CV & Shannon Transfer Entropy)
  2. Case Study 02 (cs02_maritime_decarbonization_cii_ets_fueleu.ipynb): Commercial Fleet Decarbonization Mining (Live GOMD IMO CII, EEXI, Directive 2023/959 Maritime EU ETS, FuelEU Maritime WtW & 8-Country Transport $\text{CO}_2$ Panel)
  3. Case Study 03 (cs03_blue_finance_actuarial_insurance_cwon.ipynb): Global Blue Finance, Marine Hull/P&I Underwriting (IUMI/PPMI), Parametric Reef/Mangrove Insurance (UNEP FI PSI) & World Bank CWON Natural Capital Accounting (14 Live Series)
  4. Case Study 04 (cs04_marmara_mucilage_eutrophication_acidification.ipynb): Sea of Marmara Mucilage Hazard Index, Sub-Halocline Hypoxia, Vollenweider TRIX & Carlson TSI Coastal Eutrophication & TEOS-10 Stratification (7 Live Series)
  5. Case Study 05 (cs05_mavi_vatan_icj_delimitation_causal_sdid.ipynb): Mavi Vatan (UN A/74/757) ICJ Equitable Delimitation Audit, Global Maritime Chokepoint Geodesics & Real-Panel Synthetic Difference-in-Differences (SDID) Policy Evaluation
  6. Case Study 06 (cs06_blacksea_med_fisheries_biodiversity_medits.ipynb): Black Sea & Mediterranean Fisheries, Aquaculture Transition (1970–2024) & 5-Basin MEDITS Benthic Trawl Impact Panel (panel_fixed_effects, transfer_entropy_causal)
  7. Case Study 07 (cs07_global_carbon_budget_mauna_loa_ocean_sink_cointegration.ipynb): Global Carbon Budget (1959–2025), Mauna Loa $\text{CO}_2$, Ocean Sink Cointegration (cointegration_eg_test), Ocean Heat Content & Arctic Sea Ice Attribution
  8. Case Study 08 (cs08_high_frequency_metocean_wave_extremes_chokepoints.ipynb): High-Frequency Met-Ocean Wave Extremes (extreme_return_levels), Swell Energy & Thermal Shock Across 10 Sea Basins & Maritime Chokepoints ($N=5,580$ Live Observations)
  9. Case Study 09 (cs09_energy_transition_sectoral_ghg_green_shipping_corridors.ipynb): COP31 Energy Transition, Renewable Share & Sectoral GHG Decarbonization Panel Across 6 Mediterranean & European Economies ($N=192$)
  10. Case Study 10 (cs10_maritime_trade_port_connectivity_logistics_panel.ipynb): Global Maritime Trade, Container Port Throughput (TEU), UNCTAD Liner Connectivity (LSCI) & World Bank LPI Econometric Panel ($N=96$)

What's new in 2.1.2

• Command line: python -m gomd cite --style bibtex (or --format), and python -m gomd cii with --fuel-tonnes, --distance-nm/--distance, --ship-type and --co2 now work as written in the documentation; the short forms (--fuel, --nm, --type) still work.
• R package: gomd_blue_finance_atlas() and gomd_mavi_vatan_jurisdiction() called a function that does not exist and stopped with an error; fixed.
• README examples corrected so that every code block runs against the live GOMD server.

What's new in 2.1.1

• Install from PyPI: pip install gomd.
• Citation texts (gomd.cite()) now credit the Directorate General of Environmental Management (Ministry of Environment, Urbanisation and Climate Change of the Republic of Türkiye) alongside Piri Reis University.
• R package: gomd_analyze_basin() passed two arguments under the wrong names and stopped with an error; fixed.

What's new in 2.1.0 — gomd.dynamics

Eleven pure-Python functions with the analysis methods used in the GOMD research papers (https://gomd.pirireis.edu.tr/publications.php). All are also exported at package level.

import gomd
rows = gomd.get_series("IMF_PW_PORT_CALLS_D", entity="IMF_PORT_2049", as_df=False)   # daily port calls, Ambarli
calls = [r["value"] for r in rows]
print(gomd.dispersion_index(calls))                                   # 1 = Poisson arrivals
print(gomd.weekday_profile([r["period"] for r in rows], calls))       # weekend dip
print(gomd.fdr_benjamini_hochberg([0.001, 0.02, 0.04, 0.30, 0.75]))

GOMD-derived indicators and their methods: https://gomd.pirireis.edu.tr/derived_methodology.php

Quick Start Examples

1. Data Retrieval, Multi-Indicator Panel Construction & Provenance

import gomd

# Print official APA, IEEE, and BibTeX citations
gomd.cite()

client = gomd.GOMDClient(api_key="gomd_live_ent_YOUR_KEY", enable_cache=True)

# Search across all harmonized indicators (live counts: https://gomd.pirireis.edu.tr)
matches = client.search_indicators(keyword="aragonite")

# Build an entity-by-period panel combining multiple indicators
panel = client.build_panel(
    indicator_keys=[
        "ocean_ph_in_situ_total_scale",
        "omega_aragonite_saturation_state",
        "copernicus_sst_med_anomaly",
    ],
    period_from="2015",
    period_to="2025",
    as_dataframe=True,
)

2. Oceanographic & Biogeochemical Modeling (gomd.ocean)

from gomd.ocean import seawater_density_eos80, carbonate_system, trophic_state_index, mucilage_risk_index

# UNESCO EOS-80 Seawater Density & Potential Density Anomaly
eos = seawater_density_eos80(salinity_psu=38.5, temp_c=24.2, pressure_dbar=50)
print(eos["density_kg_m3"], eos["sigma_t_kg_m3"])

# Marine Carbonate Equilibrium & Aragonite Saturation (Omega_arag)
carb = carbonate_system(ph_total=8.04, temp_c=22.0, salinity_psu=38.4, ta_umol_kg=2560)
print(carb["pCO2_uatm"], carb["omega_aragonite"], carb["calcification_regime"])

# Sea of Marmara Mucilage Hazard Index
muc = mucilage_risk_index(sst_anomaly_c=2.4, n_p_molar_ratio=4.8, wind_speed_ms=1.9, residence_time_days=45, chl_a_ug_l=11.5)
print(muc["mucilage_risk_index_0_100"], muc["alert_level"])

3. Maritime Decarbonization & Green Shipping Finance (gomd.maritime)

from gomd.maritime import calculate_imo_cii, calculate_eu_ets_exposure, poseidon_principles_alignment

# IMO CII Rating (A-E) for an 85,000 DWT Bulk Carrier burning VLSFO in 2026
cii = calculate_imo_cii(
    fuel_consumption_tonnes=6400,
    dwt_or_gt=85000,
    distance_nm=58000,
    ship_type="bulk_carrier",
    fuel_type="VLSFO",
    year=2026,
)
print(cii["attained_aer_g_co2_dwt_nm"], cii["cii_rating"], cii["rating_boundaries"])

# EU ETS Maritime Exposure (2026 = 100% phase-in + CH4 & N2O included)
ets = calculate_eu_ets_exposure(co2_tonnes=18500, ch4_tonnes=12.4, n2o_tonnes=0.8, voyage_scope="eu_extra", year=2026, eua_price_eur=74.5)
print(ets["surrender_allowances_eua"], ets["estimated_cost_eur"])

4. Climate Trend Detection & Panel Econometrics on Live GOMD Series (gomd.stats)

import gomd
from gomd.stats import mann_kendall_sen, pettitt_changepoint, multivariate_data_mining_pipeline

client = gomd.GOMDClient()
fossil_rows = client.get_series("gcb2025_fossil", entity="GCB_GLOBAL", period_from=1990, period_to=2026, as_df=False)
sink_rows   = client.get_series("gcb2025_ocean_sink", entity="GCB_GLOBAL", period_from=1990, period_to=2026, as_df=False)
atm_rows    = client.get_series("gcb2025_atmosphere", entity="GCB_GLOBAL", period_from=1990, period_to=2026, as_df=False)

sink_series = [float(r["value"]) for r in sink_rows]          # ocean carbon sink, GtC per year
years       = [int(str(r["period"])[:4]) for r in sink_rows]

mk = mann_kendall_sen(sink_series, years)
print(mk["theil_sen_slope"], mk["p_value"], mk["trend_classification"])

cp = pettitt_changepoint(sink_series, labels=years)
print(cp["changepoint_label"], cp["pre_regime_mean"], cp["post_regime_mean"])

# Multivariate Ocean & Maritime Data Mining Pipeline (PCA + K-Means++ + Time-Series CV Ridge)
n_common = min(len(fossil_rows), len(sink_rows), len(atm_rows))
dm = multivariate_data_mining_pipeline(
    feature_matrix=[[float(fossil_rows[i]["value"]), float(sink_rows[i]["value"]), float(atm_rows[i]["value"])] for i in range(n_common)],
    feature_names=["Fossil_GtC", "Ocean_sink_GtC", "Atmosphere_GtC"],
    period_labels=[str(sink_rows[i]["period"])[:4] for i in range(n_common)],
    target_index=1,
    k_clusters=3,
)
print(dm["pca"]["cumulative_variance_pc1_pc2_pct"], dm["kmeans_regimes"]["silhouette_score"])

5. Maritime Jurisdiction ("Mavi Vatan" — UN Letter A/74/757) & ICJ Equitable Delimitation Audit (gomd.spatial)

import gomd

# ICJ Coastal-Length Proportionality & Third-Party Geodatabase (VLIZ/Seville Map) Audit
audit = gomd.mavi_vatan_eez_audit(
    mainland_coast_km=1577.0,
    opposite_coast_km=19.0,
    un_registered_area_km2=462000.0,
    provider_polygon_area_km2=254026.0,
)
print(audit["provider_area_discrepancy_km2"], audit["equitable_delimitation_compliance_score"])

# Query Official Turkish MFA / SHODB / DEHUKAM Maritime Jurisdiction Series (1986-2026)
df_mv = gomd.get_mavi_vatan_jurisdiction("mavi_vatan_official_jurisdiction_km2", entities=["TUR", "TRNC", "LBY", "BGR", "ROU"])

# Project COUNTER Release 5.1 & Make Data Count Scientific Portal Usage Econometrics
usage = gomd.counter_r5_usage_econometrics(
    pageviews_by_module={"ocean_observations": 14800, "maritime_decarbonization": 9600, "blue_finance": 7400, "cop31_governance": 6200, "sdk_research": 5100},
    dau=420, wau=1850, mau=4900, authenticated_sessions=6400, total_sessions=21500
)
print(usage["counter_r51_metrics"], usage["information_theory_concentration"]["shannon_attention_entropy_bits"])

# Source-Grounded Retrieval-Augmented Data Assistant & Numeric Grounding Audit
res = gomd.ask_assistant("Ege'de 2015 sonrası deniz yüzeyi sıcaklığı eğilimi nedir?", lang="tr")
print(res["answer"], res["citations"])
grounding = gomd.audit_numeric_grounding(res["answer"], [19.4, 21.8, 0.042], question="Ege'de 2015 sonrası eğilim")
print(grounding["grounding_rate"], grounding["unverified"])

# Global Chokepoint Disruption, Kunming-Montreal 30x30 MPA Gap & Coastal Hypoxia Dead-Zone Index
choke = gomd.global_chokepoint_disruption_index(baseline_transits_yr=25840, observed_transits_yr=13120, detour_distance_nm=3500.0)
mpa_gap = gomd.kunming_montreal_30x30_gap(current_mpa_pct=8.35, eez_area_km2=137000000.0)
hypoxia = gomd.global_hypoxia_deadzone_index(subpycnocline_do_mg_l=1.42, chlorophyll_a_ug_l=11.2, secchi_depth_m=2.1, din_dip_molar_ratio=28.4)
print(choke["disruption_severity_index_0_100"], mpa_gap["required_cagr_pct"], hypoxia["oxygen_regime"])

# Coupled Teleconnections (ENSO/IOD/PDO/AMO/AMOC), Coral Bleaching DHW, UN 66 LMEs, 50 River Deltas & Subsea Corridors
tele = gomd.climate_teleconnection_regime(enso_oni_c=1.35, iod_dmi_c=0.52, amoc_26n_sv=16.1)
coral = gomd.coral_bleaching_dhw_risk(dhw_c_weeks=9.8, hotspot_sst_anomaly_c=1.6, omega_aragonite=3.05)
lme = gomd.lme_trophic_hypoxia_stress(primary_productivity_mgc_m2_d=720.0, fish_landings_mt_yr=1.85, mean_trophic_level=3.18)
delta = gomd.river_delta_plastic_nutrient_export(discharge_km3_yr=480.0, plastic_export_tonnes_yr=34000.0, din_export_kt_n_yr=620.0, dip_export_kt_p_yr=45.0)
fueleu_cost = gomd.fueleu_cii_ets_compliance_cost(annual_fuel_tonnes=12000.0, wtw_ghg_intensity_gco2e_mj=91.2, target_wtw_gco2e_mj=89.34)
subsea = gomd.subsea_corridor_vulnerability_index(daily_vessel_transits=62.0, rerouting_detour_nm=3500.0, subsea_cable_count=18)

How to Cite GOMD

If you use the GOMD platform, its curated datasets, or the gomd Python/R software packages in any scientific publication, thesis, or policy report, please cite the GOMD platform:

Piri Reis University & Directorate General of Environmental Management (Ministry of Environment, Urbanisation and Climate Change of the Republic of Türkiye). (2026). Global Ocean & Maritime Data Platform (GOMD). gomd: Official Python and R Scientific, Oceanographic, Maritime & Econometric Software Development Kits (Version 2.1.2) [Computer software & Data platform]. https://gomd.pirireis.edu.tr