Tools
Overview
Boardflare provides a comprehensive library of Python-powered tools designed to extend Excel’s analytical capabilities. By bridging the gap between the world’s most popular spreadsheet and the powerful Python ecosystem (including SciPy, NumPy, and Pandas), Boardflare enables users to perform complex mathematical modeling, statistical analysis, and data processing directly within their familiar Excel environment.
Math
Comprehensive collection of mathematical functions including calculus, linear algebra, number theory, and optimization.
Statistics
Powerful statistical tools for data analysis, probability distributions, hypothesis testing, and Bayesian inference.
Engineering
Technical computing tools for fluids, heat transfer, structural analysis, thermodynamics, and photovoltaics.
Visualization
Modern scientific and statistical visualization tools for 2D, 3D, and specialty technical plotting.
Engineering
Chemical
Chemical Properties
| Tool | Description |
|---|---|
| CAS_FROM_ANY | Resolve a chemical identifier to its standardized CAS number. |
| CHEMICAL_PROPS | Retrieve physical and thermodynamic properties for a chemical specimen. |
| DIPOLE | Retrieve the dipole moment of a chemical by CAS number. |
| OMEGA | Retrieve the acentric factor of a chemical by CAS number. |
| PC | Retrieve the critical pressure of a chemical by CAS number. |
| SEARCH_CHEMICAL | Resolve a chemical identifier and return a compact metadata summary. |
| TB | Retrieve the normal boiling temperature of a chemical by CAS number. |
| TC | Retrieve the critical temperature of a chemical by CAS number. |
| TM | Retrieve the melting temperature of a chemical by CAS number. |
| VC | Retrieve the critical molar volume of a chemical by CAS number. |
Combustion Fuels
| Tool | Description |
|---|---|
| AIR_FUEL_RATIO | Calculates molar flow rate of air or fuel from the other, using a specified air-fuel ratio. |
| COMBUSTION_STOICH | Returns a dictionary of stoichiometric coefficients of chemical combustion from an atoms dictionary. |
| FUEL_AIR_SPEC | Solves the system of equations describing a flow of air mixing with a flow of combustibles and burning completely. |
| HHV_STOICHIOMETRY | Returns the higher heating value based on theoretical combustion stoichiometry and heat of formation. |
| IS_COMBUSTIBLE | Checks if a chemical is combustible based on its CAS and atoms. |
| LHV_FROM_HHV | Returns the lower heating value (LHV) of a chemical given the higher heating value (HHV) and number of water molecules formed. |
| MON | This function handles the retrieval of a chemical’s motor octane number (MON), using CASRN. |
| RON | This function handles the retrieval of a chemical’s research octane number (RON), using CASRN. |
Process Safety
| Tool | Description |
|---|---|
| CARCINOGEN_STATUS | Looks up if a chemical is listed as a carcinogen according to specific methods. |
| CEILING_LIMIT | Handles the retrieval of ceiling limits on worker exposure to dangerous chemicals. |
| LFL | Handles the retrieval or calculation of a chemical’s Lower Flammability Limit. |
| STEL | Handles the retrieval of Short-term Exposure Limit (STEL) for worker exposure. |
| T_AUTOIGNITION | Handles the retrieval or calculation of a chemical’s autoignition temperature. |
| T_FLASH | Handles the retrieval or calculation of a chemical’s flash point. |
| TWA_LIMIT | Return the Time-Weighted Average exposure limits (TWA) for the desired chemical. |
| UFL | Handles the retrieval or calculation of a chemical’s Upper Flammability Limit. |
Control Systems
Analysis
| Tool | Description |
|---|---|
| DAMP | Compute the system’s natural frequencies, damping ratios, and poles. |
| FORCED_RESPONSE | Compute the output of a linear system given an arbitrary input signal. |
| IMPULSE_RESPONSE | Compute the impulse response for a linear system. |
| INITIAL_RESPONSE | Compute the initial condition response for a state-space system. |
| MARGIN | Calculate the gain and phase margins and crossover frequencies of a system. |
| POLES | Compute the poles of a linear system. |
| PZMAP | Compute the poles and zeros of a linear system. |
| ZEROS | Compute the zeros of a linear system. |
Design
| Tool | Description |
|---|---|
| ACKER | Pole placement using Ackermann’s formula. |
| DLQE | Linear quadratic estimator (Kalman filter) for discrete-time systems. |
| DLQR | Linear quadratic regulator design for discrete-time systems. |
| LQE | Linear quadratic estimator (Kalman filter) for continuous-time systems. |
| LQR | Linear quadratic regulator design for continuous-time systems. |
| MIXSYN | Mixed-sensitivity H-infinity controller synthesis. |
| PLACE | Pole placement for state feedback gain design. |
| ROOT_LOCUS | Calculate the root locus for an LTI system. |
Fuzzy
| Tool | Description |
|---|---|
| DEFUZZ | Defuzzify a membership function to return a crisp value. |
| FUZZY_AND | Calculate the fuzzy AND operator (intersection) of two fuzzy sets. |
| FUZZY_NOT | Calculate the fuzzy NOT operator (complement) of a fuzzy set. |
| FUZZY_OR | Calculate the fuzzy OR operator (union) of two fuzzy sets. |
| GAUSS2MF | Generate a Gaussian fuzzy membership function of two combined Gaussians. |
| GAUSSMF | Generate a Gaussian fuzzy membership function. |
| GBELLMF | Generate a Generalized Bell fuzzy membership function. |
| PIECEMF | Generate a piecewise linear fuzzy membership function. |
| PIMF | Generate a Pi-function fuzzy membership generator. |
| PSIGMF | Generate the product of two sigmoid membership functions. |
| SIGMF | Generate a basic sigmoid membership function. |
| SMF | Generate an S-function fuzzy membership generator. |
| TRAPMF | Generate a trapezoidal fuzzy membership function. |
| TRIMF | Generate a triangular fuzzy membership function. |
| ZMF | Generate a Z-function fuzzy membership generator. |
Interconnection
| Tool | Description |
|---|---|
| FEEDBACK | Feedback interconnection of two LTI systems. |
| PARALLEL | Parallel interconnection of two LTI systems. |
| SERIES | Series interconnection of two LTI systems. |
Matrix Computations
| Tool | Description |
|---|---|
| CARE | Solve the continuous-time algebraic Riccati equation. |
| CTRB | Compute the controllability matrix. |
| DARE | Solve the discrete-time algebraic Riccati equation. |
| DLYAP | Solve the discrete-time Lyapunov equation. |
| GRAM | Compute the Gramian (controllability or observability). |
| LYAP | Solve the continuous-time Lyapunov equation. |
| OBSV | Compute the observability matrix. |
Modeling
| Tool | Description |
|---|---|
| C2D | Convert a continuous-time system to discrete-time by sampling. |
| FRD | Create a frequency response data (FRD) model from measured response data. |
| PADE_DELAY | Calculate the Pade approximation of a continuous time delay. |
| SS2TF | Transform a state-space system into a transfer function. |
| STATE_SPACE | Create a state-space system model from system, control, output, and feedforward matrices. |
| TF2SS | Convert a transfer function object back into a state-space system object. |
| TRANSFER_FUNCTION | Create a transfer function system from its numerator and denominator polynomial coefficients. |
| ZPK | Create a transfer function model from zeros, poles, and gain. |
Reduction
| Tool | Description |
|---|---|
| BALRED | Balanced reduced order model of a system. |
| MINREAL | Eliminate uncontrollable or unobservable states. |
Fluids
Atmosphere
| Tool | Description |
|---|---|
| AIRMASS | Calculate the mass of air per square meter in the atmosphere along a given angle using a density profile. |
| ATMOS_NRLMSISE00 | Compute temperature, density, and pressure using the NRLMSISE-00 atmospheric model. |
| ATMOSPHERE_1976 | Calculate standard atmospheric properties at a given altitude using the US Standard Atmosphere 1976 model. |
Compressible
| Tool | Description |
|---|---|
| FRITZSCHE_FLOW | Calculate gas flow rate using the Fritzsche formula. |
| IGT_FLOW | Calculate gas flow rate using the IGT (Institute of Gas Technology) formula. |
| IS_CHOKED_FLOW | Determine if a flow is choked (critical) based on pressure ratio. |
| ISENTROPIC_EFF | Convert between isentropic and polytropic efficiency for compression. |
| ISENTROPIC_T_RISE | Calculate the temperature rise for isentropic compression or expansion. |
| ISENTROPIC_WORK | Calculate work of compression or expansion for a gas in an isentropic process. |
| ISOTHERMAL_GAS | Calculate mass flow rate for isothermal compressible gas flow in a pipe. |
| ISOTHERMAL_WORK | Calculate work of compression or expansion for a gas in an isothermal process. |
| MULLER_FLOW | Calculate gas flow rate using the Muller formula. |
| P_CRITICAL_FLOW | Calculate critical flow pressure for a fluid at Mach 1. |
| P_STAGNATION | Calculate stagnation pressure from static conditions. |
| PANHANDLE_A | Calculate gas flow rate in a pipeline using the Panhandle A formula. |
| PANHANDLE_B | Calculate gas flow rate in a pipeline using the Panhandle B formula. |
| POLYTROPIC_EXP | Calculate polytropic exponent or polytropic efficiency for compression. |
| STAGNATION_ENERGY | Calculate the increase in enthalpy due to fluid velocity. |
| T_CRITICAL_FLOW | Calculate critical flow temperature for a fluid at Mach 1. |
| T_STAG_IDEAL | Calculate ideal stagnation temperature from velocity and heat capacity. |
| T_STAGNATION | Calculate stagnation temperature from pressure ratio. |
| WEYMOUTH_FLOW | Calculate gas flow rate in a pipeline using the Weymouth formula. |
Control Valve
| Tool | Description |
|---|---|
| CV_CAV_INDEX | Calculates the cavitation index of a control valve. |
| CV_CHAR_EQ_PERC | Calculates the flow coefficient characteristic for an equal percentage control valve. |
| CV_CHAR_LINEAR | Calculates the flow coefficient characteristic for a linear control valve. |
| CV_CHAR_QUICK_OP | Calculates the flow coefficient characteristic for a quick opening control valve. |
| CV_CHOKE_PRESS_GAS | Calculates the pressure at which choked flow occurs in a gas control valve. |
| CV_CHOKE_PRESS_LIQ | Calculates the pressure at which choked flow occurs in a liquid control valve. |
| CV_CONVERT_COEFF | Converts between different flow coefficient scales (Kv, Cv, Av). |
| CV_NOISE_GAS_2011 | Calculate the A-weighted sound pressure level for gas flow through a control valve per IEC 60534-8-3 (2011). |
| CV_NOISE_LIQ_2015 | Calculates the sound made by a liquid flowing through a control valve according to the standard IEC 60534-8-4 (2015) using fluids.control_valve.control_valve_noise_l_2015. |
| FF_CRIT_PRESS_L | Calculates the liquid critical pressure ratio factor FF for IEC 60534 liquid valve sizing. |
| IS_CHOKED_GAS | Determines if a gas flow in a control valve is choked (critical) or not according to IEC 60534. |
| IS_CHOKED_LIQ | Determines if a liquid flow in a control valve is choked (critical) or not according to IEC 60534. |
| LOSS_COEFF_PIPING | Calculates the sum of loss coefficients for reducers/expanders around a control valve. |
| REYNOLDS_FACTOR | Calculates the Reynolds number factor FR for a valve according to IEC 60534. |
| REYNOLDS_VALVE | Calculates the Reynolds number of a control valve according to IEC 60534. |
| SIZE_CV_GAS | Calculates flow coefficient of a control valve passing a gas according to IEC 60534 using fluids.control_valve.size_control_valve_g. |
| SIZE_CV_LIQUID | Calculates the flow coefficient (Kv) of a control valve passing a liquid according to IEC 60534. |
Dimensionless
| Tool | Description |
|---|---|
| ARCHIMEDES | Calculate the Archimedes number (Ar) for a fluid and particle. |
| BEJAN | Compute the Bejan number (length-based or permeability-based). |
| BIOT | Calculate the Biot number for heat transfer. |
| BOILING | Calculate the Boiling number (Bg), a dimensionless number for boiling heat transfer. |
| BOND | Calculate the Bond number (Bo), also known as the Eötvös number (Eo). |
| CAPILLARY | Calculate the Capillary number (Ca) for a fluid system using fluids.core.Capillary. |
| CAVITATION | Calculate the Cavitation number (Ca) for a flowing fluid. |
| CONFINEMENT | Calculate the Confinement number (Co) for two-phase flow in a channel. |
| DEAN | Calculate the Dean number (De) for flow in a curved pipe or channel. |
| DRAG | Calculate the drag coefficient (dimensionless) for an object in a fluid. |
| ECKERT | Calculate the Eckert number using fluids.core.Eckert. |
| EULER | Calculate the Euler number (Eu) for a fluid flow. |
| FOURIER_HEAT | Calculate the Fourier number for heat transfer. |
| FOURIER_MASS | Calculate the Fourier number for mass transfer (Fo). |
| FROUDE | Calculate the Froude number (Fr) for a given velocity, length, and gravity. |
| FROUDE_DENSIMETRIC | Calculate the densimetric Froude number. |
| GRAETZ_HEAT | Calculate the Graetz number. |
| GRASHOF | Calculate the Grashof number. |
| HAGEN | Calculate the Hagen number. |
| JAKOB | Calculate the Jakob number for boiling fluid. |
| KNUDSEN | Calculate the Knudsen number. |
| LEWIS | Calculate the Lewis number. |
| MACH | Calculate the Mach number. |
| MORTON | Calculate the Morton number. |
| NUSSELT | Calculate the Nusselt number. |
| OHNESORGE | Calculate the Ohnesorge number. |
| PECLET_HEAT | Calculate the Peclet number for heat transfer. |
| PECLET_MASS | Calculate the Peclet number for mass transfer. |
| POWER_NUMBER | Calculate the Power number for an agitator. |
| PRANDTL | Calculate the Prandtl number. |
| RAYLEIGH | Calculate the Rayleigh number. |
| RELATIVE_ROUGHNESS | Calculate the relative roughness. |
| REYNOLDS | Calculate the Reynolds number. |
| SCHMIDT | Calculate the Schmidt number. |
| SHERWOOD | Calculate the Sherwood number. |
| STANTON | Calculate the Stanton number. |
| STOKES_NUMBER | Calculate the Stokes number. |
| STROUHAL | Calculate the Strouhal number. |
| SURATMAN | Calculate the Suratman number. |
| WEBER | Calculate the Weber number. |
Drag
| Tool | Description |
|---|---|
| CD_ALMEDEIJ | Calculate drag coefficient of a sphere using the Almedeij correlation. |
| CD_BARATI | Calculate drag coefficient of a sphere using the Barati correlation. |
| CD_BARATI_HIGH | Calculate drag coefficient of a sphere using the Barati high-Re correlation (valid to Re=1E6). |
| CD_CEYLAN | Calculate drag coefficient of a sphere using the Ceylan correlation. |
| CD_CHENG | Calculate drag coefficient of a sphere using the Cheng correlation. |
| CD_CLIFT | Calculate drag coefficient of a sphere using the Clift correlation. |
| CD_CLIFT_GAUVIN | Calculate drag coefficient of a sphere using the Clift-Gauvin correlation. |
| CD_ENGELUND | Calculate drag coefficient of a sphere using the Engelund-Hansen correlation. |
| CD_FLEMMER_BANKS | Calculate drag coefficient of a sphere using the Flemmer-Banks correlation. |
| CD_GRAF | Calculate drag coefficient of a sphere using the Graf correlation. |
| CD_HAIDER_LEV | Calculate drag coefficient of a sphere using the Haider-Levenspiel correlation. |
| CD_KHAN_RICH | Calculate drag coefficient of a sphere using the Khan-Richardson correlation. |
| CD_MIKHAILOV | Calculate drag coefficient of a sphere using the Mikhailov-Freire correlation. |
| CD_MORRISON | Calculate drag coefficient of a sphere using the Morrison correlation. |
| CD_MORSI_ALEX | Calculate drag coefficient of a sphere using the Morsi-Alexander correlation. |
| CD_ROUSE | Calculate drag coefficient of a sphere using the Rouse correlation. |
| CD_SONG_XU | Calculate drag coefficient of a particle using the Song-Xu correlation for spherical and non-spherical particles. |
| CD_STOKES | Calculate drag coefficient of a sphere using Stokes law (Cd = 24/Re). |
| CD_SWAMEE_OJHA | Calculate drag coefficient of a sphere using the Swamee-Ojha correlation. |
| CD_TERFOUS | Calculate drag coefficient of a sphere using the Terfous correlation. |
| CD_YEN | Calculate drag coefficient of a sphere using the Yen correlation. |
| DRAG_SPHERE | Calculate the drag coefficient of a sphere using various correlations based on Reynolds number. |
| SPHERE_FALL_DIST | Calculate distance traveled by a falling sphere after a given time. |
| SPHERE_VEL_AT_T | Calculate the velocity of a falling sphere after a given time. |
| TIME_V_TERMINAL | Calculate time for a particle in Stokes regime to reach terminal velocity. |
| V_TERMINAL | Calculate terminal velocity of a falling sphere using drag coefficient correlations. |
Filters
| Tool | Description |
|---|---|
| RND_EDGE_GRILL | Calculate the loss coefficient for a rounded edge grill or perforated plate. |
| RND_EDGE_MESH | Calculate the loss coefficient for a round edged open net or screen mesh. |
| RND_EDGE_SCREEN | Calculate the loss coefficient for a round edged wire screen or bar screen. |
| SQ_EDGE_GRILL | Calculate the loss coefficient for a square edged grill or perforated plate. |
| SQ_EDGE_SCREEN | Calculate the loss coefficient for a square edged wire screen, bar screen, or perforated plate. |
Fittings
| Tool | Description |
|---|---|
| BEND_MITER | Calculate the loss coefficient (K) for a single-joint miter bend in a pipe. |
| BEND_ROUNDED | Calculate the loss coefficient (K) for a rounded pipe bend (elbow) using various methods. |
| CONTRACTION_ROUND | Calculate the loss coefficient (K) for a rounded pipe contraction (reducer). |
| CONTRACTION_SHARP | Calculate the loss coefficient (K) for a sharp edged pipe contraction (reducer). |
| CV_TO_K | Convert imperial valve flow coefficient (Cv) to loss coefficient (K). |
| DIFFUSER_CONICAL | Calculate the loss coefficient (K) for a conical pipe expansion (diffuser). |
| DIFFUSER_SHARP | Calculate the loss coefficient (K) for a sudden pipe expansion (diffuser). |
| ENTRANCE_ANGLED | Calculate the loss coefficient (K) for an angled sharp entrance to a pipe flush with a reservoir wall. |
| ENTRANCE_BEVELED | Calculate the loss coefficient (K) for a beveled or chamfered entrance to a pipe flush with a reservoir wall. |
| ENTRANCE_ROUNDED | Calculate the loss coefficient (K) for a rounded entrance to a pipe flush with a reservoir wall. |
| ENTRANCE_SHARP | Calculate the loss coefficient (K) for a sharp entrance to a pipe flush with a reservoir wall. |
| EXIT_NORMAL | Calculate the loss coefficient (K) for a normal pipe exit discharging into a reservoir. |
| HELIX | Calculate the loss coefficient (K) for a helical coil pipe section. |
| K_BALL_VALVE | Calculate the loss coefficient (K) for a ball valve using the Crane method. |
| K_BUTTERFLY_VALVE | Calculate the loss coefficient (K) for a butterfly valve using the Crane method. |
| K_GATE_VALVE | Calculate the loss coefficient (K) for a gate valve using the Crane method. |
| K_GLOBE_VALVE | Calculate the loss coefficient (K) for a globe valve using the Crane method. |
| K_SWING_CHECK_VALVE | Calculate the loss coefficient (K) for a swing check valve using the Crane method. |
| K_TO_CV | Convert loss coefficient (K) to imperial valve flow coefficient (Cv). |
| K_TO_KV | Convert loss coefficient (K) to metric valve flow coefficient (Kv). |
| KV_TO_K | Convert metric valve flow coefficient (Kv) to loss coefficient (K). |
| SPIRAL | Calculate the loss coefficient (K) for a spiral coil pipe section. |
Flow Meter
| Tool | Description |
|---|---|
| DIFF_PRESS_BETA | Calculate the beta ratio (diameter ratio) for a differential pressure flow meter. |
| DIFF_PRESS_C_EPS | Calculate discharge coefficient and expansibility factor for differential pressure flow meters. |
| DIFF_PRESS_DP | Calculate non-recoverable pressure drop across a differential pressure flow meter. |
| FLOW_METER_DISCH | Calculate mass flow rate through a differential pressure flow meter based on measured pressures and meter geometry. |
| ORIFICE_DISCHARGE_C | Calculate the discharge coefficient for an orifice plate using the Reader-Harris-Gallagher correlation (ISO 5167 standard). |
| ORIFICE_EXPAND | Calculate the expansibility factor for an orifice plate based on geometry and pressure conditions. |
| ORIFICE_PRESS_DROP | Calculate non-recoverable pressure drop across an orifice plate based on geometry and discharge coefficient. |
Friction
| Tool | Description |
|---|---|
| BLASIUS | Calculates Darcy friction factor for turbulent flow in smooth pipes using the Blasius correlation. |
| CHURCHILL | Calculate Darcy friction factor using the Churchill (1977) universal equation for all flow regimes. |
| CLAMOND | Calculate Darcy friction factor using Clamond’s high-precision solution accurate to nearly machine precision. |
| COLEBROOK | Calculate Darcy friction factor using exact solution to the Colebrook equation. |
| DP_GRAV | Calculate gravitational pressure drop component for single-phase flow in inclined pipes. |
| FF_CURVED | Calculate friction factor for fluid flowing in a curved pipe or helical coil, supporting both laminar and turbulent regimes. |
| FP_MARTIN | Calculate Darcy friction factor for single-phase flow in Chevron-style plate heat exchangers using Martin (1999) correlation. |
| FP_MULEY_MANGLIK | Calculate Darcy friction factor for single-phase flow in Chevron-style plate heat exchangers using Muley-Manglik correlation. |
| FRICTION_FACTOR | Calculate the Darcy friction factor for fluid flow in a pipe using various correlations, automatically selecting appropriate method based on Reynolds number and relative roughness. |
| FRICTION_LAMINAR | Calculate the Darcy friction factor for laminar flow using the theoretical solution fd = 64/Re. |
| FT_CRANE | Calculate the Crane fully turbulent Darcy friction factor for flow in commercial pipe. |
| HAALAND | Calculate Darcy friction factor using the Haaland (1983) approximation. |
| HELICAL_RE_CRIT | Calculate the transition Reynolds number for fluid flowing in a curved or helical pipe between laminar and turbulent flow. |
| MOODY | Calculate Darcy friction factor using the Moody (1947) correlation. |
| ONE_PHASE_DP | Calculate single-phase pressure drop in a pipe using the Darcy-Weisbach equation. |
| SWAMEE_JAIN | Calculate Darcy friction factor using the Swamee-Jain (1976) equation. |
| TRANS_FACTOR | Convert between Darcy friction factor and transmission factor for compressible gas pipeline flow. |
| VON_KARMAN | Calculate Darcy friction factor for rough pipes at infinite Reynolds number from the von Karman equation. |
Heat Transfer
Air Cooler
| Tool | Description |
|---|---|
| AIR_NOISE_GPSA | Compute air cooler noise using the GPSA correlation. |
| AIR_NOISE_MUKHERJEE | Compute air cooler noise using the Mukherjee correlation. |
| DP_ESDU_HIGH_FIN | Compute air-side pressure drop for high-fin tube banks. |
| DP_ESDU_LOW_FIN | Compute air-side pressure drop for low-fin tube banks. |
| ESDU_TUBE_ROW_CORR | Compute the ESDU tube row correction factor for a tube bundle. |
| FIN_EFF_KERN_KRAUS | Compute circular fin efficiency for constant-thickness fins. |
| FT_AIRCOOLER | Compute the LMTD correction factor for an air cooler crossflow exchanger. |
| H_BRIGGS_YOUNG | Compute air-side heat transfer coefficient using Briggs and Young correlations. |
| H_ESDU_HIGH_FIN | Compute air-side heat transfer coefficient for high-fin tube banks. |
| H_ESDU_LOW_FIN | Compute air-side heat transfer coefficient for low-fin tube banks. |
| H_GANGULI_VDI | Compute air-side heat transfer coefficient using the Ganguli VDI method. |
| LMTD | Compute the log-mean temperature difference for a heat exchanger. |
| WALL_FACTOR | Compute wall property correction factors for heat transfer correlations. |
Boiling Flow
| Tool | Description |
|---|---|
| CHEN_BENNETT | Compute the Chen-Bennett boiling heat transfer coefficient. |
| CHEN_EDELSTEIN | Compute the Chen-Edelstein boiling heat transfer coefficient. |
| COOPER | Compute the Cooper nucleate boiling heat transfer coefficient. |
| FORSTER_ZUBER | Compute the Forster-Zuber nucleate boiling heat transfer coefficient. |
| LAZAREK_BLACK | Compute the Lazarek-Black boiling heat transfer coefficient. |
| LI_WU | Compute the Li-Wu boiling heat transfer coefficient. |
| LIU_WINTERTON | Compute the Liu-Winterton boiling heat transfer coefficient. |
| LOCKHART_XTT | Compute the Lockhart-Martinelli Xtt two-phase flow parameter. |
| SUN_MISHIMA | Compute the Sun-Mishima boiling heat transfer coefficient. |
| THOME | Compute the Thome microchannel boiling heat transfer coefficient. |
| TO_SOLVE_Q_THOME | Compute the Thome heat flux residual for a specified wall temperature. |
| TURBULENT_DITTUS | Compute the Dittus-Boelter turbulent Nusselt number. |
| TURBULENT_GNIEL | Compute the Gnielinski turbulent Nusselt number. |
| YUN_HEO_KIM | Compute the Yun-Heo-Kim boiling heat transfer coefficient. |
Boiling Nucleic
| Tool | Description |
|---|---|
| BIER | Compute nucleate boiling heat transfer coefficient using the Bier correlation. |
| GORENFLO | Compute nucleate boiling heat transfer coefficient using the Gorenflo correlation. |
| H_NUCLEIC | Compute nucleate boiling heat transfer coefficient with method selection. |
| H_NUCLEIC_METHODS | List available nucleate boiling correlations based on provided inputs. |
| HEDH_MONTINSKY | Compute nucleate boiling critical heat flux using the HEDH-Montinsky correlation. |
| HEDH_TABOREK | Compute nucleate boiling heat transfer coefficient using the HEDH-Taborek correlation. |
| MCNELLY | Compute nucleate boiling heat transfer coefficient using the McNelly correlation. |
| MONTINSKY | Compute nucleate boiling heat transfer coefficient using the Montinsky correlation. |
| QMAX_BOIL_METHODS | List available nucleate boiling critical heat flux correlations. |
| QMAX_BOILING | Compute nucleate boiling critical heat flux with method selection. |
| ROHSENOW | Compute nucleate boiling heat transfer coefficient using the Rohsenow correlation. |
| SERTH_HEDH | Compute nucleate boiling critical heat flux for tube bundles using the Serth-HEDH correlation. |
| STEPHAN_ABDELSALAM | Compute nucleate boiling heat transfer coefficient using the Stephan-Abdelsalam correlation. |
| ZUBER | Compute nucleate boiling critical heat flux using the Zuber correlation. |
Boiling Plate
| Tool | Description |
|---|---|
| H_BOIL_HAN_LEE_KIM | Calculate boiling heat transfer coefficient using Han Lee Kim correlation. |
| H_BOIL_HANLEEKIM | Calculate boiling heat transfer coefficient using Han Lee Kim correlation. |
| H_BOIL_HUANG_SHEER | Calculate boiling heat transfer coefficient using Huang Sheer correlation. |
| H_BOIL_HUANGSHEER | Calculate boiling heat transfer coefficient using Huang Sheer correlation. |
| H_BOIL_LEE_KANG_KIM | Calculate boiling heat transfer coefficient using Lee Kang Kim correlation. |
| H_BOIL_LEEKANGKIM | Calculate boiling heat transfer coefficient using Lee Kang Kim correlation. |
| H_BOILING_AMALFI | Calculate boiling heat transfer coefficient using Amalfi correlation. |
| H_BOILING_YAN_LIN | Calculate boiling heat transfer coefficient using Yan Lin correlation. |
| THERMAL_DIFFUSIVITY | Calculate thermal diffusivity for a fluid. |
Condensation
| Tool | Description |
|---|---|
| AKERS_DEANS_CROSSER | Calculate condensation heat transfer coefficient in tubes using the Akers-Deans-Crosser correlation. |
| BOYKO_KRUZHILIN | Calculate condensation heat transfer coefficient using the Boyko-Kruzhilin correlation. |
| CAVALLINI_SMITH_Z | Calculate condensation heat transfer coefficient using the Cavallini-Smith-Zecchin correlation. |
| CAVALLINI_SZ | Calculate condensation heat transfer coefficient using the Cavallini-Smith-Zecchin correlation. |
| H_KINETIC | Calculate kinetic theory condensation heat transfer coefficient. |
| NUSSELT_LAMINAR | Calculate laminar film condensation heat transfer on a flat plate using Nusselt theory. |
| SHAH | Calculate condensation heat transfer coefficient using the Shah correlation. |
Conduction
| Tool | Description |
|---|---|
| ACOSH | Compute the inverse hyperbolic cosine. |
| CYL_HEAT_TRANSFER | Compute heat transfer through a multilayer cylindrical wall. |
| K_TO_R | Compute thermal resistance from thermal conductivity. |
| K_TO_R_VALUE | Convert thermal conductivity to R-value. |
| K_TO_THERM_RESIST | Convert thermal conductivity to thermal resistivity. |
| LEGACY_CYL_HT | Deprecated alias for cyl_heat_transfer. |
| LEGACY_K_THERM_RES | Deprecated alias for k_to_therm_resist. |
| LEGACY_S_PIPE_ECC | Deprecated alias for S_pipe_ecc_to_pipe. |
| LEGACY_S_PIPE_NORM | Deprecated alias for S_pipe_norm_plane. |
| LEGACY_S_PIPE_PAIR | Deprecated alias for S_pipe_to_pipe. |
| LEGACY_S_PIPE_PLANE | Deprecated alias for S_pipe_to_plane. |
| LEGACY_S_PIPE_PLNS | Deprecated alias for S_pipe_two_planes. |
| LEGACY_S_SPH_PLANE | Deprecated alias for S_sphere_to_plane. |
| LEGACY_THERM_RES_K | Deprecated alias for therm_resist_to_k. |
| LOG | Compute the logarithm of a value with optional base. |
| R_CYLINDER | Compute thermal resistance of a cylindrical wall. |
| R_TO_K | Compute thermal conductivity from thermal resistance. |
| R_VALUE_TO_K | Convert R-value to thermal conductivity. |
| S_PIPE_ECC_TO_PIPE | Compute the shape factor for eccentric isothermal pipes. |
| S_PIPE_NORM_PLANE | Compute the shape factor for a pipe normal to a plane. |
| S_PIPE_TO_PIPE | Compute the shape factor for two isothermal pipes. |
| S_PIPE_TO_PLANE | Compute the shape factor for a pipe near a plane. |
| S_PIPE_TWO_PLANES | Compute the shape factor for a pipe between two planes. |
| S_SPHERE_TO_PLANE | Compute the shape factor for a sphere near a plane. |
| THERM_RESIST_TO_K | Convert thermal resistivity to thermal conductivity. |
Conv External
| Tool | Description |
|---|---|
| NU_CYL_CB | Calculate the Nusselt number for crossflow across a single cylinder using the Churchill-Bernstein correlation. |
| NU_CYL_PL62 | Calculate the Nusselt number for crossflow across a single cylinder using the Perkins-Leppert 1962 correlation. |
| NU_CYL_PL64 | Calculate the Nusselt number for crossflow across a single cylinder using the Perkins-Leppert 1964 correlation. |
| NU_CYL_SG | Calculate the Nusselt number for crossflow across a single cylinder using the Sanitjai-Goldstein correlation. |
| NU_CYL_WHITAKER | Calculate the Nusselt number for crossflow across a single cylinder using the Whitaker correlation. |
| NU_CYL_ZUKAUSKAS | Calculate the Nusselt number for crossflow across a single cylinder using the Zukauskas correlation. |
| NU_CYLINDER_FAND | Calculate the Nusselt number for crossflow across a single cylinder using the Fand correlation. |
| NU_CYLINDER_MCADAMS | Calculate the Nusselt number for crossflow across a single cylinder using the McAdams correlation. |
| NU_EXT_CYL | Calculate the Nusselt number for crossflow across a single cylinder using a selected correlation. |
| NU_EXT_CYL_METHODS | List available correlations for external cylinder forced convection. |
| NU_EXT_HORZ_METHODS | List available correlations for forced convection across a horizontal plate. |
| NU_EXT_HORZ_PLATE | Calculate the Nusselt number for forced convection across a horizontal plate. |
| NU_HORZ_LAM_BAEHR | Calculate the Nusselt number for laminar flow across an isothermal flat plate using the Baehr correlation. |
| NU_HORZ_LAM_COZOE | Calculate the Nusselt number for laminar flow across an isothermal flat plate using the Churchill-Ozoe correlation. |
| NU_HORZ_TURB_KREITH | Calculate the Nusselt number for turbulent flow across an isothermal flat plate using the Kreith correlation. |
| NU_HORZ_TURB_SCHL | Calculate the Nusselt number for turbulent flow across an isothermal flat plate using the Schlichting correlation. |
Conv Free Enclosed
| Tool | Description |
|---|---|
| NU_RA_HOLLANDS | Calculate the Nusselt number between horizontal plates using the Hollands correlation. |
| NU_RA_HOLLINGHERWIG | Calculate the Nusselt number between infinite horizontal plates using the Holling-Herwig correlation. |
| NU_RA_PROBERT | Calculate the Nusselt number between infinite horizontal plates using the Probert correlation. |
| NU_VERT_THESS | Calculate the Nusselt number between vertical plates using the Thess correlation. |
| NU_VHELIX_ALI | Calculate the Nusselt number for natural convection around a vertical helical coil using the Ali correlation. |
| NU_VHELIX_PRR | Calculate the Nusselt number for natural convection around a vertical helical coil using the Prabhanjan-Rennie-Raghavan correlation. |
| RAC_RAYLEIGH | Calculate the critical Rayleigh number for enclosed parallel plates. |
| RAC_RAYLEIGH_DISK | Calculate the critical Rayleigh number for enclosed parallel disks. |
Conv Free Immersed
| Tool | Description |
|---|---|
| NU_COIL_XIN_EBADIAN | Calculate the Nusselt number for natural convection around a helical coil. |
| NU_FREE_HPLATE | Calculate the Nusselt number for free convection from a horizontal plate. |
| NU_FREE_HPLATE_METH | List available correlations for free convection from a horizontal plate. |
| NU_FREE_VPLATE | Calculate the Nusselt number for free convection from a vertical plate. |
| NU_FREE_VPLATE_METH | List available correlations for free convection from a vertical plate. |
| NU_HCYL_CHURCHILL | Calculate the Nusselt number for a horizontal cylinder using Churchill-Chu. |
| NU_HCYL_KUEHNGOLD | Calculate the Nusselt number for a horizontal cylinder using Kuehn-Goldstein. |
| NU_HCYL_METHODS | List available correlations for free convection from a horizontal cylinder. |
| NU_HCYL_MORGAN | Calculate the Nusselt number for a horizontal cylinder using Morgan. |
| NU_HORIZ_CYL | Select and calculate a Nusselt number correlation for a horizontal cylinder. |
| NU_HPLATE_MCADAMS | Calculate the Nusselt number for a horizontal plate using McAdams. |
| NU_HPLATE_ROHSENOW | Calculate the Nusselt number for a horizontal plate using Rohsenow. |
| NU_HPLATE_VDI | Calculate the Nusselt number for a horizontal plate using VDI. |
| NU_SPHERE_CHURCHILL | Calculate the Nusselt number for a sphere using Churchill. |
| NU_VCYL_ALARABI | Calculate the Nusselt number for a vertical cylinder using Al-Arabi and Khamis. |
| NU_VCYL_CARNEMORGAN | Calculate the Nusselt number for a vertical cylinder using Carne-Morgan. |
| NU_VCYL_EIGENSON | Calculate the Nusselt number for a vertical cylinder using Eigenson-Morgan. |
| NU_VCYL_GRIFFITHS | Calculate the Nusselt number for a vertical cylinder using Griffiths-Davis-Morgan. |
| NU_VCYL_HANESIAN | Calculate the Nusselt number for a vertical cylinder using Hanesian-Kalish-Morgan. |
| NU_VCYL_JAKOB | Calculate the Nusselt number for a vertical cylinder using Jakob-Linke-Morgan. |
| NU_VCYL_KREITH | Calculate the Nusselt number for a vertical cylinder using Kreith-Eckert. |
| NU_VCYL_MCADAMS | Calculate the Nusselt number for a vertical cylinder using McAdams-Weiss-Saunders. |
| NU_VCYL_METHODS | List available correlations for free convection from a vertical cylinder. |
| NU_VCYL_POPIEL | Calculate the Nusselt number for a vertical cylinder using Popiel-Churchill. |
| NU_VCYL_TOULOUKIAN | Calculate the Nusselt number for a vertical cylinder using Touloukian-Morgan. |
| NU_VERT_CYL | Select and calculate a Nusselt number correlation for a vertical cylinder. |
| NU_VPLATE_CHURCHILL | Calculate the Nusselt number for a vertical plate using Churchill-Chu. |
Conv Internal
| Tool | Description |
|---|---|
| HEL_TURB_NU_MORI | Calculate the turbulent helical coil Nusselt number using Mori-Nakayama. |
| HEL_TURB_NU_SCHM | Calculate the turbulent helical coil Nusselt number using Schmidt. |
| HEL_TURB_NU_XIN | Calculate the turbulent helical coil Nusselt number using Xin-Ebadian. |
| LAM_ENTRY_BAEHR | Calculate laminar entry Nusselt number using Baehr-Stephan. |
| LAM_ENTRY_HAUSEN | Calculate laminar thermal entry Nusselt number using Hausen. |
| LAM_ENTRY_SEIDER | Calculate laminar entry Nusselt number using Seider-Tate. |
| LAMINAR_Q_CONST | Return the laminar constant-heat-flux Nusselt number for a pipe. |
| LAMINAR_T_CONST | Return the laminar constant-wall-temperature Nusselt number for a pipe. |
| MORIMOTO_HOTTA | Calculate the Nusselt number for flow in a spiral heat exchanger. |
| NU_CONV_INT_METHODS | List available internal convection correlations for a pipe. |
| NU_CONV_INTERNAL | Compute the Nusselt number for internal pipe convection. |
| NU_LAM_RECT_SHAN | Calculate the laminar Nusselt number for a rectangular duct. |
| TURB_BHATTI_SHAH | Calculate turbulent Nusselt number using the Bhatti-Shah correlation. |
| TURB_CHURCHILL | Calculate turbulent Nusselt number using Churchill-Zajic. |
| TURB_COLBURN | Calculate turbulent Nusselt number using the Colburn correlation. |
| TURB_DIPPREY | Calculate turbulent Nusselt number using Dipprey-Sabersky. |
| TURB_DITTUS | Calculate turbulent Nusselt number using Dittus-Boelter. |
| TURB_DREXEL | Calculate turbulent Nusselt number using Drexel-McAdams. |
| TURB_ENTRY_HAUSEN | Calculate turbulent entry-region Nusselt number using Hausen. |
| TURB_ESDU | Calculate turbulent Nusselt number using the ESDU correlation. |
| TURB_FRIEND | Calculate turbulent Nusselt number using Friend-Metzner. |
| TURB_GNIEL_S1 | Calculate turbulent Nusselt number using Gnielinski smooth pipe case 1. |
| TURB_GNIEL_S2 | Calculate turbulent Nusselt number using Gnielinski smooth pipe case 2. |
| TURB_GNIELINSKI | Calculate turbulent Nusselt number using the Gnielinski correlation. |
| TURB_PETUKHOV | Calculate turbulent Nusselt number using Petukhov-Kirillov-Popov. |
| TURB_PRANDTL | Calculate turbulent Nusselt number using the Prandtl correlation. |
| TURB_SIEDER | Calculate turbulent Nusselt number using the Sieder-Tate correlation. |
| TURB_VON_KARMAN | Calculate turbulent Nusselt number using the von Karman correlation. |
| TURB_WEBB | Calculate turbulent Nusselt number using the Webb correlation. |
Conv Jacket
| Tool | Description |
|---|---|
| LEHRER | Calculate the average heat transfer coefficient for a jacket around a vessel. |
| STEIN_SCHMIDT | Calculate the average heat transfer coefficient for a jacket around a vessel. |
Conv Packed Bed
| Tool | Description |
|---|---|
| NU_ACHENBACH | Calculate Nusselt number for a packed bed using the Achenbach correlation. |
| NU_KTA | Calculate Nusselt number for a packed bed using the KTA correlation. |
| NU_PACKED_BED_GN | Calculate Nusselt number for a packed bed using the Gnielinski correlation. |
| NU_WAKAO_KAGEI | Calculate Nusselt number for a packed bed using the Wakao-Kagei correlation. |
Conv Plate
| Tool | Description |
|---|---|
| FRIC_PLATE_MARTIN99 | Calculate Darcy friction factor for chevron plate exchangers (Martin 1999). |
| FRIC_PLATE_MARTINV | Calculate Darcy friction factor for chevron plate exchangers (VDI Heat Atlas variant). |
| NU_PLATE_KHAN_KHAN | Calculate Nusselt number for single-phase flow in a chevron-style plate heat exchanger (Khan and Khan). |
| NU_PLATE_KUMAR | Calculate Nusselt number for a well-designed chevron plate heat exchanger (Kumar correlation). |
| NU_PLATE_MARTIN | Calculate Nusselt number for chevron plate exchangers using the Martin correlation. |
| NU_PLATE_MULEYMANG | Calculate Nusselt number for chevron plate exchangers using the Muley-Manglik correlation. |
Conv Supercritical
| Tool | Description |
|---|---|
| NU_BISHOP | Calculate Nusselt number for supercritical pipe flow using the Bishop correlation. |
| NU_BRINGER_SMITH | Calculate Nusselt number for near-supercritical flow using the Bringer-Smith correlation. |
| NU_GORBAN | Calculate Nusselt number for supercritical flow using the Gorban correlation. |
| NU_GRIEM | Calculate Nusselt number for supercritical flow using the Griem correlation. |
| NU_GUPTA | Calculate Nusselt number for supercritical flow using the Gupta correlation. |
| NU_JACKSON | Calculate Nusselt number for supercritical flow using the Jackson correlation. |
| NU_KITOH | Calculate Nusselt number for supercritical flow using the Kitoh correlation. |
| NU_KRASN_PROTO | Calculate Nusselt number for supercritical flow using the Krasnoshchekov-Protopopov correlation. |
| NU_KRASNOSH_PROTO | Calculate Nusselt number for supercritical flow using the Krasnoshchekov-Protopopov correlation. |
| NU_KRASNOSHCHEKOV | Calculate Nusselt number for supercritical flow using the Krasnoshchekov correlation. |
| NU_MCADAMS | Calculate Nusselt number for supercritical flow using the McAdams correlation. |
| NU_MOKRY | Calculate Nusselt number for supercritical flow using the Mokry correlation. |
| NU_ORNATSKY | Calculate Nusselt number for supercritical flow using the Ornatsky correlation. |
| NU_PETUKHOV | Calculate Nusselt number for supercritical flow using the Petukhov correlation. |
| NU_SHITSMAN | Calculate Nusselt number for supercritical flow using the Shitsman correlation. |
| NU_SWENSON | Calculate Nusselt number for supercritical flow using the Swenson correlation. |
| NU_XU | Calculate Nusselt number for supercritical flow using the Xu correlation. |
| NU_YAMAGATA | Calculate Nusselt number for supercritical flow using the Yamagata correlation. |
| NU_ZHU | Calculate Nusselt number for supercritical flow using the Zhu correlation. |
Conv Tube Bank
| Tool | Description |
|---|---|
| CTB_BAFFLE_CORR | Compute Bell-Delaware baffle correction factor for crossflow. |
| CTB_BAFFLE_LEAK | Compute Bell-Delaware baffle leakage correction factor. |
| CTB_BUNDLE_BYPASS | Compute Bell-Delaware bundle bypass correction factor. |
| CTB_DP_KERN | Compute tube bank pressure drop using the Kern method. |
| CTB_DP_ZUKAUSKAS | Compute tube bank pressure drop using the Zukauskas method. |
| CTB_ESDU_ANG_CORR | Compute the ESDU tube bank inclination correction factor. |
| CTB_ESDU_ROW_CORR | Compute the ESDU tube row correction factor for a tube bundle. |
| CTB_HORNER | Evaluate a polynomial using Horner’s method. |
| CTB_LAMINAR_CORR | Compute Bell-Delaware laminar flow correction factor. |
| CTB_NU_ESDU_73031 | Compute tube bank Nusselt number using the ESDU 73031 correlation. |
| CTB_NU_GRIMISON | Compute tube bank Nusselt number using the Grimison correlation. |
| CTB_NU_HEDH | Compute tube bank Nusselt number using the HEDH correlation. |
| CTB_NU_ZUK_BEJAN | Compute tube bank Nusselt number using the Zukauskas-Bejan correlation. |
| CTB_UNEQUAL_BAFFLE | Compute Bell-Delaware unequal baffle spacing correction factor. |
| CTB_WALL_FACTOR | Compute wall correction factor for heat transfer properties. |
| CTB_ZUK_ROW_CORR | Compute Zukauskas tube row correction factor for a tube bundle. |
Conv Two Phase
| Tool | Description |
|---|---|
| AGGOUR | Calculate two-phase heat transfer coefficient using the Aggour correlation. |
| DAVIS_DAVID | Calculate two-phase heat transfer coefficient using the Davis-David correlation. |
| ELAMVALUTHI_SRIN | Calculate two-phase heat transfer coefficient using the Elamvaluthi-Srinivas correlation. |
| GROOTHUIS_HENDAL | Calculate two-phase heat transfer coefficient using the Groothuis-Hendal correlation. |
| H_TWO_PHASE | Calculate two-phase heat transfer coefficient using a selected correlation. |
| H_TWO_PHASE_METHODS | List available two-phase heat transfer correlations for a tube. |
| HUGHMARK | Calculate two-phase laminar heat transfer coefficient using the Hughmark correlation. |
| KNOTT | Calculate two-phase heat transfer coefficient using the Knott correlation. |
| KUDIRKA_GROSH_MCF | Calculate two-phase heat transfer coefficient using the Kudirka-Grosh-McFadden correlation. |
| LAMINAR_ENTRY_ST | Calculate laminar entry-region Nusselt number using Seider-Tate. |
| MARTIN_SIMS | Calculate two-phase heat transfer coefficient using the Martin-Sims correlation. |
| RAVIPUDI_GODBOLD | Calculate two-phase heat transfer coefficient using the Ravipudi-Godbold correlation. |
Core
| Tool | Description |
|---|---|
| CC_HX_TEMP_CHECK | Check whether two fluid temperature profiles are plausible for countercurrent exchange. |
| FIN_EFFICIENCY_KK | Compute circular fin efficiency using the Kern-Kraus correlation. |
| IS_HEATING_PROPERTY | Determine whether a wall heats or cools a flow from a property ratio. |
| IS_HEATING_TEMP | Determine whether a wall heats or cools a flow from temperatures. |
| WALL_FACTOR_FD | Compute a wall correction factor for frictional pressure loss. |
| WALL_FACTOR_NU | Compute a wall correction factor for Nusselt number calculations. |
Hx
| Tool | Description |
|---|---|
| BAFFLE_THICKNESS | Compute baffle thickness from shell diameter and support spacing. |
| BUNDLE_FROM_TUBES | Calculate bundle diameter required for a specified tube count. |
| CALC_CMAX | Calculate the maximum heat capacity rate of two streams. |
| CALC_CMIN | Calculate the minimum heat capacity rate of two streams. |
| CALC_CR | Calculate the heat capacity rate ratio for two streams. |
| CHECK_TUBING_TEMA | Check whether a tubing size and gauge are valid per TEMA. |
| D_BAFFLE_HOLES | Calculate baffle hole diameter for tubes using TEMA guidance. |
| D_FOR_NTUBES_VDI | Estimate tube bundle diameter from tube count using the VDI method. |
| DBUNDLE_MIN | Estimate a minimum bundle diameter for a given tube outer diameter. |
| DBUNDLE_NT_HEDH | Estimate tube bundle diameter from tube count using the HEDH correlation. |
| DBUNDLE_NT_PHADK | Calculate tube bundle diameter for a given tube count using Phadke’s method. |
| EFF_FROM_NTU | Calculate effectiveness from NTU, capacity ratio, and configuration. |
| EFF_NTU_METHOD | Solve a heat exchanger with the effectiveness-NTU method. |
| F_LMTD_FAKHERI | Compute the LMTD correction factor for shell-and-tube exchangers. |
| L_UNSUPPORTED_MAX | Get the maximum unsupported tube length from TEMA guidance. |
| NTU_FROM_EFF | Solve NTU from effectiveness, capacity ratio, and configuration. |
| NTU_FROM_P_BASIC | Solve NTU for a basic exchanger from effectiveness and capacity ratio. |
| NTU_FROM_P_E | Solve NTU for a TEMA E exchanger from effectiveness and capacity ratio. |
| NTU_FROM_P_PLATE | Solve NTU for a plate exchanger from effectiveness and capacity ratio. |
| NTU_FROM_UA | Calculate NTU from UA and the minimum heat capacity rate. |
| NTUBES | Calculate the number of tubes that fit in a tube bundle. |
| NTUBES_HEDH | Estimate tube count from bundle diameter using the HEDH correlation. |
| NTUBES_PHADKEB | Calculate tube count from bundle diameter using Phadke’s method. |
| NTUBES_VDI | Estimate tube count from bundle diameter using the VDI method. |
| P_NTU_METHOD | Solve a heat exchanger with the P-NTU method. |
| SHELL_CLEARANCE | Look up shell-to-bundle clearance from TEMA guidance. |
| TEMP_EFF_BASIC | Compute temperature effectiveness for a basic exchanger type. |
| TEMP_EFF_PLATE | Compute temperature effectiveness for a plate exchanger. |
| TEMP_EFF_TEMA_E | Compute temperature effectiveness for a TEMA E exchanger. |
| UA_FROM_NTU | Calculate UA from NTU and the minimum heat capacity rate. |
Insulation
| Tool | Description |
|---|---|
| ASHRAE_K | Return thermal conductivity for an ASHRAE material. |
| CP_MATERIAL | Return heat capacity for an insulating or building material. |
| INTERP | Perform one-dimensional linear interpolation. |
| K_MATERIAL | Return thermal conductivity for an insulating or building material. |
| NEAREST_MATERIAL | Return the nearest material match from insulation tables. |
| REFRACTORY_VDI_CP | Return refractory heat capacity from VDI data. |
| REFRACTORY_VDI_K | Return refractory thermal conductivity from VDI data. |
| RHO_MATERIAL | Return density for an insulating or building material. |
Radiation
| Tool | Description |
|---|---|
| BB_SPECTRAL_RAD | Compute blackbody spectral radiance at a wavelength. |
| GREY_TRANSMITTANCE | Compute grey-body transmittance from extinction and path length. |
| Q_RAD | Compute radiant heat flux between a surface and surroundings. |
Photovoltaics
Data Quality
| Tool | Description |
|---|---|
| CHECK_DHI_QCRAD | Return a pass/fail QC flag array for each DHI reading against QCRad physical or extreme limits. |
| CHECK_DNI_QCRAD | Return a pass/fail QC flag array for each DNI reading against QCRad physical or extreme limits. |
| CHECK_GHI_QCRAD | Return a pass/fail QC flag array for each GHI reading against QCRad physical or extreme limits. |
| CHECK_IRRAD_QCRAD | Return consistency flags for irradiance-component balance from GHI, DNI, DHI, and zenith ranges. |
| COMPLETENESS_SCORE | Calculate a data completeness score for each day from a timestamped PV series. |
| PERF_RATIO_NREL | Calculate the NREL weather-corrected performance ratio from irradiance, temperature, and power data. |
| SPACING | Check that the spacing between timestamps conforms to an expected frequency. |
| STALE_VALUES_ROUND | Identify stale or stuck-sensor values by rounding and checking for repeating sequences. |
| TRIM_INCOMPLETE | Return a boolean mask to trim leading and trailing low-completeness periods from a PV series. |
Irradiance Models
| Tool | Description |
|---|---|
| DIRINT | Estimate Direct Normal Irradiance (DNI) from GHI using the DIRINT model. |
| DISC | Convert GHI to DNI using the Direct Insolation Simulation Code (DISC) model. |
| ERBS | Estimate DNI and DHI from GHI and solar zenith using the Erbs model. |
| GET_EXTRA_RADIATION | Determine extraterrestrial radiation (DNI_extra) for a given day of the year. |
| GET_GROUND_DIFFUSE | Estimate diffuse irradiance on a tilted surface from ground reflections. |
| HAYDAVIES | Determine diffuse irradiance from the sky on a tilted surface using the Hay and Davies model. |
| PEREZ | Determine diffuse irradiance from the sky on a tilted surface using one of the Perez models. |
| TOTAL_IRRADIANCE | Determine total in-plane irradiance and its beam, sky diffuse and ground reflected components. |
Module Models
| Tool | Description |
|---|---|
| CALCPARAMS_DESOTO | Calculate five single-diode model parameter values using the De Soto model. |
| CALCPARAMS_PVSYST | Calculate five single-diode parameter values using the PVsyst v6 model. |
| MAX_POWER_POINT | Calculate the maximum power point (MPP) from single-diode equation coefficients. |
| PVWATTS_LOSSES | Implement NREL’s PVWatts system loss model. |
| SAPM | Sandia Photovoltaic Array Performance Model (SAPM) solver. |
| SAPM_EFF_IRRAD | Calculate SAPM effective irradiance accounting for spectral and incidence losses. |
| SINGLEDIODE | Solve the single-diode equation to obtain a photovoltaic IV curve and its key operating points. |
| V_FROM_I | Calculate device voltage at a given current for the single-diode model. |
Solar Geometry
| Tool | Description |
|---|---|
| AOI | Calculate the angle of incidence (AOI) for a surface. |
| AOI_PROJECTION | Calculate the dot product of the sun position and surface normal (cosine of AOI). |
| CALC_AXIS_TILT | Calculate tracker axis tilt on sloped terrain. |
| CROSS_AXIS_TILT | Calculate cross-axis tilt for single-axis trackers on sloped terrain. |
| MASK_ANGLE_PASSIAS | Calculate the average masking angle over the slant height of a row. |
| MASKING_ANGLE | Calculate the elevation angle below which diffuse irradiance is blocked. |
| PROJ_SOLAR_ZENITH | Calculate the projected solar zenith angle in the tracker reference plane. |
| SHADED_FRACTION1D | Calculate the fraction of a collector width shaded by an adjacent row. |
| SINGLEAXIS | Determine the rotation angle and incidence angle for a single-axis tracker. |
| SURFACE_ORIENT | Calculate surface tilt and azimuth for a given tracker rotation. |
Solar Position
| Tool | Description |
|---|---|
| DECLINATION_SPENCER | Compute the solar declination angle using Spencer’s (1971) formula. |
| EARTHSUN_DISTANCE | Calculate the Earth-Sun distance in AU using the NREL SPA algorithm. |
| EOT_SPENCER | Compute the equation of time (EOT) using Spencer’s (1971) formula. |
| SOLAR_AZIM_AN | Calculate the solar azimuth angle using an analytical expression. |
| SOLAR_ZEN_AN | Calculate the solar zenith angle using an analytical expression. |
| SOLARPOSITION | Calculate solar azimuth, elevation, and apparent zenith for given times and location. |
| SUN_RISE_SET_GEOM | Geometric calculation of solar sunrise, sunset, and transit. |
| SUN_RISE_SET_SPA | Calculate sunrise, sunset, and solar transit times using the NREL SPA algorithm. |
Signal Processing
Filtering
| Tool | Description |
|---|---|
| BUTTER | Butterworth digital and analog filter design. |
| CHEBY1 | Chebyshev Type I digital and analog filter design (passband ripple). |
| CHEBY2 | Chebyshev Type II digital and analog filter design (stopband ripple). |
| ELLIP | Elliptic (Cauer) digital and analog filter design. |
| FILTFILT | Apply a digital filter forward and backward to a signal for zero phase distortion. |
| FIRWIN | FIR filter design using the window method. |
| FREQZ | Compute the frequency response of a digital filter. |
| GAUSSIAN_FILTER1D | 1-D Gaussian filter for signal denoising and smoothing. |
| GET_WINDOW | Generate a window function vector for signal processing. |
| IIRDESIGN | Complete IIR digital and analog filter design from passband and stopband specs. |
| MEDFILT | Perform a median filter on a signal array to remove spike noise. |
| REMEZ | Calculate the minimax optimal filter using the Remez exchange algorithm. |
| SAVGOL_FILTER | Apply a Savitzky-Golay filter to a signal array for smoothing. |
| SOSFILTFILT | Forward-backward digital filter using cascaded second-order sections. |
Resampling
| Tool | Description |
|---|---|
| DECIMATE | Downsample the signal after applying an anti-aliasing filter. |
| RESAMPLE | Resample x to num samples using Fourier method. |
| RESAMPLE_POLY | Resample x along the matrix using polyphase filtering. |
| UPFIRDN | Upsample, FIR filter, and downsample. |
Spectral Analysis
| Tool | Description |
|---|---|
| COHERENCE | Estimate the magnitude squared coherence (Cxy) using Welch’s method. |
| CSD | Estimate the cross power spectral density (Pxy) using Welch’s method. |
| ENG_PERIODOGRAM | Estimate power spectral density using a periodogram. |
| ENG_WELCH | Estimate power spectral density using Welch’s method. |
| ISTFT | Perform the Inverse Short-Time Fourier Transform (ISTFT). |
| LOMBSCARGLE | Estimate a Lomb-Scargle periodogram for unevenly sampled data. |
| RFFT | Compute the one-dimensional discrete Fourier transform for real input. |
| RFFTFREQ | Return sample frequencies for one-sided real FFT bins. |
| SPECTROGRAM | Compute a spectrogram with consecutive Fourier transforms. |
| STFT | Compute the Short Time Fourier Transform (STFT). |
Wavelets
| Tool | Description |
|---|---|
| CASCADE | Compute scaling and wavelet functions at dyadic points from filter coefficients. |
| CWT | Perform a Continuous Wavelet Transform (CWT). |
| DAUB | Get coefficients for the low-pass filter producing Daubechies wavelets. |
| MORLET2 | Generate a complex Morlet wavelet for a given length and width. |
| QMF | Return a Quadrature Mirror Filter (QMF) from low-pass coefficients. |
| RICKER | Return a Ricker wavelet (Mexican hat wavelet). |
| THRESHOLD | Apply elementwise wavelet thresholding to numeric data. |
| WAVEDEC | Compute a multilevel one-dimensional discrete wavelet decomposition. |
Structural
Beam Numerical
| Tool | Description |
|---|---|
| ANALYZE_BEAM | Perform numerical analysis of a structural beam to determine reactions and internal forces. |
| BEAM_DEFLECTION | Calculate the deflection of a beam at a specific coordinate. |
| BEAM_REACTION | Calculate the reaction force or moment at a specific support position. |
Beam Symbolic
| Tool | Description |
|---|---|
| SOLVE_BEAM_SYMBOLIC | Solve a statically determinate beam symbolically and return reactions plus internal-force equations. |
Finite Elements
| Tool | Description |
|---|---|
| BEAM_2D | Analyze continuous 2D beams with auto-meshing. |
| FRAME_2D | Analyze 2D rigid frames. |
| PLATE | Analyze 2D plates and shells. |
| TRUSS_2D | Analyze 2D pin-jointed trusses. |
Thermodynamics
Fluid Properties
| Tool | Description |
|---|---|
| CP_FLUID_PARAM | Retrieve a CoolProp metadata string for a specified fluid and metadata field. |
| CP_FLUIDS_LIST | Return a list of CoolProp fluid names, optionally capped to a maximum count. |
| CP_SAT_ANC | Evaluate a CoolProp saturation ancillary property for a fluid and branch quality. |
| IAPWS11_PSUB | Compute water-ice sublimation pressure from temperature using IAPWS-11. |
| IAPWS92_DPSAT_DT | Compute saturation pressure derivative with respect to temperature using IAPWS-92. |
| IAPWS92_RHOG_SAT | Compute saturated vapor water density from temperature using IAPWS-92. |
| IAPWS92_RHOL_SAT | Compute saturated liquid water density from temperature using IAPWS-92. |
| IAPWS95_PROPERTIES | Return key water thermodynamic properties from temperature and pressure using IAPWS-95. |
| IAPWS95_PSAT | Compute saturation pressure from temperature using IAPWS-95 polynomial fits. |
| IAPWS95_RHO | Compute water density from temperature and pressure using IAPWS-95. |
| IAPWS95_SATURATION | Solve IAPWS-95 saturation state from temperature. |
| IAPWS95_T | Compute water temperature from pressure and density using IAPWS-95. |
| IAPWS95_TSAT | Compute saturation temperature from pressure using IAPWS-95. |
| IAPWS97_REGION_TP | Identify the IF-97 region from temperature and pressure. |
| IAPWS97_RHO | Compute water density from temperature and pressure using IAPWS-97. |
| PHASE_SI | Identify the phase of a fluid at a given state using CoolProp. |
| PROPS_SI | Calculate thermophysical properties of fluids using CoolProp. |
| PSAT_IAPWS | Compute water saturation pressure from temperature with the explicit IAPWS equation. |
| TSAT_IAPWS | Compute water saturation temperature from pressure with the explicit IAPWS equation. |
Phase Equilibrium
| Tool | Description |
|---|---|
| FLASH_INNER_LOOP | Solve flash inner-loop vapor fraction and phase compositions from overall composition and K-values. |
| FLASH_TB_TC_PC | Perform a low-data flash calculation using boiling and critical properties. |
| FLASH_WILSON | Perform a Wilson-model flash calculation and return state and phase compositions. |
| K_VALUE | Calculate a component equilibrium K-value from available pressure, fugacity, and activity inputs. |
| LI_JOHNS_AHMADI | Solve the Li-Johns-Ahmadi flash equation for vapor fraction and phase compositions. |
| MIXTURE_FLASH | Perform a flash calculation for a chemical mixture and return key properties. |
| MIXTURE_STRING | Create a formatted CoolProp mixture string from component fluids and mole fractions. |
| PR | Solve pure-component Peng-Robinson EOS and return key phase-state properties. |
| PR_WATER_K_VALUE | Estimate hydrocarbon-water equilibrium K-value with the Peng-Robinson heuristic. |
| PRMIX | Solve Peng-Robinson mixture EOS and return key phase and fugacity metrics. |
| PRSV | Solve pure-component PRSV EOS and return key phase-state properties. |
| PRSVMIX | Solve PRSV mixture EOS and return key phase and fugacity metrics. |
| RACHFORD_RICE | Solve the classical Rachford-Rice flash equation. |
| SAT_ANCILLARY | Evaluate a CoolProp saturation ancillary correlation value. |
| SRK | Solve pure-component Soave-Redlich-Kwong EOS and return key phase-state properties. |
| SRKMIX | Solve SRK mixture EOS and return key phase and fugacity metrics. |
| WILSON_K_VALUE | Estimate a component equilibrium K-value using Wilson’s correlation. |
Psychrometrics
| Tool | Description |
|---|---|
| HA_PROPS_AUX | Compute auxiliary humid-air properties from CoolProp. |
| HA_PROPS_SI | Calculate humid air properties using CoolProp psychrometrics. |
| IAPWS92_PSAT | Compute saturation vapor pressure using the IAPWS-92 correlation. |
| PSYCHRO_DPSATDT | Compute temperature derivative of saturation pressure using IAPWS-92. |
Machine Learning
Clustering
| Tool | Description |
|---|---|
| CMEANS | Perform fuzzy c-means clustering on data. |
| CMEANS_PREDICT | Predict cluster membership for new data given a trained fuzzy c-means model. |
Math
Calculus
Differentiation
| Tool | Description |
|---|---|
| HESSIAN | Compute the Hessian matrix (second derivatives) of a scalar function using CasADi symbolic differentiation. |
| JACOBIAN | Calculate the Jacobian matrix of mathematical expressions with respect to specified variables. |
| SENSITIVITY | Compute the sensitivity of a scalar model with respect to its parameters using CasADi. |
Integration
| Tool | Description |
|---|---|
| DBLQUAD | Compute the double integral of a function over a two-dimensional region. |
| QUAD | Numerically integrate a function defined by a table of x, y values over [a, b] using adaptive quadrature. |
| TRAPEZOID | Integrate sampled data using the composite trapezoidal rule. |
Ode Models
| Tool | Description |
|---|---|
| BRUSSELATOR | Numerically solves the Brusselator system of ordinary differential equations for autocatalytic chemical reactions. |
| COMPARTMENTAL_PK | Numerically solves the basic one-compartment pharmacokinetics ODE using scipy.integrate.solve_ivp. |
| FITZHUGH_NAGUMO | Numerically solves the FitzHugh-Nagumo system of ordinary differential equations for neuron action potentials using scipy.integrate.solve_ivp. |
| HODGKIN_HUXLEY | Numerically solves the Hodgkin-Huxley system of ordinary differential equations for neuron action potentials. |
| LORENZ | Numerically solves the Lorenz system of ordinary differential equations for chaotic dynamics. |
| LOTKA_VOLTERRA | Numerically solves the Lotka-Volterra predator-prey system of ordinary differential equations. |
| MICHAELIS_MENTEN | Numerically solves the Michaelis-Menten system of ordinary differential equations for enzyme kinetics using scipy.integrate.solve_ivp. |
| SEIR | Numerically solves the SEIR system of ordinary differential equations for infectious disease modeling using scipy.integrate.solve_ivp. |
| SIR | Solves the SIR system of ordinary differential equations for infection dynamics using scipy.integrate.solve_ivp (see scipy.integrate.solve_ivp). |
| VAN_DER_POL | Numerically solves the Van der Pol oscillator system of ordinary differential equations. |
Ode Systems
| Tool | Description |
|---|---|
| SOLVE_BVP | Solve a boundary value problem for a second-order system of ODEs. |
| SOLVE_IVP | Solve an initial value problem for a system of ODEs of the form dy/dt = A @ y. |
Curve Fitting
Least Squares
| Tool | Description |
|---|---|
| CA_CURVE_FIT | Fit an arbitrary symbolic model to data using CasADi and automatic differentiation. |
| CURVE_FIT | Fit a model expression to xdata, ydata using scipy.optimize.curve_fit. |
| LM_FIT | Fit data using lmfit’s built-in models with optional model composition. |
| MINUIT_FIT | Fit an arbitrary model expression to data using iminuit least-squares minimization with uncertainty estimates. |
Models
| Tool | Description |
|---|---|
| ADSORPTION | Fits adsorption models to data using scipy.optimize.curve_fit. |
| AGRICULTURE | Fits agriculture models to data using scipy.optimize.curve_fit. |
| BINDING_MODEL | Fits binding_model models to data using scipy.optimize.curve_fit. |
| CHROMA_PEAKS | Fits chroma_peaks models to data using scipy.optimize.curve_fit. |
| DOSE_RESPONSE | Fits dose_response models to data using scipy.optimize.curve_fit. |
| ELECTRO_ION | Fits electro_ion models to data using scipy.optimize.curve_fit. |
| ENZYME_BASIC | Fits enzyme_basic models to data using scipy.optimize.curve_fit. |
| ENZYME_INHIBIT | Fits enzyme_inhibit models to data using scipy.optimize.curve_fit. |
| EXP_ADVANCED | Fits exp_advanced models to data using scipy.optimize.curve_fit. |
| EXP_DECAY | Fits exp_decay models to data using scipy.optimize.curve_fit. |
| EXP_GROWTH | Fits exponential growth models to data using scipy.optimize.curve_fit. |
| GROWTH_POWER | Fits growth_power models to data using scipy.optimize.curve_fit. |
| GROWTH_SIGMOID | Fits growth_sigmoid models to data using scipy.optimize.curve_fit. |
| MISC_PIECEWISE | Fits misc_piecewise models to data using scipy.optimize.curve_fit. |
| PEAK_ASYM | Fits peak_asym models to data using scipy.optimize.curve_fit. |
| POLY_BASIC | Fits poly_basic models to data using scipy.optimize.curve_fit. |
| RHEOLOGY | Fits rheology models to data using scipy.optimize.curve_fit. |
| SPECTRO_PEAKS | Fits spectro_peaks models to data using scipy.optimize.curve_fit. |
| STAT_DISTRIB | Fits stat_distrib models to data using scipy.optimize.curve_fit. |
| STAT_PARETO | Fits stat_pareto models to data using scipy.optimize.curve_fit. |
| WAVEFORM | Fits waveform models to data using scipy.optimize.curve_fit. |
Geometry
Gis
| Tool | Description |
|---|---|
| SHAPELY_AREA | Calculate the area of a geometry. |
| SHAPELY_BUFFER | Returns a representation of all points within a given distance of the this geometric object. |
| SHAPELY_CONTAINS | Returns True if the first geometry contains the second. |
| SHAPELY_CONVEX_HULL | Returns the smallest convex polygon that contains all the points in the object. |
| SHAPELY_DISTANCE | Calculate the minimum distance between two geometries. |
| SHAPELY_INTERSECT | Returns a representation of the intersection of this object with another geometric object. |
| SHAPELY_LINESTRING | Create a geometric line string from a list of points. |
| SHAPELY_PLOT | Plot geometries and return an image. |
| SHAPELY_POINT | Create a geometric point. |
| SHAPELY_POLYGON | Create a geometric polygon from a shell of points and optional holes. |
| SHAPELY_SIMPLIFY | Returns a simplified representation of the geometric object. |
| SHAPELY_UNION_ALL | Returns the union of all geometries in the input list. |
Triangle Solvers
| Tool | Description |
|---|---|
| TRI_AAAS | Solve a triangle from three angles and one side for scale. |
| TRI_SAS | Solve a triangle from two sides and their included angle. |
| TRI_SOLVE | Solve a triangle from any valid combination of three known side/angle values. |
| TRI_SSA | Solve a triangle from two sides and a non-included angle using SSA branch selection. |
| TRI_SSS | Solve a triangle from three known side lengths. |
Trig Identities
| Tool | Description |
|---|---|
| TRIG_EXPAND | Expand trigonometric compound-angle expressions into component terms. |
| TRIG_FU | Simplify trigonometric expressions using the Fu transformation pipeline. |
| TRIG_TR1 | Rewrite secant and cosecant into reciprocal cosine and sine forms. |
| TRIG_TR10 | Expand sine and cosine of summed angles into separated component terms. |
| TRIG_TR2 | Rewrite tangent and cotangent into sine-cosine ratio forms. |
| TRIG_TR3 | Normalize trig signs and induced odd/even identity forms. |
| TRIG_TR4 | Evaluate exact trigonometric values at standard special angles. |
| TRIG_TR5 | Replace even powers of sine using a cosine-based identity. |
| TRIG_TR6 | Replace even powers of cosine using a sine-based identity. |
| TRIG_TR7 | Apply power-reduction to cosine-squared terms. |
| TRIG_TR8 | Convert products of sine and cosine terms into sum or difference forms. |
| TRIG_TR9 | Convert sums of sine or cosine terms into product forms. |
| TRIG_TRIGSIMP | Simplify a trigonometric expression using identity transformations. |
Graph Theory
Centrality
| Tool | Description |
|---|---|
| BETWEENNESS_CENT | Calculate the shortest-path betweenness centrality for nodes. |
| CLOSENESS_CENT | Calculate the closeness centrality for nodes. |
| DEGREE_CENTRALITY | Calculate the degree centrality for nodes in a graph. |
| EIGENVECTOR_CENT | Calculate the eigenvector centrality for nodes. |
| PAGERANK | Calculate the PageRank of the nodes in the graph. |
Network Flow
| Tool | Description |
|---|---|
| MAX_FLOW_VALUE | Calculate the maximum flow value between a source and sink. |
| MIN_COST_FLOW_COST | Find the minimum cost to satisfy all demands in a network. |
| MIN_CUT_VALUE | Calculate the capacity of the minimum (source, sink) cut. |
Shortest Path
| Tool | Description |
|---|---|
| MIN_SPANNING_TREE | Find the minimum spanning tree (MST) of an undirected graph. |
| SHORTEST_PATH | Find the shortest path and its length between two nodes in a network. |
Interpolation
Approximation
| Tool | Description |
|---|---|
| LAGRANGE_INTERP | Compute the Lagrange interpolating polynomial through a set of points. |
| PADE | Compute Pade rational approximation to a polynomial. |
Multivariate
| Tool | Description |
|---|---|
| CLOUGH_TOCHER | Piecewise cubic C1 interpolation for scattered 2D data. |
| GRID_INTERP | Interpolator on a regular grid in 2D. |
| GRIDDATA | Interpolate unstructured D-D data. |
| INTERPN | Multidimensional interpolation on regular grids (2D). |
| LINEAR_ND_INTERP | Piecewise linear interpolator in N > 1 dimensions. |
| LSQ_BIVAR_SPL | Weighted least-squares bivariate spline with user-specified knots. |
| NEAREST_ND_INTERP | Nearest neighbor interpolation in N > 1 dimensions. |
| RBF_INTERPOLATOR | Radial basis function interpolation in N dimensions. |
| SMOOTH_BIVAR_SPL | Smooth bivariate spline fit for scattered 2D observations. |
Splines
| Tool | Description |
|---|---|
| INTERP_UV_SPLINE | 1-D interpolating spline for data. |
| MAKE_INTERP_SPLINE | Compute interpolating B-spline and evaluate at new points. |
| MAKE_LSQ_SPLINE | Compute LSQ-based fitting B-spline. |
| SMOOTH_SPLINE | Smoothing cubic spline. |
| UNIVARIATE_SPLINE | 1-D smoothing spline fit to data. |
Univariate
| Tool | Description |
|---|---|
| AKIMA_INTERP | Akima 1D interpolation. |
| BARYCENTRIC_INTERP | Interpolating polynomial for a set of points using barycentric interpolation. |
| CUBIC_SPLINE | Cubic spline data interpolator. |
| HERMITE_SPLINE | Piecewise-cubic interpolator matching values and first derivatives. |
| INTERP1D | Interpolate a 1-D function. |
| KROGH_INTERPOLATE | Krogh polynomial interpolation. |
| PCHIP_INTERPOLATE | PCHIP 1-D monotonic cubic interpolation. |
Linear Algebra
Decompositions
| Tool | Description |
|---|---|
| CHOLESKY | Compute the Cholesky decomposition of a real, symmetric positive-definite matrix. |
| HESSENBERG | Compute Hessenberg form of a matrix. |
| LDL | Compute the LDLt or Bunch-Kaufman factorization of a symmetric matrix. |
| LU | Compute LU decomposition of a matrix with partial pivoting. |
| POLAR | Compute the polar decomposition of a matrix. |
| QR | Compute the QR decomposition of a matrix and return either Q or R. |
| SCHUR | Compute Schur decomposition of a matrix. |
| SVD | Compute the Singular Value Decomposition (SVD) of a matrix using scipy.linalg.svd. |
Eigenvalues
| Tool | Description |
|---|---|
| EIG | Compute eigenvalues and eigenvectors of a general square matrix. |
| EIGH | Solve eigenvalue problem for a real symmetric or complex Hermitian matrix. |
| EIGVALS | Compute eigenvalues of a general square matrix. |
| EIGVALSH | Compute eigenvalues of a real symmetric or complex Hermitian matrix. |
Equations
| Tool | Description |
|---|---|
| LSQ_LINEAR | Solve a bounded linear least-squares problem. |
| LSTSQ | Compute the least-squares solution to Ax = B using scipy.linalg.lstsq. |
| SOLVE | Solve a linear matrix equation, or system of linear scalar equations. |
| SOLVE_BANDED | Solve the equation Ax = b for x, assuming A is a banded matrix. |
| SOLVE_TOEPLITZ | Solve a Toeplitz system using Levinson Recursion. |
| SOLVE_TRIANGULAR | Solve the equation Ax = b for x, assuming A is a triangular matrix. |
Matrix Functions
| Tool | Description |
|---|---|
| COSM | Compute the matrix cosine. |
| EXPM | Compute the matrix exponential of a square matrix using scipy.linalg.expm |
| FRAC_MAT_POW | Compute the fractional power of a square matrix. |
| LOGM | Compute matrix logarithm. |
| SINM | Compute the matrix sine. |
| SQRTM | Compute the matrix square root. |
Matrix Operations
| Tool | Description |
|---|---|
| DET | Compute the determinant of a square matrix. |
| INV | Compute the inverse of a square matrix. |
| KHATRI_RAO | Compute the Khatri-Rao product of two matrices. |
| KRON | Compute the Kronecker product of two matrices. |
| MATRIX_NORM | Compute matrix or vector norm. |
| PINV | Compute the Moore-Penrose pseudoinverse of a matrix using singular value decomposition (SVD). |
Special Matrices
| Tool | Description |
|---|---|
| CIRCULANT | Construct a circulant matrix. |
| HADAMARD | Construct a Hadamard matrix. |
| HANKEL | Construct a Hankel matrix. |
| HILBERT | Construct a Hilbert matrix. |
| PASCAL | Construct a Pascal matrix. |
| TOEPLITZ | Construct a Toeplitz matrix. |
Number Theory
Gcd Lcm Divisors
| Tool | Description |
|---|---|
| DIVCOUNT | Count divisors of an integer with optional modulus filtering. |
| DIVISORS | List divisors of an integer with optional proper divisor mode. |
| GCD | Compute the greatest common divisor of one or more integers. |
| ISQRT | Compute the integer square root of a nonnegative integer. |
| LCM | Compute the least common multiple of one or more integers. |
| PPY_PHI | Compute Euler’s totient function using the primePy implementation. |
| PROPDIVCNT | Count proper divisors of an integer with optional modulus filtering. |
| PROPDIVS | List proper divisors of an integer. |
| REDUCEDTOT | Compute Carmichael’s reduced totient function for an integer. |
| TOTIENT | Compute Euler’s totient function for an integer. |
Modular Arithmetic
| Tool | Description |
|---|---|
| DLOG | Solve discrete logarithms in modular arithmetic. |
| ISPRIMROOT | Check whether a value is a primitive root modulo n. |
| ISQUADRES | Check whether a value is a quadratic residue modulo p. |
| JACOBI | Compute the Jacobi symbol for two integers. |
| LEGENDRE | Compute the Legendre symbol modulo an odd prime. |
| NORDER | Find the multiplicative order of an integer modulo n. |
| NTHROOTMOD | Solve nth-power congruences modulo an integer. |
| PRIMROOT | Find a primitive root modulo n when one exists. |
| QUADCONG | Solve quadratic congruences modulo n. |
| QUADRES | List all quadratic residues modulo p. |
| SQRTMOD | Solve quadratic congruences of the form x squared congruent to a. |
Prime Numbers
| Tool | Description |
|---|---|
| FACTORINT | Compute the prime factorization of an integer with multiplicities. |
| ISPRIME | Determine whether an integer is prime. |
| NEXTPRIME | Return the ith prime number greater than a given integer. |
| PPY_PRIMEBETWEEN | List prime numbers between two bounds using primePy. |
| PPY_PRIMECHECK | Check primality using the primePy implementation. |
| PPY_PRIMEUPTO | List all prime numbers up to a maximum value using primePy. |
| PREVPRIME | Return the largest prime smaller than a given integer. |
| PRIME | Return the nth prime number. |
| PRIMEFACTORS | Return distinct prime factors of an integer. |
| PRIMEPI | Count the number of primes less than or equal to an integer. |
| PRIMERANGE | Generate all prime numbers in a specified interval. |
| RANDPRIME | Sample a prime number from a half-open integer interval. |
Optimization
Assignment Problems
| Tool | Description |
|---|---|
| LINEAR_ASSIGNMENT | Solve the linear assignment problem using scipy.optimize.linear_sum_assignment. |
| QUAD_ASSIGN | Solve a quadratic assignment problem using SciPy’s implementation. |
Global Optimization
| Tool | Description |
|---|---|
| BASIN_HOPPING | Minimize a single-variable expression with SciPy’s basinhopping algorithm. |
| BRUTE | Perform a brute-force grid search to approximate the global minimum of a function. |
| DIFF_EVOLUTION | Minimize a multivariate function using differential evolution. |
| DUAL_ANNEALING | Minimize a multivariate function using dual annealing. |
| SHGO | Find global minimum using Simplicial Homology Global Optimization. |
Linear Programming
| Tool | Description |
|---|---|
| CA_QUAD_PROG | Solve a quadratic programming problem using CasADi’s qpsol solver. |
| LINEAR_PROG | Solve a linear programming problem using SciPy’s linprog function. |
| MILP | Solve a mixed-integer linear program using scipy.optimize.milp. |
Local Optimization
| Tool | Description |
|---|---|
| CA_MINIMIZE | Minimize a multivariate function using CasADi with automatic differentiation. |
| MINIMIZE | Minimize a multivariate function using SciPy’s minimize routine. |
| MINIMIZE_SCALAR | Minimize a single-variable function using SciPy’s minimize_scalar. |
Root Finding
| Tool | Description |
|---|---|
| CA_ROOT | Solve a system of nonlinear equations using CasADi with automatic Jacobian. |
| FIXED_POINT | Find a fixed point x such that f(x) = x for a scalar function expression. |
| ROOT | Solve a square nonlinear system using SciPy’s root solver. |
| ROOT_SCALAR | Find a real root of a scalar function using SciPy’s root_scalar. |
Special Functions
Bessel Functions
| Tool | Description |
|---|---|
| BESSEL_HANKEL1 | Compute the cylindrical Hankel function of the first kind and return real and imaginary parts. |
| BESSEL_HANKEL2 | Compute the cylindrical Hankel function of the second kind and return real and imaginary parts. |
| BESSEL_IV | Compute the modified cylindrical Bessel function of the first kind for real order. |
| BESSEL_JN_ZEROS | Compute the first positive zeros of the integer-order Bessel function of the first kind. |
| BESSEL_JV | Compute the cylindrical Bessel function of the first kind for real order. |
| BESSEL_KV | Compute the modified cylindrical Bessel function of the second kind for real order. |
| BESSEL_YN_ZEROS | Compute the first positive zeros of the integer-order Bessel function of the second kind. |
| BESSEL_YV | Compute the cylindrical Bessel function of the second kind for real order. |
| SPHERICAL_IN | Compute the modified spherical Bessel function of the first kind or its derivative. |
| SPHERICAL_JN | Compute the spherical Bessel function of the first kind or its derivative. |
| SPHERICAL_KN | Compute the modified spherical Bessel function of the second kind or its derivative. |
| SPHERICAL_YN | Compute the spherical Bessel function of the second kind or its derivative. |
Elliptic Integrals
| Tool | Description |
|---|---|
| ELLIPE | Compute the complete elliptic integral of the second kind. |
| ELLIPEINC | Compute the incomplete elliptic integral of the second kind. |
| ELLIPJ | Compute Jacobi elliptic functions sn, cn, dn and amplitude for scalar input. |
| ELLIPK | Compute the complete elliptic integral of the first kind. |
| ELLIPKINC | Compute the incomplete elliptic integral of the first kind. |
| ELLIPKM1 | Compute the complete elliptic integral of the first kind near m equals one. |
| ELLIPRC | Compute Carlson’s degenerate symmetric elliptic integral RC. |
| ELLIPRD | Compute Carlson’s symmetric elliptic integral RD. |
| ELLIPRF | Compute Carlson’s completely symmetric elliptic integral RF. |
| ELLIPRG | Compute Carlson’s completely symmetric elliptic integral RG. |
| ELLIPRJ | Compute Carlson’s symmetric elliptic integral RJ. |
Error And Fresnel
| Tool | Description |
|---|---|
| DAWSN | Evaluate Dawson’s integral for a real input. |
| ERF | Evaluate the Gauss error function for a real input. |
| ERFC | Evaluate the complementary error function for a real input. |
| ERFCINV | Compute the inverse complementary error function on its real domain. |
| ERFCX | Evaluate the exponentially scaled complementary error function. |
| ERFI | Evaluate the imaginary error function for a real input. |
| ERFINV | Compute the inverse error function on its real domain. |
| FRESNEL | Compute Fresnel sine and cosine integrals for a real input. |
| WOFZ | Compute the Faddeeva function and return real and imaginary parts. |
Gamma Beta Functions
| Tool | Description |
|---|---|
| BETAINC | Compute the regularized incomplete beta function. |
| BETAINCINV | Invert the regularized incomplete beta function with respect to x. |
| BETALN | Compute the natural logarithm of the absolute beta function. |
| DIGAMMA | Compute the digamma function for a real input. |
| EULER_BETA | Evaluate the Euler beta function for two real parameters. |
| GAMMA | Evaluate the gamma function for a real input. |
| GAMMAINC | Compute the regularized lower incomplete gamma function. |
| GAMMAINCC | Compute the regularized upper incomplete gamma function. |
| GAMMAINCCINV | Invert the regularized upper incomplete gamma function. |
| GAMMAINCINV | Invert the regularized lower incomplete gamma function. |
| GAMMALN | Compute the natural logarithm of the absolute gamma function. |
| POCH | Evaluate the rising factorial using the Pochhammer symbol. |
| POLYGAMMA | Compute the n-th derivative of the digamma function. |
| RGAMMA | Compute the reciprocal of the gamma function. |
Symbolic
| Tool | Description |
|---|---|
| DIFF | Compute a symbolic derivative of an expression. |
| DSOLVE | Solve an ordinary differential equation symbolically. |
| EXPAND | Expand products and powers in a symbolic expression. |
| FACTOR | Factor a symbolic expression into simpler multiplicative terms. |
| INTEGRATE | Compute an indefinite or definite symbolic integral. |
| LIMIT | Compute the symbolic limit of an expression. |
| SIMPLIFY | Simplify a symbolic expression. |
| SYM_SOLVE | Solve an algebraic equation for a variable. |
Wavelets
| Tool | Description |
|---|---|
| CWT_PYWT | Compute the continuous wavelet transform and associated pseudo-frequencies. |
| DOWNCOEF | Compute approximation or detail coefficients at a specified decomposition level. |
| DWT | Perform a single-level one-dimensional discrete wavelet transform. |
| DWT2 | Perform a single-level two-dimensional discrete wavelet transform. |
| IDWT | Reconstruct a one-dimensional signal from approximation and detail coefficients. |
| THRESHOLD_PYWT | Apply elementwise wavelet thresholding to numeric data. |
| UPCOEF | Reconstruct approximation or detail contribution from one-dimensional coefficients. |
| WAVEDEC_PYWT | Compute a multilevel one-dimensional discrete wavelet decomposition. |
| WAVEREC | Reconstruct a one-dimensional signal from multilevel wavelet coefficients. |
Statistics
Bayesian
Conjugate Priors
| Tool | Description |
|---|---|
| BB_LOGBETA | Compute the log-Beta term used in conjugate posterior calculations. |
| BB_POST_UPDATE | Update Beta-Binomial posterior hyperparameters from observed counts. |
| BB_QBETA | Compute a Beta posterior quantile for Beta-Binomial models. |
| GAMMA_POST_Q | Compute a Gamma posterior quantile from shape-rate parameters. |
| INVGAMMA_POST_Q | Compute an inverse-Gamma posterior quantile. |
| NIG_POST_UPDATE | Update Normal-Inverse-Gamma posterior hyperparameters from sample summaries. |
| NN_POST_UPDATE | Update Normal posterior parameters for unknown mean with known variance. |
Credible Intervals
| Tool | Description |
|---|---|
| BAYES_MVS_CI | Compute Bayesian credible intervals for mean, variance, and standard deviation from sample data. |
| BETA_CI_BOUNDS | Compute an equal-tailed Bayesian credible interval for a proportion using a Beta posterior. |
| GAMMA_CI_BOUNDS | Compute an equal-tailed Bayesian credible interval for a positive rate parameter using Gamma quantiles. |
| INVGAMMA_CI_BOUNDS | Compute an equal-tailed Bayesian credible interval for a positive scale or variance parameter using Inverse-Gamma quantiles. |
| MVSDIST_CI | Compute Bayesian credible intervals from posterior distributions of mean, variance, and standard deviation. |
| SAMPLE_EQTAIL_CI | Compute an equal-tailed credible interval from posterior samples using empirical quantiles. |
| SAMPLE_HPD_CI | Approximate a highest posterior density interval from posterior samples using the narrowest empirical window. |
Dirichlet Multinomial
| Tool | Description |
|---|---|
| DM_CRED_INT | Compute category-wise credible intervals from posterior Dirichlet parameters. |
| DM_DIRICHLET_SUM | Compute Dirichlet density and moments for a category-probability vector. |
| DM_LOGBETA | Compute the Dirichlet log-normalization term using log-gamma values. |
| DM_LOGSUM_NORM | Compute a stable log normalizer and normalized probabilities from log-values. |
| DM_POST_UPDATE | Update Dirichlet posterior parameters from prior hyperparameters and observed counts. |
| DM_PREDICTIVE | Compute posterior predictive category probabilities from Dirichlet parameters. |
Posterior Summarization
| Tool | Description |
|---|---|
| POSTERIOR_BMV | Compute Bayesian posterior summaries for mean, variance, and standard deviation. |
| POSTERIOR_ENTROPY | Compute Shannon or relative entropy for posterior probability tables. |
| POSTERIOR_LOGSUMEXP | Compute stable log-sum-exp aggregates for posterior normalization and evidence calculations. |
| POSTERIOR_MAP | Extract the MAP estimate from a tabulated posterior distribution. |
| POSTERIOR_TAILPROB | Compute posterior tail probabilities relative to a decision threshold. |
| POSTERIOR_WMEANVAR | Compute posterior weighted mean and variance summaries from values and weights. |
| POSTERIOR_XLOGY | Compute numerically stable x times log y terms for posterior information calculations. |
Frequency Statistics
| Tool | Description |
|---|---|
| BINNED_STATISTIC | Computes a binned statistic (mean, sum, median, etc.) for the input data. |
| BINNED_STATISTIC_2D | Computes a bidimensional binned statistic (mean, sum, median, etc.) for the input data. |
| CUMFREQ | Compute the cumulative frequency histogram for the input data. |
| PERCENTILEOFSCORE | Computes the percentile rank of a score relative to the input data. |
| RELFREQ | Returns the relative frequency histogram for the input data. |
| SCOREATPERCENTILE | Calculates the score at the given percentile of the input data. |
Hypothesis Tests
| Tool | Description |
|---|---|
| ANOVA | Perform one-way ANOVA on tabular data using Pingouin. |
| GAMESHOWELL | Run Games-Howell pairwise comparisons using Pingouin. |
| HOMOSCEDASTICITY | Test equality of variances across groups using Pingouin. |
| MIXED_ANOVA | Perform mixed ANOVA with within- and between-subject factors using Pingouin. |
| NORMALITY | Test normality by group or overall using Pingouin. |
| PAIRWISE_TUKEY | Run Tukey HSD pairwise comparisons using Pingouin. |
| RM_ANOVA | Perform repeated-measures ANOVA on tabular data using Pingouin. |
| WELCH_ANOVA | Perform Welch ANOVA for unequal variances using Pingouin. |
Association Correlation
| Tool | Description |
|---|---|
| BARNARD_EXACT | Perform Barnard’s exact test on a 2x2 contingency table. |
| BOSCHLOO_EXACT | Perform Boschloo’s exact test on a 2x2 contingency table. |
| CHI2_CONTINGENCY | Perform the chi-square test of independence for variables in a contingency table. |
| FISHER_EXACT | Perform Fisher’s exact test on a 2x2 contingency table. |
| KENDALLTAU | Calculate Kendall’s tau, a correlation measure for ordinal data. |
| LINREGRESS | Calculate a linear least-squares regression for two sets of measurements. |
| MULTISCALE_GRAPH | Compute the Multiscale Graph Correlation (MGC) test statistic and p-value. |
| PAGE_TREND_TEST | Perform Page’s L trend test for monotonic trends across treatments. |
| PEARSONR | Calculate the Pearson correlation coefficient and p-value for two datasets. |
| POINTBISERIALR | Calculate a point biserial correlation coefficient and its p-value. |
| SIEGELSLOPES | Compute the Siegel repeated medians estimator for robust linear regression using scipy.stats.siegelslopes. |
| SOMERSD | Calculate Somers’ D, an asymmetric measure of ordinal association between two variables. |
| SPEARMANR | Calculate a Spearman rank-order correlation coefficient with associated p-value. |
| THEILSLOPES | Compute the Theil-Sen estimator for a set of points (robust linear regression). |
| WEIGHTEDTAU | Compute a weighted version of Kendall’s tau correlation coefficient. |
Independent Sample
| Tool | Description |
|---|---|
| ALEXANDERGOVERN | Performs the Alexander-Govern test for equality of means across multiple independent samples with possible heterogeneity of variance. |
| ANDERSON_KSAMP | Performs the k-sample Anderson-Darling test to determine if samples are drawn from the same population. |
| ANSARI | Performs the Ansari-Bradley test for equal scale parameters (non-parametric) using scipy.stats.ansari. |
| BARTLETT | Performs Bartlett’s test for equal variances across multiple samples. |
| BRUNNERMUNZEL | Computes the Brunner-Munzel nonparametric test for two independent samples. |
| BWS_TEST | Performs the Baumgartner-Weiss-Schindler test on two independent samples. |
| CVM_2SAMP | Performs the two-sample Cramér-von Mises test using scipy.stats.cramervonmises_2samp. |
| DUNNETT | Performs Dunnett’s test for multiple comparisons of means against a control group. |
| EPPS_SINGLE_2SAMP | Compute the Epps-Singleton test statistic and p-value for two samples. |
| F_ONEWAY | Performs a one-way ANOVA test for two or more independent samples. |
| FLIGNER | Performs the Fligner-Killeen test for equality of variances across multiple samples. |
| FRIEDMANCHISQUARE | Computes the Friedman test for repeated samples. |
| KRUSKAL | Computes the Kruskal-Wallis H-test for independent samples. |
| KS_2SAMP | Performs the two-sample Kolmogorov-Smirnov test for goodness of fit. |
| LEVENE | Performs the Levene test for equality of variances across multiple samples. |
| MANNWHITNEYU | Performs the Mann-Whitney U rank test on two independent samples using scipy.stats.mannwhitneyu. |
| MEDIAN_TEST | Performs Mood’s median test to determine if two or more independent samples come from populations with the same median. |
| MOOD | Perform Mood’s two-sample test for scale parameters. |
| POISSON_MEANS_TEST | Performs the Poisson means test (E-test) to compare the means of two Poisson distributions. |
| RANKSUMS | Computes the Wilcoxon rank-sum statistic and p-value for two independent samples. |
| TTEST_IND | Performs the independent two-sample t-test for the means of two groups. |
| TTEST_IND_STATS | Perform a t-test for means of two independent samples using summary statistics. |
One Sample
| Tool | Description |
|---|---|
| BINOMTEST | Perform a binomial test for the probability of success in a Bernoulli experiment. |
| JARQUE_BERA | Perform the Jarque-Bera goodness of fit test for normality. |
| KSTEST | Performs the one-sample Kolmogorov-Smirnov test for goodness of fit. |
| KURTOSISTEST | Test whether the kurtosis of a sample is different from that of a normal distribution. |
| NORMALTEST | Test whether a sample differs from a normal distribution (omnibus test). |
| QUANTILE_TEST | Perform a quantile test to determine if a population quantile equals a hypothesized value. |
| SHAPIRO | Perform the Shapiro-Wilk test for normality. |
| SKEWTEST | Test whether the skewness of a sample is different from that of a normal distribution. |
| TTEST_1SAMP | Perform a one-sample t-test for the mean of a group of scores. |
Models
| Tool | Description |
|---|---|
| MEDIATION_ANALYSIS | Perform causal mediation analysis with bootstrap confidence intervals. |
Count
| Tool | Description |
|---|---|
| HURDLE_COUNT_MODEL | Fits a Hurdle model for count data with two-stage process (zero vs. |
| ZINB_MODEL | Fits a Zero-Inflated Negative Binomial (ZINB) model for overdispersed count data with excess zeros. |
| ZIP_MODEL | Fits a Zero-Inflated Poisson (ZIP) model for count data with excess zeros. |
Discrete Choice
| Tool | Description |
|---|---|
| LOGIT_MODEL | Fits a binary logistic regression model to predict binary outcomes using maximum likelihood estimation. |
| MULTINOMIAL_LOGIT | Fits a multinomial logistic regression model for multi-category outcomes. |
| ORDERED_LOGIT | Fits an ordered logistic regression model for ordinal outcomes. |
| PROBIT_MODEL | Fits a binary probit regression model using maximum likelihood estimation. |
Generalized Linear
| Tool | Description |
|---|---|
| GLM_BINOMIAL | Fits a Generalized Linear Model with binomial family for binary or proportion data. |
| GLM_GAMMA | Fit a Generalized Linear Model with Gamma family for positive continuous data. |
| GLM_INV_GAUSS | Fits a Generalized Linear Model with Inverse Gaussian family for right-skewed positive data. |
| GLM_NEG_BINOM | Fits a Generalized Linear Model with Negative Binomial family for overdispersed count data. |
| GLM_POISSON | Fits a Generalized Linear Model with Poisson family for count data. |
| GLM_TWEEDIE | Fits a Generalized Linear Model with Tweedie family for flexible distribution modeling. |
Mixed Effects
| Tool | Description |
|---|---|
| GEE_MODEL | Fits a Generalized Estimating Equations (GEE) model for correlated data. |
| GLMM_BINOMIAL | Fits a Generalized Linear Mixed Model (GLMM) with binomial family for binary clustered data. |
| GLMM_POISSON | Fits a Generalized Linear Mixed Model (GLMM) with Poisson family for count clustered data. |
| MIXED_LINEAR_MODEL | Fits a Linear Mixed Effects Model (LMM) with random intercepts and slopes. |
Regression
| Tool | Description |
|---|---|
| GLS_REGRESSION | Fits a Generalized Least Squares (GLS) regression model. |
| INFLUENCE_DIAG | Computes regression influence diagnostics for identifying influential observations. |
| OLS_DIAGNOSTICS | Performs diagnostic tests on OLS regression residuals. |
| OLS_REGRESSION | Fits an Ordinary Least Squares (OLS) regression model. |
| QUANTILE_REGRESSION | Fits a quantile regression model to estimate conditional quantiles of the response distribution. |
| REGRESS_DIAG | Performs comprehensive regression diagnostic tests. |
| ROBUST_LINEAR_MODEL | Fits a robust linear regression model using M-estimators. |
| SPECIFICATION_TESTS | Performs regression specification tests to detect model misspecification. |
| WLS_REGRESSION | Fits a Weighted Least Squares (WLS) regression model. |
Survival
| Tool | Description |
|---|---|
| COX_HAZARDS | Fits a Cox Proportional Hazards regression model for survival data. |
| EXP_SURVIVAL_REG | Fits a parametric exponential survival regression model. |
| KAPLAN_MEIER | Computes the Kaplan-Meier survival function estimate for time-to-event data. |
Multivariate Analysis
| Tool | Description |
|---|---|
| CANCORR | Performs Canonical Correlation Analysis (CCA) between two sets of variables. |
| FACTOR_ANALYSIS | Performs exploratory factor analysis with rotation. |
| MANOVA_TEST | Performs Multivariate Analysis of Variance (MANOVA) for multiple dependent variables. |
| PCA_ANALYSIS | Performs Principal Component Analysis (PCA) for dimensionality reduction. |
Probability Distributions
Continuous Distributions
| Tool | Description |
|---|---|
| BETA | Wrapper for scipy.stats.beta distribution providing multiple statistical methods. |
| CAUCHY | Wrapper for scipy.stats.cauchy distribution providing multiple statistical methods. |
| CHISQ | Compute various statistics and functions for the chi-squared distribution from scipy.stats.chi2. |
| EXPON | Exponential distribution function wrapping scipy.stats.expon. |
| F_DIST | Unified interface to the main methods of the F-distribution, including PDF, CDF, inverse CDF, survival function, and distribution statistics. |
| LAPLACE | Laplace distribution function supporting multiple methods. |
| LOGNORM | Compute lognormal distribution statistics and evaluations. |
| NORM | Normal (Gaussian) distribution function supporting multiple methods. |
| PARETO | Pareto distribution function supporting multiple methods. |
| T_DIST | Student’s t distribution function supporting multiple methods from scipy.stats.t. |
| UNIFORM | Uniform distribution function supporting multiple methods. |
| WEIBULL_MIN | Compute various functions of the Weibull minimum distribution using scipy.stats.weibull_min. |
Discrete Distributions
| Tool | Description |
|---|---|
| BERNOULLI | Calculates properties of a Bernoulli discrete random variable. |
| BETABINOM | Compute Beta-binomial distribution values from scipy.stats.betabinom. |
| BETANBINOM | Compute Beta-negative-binomial distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| BINOM | Compute Binomial distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| BOLTZMANN | Compute Boltzmann distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| DLAPLACE | Compute Discrete Laplace distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| GEOM | Compute Geometric distribution values using scipy.stats.geom. |
| HYPERGEOM | Compute Hypergeometric distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| LOGSER | Compute Log-Series distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| NBINOM | Compute Negative Binomial distribution values using scipy.stats.nbinom. |
| NHYPERGEOM | Compute Negative Hypergeometric distribution values using scipy.stats.nhypergeom. |
| PLANCK | Compute Planck distribution values using scipy.stats.planck. |
| POISSON_DIST | Compute Poisson distribution values using scipy.stats.poisson. |
| RANDINT | Compute Uniform discrete distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| SKELLAM | Compute Skellam distribution values using scipy.stats.skellam. |
| VAL_DISCRETE | Select a value from a list based on a discrete probability distribution. |
| YULESIMON | Compute Yule-Simon distribution values using scipy.stats.yulesimon. |
| ZIPF | Compute Zipf distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
| ZIPFIAN | Compute Zipfian distribution values: PMF, CDF, SF, ICDF, ISF, mean, variance, std, or median. |
Multivariate Distributions
| Tool | Description |
|---|---|
| DIRICHLET | Computes the PDF, log-PDF, mean, variance, covariance, entropy, or draws random samples from a Dirichlet distribution. |
| DIRICHLET_MULTINOM | Computes the probability mass function, log probability mass function, mean, variance, or covariance of the Dirichlet multinomial distribution. |
| MATRIX_NORMAL | Computes the PDF, log-PDF, or draws random samples from a matrix normal distribution. |
| MULTINOMIAL | Compute the probability mass function, log-PMF, entropy, covariance, or draw random samples from a multinomial distribution. |
| MULTIVARIATE_NORMAL | Computes the PDF, CDF, log-PDF, log-CDF, entropy, or draws random samples from a multivariate normal distribution. |
| MULTIVARIATE_T | Computes the PDF, CDF, or draws random samples from a multivariate t-distribution. |
| MV_HYPERGEOM | Computes probability mass function, log-PMF, mean, variance, covariance, or draws random samples from a multivariate hypergeometric distribution. |
| ORTHO_GROUP | Draws random samples of orthogonal matrices from the O(N) Haar distribution using scipy.stats.ortho_group. |
| RANDOM_CORRELATION | Generates a random correlation matrix with specified eigenvalues. |
| SPECIAL_ORTHO_GROUP | Draws random samples from the special orthogonal group SO(N), returning orthogonal matrices with determinant +1. |
| UNIFORM_DIRECTION | Draws random unit vectors uniformly distributed on the surface of a hypersphere in the specified dimension. |
| UNITARY_GROUP | Generate a random unitary matrix of dimension N from the Haar distribution. |
| VONMISES_FISHER | Computes the PDF, log-PDF, entropy, or draws random samples from a von Mises-Fisher distribution on the unit hypersphere. |
| WISHART | Computes the PDF, log-PDF, or draws random samples from the Wishart distribution using scipy.stats.wishart. |
Summary Statistics
| Tool | Description |
|---|---|
| CRONBACH_ALPHA | Compute Cronbach’s alpha reliability coefficient for a set of items. |
| DESCRIBE | Compute descriptive statistics using scipy.stats.describe. |
| DISTANCE_CORR | Compute distance correlation between two numeric variables. |
| EXPECTILE | Calculates the expectile of a dataset using scipy.stats.expectile. |
| GMEAN | Compute the geometric mean of the input data, flattening the input and ignoring non-numeric values. |
| HMEAN | Calculates the harmonic mean of the input data, flattening the input and ignoring non-numeric values. |
| KURTOSIS | Compute the kurtosis (Fisher or Pearson) of a dataset. |
| MODE | Return the modal (most common) numeric value in the input data, returning the smallest if there are multiple modes. |
| MOMENT | Calculates the nth moment about the mean for a sample. |
| PARTIAL_CORR | Compute partial or semi-partial correlation between two variables. |
| PMEAN | Computes the power mean (generalized mean) of the input data for a given power p. |
| SKEWNESS | Calculate the skewness of a dataset. |
Time Series
Autocorrelation And Stationarity Tests
| Tool | Description |
|---|---|
| ACF | Compute autocorrelation values across lags with optional confidence intervals and Ljung-Box statistics. |
| ACOVF | Estimate autocovariance values of a time series across lags. |
| ADFULLER | Run the Augmented Dickey-Fuller unit-root test for stationarity diagnostics. |
| CCF | Compute cross-correlation between two time series across nonnegative lags. |
| CCOVF | Estimate cross-covariance values between two time series across lags. |
| KPSS | Run the KPSS stationarity test under level or trend null hypotheses. |
| PACF | Compute partial autocorrelation values across lags for lag-order diagnostics. |
| Q_STAT | Compute Ljung-Box Q statistics and p-values from autocorrelation coefficients. |
| RURTEST | Run the range unit-root test as an alternative stationarity diagnostic. |
| ZIVOT_ANDREWS | Run the Zivot-Andrews unit-root test allowing one endogenous structural break. |
Decomposition And Seasonality
| Tool | Description |
|---|---|
| DETREND | Remove linear or constant trend from input data. |
| MSTL | Perform multi-seasonal STL decomposition on a time series. |
| PERIODOGRAM | Estimate the power spectral density of a time series using a periodogram. |
| SEASDECOMP | Decompose a time series into trend, seasonal, and residual components. |
| STL | Perform STL decomposition of a univariate time series. |
| WELCH | Estimate the power spectral density of a time series using Welch’s method. |
Forecasting Models
| Tool | Description |
|---|---|
| ARIMA_FORECAST | Fit an ARIMA model and return out-of-sample forecasts. |
| ARMA_ORDER_IC | Select ARMA order using an information criterion. |
| HANNAN_RISSANEN | Estimate ARMA parameters using the Hannan-Rissanen procedure. |
| HOLT_FORECAST | Fit Holt trend exponential smoothing and return forecasts. |
| HW_FORECAST | Fit Holt-Winters exponential smoothing and return forecasts. |
| INNOVATIONS_MLE | Estimate SARIMA parameters using innovations maximum likelihood. |
| SARIMAX_FORECAST | Fit a SARIMAX model and return out-of-sample forecasts. |
| SES_FORECAST | Fit simple exponential smoothing and return forecasts. |
Moving Averages
| Tool | Description |
|---|---|
| EMA_LFILTER | Compute an exponential moving average using recursive linear filtering. |
| EMA_PERIOD | Compute an exponential moving average using a period-derived smoothing constant. |
| SMA_CONV | Compute a simple moving average using discrete convolution with a uniform window. |
| SMA_CUMSUM | Compute a simple moving average using cumulative-sum differencing. |
| WINMA_CONV | Compute a weighted moving average by convolving data with a user-defined weight window. |
| WMA | Compute a rolling weighted moving average using user-supplied weights. |
Visualization
Charts
3d
| Tool | Description |
|---|---|
| AREA_3D | Create a 3D filled area chart between two 3D lines. |
| BAR_3D | Create a 3D bar chart. |
| LINE_3D | Create a 3D line plot. |
| QUIVER_3D | Create a 3D quiver (vector) plot. |
| SCATTER_3D | Create a 3D scatter plot. |
| STEM_3D | Create a 3D stem plot. |
| SURFACE_3D | Create a 3D surface plot. |
| TRISURF_3D | Create a 3D triangular surface plot. |
| VOXELS | Create a 3D voxel plot from a 3D grid of values. |
| WIREFRAME_3D | Create a 3D wireframe plot. |
Basic
| Tool | Description |
|---|---|
| AREA | Create a filled area chart from data. |
| BAR | Create a bar chart (vertical or horizontal) from data. |
| GROUPED_BAR | Create a grouped/dodged bar chart from data. |
| LINE | Create a line chart from data. |
| PIE | Create a pie chart from data. |
| SCATTER | Create an XY scatter plot from data. |
| STACKED_BAR | Create a stacked bar chart from data. |
| STEP | Create a step plot from data. |
Categorical
| Tool | Description |
|---|---|
| DONUT | Create a donut chart from data. |
| DOT_PLOT | Create a Cleveland dot plot from data. |
| DUMBBELL | Create a dumbbell plot (range comparison) from data. |
| FUNNEL | Create a funnel chart for stages in a process. |
| PARETO_CHART | Create a Pareto chart (bar chart + cumulative line). |
| SLOPE | Create a slope chart for comparing paired changes across categories. |
| STEM | Create a stem/lollipop plot from data. |
| WATERFALL | Create a waterfall chart (change analysis) from data. |
Matrix
| Tool | Description |
|---|---|
| CLUSTER_MAP | Create a hierarchically-clustered heatmap. |
| CORRELATION | Create a correlation matrix heatmap from data. |
| HEATMAP | Create a heatmap from a matrix of data. |
| TRIANGULAR_HEATMAP | Create a lower or upper triangular heatmap. |
Scientific
| Tool | Description |
|---|---|
| BARBS | Plot a 2D field of wind barbs. |
| CONTOUR | Create a contour plot. |
| CONTOUR_FILLED | Create a filled contour plot. |
| LOGLOG | Create a log-log plot from data. |
| PCOLORMESH | Create a pseudocolor plot with a rectangular grid. |
| POLAR_BAR | Create a bar chart in polar coordinates (also known as a Rose diagram). |
| POLAR_LINE | Create a line plot in polar coordinates. |
| POLAR_SCATTER | Create a scatter plot in polar coordinates. |
| QUIVER | Create a quiver plot (vector field arrows). |
| RADAR | Create a radar (spider) chart. |
| SEMILOGX | Create a plot with a log-scale X-axis. |
| SEMILOGY | Create a plot with a log-scale Y-axis. |
| STREAMPLOT | Create a streamplot (vector field streamlines). |
| TRICONTOUR | Draw contour lines on an unstructured triangular grid. |
| TRICONTOUR_FILLED | Draw filled contour regions on an unstructured triangular grid. |
| TRIPCOLOR | Create a pseudocolor plot of an unstructured triangular grid. |
| TRIPLOT | Draw an unstructured triangular grid as lines and/or markers. |
Specialty
| Tool | Description |
|---|---|
| BULLET | Create a bullet chart for visual comparison against a target. |
| GANTT | Create a Gantt chart (timeline of tasks). |
| GAUGE | Create a speedometer/gauge style chart. |
| SANKEY | Create a Sankey flow diagram. |
| TABLE | Render data as a graphical table image. |
| WORDCLOUD | Generates a word cloud image from provided text data and returns a PNG image as a base64 string. |
Statistical
| Tool | Description |
|---|---|
| BOXPLOT | Create a box-and-whisker plot from data. |
| DENDROGRAM | Performs hierarchical (agglomerative) clustering and returns a dendrogram as an image. |
| DENSITY | Create a Kernel Density Estimate (KDE) plot. |
| ECDF | Create an Empirical Cumulative Distribution Function plot. |
| ERRORBAR | Create an XY plot with error bars. |
| EVENTPLOT | Create a spike raster or event plot from data. |
| HEXBIN | Create a hexagonal binning plot from data. |
| HIST2D | Create a 2D histogram plot from data. |
| HISTOGRAM | Create a frequency distribution histogram from data. |
| VIOLIN | Create a violin plot from data. |
Dashboards
Geo Allocation
| Tool | Description |
|---|---|
| Capacity Coverage Balance | Balances workforce and asset capacity allocation against geographic coverage goals to avoid solving one deficit while creating another. |
| Catchment Analysis View | Examines each location’s catchment profile using deterministic trade-area demand, penetration, and overlap indicators. |
| Coverage Overlap Audit | Audits account and postal-code coverage to detect overlapping ownership, uncovered white space, and routing conflicts between adjacent territories. |
| Coverage Planning Map | Provides a map-first operating view that compares serviceable capacity against demand by zone, cluster, and service tier. |
| Coverage Variance Monitor | Monitors whether coverage performance is stable or drifting away from expected operating ranges across regions and service tiers. |
| Expansion Action Queue | Converts identified coverage deficits into an executable queue of actions with effort, impact, and dependency visibility. |
| Exposure Variance Monitor | Tracks exposure movement across periods and compares realized variance against approved tolerance bands by region and asset class. |
| Hazard Scenario Simulator | Simulates plausible hazard scenarios and estimates resulting exposure, service disruption, and recovery burden across regions. |
| Local Risk Overlay | Overlays local risk signals on top of location performance to reveal where operational fragility and external exposure intersect. |
| Location Performance Map | Provides a map-first operating view of site, store, and facility performance with deterministic scoring for revenue, throughput, quality, and service reliability. |
| Mitigation Action Queue | Converts diagnosed risk issues into an execution-ready action queue ranked by expected reduction, urgency, and implementation effort. |
| New Location Scenario Planner | Evaluates multiple new-site scenarios and compares expected coverage gain, travel-time reduction, and financial feasibility under common assumptions. |
| Path Efficiency Simulator | Simulates how path redesign choices affect trip time, transfer burden, fuel use, and service reliability before committing schedule changes. |
| Peak Window Risk Tracker | Tracks route-level risk during predefined peak windows by combining demand spikes, delay volatility, fleet readiness, and staffing exposure. |
| Quota Variance Monitor | Tracks quota variance continuously across territories and regions against plan, commit, and prior-period baselines. |
| Region Gap Diagnostics | Decomposes regional attainment gaps into coverage, conversion, deal-size, and cycle-time contributors with signed impact values. |
| Regional Alert Timeline | Provides a time-ordered view of risk alerts, escalation decisions, and response milestones to evaluate operational responsiveness. |
| Regional Potential Tracker | Tracks realized revenue against modeled addressable potential to show where each region is overperforming, saturated, or underpenetrated. |
| Regional Risk Map | Presents a map-first view of regional exposure, hazard intensity, and control readiness so leaders can rapidly identify where risk is clustering. |
| Resilience Score Tracker | Tracks resilience capability scores by region across preparedness, response, recovery, and adaptation dimensions. |
| Risk Hotspot Diagnostics | Decomposes each hotspot into hazard, concentration, control, and recovery components so teams can isolate the dominant driver of current risk. |
| Route Action Queue | Translates congestion and variance findings into a ranked execution queue with owners, due dates, and expected throughput lift. |
| Route Congestion Diagnostics | Decomposes corridor congestion into traffic friction, boarding pressure, intersection delay, and dispatch spacing effects with signed impact values. |
| Route Density Map | Presents a map-first view of route movement density, active vehicle concentration, and stop-load intensity across the operating network. |
| Service Gap Diagnostics | Focuses on why service gaps persist after routine dispatch and route changes, separating structural gaps from temporary operational noise. |
| Service Radius Optimizer | Optimizes service radius policies by balancing incremental demand capture against travel-time reliability and utilization constraints. |
| Site Comparison Explorer | Enables structured side-by-side comparison of selected sites across outcome metrics, operating inputs, and quality signals. |
| Site Driver Diagnostics | Decomposes site performance gaps into explicit demand, staffing, process, and asset reliability drivers with signed impact values. |
| Site Intervention Queue | Converts diagnostic and variance findings into an actionable queue of interventions with ranked priority, named owners, and expected business impact. |
| Stop Density Analyzer | Analyzes stop density, spacing variability, and boarding concentration to identify where stop patterns are too sparse, too dense, or unevenly loaded. |
| Target Variance Monitor | Monitors variance between plan targets and observed results for each location with deterministic escalation states. |
| Territory Action Queue | Converts diagnostic findings into a prioritized queue of territory interventions with owners, due dates, and expected impact. |
| Territory Performance Map | Presents a map-first operating view of quota attainment, run-rate, and risk across all active territories in the selected period. |
| Territory Rebalance Simulator | Simulates territory rebalance scenarios to estimate impact on quota equity, coverage load, and projected attainment before execution. |
| Throughput Variance Monitor | Tracks variance between planned and observed route throughput to detect deterioration before reliability and customer wait times worsen. |
Lifecycle Retention
| Tool | Description |
|---|---|
| Advisor Intervention Queue | Produces a deterministic intervention queue that prioritizes students requiring advisor outreach based on risk severity, intervention readiness, and timing sensitivity. |
| At-Risk Account Queue | Converts churn analytics into a deterministic intervention queue so frontline teams know exactly which accounts to engage, what intervention to apply, who owns each action, and the latest acceptable due date. |
| Attendance Outcome Analyzer | Quantifies the relationship between attendance behavior and persistence outcomes at course, section, and cohort levels. |
| Attrition Driver Diagnostics | Decomposes employee attrition into deterministic cause categories so teams can identify why exits are occurring, where exits are accelerating, and which drivers are economically and operationally material. |
| Cancel Reason Audit | Audits cancellation reasons for signal quality, consistency, and actionability so churn analysis is based on reliable evidence rather than inconsistent free-text coding. |
| Churn Driver Diagnostics | Decomposes subscription churn into deterministic drivers so teams can isolate whether losses are caused by onboarding friction, product value gaps, support instability, competitive pressure, pricing mismatch, or procurement constraints. |
| Renewal Variance Monitor | Tracks renewal performance against committed subscription plan assumptions and identifies where variance is accumulating by month, segment, and plan family. |
| Churn Scenario Simulator | Simulates deterministic churn outcomes under configurable intervention assumptions so leaders can compare realistic mitigation paths before allocating budget or setting quarterly commitments. |
| Cohort Retention Diagnostics | Diagnoses retention behavior by signup cohort, tenure band, product adoption profile, and contract model so teams can identify where persistence is deteriorating and why. |
| Cohort Risk Explorer | Maps persistence risk concentration across cohort definitions such as entry term, major family, aid status, residency, modality, and demographic segments. |
| Customer Retention Hub | Provides a unified retention command view that consolidates logo retention, gross revenue retention, net revenue retention, renewal attainment, and active risk concentration across segments and geographies. |
| Employee Retention Hub | Provides a unified command view for workforce retention by combining headcount retention, voluntary attrition, regrettable attrition, critical-role vacancy pressure, and manager-level risk concentration into one deterministic operating layer. |
| Exit Signal Analyzer | Analyzes deterministic pre-exit signals captured from engagement pulses, manager one-on-one completion, workload telemetry, internal application behavior, and compensation event timing to detect acceleration in exit propensity before resignation events occur. |
| Expansion Opportunity Tracker | Tracks expansion opportunities embedded in existing customers by combining retention health, product adoption depth, seat utilization, feature gap signals, and buying-center engagement. |
| Manager Intervention Queue | Converts attrition-risk analytics into a deterministic manager intervention queue that identifies which managers require immediate support, why they are flagged, what intervention should be executed, and the expected retention impact. |
| Mobility Impact Tracker | Tracks how internal mobility pathways influence retention, regrettable attrition reduction, skill redeployment, and manager stability across the enterprise. |
| Persistence Driver Diagnostics | Diagnoses which academic, behavioral, and financial factors most strongly influence term persistence for specific cohorts. |
| Plan Mix Retention Analyzer | Analyzes how subscription plan mix changes influence retention quality and churn exposure so pricing and packaging teams can make deterministic portfolio decisions. |
| Renewal Variance Monitor | Monitors renewal outcomes against committed operating plan assumptions and flags where variance is accumulating by segment, contract type, and renewal month. |
| Retention Action Queue | Converts retention risk analytics into a deterministic, account-level intervention queue that clarifies which accounts require immediate action, what action should be taken, who owns it, and by when it must be completed. |
| Retention Variance Monitor | Monitors retention execution against workforce plan assumptions and flags where variance is accumulating by function, location, and role criticality. |
| Risk Segment Explorer | Explores how churn and downgrade risk concentrates across customer segments, product tiers, geography, tenure bands, and engagement profiles. |
| Save Offer Simulator | Simulates the impact of save-offer strategies on renewal probability, gross margin, and net retained ARR so teams can choose interventions that maximize long-term value rather than short-term logo saves alone. |
| Student Retention Hub | Provides a unified retention command center that consolidates first-year retention, term-to-term persistence, stop-out incidence, credit momentum, and advisor capacity pressure into one deterministic operating layer. |
| Subscription Churn Hub | Provides a unified executive command center for subscription churn risk by combining logo churn, gross revenue churn, net revenue retention, renewal attainment, and segment-level risk concentration into one deterministic operating view. |
| Support Program Tracker | Tracks performance of student support programs such as tutoring, emergency aid, peer mentoring, learning communities, and first-year seminars against persistence and completion goals. |
| Tenure Risk Explorer | Explores how attrition risk concentrates across tenure bands, role families, manager cohorts, performance distributions, and location clusters. |
| Term Variance Monitor | Tracks variance between retention plan and observed outcomes across terms, colleges, and cohort bands. |
Monitoring Anomalies
| Tool | Description |
|---|---|
| Anomaly Detection Console | Centralizes anomaly scoring across key metrics so analysts can confirm whether a current signal is outside expected operating behavior. |
| Anomaly Triage Queue | Converts detected anomalies into a prioritized work queue with ownership, urgency, and disposition tracking. |
| Availability Incident Diagnostics | Focuses on active availability incidents with drill-down diagnostics that explain why uptime dropped and how fast impact is spreading. |
| Baseline Variance Monitor | Monitors variance drift between short-horizon and reference baselines to determine if detection parameters remain trustworthy. |
| Change Point Explorer | Identifies potential structural breaks in metric trajectories and supports evidence-based acceptance or rejection of each candidate break. |
| Data Issue Triage Queue | Converts quality detections into an execution queue with severity, ownership, and SLA timers for dependable incident throughput. |
| Data Quality Monitor Console | Aggregates freshness, completeness, and quality conformance indicators into a single operations surface for hourly monitoring. |
| Dependency Failure Map | Maps reliability of shared dependencies to downstream service availability so teams can quantify blast radius before failures cascade. |
| Downstream Impact Analyzer | Quantifies how active upstream quality issues propagate into downstream dashboards, machine-learning features, and operational decisions. |
| Error Budget Burn Tracker | Tracks error budget spend and burn rate to determine whether services are operating within reliability policy boundaries. |
| Event Correlation View | Maps temporal and topological relationships between alerts, logs, and metric anomalies to reveal correlated event clusters. |
| False Positive Audit | Audits closed anomaly cases to quantify false-positive patterns by metric, rule, team, and time window. |
| Freshness Completeness Diagnostics | Investigates whether each critical feed arrived on time and delivered expected record coverage for its scheduled batch window. |
| Live Alert Queue | Organizes incoming alerts into a deterministic queue with severity, freshness, assignment, and escalation state so on-call teams can process work in strict order. |
| Noise Reduction Tuner | Provides a controlled environment for testing suppression rules, deduplication windows, and threshold sensitivity against deterministic historical alert outcomes. |
| Outlier Cluster Diagnostics | Examines anomaly points as spatial and temporal clusters to determine whether outliers share a common operational mechanism. |
| Quality Variance Monitor | Monitors short-horizon quality variance against stable historical baselines to detect emerging drift before it becomes operationally severe. |
| Realtime Status Wall | Presents a continuously updated wallboard view of service availability, latency pressure, and regional degradation so operators can identify instability in seconds. |
| Recovery Time Analyzer | Analyzes recovery timelines to show where incident response and restoration workflows lose time. |
| Reliability Action Queue | Converts reliability findings into an execution-ready queue so teams can sequence remediation work with high operational leverage. |
| Schema Drift Detector | Detects structural and semantic schema changes between current source payloads and governed contract baselines. |
| Seasonality Break Detector | Detects when established seasonal patterns no longer explain observed behavior, signaling potential process or demand regime shifts. |
| Signal Variance Monitor | Tracks statistical variance shifts across high-frequency telemetry streams to detect unstable operating regimes before hard failures occur. |
| SLO Variance Monitor | Tracks variance between observed reliability performance and committed SLO objectives across services and customer journeys. |
| Source Reliability Tracker | Tracks data source reliability using delivery timeliness, failure incidence, retry behavior, and recovery performance metrics. |
| Stream Health Tracker | Monitors streaming pipeline reliability through partition lag, throughput balance, consumer commit behavior, and dropped-message risk indicators. |
| Threshold Breach Diagnostics | Focuses on breach-first triage by ranking active violations against latency, error-rate, and throughput thresholds with deterministic severity scoring. |
| Uptime Reliability Console | Consolidates service uptime, incident pressure, and reliability trend signals into a single operating console for daily review. |
Operational Control
| Tool | Description |
|---|---|
| Aging Inventory Audit | Audits aging inventory to identify excess and obsolescence exposure by age bucket, SKU class, and location. |
| Allocation Replan Simulator | Simulates deterministic reallocation scenarios that rebalance constrained supply across channels, regions, and priority tiers. |
| Carrier Performance Audit | Audits carrier execution quality using deterministic service and cost records across lanes, service classes, and claim categories. |
| Cost-to-Serve Tracker | Tracks cost-to-serve performance by combining transportation spend, handling cost, expedite leakage, claim burden, and customer-specific service overhead into a deterministic lane view. |
| Delivery Variance Monitor | Monitors delivery promise adherence by quantifying variance between committed delivery windows and actual drop-off completion times across customer tiers, channels, and service classes. |
| Dependency Risk Map | Maps deterministic dependency networks across projects to show where upstream slippage, vendor uncertainty, and environment readiness can propagate into milestone failures. |
| Dispatch Action Queue | Converts live exception signals into a deterministic dispatch action queue prioritized by service risk, customer impact, and time-to-deadline urgency. |
| Escalation Path Analyzer | Evaluates deterministic escalation pathways from initial incident declaration through managerial, specialist, and executive decision nodes. |
| Expedite Action Queue | Consolidates expediting interventions into a deterministic ranked backlog based on service risk, due-date pressure, financial impact, and unblock readiness. |
| Incident Control Tower | Provides a deterministic command view of active and recently resolved incidents across severity, customer impact, containment posture, escalation pressure, and restoration confidence. |
| Incident Flow Diagnostics | Decomposes incident throughput from alert intake to closure by measuring deterministic transition times between acknowledge, diagnose, contain, resolve, and verify stages. |
| Intervention Queue | Consolidates high-priority project interventions into a deterministic queue ranked by urgency, value at risk, due-date proximity, and execution confidence. |
| Inventory Control Tower | Provides a unified operating view of inventory health across plants, distribution centers, and channels by combining on-hand position, days of supply, stockout risk, overstock burden, and service-level attainment into a deterministic command layer. |
| Lead-Time and Fulfillment Diagnostics | Decomposes late fulfillment outcomes into deterministic drivers spanning supplier delay, customs hold, plant release latency, allocation lag, and carrier handoff slippage. |
| Logistics Control Tower | Provides a single-screen operational command view across active shipments, lane status, delay exposure, dispatch workload, and same-day service risk using deterministic seeded records. |
| Milestone Blocker Diagnostics | Decomposes milestone slippage into deterministic blocker classes such as dependency wait, scope churn, environment instability, approval latency, and staffing shortfall. |
| Node Bottleneck Map | Maps deterministic bottleneck pressure across critical network nodes by comparing planned capacity, realized throughput, queue buildup, and downstream service impact. |
| OTIF Variance Monitor | Tracks deterministic OTIF performance against commitments and highlights where variance is persistent enough to require escalation. |
| Postmortem Follow-up Tracker | Tracks deterministic execution of postmortem commitments from action definition through owner assignment, due-date governance, validation, and closure evidence. |
| Project Control Tower | Provides a deterministic operational command view for active projects across schedule, budget, milestone confidence, dependency health, and escalation load. |
| Delivery Variance Monitor | Tracks deterministic variance between approved baseline and current forecast across schedule, cost, scope completion, and milestone attainment. |
| Recovery Plan Simulator | Simulates deterministic recovery plans that rebalance scope, staffing, sequencing, and contingency usage to restore milestone commitments. |
| Replenishment Action Queue | Organizes replenishment interventions into a deterministic execution queue based on service risk, economic impact, and action urgency. |
| Resolution Variance Monitor | Tracks deterministic variance between actual incident resolution duration and committed restoration targets across severity tiers, services, and incident archetypes. |
| Resource Conflict Tracker | Tracks deterministic resource allocation conflicts where critical roles are overcommitted across projects, phases, and delivery windows. |
| Response Action Queue | Prioritizes deterministic response actions across active incidents by balancing urgency, customer impact reduction potential, dependency readiness, and execution effort. |
| Root Cause Cluster Map | Maps incident root causes into deterministic clusters so teams can identify recurring systemic failure patterns rather than treating each event as isolated. |
| Route Delay Diagnostics | Diagnoses route-level delay accumulation by decomposing lateness into departure slippage, transit variance, transfer dwell overages, and final-mile execution misses. |
| Route Efficiency Analyzer | Analyzes route efficiency by linking miles traveled, load utilization, stop productivity, and cycle-time outcomes for each lane and route template. |
| Safety Stock Simulator | Simulates the effect of safety-stock policy changes on service reliability, stockout probability, and working-capital investment under deterministic demand and lead-time assumptions. |
| Service Level Variance | Tracks service-level performance against target across channels, customers, and product tiers to surface where fulfillment reliability is drifting outside acceptable limits. |
| Stockout and Overstock Diagnostics | Diagnoses simultaneous stockout and overstock conditions by isolating where forecast error, MOQ constraints, replenishment delay, and network allocation rules create inventory mismatch at SKU-site level. |
| Supplier Delay Impact | Quantifies how supplier lead-time delays propagate through inventory positions, customer service levels, and expedite costs at SKU and supplier lane granularity. |
| Supplier Risk Tracker | Tracks deterministic supplier risk exposure by combining punctuality performance, quality incidents, financial stress signals, and concentration dependency in one repeatable monitoring frame. |
| Supply Chain Control Tower | Provides the deterministic command view of network health by combining supplier lead-time adherence, node throughput, OTIF attainment, expedite burden, and backlog pressure into a single operational layer. |
Performance Diagnostics
| Tool | Description |
|---|---|
| Agent Performance Explorer | Explores agent-level performance by combining productivity, quality, and workload fairness diagnostics in a unified review interface. |
| Attribution Gap Review | Reviews attribution gaps by comparing deterministic credit assignment outcomes across first-touch, last-touch, linear, and position-based models. |
| Backlog Aging Diagnostics | Diagnoses backlog pressure by age band, queue, issue type, and assignment state to reveal where work is accumulating faster than resolution throughput. |
| Channel & Campaign Drilldown | Enables drilldown from aggregate marketing outcomes into channel and campaign-level efficiency, conversion quality, and contribution diagnostics. |
| Corrective Action Queue | Presents a prioritized queue of corrective and preventive actions ranked by quality risk, regulatory exposure, and closure urgency. |
| Creative Performance Explorer | Explores creative-level performance by linking message themes, formats, and audience segments to downstream conversion and revenue influence. |
| CSAT Driver Analysis | Analyzes customer satisfaction outcomes to isolate controllable drivers across response timeliness, resolution quality, communication clarity, and follow-up effectiveness. |
| Deal Velocity Diagnostics | Diagnoses deal velocity by decomposing cycle time across pipeline stages, regions, and deal-size bands. |
| Defect Source Drilldown | Enables targeted drilldown from aggregate defect rates into source-level diagnostics by line, station, shift, product variant, and defect category. |
| Funnel Stage Diagnostics | Diagnoses marketing funnel performance by stage using deterministic conversion, velocity, and leakage metrics from lead capture through closed-won influence. |
| Intervention Action Queue | Presents a prioritized queue of portfolio interventions ranked by schedule risk, value at risk, governance urgency, and dependency criticality. |
| Marketing Performance Overview | Provides a single-screen diagnostic view of marketing performance with deterministic KPI tiles for spend, pipeline contribution, sourced revenue, CAC, and ROI across the selected reporting period. |
| Milestone Slippage Review | Reviews milestone slippage across programs and projects by comparing planned versus actual completion, critical path movement, and gate-readiness quality. |
| Optimization Action Queue | Presents a prioritized queue of campaign optimization actions ranked by expected pipeline impact, urgency, and implementation effort. |
| Pipeline Action Queue | Presents a prioritized queue of in-flight opportunities requiring action, ranked by close-date risk, slippage probability, and forecast impact. |
| Portfolio Performance Overview | Provides a single-screen diagnostic summary of portfolio performance with deterministic KPI cards for strategic initiative attainment, on-time milestone rate, budget adherence, risk exposure, and value capture against plan. |
| Quality Performance Overview | Provides a single-screen quality diagnostics summary with deterministic KPI cards for first-pass yield, defect rate per thousand units, customer returns rate, corrective action closure rate, and cost of poor quality. |
| Quota Variance Diagnostics | Breaks down quota variance by region, segment, and rep cohort to show where attainment misses are structural versus execution-related. |
| Rep & Segment Drilldown | Enables manager-led drilldown from regional outcomes into rep and segment-level performance with conversion and cycle diagnostics. |
| Resolution Variance Diagnostics | Decomposes resolution-time variance by queue, issue category, priority band, and escalation path. |
| Resource Mix Analysis | Analyzes portfolio resource mix to determine whether labor, contractor, platform, and vendor allocations are aligned with delivery goals and value outcomes. |
| Rework Cost Analysis | Quantifies rework cost burden by defect type, line, shift, and product family, linking quality losses to labor, scrap, downtime, and expedited handling components. |
| ROI Variance Diagnostics | Decomposes marketing ROI variance by channel, campaign objective, audience tier, and conversion stage to show where returns diverge from plan assumptions. |
| Sales Performance Overview | Provides a single-screen diagnostic overview of sales performance with headline tiles for bookings, quota attainment, win rate, average sales cycle, and pipeline coverage. |
| Service Performance Overview | Provides a single-screen diagnostic summary of service operations with deterministic KPI tiles for ticket volume, first response SLA attainment, median time to resolution, backlog pressure, escalation rate, and CSAT. |
| SLA Breach Drilldown | Enables focused drilldown on SLA breaches by queue, ticket priority, support channel, and shift window. |
| SPC Signal Explorer | Explores statistical process control signals across critical quality characteristics, surfacing special-cause variation and rule violations with deterministic chart-ready datasets. |
| Supplier Quality Breakdown | Breaks down supplier quality performance across incoming defect rates, lot acceptance outcomes, response timeliness, and corrective action effectiveness. |
| Target Variance Diagnostics | Decomposes portfolio target variance across business units, initiative classes, and delivery phases to quantify where misses are structural versus temporary. |
| Territory Gap Explorer | Explores territory-level performance gaps by comparing quota, pipeline, conversion, and account coverage against potential market demand. |
| Ticket Action Queue | Presents a prioritized queue of active tickets requiring intervention based on SLA risk, customer impact, escalation probability, and dependency blockers. |
| Unit & Project Drilldown | Enables deterministic drilldown from portfolio outcomes into business-unit, program, and project-level delivery performance. |
| Value Realization Tracker | Tracks value realization across the portfolio by measuring approved business case benefits, realized outcomes to date, confidence of future capture, and timing of benefit ramp. |
| Win/Loss Driver Analysis | Analyzes won and lost opportunities to isolate outcome drivers across pricing, product fit, competition, response speed, and stakeholder engagement. |
| Yield Variance Diagnostics | Decomposes first-pass yield variance by line, shift, product family, and defect mechanism to quantify where target misses are structural versus temporary. |
Pipeline Funnel
| Tool | Description |
|---|---|
| Activation Variance Monitor | Monitors plan-versus-actual activation performance across onboarding stages, segments, and motion types, with explicit decomposition of where variance pressure is accumulating. |
| Backlog Pressure Audit | Audits deterministic backlog pressure by combining inflow-outflow imbalance, aging distribution, severity-weighted exposure, and available handling capacity. |
| Campaign Follow-Up Queue | Produces an ordered, deterministic action queue of campaigns and lead cohorts requiring immediate follow-up, ranked by expected pipeline recovery and SLA breach risk. |
| Candidate Stage Diagnostics | Isolates stage-by-stage loss and delay signals so recruiting teams can identify whether conversion friction is driven by qualification mismatch, interviewer latency, compensation misalignment, or candidate experience issues. |
| Channel Conversion Audit | Audits conversion quality across paid, owned, partner, and organic channels with an emphasis on consistency between volume generation and downstream qualification outcomes. |
| Cohort Funnel Analyzer | Analyzes funnel outcomes by acquisition cohort to reveal whether conversion quality is improving, decaying, or remaining stable as campaigns scale. |
| Conversion Variance Monitor | Monitors conversion performance versus plan across each funnel step, decomposing variance into volume mix, response-time effects, and campaign-quality effects. |
| Creative Drop-Off Inspector | Diagnoses where individual creatives lose audience quality between click, inquiry, and qualified lead stages, helping teams separate high-CTR but low-intent assets from creatives that sustain downstream conversion quality. |
| Customer Follow-Up Queue | Produces a deterministic action queue for account-level outreach by combining onboarding stage, inactivity age, milestone risk, expansion potential, and assigned owner capacity. |
| Deal Followup Queue | Converts pipeline risk analysis into a deterministic follow-up queue so managers and reps can execute the highest-impact deal actions first. |
| Escalation Path Inspector | Evaluates the deterministic performance of escalation pathways from frontline queues to specialist teams, focusing on transfer delay, loopback frequency, ownership clarity, and resolution quality after escalation. |
| Forecast Commit Inspector | Evaluates forecast commit integrity by reconciling committed deals against stage readiness, historical slippage patterns, and confidence-scoring rules. |
| Friction Point Inspector | Identifies high-friction checkpoints in the onboarding experience by combining event completion, retry frequency, abandonment signals, and support touchpoint demand. |
| Intake to Resolve Diagnostics | Isolates where tickets stall, reroute, or exit expected workflows between intake and resolution, allowing teams to pinpoint whether losses are driven by classification accuracy, assignment latency, dependency wait states, or escalation routing friction. |
| Lead Stage Leakage Diagnostics | Isolates and ranks leakage by stage transition so operators can determine whether conversion loss is driven by audience fit, lead quality, response latency, routing issues, or qualification criteria drift. |
| Marketing Funnel Tracker | Provides a deterministic, executive-grade view of the entire demand funnel from first inquiry to closed won, combining stage volumes, stage-to-stage conversion rates, influenced pipeline value, and elapsed time to progression in a single operating screen. |
| Offer Acceptance Audit | Audits offer outcomes to identify acceptance risk drivers across compensation competitiveness, decision latency, candidate seniority, and competing-offer pressure. |
| Onboarding Funnel Tracker | Provides an executive-grade, deterministic view of customer onboarding progression from signed account through kickoff scheduling, workspace setup, first key action, and verified activation. |
| Persona Path Analyzer | Compares onboarding path performance across buyer and end-user personas to reveal where each group experiences different completion behavior, timing, and activation quality. |
| Pipeline Quality Audit | Audits pipeline record quality to ensure opportunities used in forecast and funnel analytics meet defined completeness, consistency, and timeliness standards. |
| Pipeline Velocity Analyzer | Measures pipeline velocity by combining stage dwell time, stage throughput, and transition probability into a unified flow-efficiency view. |
| Queue Mix Analyzer | Decomposes queue volume into deterministic mix components so teams can understand whether pressure is caused by demand growth, case-complexity shift, channel-routing change, or service-policy updates. |
| Recruiter Action Queue | Converts funnel risk diagnostics into a deterministic recruiter work queue ranked by time sensitivity, candidate quality, requisition criticality, and expected contribution to near-term hires. |
| Conversion Variance Monitor | Monitors conversion plan-versus-actual outcomes across recruiting stages and quantifies hiring impact attributable to each stage variance. |
| Recruiting Funnel Tracker | Provides an executive-grade, deterministic view of the end-to-end recruiting funnel across requisitions, from initial application volume to accepted offers. |
| Conversion Variance Monitor | Monitors conversion variance by stage, region, and segment against committed operating plan assumptions, with explicit decomposition into favorable and unfavorable contributors. |
| Sales Pipeline Funnel | Provides an executive view of the full opportunity funnel from lead qualification through closed won, combining stage counts, weighted pipeline value, conversion rates, and average days-in-stage in one deterministic operating view. |
| SLA Variance Monitor | Monitors plan-versus-actual SLA performance across severity tiers, support queues, and handoff stages, with explicit decomposition of where breach pressure is accumulating. |
| Source Quality Analyzer | Audits candidate source performance across referral, job board, outbound, campus, and agency channels, with emphasis on downstream quality, interview progression, and accepted-offer yield instead of top-of-funnel volume alone. |
| Stage Leakage Diagnostics | Isolates where and why opportunities leak between pipeline stages by decomposing conversion loss into volume decay, qualification mismatch, pricing friction, and legal/procurement delay signals. |
| Step Completion Diagnostics | Isolates where customers fail to complete onboarding milestones so teams can determine whether drop-off is driven by missing configuration data, delayed stakeholder response, unresolved integration prerequisites, or unclear in-product guidance. |
| Support Queue Funnel | Provides an executive-grade, deterministic view of the full support lifecycle from new intake through triage, assignment, active work, escalation, and resolved closure. |
| Ticket Triage Queue | Converts queue risk signals into a deterministic triage worklist ranked by severity, age, customer impact, and predicted breach proximity. |
| Time-to-Fill Inspector | Inspects time-to-fill performance across requisitions and stage segments to identify where hiring cycle duration exceeds staffing commitments. |
| Time to Value Audit | Audits whether customers reach first measurable business value within committed onboarding windows, and quantifies where delays are concentrated by segment, implementation type, and value milestone. |
Planning Forecasting
| Tool | Description |
|---|---|
| Allocation Action Queue | Centralizes allocation interventions into a deterministic queue ranked by customer impact, due-date urgency, and expected capacity recovery. |
| Budget Action Queue | Centralizes budget interventions into a deterministic action queue sorted by financial impact, deadline risk, and owner accountability. |
| Budget Plan Control Center | Provides a single control surface for budget plan governance with deterministic KPI cards, period trend lines, and department status indicators aligned to monthly operating cadence. |
| Budget Timeline Variance | Quantifies deterministic variance between planned and actual project performance across budget consumption and timeline adherence in a single integrated bridge. |
| Capacity Gap Variance | Quantifies the gap between required demand load and feasible capacity across plants, lines, and shift structures using a deterministic bridge from nominal capacity to realized output. |
| Cash Action Queue | Centralizes liquidity interventions into a deterministic action queue sorted by impact, due-date risk, and controllability to support daily treasury execution. |
| Cash Scenario Simulator | Simulates deterministic what-if cash trajectories under configurable collection, disbursement, financing, and macro-stress assumptions. |
| Cashflow Planning Console | Provides a single operating console for cash flow governance with deterministic KPI cards, multi-week trend context, and legal-entity cash posture indicators aligned to treasury cadence. |
| Constraint Impact Simulator | Simulates deterministic impact of discrete constraints such as supplier shortages, line downtime, labor deficits, and logistics disruptions on fulfillment and revenue. |
| Cost Category Diagnostics | Decomposes budget outcomes by category, department, and supplier concentration to identify which spend classes are driving unfavorable plan movement. |
| Critical Path Projection Panel | Projects deterministic critical path timelines by combining current task progress, dependency readiness, and risk-adjusted duration assumptions. |
| Demand Capacity Console | Provides a single planning console that aligns demand outlook, available capacity, and service-level risk in one deterministic operating view for weekly execution cadence. |
| Demand Driver Diagnostics | Decomposes demand changes into explicit drivers such as promotions, pricing, channel mix, and regional seasonality so teams can identify what is materially changing the short-term load on constrained capacity. |
| Department Commitment Tracker | Tracks department-level budget commitments against approved targets with deterministic visibility into pledge quality, delivery timing, and closure confidence. |
| Dependency Risk Planner | Converts cross-project dependency exposure into deterministic mitigation plans with quantified schedule risk reduction and owner accountability. |
| Headcount Variance Monitor | Quantifies deterministic headcount variance from approved plan to current and forecasted staffing, using a bridge that isolates hiring, attrition, transfers, and organizational change effects. |
| Hiring Scenario Simulator | Simulates deterministic hiring strategies under configurable assumptions for offer acceptance, recruiter throughput, compensation pressure, and onboarding capacity. |
| Inflow Outflow Diagnostics | Decomposes cash movement into inflow and outflow drivers across customer segments, payment channels, and disbursement classes to expose root causes of cash volatility. |
| Labor Cost Capacity Planner | Aligns labor cost and workforce capacity decisions in a deterministic planning model that balances staffing mix, productivity assumptions, and budget constraints. |
| Liquidity Variance Monitor | Tracks liquidity headroom versus plan and policy floors across rolling daily and weekly horizons. |
| Plan vs Actual Variance | Quantifies budget variance between plan and actuals with a deterministic bridge from baseline assumptions to observed spend outcomes. |
| Project Action Queue | Consolidates project remediation tasks into a deterministic queue ranked by delivery impact, deadline proximity, and recovery feasibility. |
| Project Planning Console | Provides a single deterministic command view for project delivery health across schedule, effort, budget, and milestone confidence in one governance-ready surface. |
| Role Supply-Demand Diagnostics | Breaks workforce pressure into role-by-role supply versus demand detail so planners can pinpoint where shortages are concentrated and whether internal mobility can close the gap. |
| Run-Rate Projection Panel | Projects year-end budget outcomes from observed run-rate and seasonality assumptions, allowing early intervention before variance becomes non-recoverable. |
| Runway Projection Panel | Projects deterministic cash runway by combining opening liquidity, expected net burn, and financing availability over monthly horizons. |
| Scenario Capacity Optimizer | Simulates deterministic what-if scenarios that re-balance demand and capacity using levers such as overtime, subcontracting, line re-sequencing, and allocation policy changes. |
| Scenario Rebaseline Simulator | Simulates deterministic rebaseline scenarios that adjust scope, sequencing, staffing intensity, and contingency allocation to restore schedule confidence. |
| Schedule Effort Diagnostics | Decomposes schedule and effort variance into actionable root causes such as scope churn, dependency wait time, staffing shortfall, and rework intensity. |
| Seasonality Projection Panel | Projects deterministic seasonal demand curves across future periods to expose upcoming load peaks and troughs before they convert into service failure risk. |
| Skill Gap Projection Panel | Projects deterministic skill supply versus projected skill demand across future periods to identify capability deficits before critical initiatives enter execution. |
| Staffing Action Queue | Consolidates workforce interventions into a deterministic action queue ranked by delivery impact, skill criticality, and due-date urgency. |
| What-If Budget Scenarios | Supports deterministic what-if simulation for budget reallocation across departments, categories, and initiatives under policy and capacity constraints. |
| Workforce Planning Console | Delivers a single deterministic command view for workforce supply, approved demand, open requisitions, and labor-capacity coverage across the planning horizon. |
| Working Capital Planner | Converts working-capital levers into deterministic cash impact plans across receivables, payables, and inventory with owner-based execution tracking. |
Risk Compliance
| Tool | Description |
|---|---|
| Audit Follow-Up Queue | Centralizes all active follow-up actions from internal audit findings into a deterministic, prioritized execution queue that supports daily coordination and weekly escalation review. |
| Closure Variance Monitor | Tracks deterministic variance between planned and actual closure outcomes for internal audit findings, with explicit linkage to due dates, evidence sufficiency, and residual risk reduction. |
| Committee Effectiveness Tracker | Tracks deterministic committee effectiveness outcomes across participation, decision throughput, action closure, and documentation quality dimensions. |
| Compliance Control Hub | Serves as the canonical executive summary for compliance controls with deterministic snapshots of control inventory, ownership coverage, testing outcomes, and evidence completeness by framework and business process. |
| Compliance Variance Monitor | Tracks deterministic variance between committed compliance plans and actual delivery performance across control testing, issue closure, evidence collection, and policy attestation milestones. |
| Control Effectiveness Analyzer | Evaluates control health across design adequacy, operating performance, test outcomes, and issue recurrence with deterministic scoring at control and process levels. |
| Control Gap Diagnostics | Decomposes compliance control gaps into deterministic drivers across frameworks, obligations, process steps, and operating entities so teams can isolate root causes behind weak control coverage and recurring assurance findings. |
| Control Maturity Analyzer | Assesses deterministic cyber control maturity across design, implementation, operating effectiveness, and evidence sustainability to identify where control stacks remain fragile despite policy compliance claims. |
| Cyber Risk Command Center | Provides the canonical operating view for cyber risk management with deterministic visibility into current exposure, unresolved vulnerabilities, overdue patch obligations, and open security actions. |
| Decision Latency Analyzer | Measures deterministic governance decision-cycle performance from submission through committee review and formal resolution. |
| Enterprise Risk Register | Provides the canonical enterprise risk register with deterministic scoring for impact, likelihood, control maturity, and residual risk to support board and committee governance cycles. |
| Escalation Compliance Audit | Audits deterministic escalation records against governance protocol requirements, including trigger criteria, notification timelines, approval authority, and closure evidence standards. |
| Finding Theme Diagnostics | Decomposes internal audit findings into deterministic themes, sub-themes, and root-cause drivers so teams can identify systemic control breakdowns rather than treating each finding as an isolated event. |
| Governance Action Queue | Centralizes deterministic governance actions into a ranked execution queue so teams can triage policy, committee, and escalation follow-up tasks by urgency, governance impact, and dependency readiness. |
| Governance Control Board | Provides the canonical operating view for enterprise governance with deterministic visibility into active policies, adherence posture, committee obligations, and unresolved governance exceptions. |
| Incident Impact Tracker | Tracks deterministic business impact of cyber incidents across detection-to-recovery stages, including service disruption, customer effect, regulatory exposure, and cost accumulation. |
| Internal Audit Command Center | Provides the canonical operating view for the internal audit function with deterministic visibility into audit plan progress, active engagements, open findings, and closure risk. |
| Issue Severity Heatmap | Maps internal audit issue severity concentration across control domains and business entities using deterministic severity and exposure scoring to surface where aggregate assurance risk is accumulating. |
| Mitigation Variance Monitor | Monitors mitigation initiative execution against approved plan with deterministic tracking of milestone adherence, spend-to-plan, and realized residual-risk reduction for each high-priority risk theme. |
| Oversight Variance Monitor | Tracks deterministic variance between planned governance oversight commitments and actual completion outcomes across committee deliverables, policy attestations, escalation handling, and decision documentation timeliness. |
| Owner Timeliness Analyzer | Evaluates deterministic closure timeliness performance for remediation owners to identify consistent delivery strengths, chronic delays, and escalation hotspots. |
| Patch Variance Monitor | Tracks deterministic variance between committed patch plans and actual deployment outcomes, with explicit linkage to risk reduction objectives, maintenance windows, and SLA obligations. |
| Policy Adherence Diagnostics | Decomposes policy adherence into deterministic drivers across policy families, business units, attestation cycles, and control evidence quality so governance teams can isolate where non-adherence originates. |
| Policy Coverage Analyzer | Analyzes deterministic policy coverage by mapping policy clauses to implemented controls, evidence artifacts, and ownership accountability across regulatory frameworks. |
| Remediation Action Queue | Provides a deterministic, prioritized queue of remediation actions tied to open findings, control deficiencies, and policy exceptions. |
| Repeat Finding Tracker | Tracks deterministic recurrence of previously reported findings to identify persistent control weaknesses that survive one or more remediation cycles. |
| Risk Action Queue | Centralizes open risk actions into a deterministic queue ranked by residual exposure, due-date pressure, and control dependency criticality for daily execution management. |
| Risk Exposure Diagnostics | Decomposes enterprise risk exposure into deterministic contributors by business unit, geography, risk type, and control environment maturity so teams can isolate concentrated risk pockets. |
| Risk Heatmap Explorer | Visualizes enterprise risks on a deterministic impact-likelihood heatmap with overlays for residual score, control strength, and mitigation status to support prioritization and escalation decisions. |
| Risk Scenario Simulator | Simulates deterministic enterprise risk outcomes under configurable macro, operational, and control-disruption assumptions to quantify potential exposure range and resilience capacity. |
| Security Action Queue | Centralizes deterministic security action routing so vulnerability, detection, hardening, and incident follow-up tasks can be prioritized by risk, urgency, and dependency readiness. |
| Threat Surface Explorer | Maps deterministic threat surface exposure across internet-facing assets, identity trust paths, cloud entry points, and third-party connections so teams can understand where structural attack opportunity is expanding faster than control coverage. |
| Vulnerability Exposure Diagnostics | Decomposes vulnerability exposure into deterministic drivers across asset criticality, exploit availability, internet reachability, and compensating control strength so teams can isolate where technical debt creates disproportionate business risk. |
Status Scorecards
| Tool | Description |
|---|---|
| Adoption & Engagement Scorecard | Provides a focused scorecard for end-to-end adoption and engagement performance across activation funnel stages, feature depth, and repeat-use stability. |
| Attrition Risk Brief | Produces a concise attrition risk brief with segment-level risk concentrations, top predicted drivers, and intervention recommendations by owner. |
| Board Metric Briefing | Structures board-level metrics into three zones: growth, resilience, and execution, each with current performance, quarter-over-quarter delta, and commentary cues. |
| Budget Attainment Board | Displays budget attainment as a board-style grid with rows by department and columns for spend, revenue, headcount cost, and discretionary programs. |
| Capacity Bottleneck Brief | Summarizes the highest-impact bottlenecks across lines, stations, and support resources with explicit quantification of lost throughput and queue accumulation. |
| Cash & Margin Scorecard | Combines margin quality and cash realization signals into a compact scorecard with side-by-side cards for gross margin, contribution margin, operating cash flow margin, and cash conversion ratio. |
| Downtime Impact Panel | Quantifies the operational and service impact of planned and unplanned downtime across critical assets, translating stoppage minutes into lost output, delayed orders, and cost exposure. |
| Engagement Signal Rollup | Consolidates engagement signals from pulse surveys, participation behavior, manager effectiveness indicators, and wellbeing flags into a single rollup. |
| Executive KPI Cockpit | Presents a single-screen executive cockpit with a top KPI ribbon (revenue, gross margin, EBITDA, NPS, on-time delivery), a trend panel for the last 8 periods, and a status grid grouped by business pillar. |
| Expense Efficiency Brief | Summarizes expense efficiency through a structured brief that compares spend levels against output indicators such as revenue, customers served, and project throughput. |
| Feature Goal Attainment | Organizes feature initiatives by objective and release train, pairing goal definitions with measured outcome attainment and confidence in sustained impact. |
| Finance Alert Digest | Aggregates finance-specific exceptions into a daily digest covering cash floor breaches, overdue receivables spikes, margin drops, and budget overrun alerts. |
| Finance KPI Cockpit | Presents a one-screen finance control tower with headline tiles for revenue, gross margin, operating expense ratio, free cash flow, and net working capital days. |
| Forecast Confidence Panel | Provides a confidence-centered forecast panel that scores forecast quality across revenue, margin, cash flow, and expense lines using bias and accuracy diagnostics. |
| Headcount Capacity Scorecard | Provides a focused scorecard for workforce capacity using side-by-side views of approved positions, filled positions, vacancy load, and critical-role coverage. |
| Hiring Velocity Panel | Tracks end-to-end hiring execution across requisition aging, stage conversion, time-to-fill, and offer acceptance quality in a single operational panel. |
| Initiative Health Rollup | Aggregates major strategic initiatives into a unified health rollup, blending schedule confidence, budget burn, risk load, and dependency exposure. |
| KPI Alert Digest | Provides an alert-centric dashboard that aggregates breached KPI thresholds, trend-break anomalies, and stale owner updates into a single digest. |
| Operations KPI Cockpit | Presents an operations control-tower view that consolidates throughput, first-pass yield, schedule adherence, service level attainment, and unplanned downtime into a single, scan-first cockpit. |
| Ops Alert Digest | Consolidates operational exceptions into a daily digest covering throughput shortfalls, quality excursions, downtime spikes, and service-level breaches. |
| People Alert Digest | Aggregates people-related exceptions into a daily digest covering attrition spikes, hiring SLA breaches, engagement drops, policy compliance gaps, and manager-risk anomalies. |
| People KPI Cockpit | Presents a single-screen workforce cockpit with headline tiles for total headcount, regrettable attrition, hiring plan attainment, engagement index, and manager span-of-control stability. |
| Product Alert Digest | Aggregates product-related alert events into a daily digest covering adoption drops, engagement anomalies, reliability regressions, and conversion deterioration. |
| Product KPI Cockpit | Presents a single-screen product cockpit that consolidates active adoption, engagement depth, release reliability, trial-to-paid conversion, and retained revenue influence into one scan-first view. |
| Quarterly Variance Brief | Summarizes quarter-level performance variance against plan, forecast, and prior-year comparables with explicit driver decomposition. |
| Release Impact Rollup | Synthesizes release-level outcomes into a rollup that compares expected impact versus observed impact across adoption lift, engagement quality, reliability side effects, and monetization contribution. |
| Reliability Health Board | Tracks reliability health using a board layout that combines service availability, incident burden, defect escape, and mean time to recovery by product surface. |
| Retention Health Brief | Delivers a concise retention brief that combines gross retention, net retention influence, churn-risk concentration, and save-program effectiveness. |
| Service Level Board | Tracks service-level attainment across order-to-delivery and case-resolution commitments, with explicit segmentation by channel, region, and customer tier. |
| Shift Performance Rollup | Aggregates shift-level performance across safety, quality, throughput, and adherence dimensions to provide a consistent view of crew execution quality. |
| Strategic Goal Scorecard | Organizes strategic goals by pillar and objective, pairing lagging outcomes with leading milestone indicators in one scorecard. |
| Talent Health Board | Tracks talent health using a board layout that combines capability depth, internal mobility, succession coverage, and performance distribution indicators. |
| Target Commitment Tracker | Tracks committed targets by owner, period, and confidence, highlighting where commitments repeatedly miss delivery windows. |
| Throughput & Utilization Scorecard | Provides a focused scorecard for line throughput and asset utilization, combining achieved output, planned capacity, and utilization dispersion by line, shift, and product family. |
| Working Capital Watch | Focuses on the three working-capital levers (DSO, DPO, DIO) with a composite cash-cycle indicator and directional signal badges. |
Interactive
Area
| Tool | Description |
|---|---|
| Area Chart | Interactive area chart based on Observable Plot’s area-chart gallery example, plotting stock closing price by date with an area fill and overlaid line. |
| Area Chart with Missing Data | Interactive area chart adapted from Observable Plot’s plot-area-chart-missing-data gallery example, showing a weekly closing-price series with missing values at the beginning of the timeline. |
| Area Chart with Gradient Fill | Interactive area chart adapted from Observable Plot’s area-chart-with-gradient example, showing weekly closing prices over time with a gradient-filled area under the series and a line overlay for trend clarity. |
| Area + Line Custom Mark | Interactive area-and-line chart adapted from Observable Plot’s plot-arealiney-custom-mark gallery example, implemented as a custom composite mark that combines a zero baseline rule, an area fill, and a line over weekly closing prices. |
| Burndown Chart | Interactive burndown chart adapted from Observable Plot’s plot-burndown-chart gallery example, showing daily open issues as a stacked area where each issue contributes to every day from creation until close (or the selected burn date). |
| Difference Chart | Interactive difference chart adapted from Observable Plot’s plot-difference-chart gallery example, comparing monthly temperatures between New York and San Francisco and shading the gap between the two series. |
| Faceted Area Chart | Interactive faceted area chart adapted from Observable Plot’s plot-faceted-areas example, showing monthly unemployment trends as small-multiple area panels split by industry. |
| Horizon Chart of Unemployment by Industry | Interactive horizon chart adapted from Observable Plot’s plot-unemployment-horizon-chart gallery example, showing monthly unemployment trends as faceted horizon bands for multiple industries. |
| Urban Traffic Horizon Bands | Interactive horizon chart adapted from Observable Plot’s plot-horizon gallery example, showing hourly vehicles-per-hour patterns as faceted horizon bands across multiple transit routes. |
| Normalized Stack: Music Revenue Mix | Interactive normalized stacked area chart adapted from Observable Plot’s plot-normalized-stack gallery example, showing each format’s share of annual music revenue so every year sums to 100%. |
| Population Pyramid by Marital Status | Interactive population pyramid adapted from Observable Plot’s plot-population-pyramid gallery example, showing mirrored male and female age distributions where male values render on the left and female values on the right. |
| Proportion Plot Across Workforce Measures | Interactive proportion plot adapted from Observable Plot’s plot-proportion-plot gallery example, using stacked flowing areas to show how age-group shares change across four measures: population, labor force, employed, and full-time workers. |
| Ribbon Chart: U.S. Recorded Music Revenue | Interactive ribbon-style stacked area chart adapted from Observable Plot’s plot-ribbon-chart gallery example, showing annual U.S. |
| Stacked Area Unemployment Trends | Interactive stacked area chart adapted from Observable Plot’s plot-stacked-area-chart gallery example, showing monthly unemployment levels by industry as stacked layers over time. |
| Variable Fill Area | Interactive area chart adapted from Observable Plot’s plot-variable-fill-area gallery example, showing weekly closing price over time with area fill intensity mapped to a selected metric. |
| Wiggle Streamgraph by Stack Offset | Interactive streamgraph adapted from Observable Plot’s plot-stack-offset gallery example, using stacked area layers for music-format revenue over time and exposing stack offset behavior directly in the UI. |
Arrow Link Vector
| Tool | Description |
|---|---|
| Arc Diagram | A network arc diagram visualizing relationships in a JavaScript ecosystem (25 nodes across 7 groups: Frontend Frameworks, Server Frameworks, Build Tools, Compilers, Testing, Visualization, Styling). |
| Arrow Variation Chart | An arrow variation chart showing changes in population and income inequality across 25 US metro areas from 1980 to 2015. |
| Barley Trellis Plot with Arrows | A trellis (small multiples) chart showing year-over-year barley yield changes across 6 Minnesota trial sites (University Farm, Waseca, Morris, Crookston, Grand Rapids, Duluth) and 10 barley varieties. |
| Difference Arrows | A difference arrows chart showing the gender participation gap across 25 Olympic sports. |
| Finite State Machine | A directed graph visualization of a finite state machine (Markov chain) with three states (A, B, C) arranged in a circle. |
| Phases of the Moon | A lunar calendar visualization showing the phases of the moon for any given year, in the style of Irwin Glusker. |
Bar
| Tool | Description |
|---|---|
| Bar and Tick | Interactive bar-and-tick chart based on Observable Plot’s bar-and-tick gallery example, using deterministic English letter frequency data with bars and overlaid tick marks on a percent-scaled y-axis. |
| State Population Change Diverging Bars | Interactive diverging horizontal bar chart based on Observable Plot’s state population change example, showing percent change from 2010 to 2019 for seeded U.S. |
| Diverging Stacked Likert Bars | Interactive diverging stacked bar chart based on Observable Plot’s diverging stacked bar gallery example, using deterministic survey responses grouped by question and Likert response category. |
| Faceted Lollipop by State | Interactive faceted lollipop chart based on Observable Plot’s faceted lollipop gallery example, using deterministic state-by-year population rows with state, year, and population columns. |
| Grouped Bar Chart by State and Age Group | Interactive grouped bar chart based on Observable Plot’s grouped bar gallery example, using faceted state panels (fx) and age-group categories on each panel with population on the y-axis. |
| Olympians Grouped Bar Chart | Interactive grouped bar chart based on Observable Plot’s olympians grouped bar gallery example, faceting by sport (fx) and grouping bars by sex on the x-axis. |
| Horizontal Bar Chart | Interactive horizontal bar chart based on Observable Plot’s horizontal bar gallery example, using English letter frequencies on a top-oriented percent axis with grid lines. |
| Horizontal Bars with a Label | Interactive horizontal bar chart based on Observable Plot’s horizontal-bar-with-label gallery example, using deterministic company market-value data in billions. |
| Horizontal Stacked Bars | Interactive horizontal stacked bar chart based on Observable Plot’s horizontal stacked bars gallery example, using deterministic congressional-style records with party, gender, chamber, and region dimensions. |
| Lollipop Chart | Interactive lollipop chart based on Observable Plot’s lollipop gallery example, using deterministic English letter frequency data with Plot.ruleX stems and Plot.dot heads on a percent-scaled y-axis. |
| Ordinal Time Bar Chart | Interactive quarterly bar chart based on Observable Plot’s ordinal bar chart example, using deterministic labor-market style data with quarter, vacancies, and applications columns. |
| Single Stacked Percentage Bar | Interactive single stacked percentage bar based on Observable Plot’s stacked percentages gallery example, using deterministic English letter-frequency data where all segments compose one 100% horizontal bar. |
| Single Stacked Bar by Olympian Sport Share | Interactive single stacked horizontal bar based on Observable Plot’s single stacked bar example, using deterministic olympian sport participation counts converted into share of total athletes. |
| Stacked Bars by Island and Species | Interactive stacked bar chart based on Observable Plot’s stacked bars gallery example, showing deterministic penguin island data with species-specific stacked segments. |
| Crimean War Stacked Bars by Cause | Interactive stacked monthly bar chart based on Observable Plot’s Crimean War barY example, using deterministic long-form data with date, cause, and deaths columns. |
| Stacked Vertical Histogram | Interactive vertical histogram based on Observable Plot’s stacked olympians histogram example, using deterministic athlete records with weight, sex, sport, and athlete name fields. |
| Stacked Waffles by Weight and Sex | Interactive faceted waffle chart based on Observable Plot’s stacked waffles gallery example, using deterministic athlete rows with weight, sex, and sport fields. |
| Survey Waffle | Interactive waffle chart based on Observable Plot’s survey waffle gallery example, using deterministic survey question rows with baseline yes counts from a 120-person survey. |
| Temporal Bar Chart | Interactive temporal bar chart based on Observable Plot’s temporal bar chart example, using deterministic daily weather-style records with date, wind, temperature, and precipitation columns. |
| Vertical Bar Chart | Interactive vertical bar chart inspired by Observable Plot’s vertical bar gallery example, using English letter frequencies on a percent-scaled y-axis with a zero baseline rule. |
| Vertical Bars with Rotated Labels | Interactive vertical bar chart based on Observable Plot’s rotated-label bars gallery example, showing deterministic brand market values with long category names on the x-axis. |
Cell
| Tool | Description |
|---|---|
| Auto Mark Heatmap Explorer | Visualizes deterministic seeded athlete records with weight on the x-axis and height on the y-axis, using color to represent local density as a heatmap. |
| Auto Mark Heatmap by Weight and Sex | Recreates the Observable Plot auto-mark heatmap 2 pattern by plotting athlete weight on the x-axis and sex on the y-axis, with color encoding binned count density. |
| Calendar Activity Heatmap | Recreates the Observable Plot calendar example as a faceted year-by-year calendar heatmap where x is week-of-year and y is weekday, with each cell encoding a deterministic daily activity value. |
| Correlation Heatmap Explorer | Displays a pairwise correlation matrix across six deterministic seeded numeric fields (Demand, Temperature, Humidity, Wind, Solar, Price), with variables shown on both axes and color encoding Pearson correlation from -1 to 1. |
| Continuous Dimensions Heatmap | Recreates the Observable Plot continuous dimensions heatmap style by binning two quantitative dimensions: carat on the x-axis and price on the y-axis. |
| Seattle Temperature Temporal Heatmap | Recreates the Observable Plot Seattle temperature heatmap pattern using a calendar-like grid where day-of-month is on the x-axis and month is on the y-axis. |
| Simplified Dow Jones Calendar | Recreates the Observable Plot Dow Jones simplified calendar by faceting each year into horizontal calendar strips, encoding week-of-year on x and weekday on y with cell color representing daily percentage change in close. |
| Simpsons IMDb Ratings Heatmap | Recreates the Observable Plot Simpsons ratings example as a season-by-episode heatmap with season on the top x-axis, episode index on the y-axis, and cell color encoding IMDb rating. |
| Sorted Heatmap of Traffic by Hour | Shows a heatmap of traffic volume by hour (x-axis) and location (y-axis), where each cell color encodes an aggregated traffic metric. |
Contour Density
| Tool | Description |
|---|---|
| Blurred Contours | Geomagnetic field anomalies over Southern California visualized using Observable Plot’s contour mark with blur interpolation, based on the Plot blurred contours example. |
| Water Vapor Contour Map | Global precipitable water vapor visualized as filled contours on a geographic projection, based on the Observable Plot contours-projection example. |
| Faceted Density | Faceted density contour plot of penguin morphometric measurements, based on the Observable Plot density-faceted example. |
| Diamond Density (Stroke) | Density contour plot of diamonds showing carat vs price relationship, based on the Observable Plot density-stroke example. |
| Faceted Function Contour | A 2×2 grid of contour plots where each cell visualizes fill = fx(x) × fy(y), faceted by combinations of trigonometric (sin, cos) and linear functions. |
| Filled Contours | Volcanic elevation data rendered as filled contour bands, based on the Observable Plot filled contours example. |
| Function Contour Plot | Visualizes mathematical functions as filled contour maps using Observable Plot’s contour mark. |
| IGRF90 Contours | International Geomagnetic Reference Field (1990) total intensity contours over Southern California, based on the Observable Plot IGRF90 contours example. |
| Olympians Density | Density contour plot of Olympic athletes showing height (cm) vs. |
| One-Dimensional Density | Old Faithful geyser data visualized as layered one-dimensional density contours with individual data points, based on the Observable Plot one-dimensional density example. |
| Point Cloud Density | Old Faithful geyser eruption data visualized as a two-layer density estimation over a point cloud, based on the Observable Plot point-cloud-density example. |
| Stroked Contours | Elevation contour lines of the Maunga Whau volcano rendered as stroke-only contours (no fill), based on the Observable Plot stroked contours example. |
| Walmart Store Density | Kernel density estimation of Walmart store locations across the continental United States, based on the Observable Plot walmart-density example. |
Delaunay Voronoi
| Tool | Description |
|---|---|
| Centroid Voronoi | Displays a Voronoi tessellation computed from the centroids of all US counties, using Observable Plot’s centroid transform on GeoJSON county features loaded from the us-atlas CDN. |
| Delaunay Hull | Displays a Delaunay mesh triangulation with convex hulls overlaid, using Palmer Penguins data. |
| Delaunay Links | Displays a Delaunay triangulation link chart using Palmer Penguins data. |
| Planar vs Spherical Voronoi | Compares planar (Euclidean) and spherical (geodesic) Voronoi tessellations of 50 world airports on various map projections. |
| Voronoi Map | Displays a Voronoi tessellation of US state capitals on an azimuthal projection, showing each capital’s nearest-neighbor geographic region. |
| Voronoi Scatterplot | Displays a Voronoi tessellation scatterplot using Palmer Penguins data. |
| Walmart Store Voronoi | Displays a Voronoi mesh of Walmart store locations across the United States on a geographic projection, showing the territory nearest to each store. |
Dot
| Tool | Description |
|---|---|
| Anscombe’s Quartet Explorer | Interactive faceted scatterplot based on Observable Plot’s Anscombe’s quartet example, using Plot.frame, Plot.line, and Plot.dot marks over the canonical quartet dataset. |
| Beeswarm Dodge of Car Metrics | Interactive dodged beeswarm chart inspired by Observable Plot’s cars dodge example, drawing car records as Plot.dotX marks with Plot.dodgeY so overlapping values stack into a readable swarm. |
| Centroid Dot Explorer | Interactive map-style dot chart inspired by Observable Plot’s centroid-dot example, overlaying Plot.geoCentroid and Plot.centroid markers on seeded region polygons in an Albers USA projection. |
| Diverging Color Scatterplot | Interactive scatterplot of yearly global surface temperature anomalies with a diverging BuRd color scale centered around zero. |
| Dot Heatmap of Athlete Size | Interactive dot heatmap based on Observable Plot’s dot-heatmap gallery example, binning athlete weight on the x-axis and height on the y-axis. |
| Dot-Bin Weight Histogram | Interactive dot-bin histogram based on Observable Plot’s dot-bins gallery example, using athlete weight values binned across the x-axis with dot radius proportional to count in each bin. |
| Dot Sort Bubble Map | Interactive bubble map inspired by Observable Plot’s dot-sort gallery example, plotting seeded U.S. |
| Faceted Dodge Penguins | Interactive faceted dodge chart based on Observable Plot’s plot-dodge-penguins example, drawing penguin records as Plot.dot marks with Plot.dodgeX and faceting by species or island. |
| Olympians Hexbin Heatmap | Interactive hexbin heatmap based on Observable Plot’s olympians-hexbin gallery example, aggregating athlete weight on the x-axis and height on the y-axis into hexbin cells. |
| Ordinal Scatterplot | Interactive categorical scatterplot based on Observable Plot’s ordinal-scatterplot gallery example, where bubble size encodes the number of penguin records at each category intersection. |
| Proportional Symbol Scatterplot | Interactive stock-move scatterplot modeled on Observable Plot’s proportional-symbol example, with x-position as daily percent change and y-position as log-scaled daily trading volume. |
| Quantile-Quantile Plot | Interactive QQ plot inspired by Observable Plot’s qq-plot example, comparing two selected sample batches by plotting matched quantiles against each other. |
| Penguin Measurement Scatterplot | Interactive scatterplot of penguin measurements with selectable X and Y numeric fields and adjustable point radius. |
| Scatterplot with Color | Interactive color scatterplot inspired by Observable Plot’s gallery example, plotting penguin measurements with species encoded by color. |
| Scatterplot with Interactive Tips | Interactive athlete scatterplot based on Observable Plot’s interactive tips example, plotting weight on the x-axis and height on the y-axis with sex encoded by color. |
| Scatterplot with Ordinal Dimension | Interactive penguin scatterplot based on Observable Plot’s ordinal-dimension gallery example, plotting a numeric measure on the x-axis against an ordinal category on the y-axis. |
| Sized Hexbin Heatmap | Interactive sized-hexbin chart based on Observable Plot’s hexbin-binWidth gallery example, plotting athlete weight on the x-axis and height on the y-axis. |
| Stacked Dots by Age and Gender | Interactive mirrored stacked-dot chart inspired by Observable Plot’s stacked-dots example, using member age on the x-axis and stacking count above/below zero by gender. |
| Symbol Channel Scatterplot | Interactive penguin scatterplot modeled on Observable Plot’s symbol-channel example, encoding one categorical field by marker symbol and another by color. |
| Wealth & Health of Nations | Interactive bubble scatterplot based on Observable Plot’s wealth-health-nations example, plotting income per person on a logarithmic x-axis against life expectancy on the y-axis. |
Geo
| Tool | Description |
|---|---|
| US Map — Albers USA Projection | An interactive map of the United States using the Albers USA composite projection, inspired by the Observable Plot Albers USA projection example. |
| Bivariate Choropleth Map | An interactive bivariate choropleth map of the United States inspired by the Observable Plot bivariate choropleth example. |
| US County Choropleth Map | An interactive choropleth map of the United States showing county-level data, inspired by the Observable Plot choropleth example. |
| Earthquake Globe | An interactive earthquake globe inspired by the Observable Plot earthquake globe example. |
| Election Wind Map | An interactive election wind map of the United States inspired by the Observable Plot election wind map example. |
| Floor Plan | An interactive architectural floor plan rendered using Plot.geo with an identity projection, inspired by the Observable Plot floor plan example. |
| GeoTIFF Contours | An interactive filled contour visualization of global geographic data fields, inspired by the Observable Plot GeoTIFF contours example. |
| Hexbin Map | An interactive hexbin map of the United States inspired by the Observable Plot hexbin map example. |
| Store Expansion Map — Small Multiples | An interactive small-multiples geographic map inspired by the Observable Plot map small multiples example. |
| Polar Projection Explorer | An interactive azimuthal map projection explorer inspired by the Observable Plot polar projection example. |
| Shockwave Distance Rings | An interactive geographic visualization of concentric distance rings radiating from a geological epicenter, inspired by the Observable Plot shockwave example showing the Hunga Tonga eruption. |
| US Population Spike Map | An interactive spike map of the United States showing county-level population as vertical spikes, inspired by the Observable Plot spike map example. |
| US State Centroids | An interactive map of the United States showing centroid dots at the geographic center of each state, inspired by the Observable Plot state centroids example. |
| US Counties by First Letter | An interactive map of the United States showing county centroids filtered by the starting letter of the county name, inspired by the Observable Plot “V counties” example. |
| Wind Map | An interactive wind vector field visualization inspired by the Observable Plot wind map example. |
| World Map Projections | An interactive world map projection explorer inspired by the Observable Plot world projections example. |
Image
| Tool | Description |
|---|---|
| Background Image Scatterplot | Interactive scatterplot inspired by Observable Plot’s background-image gallery example, plotting penguin culmen length (x) and culmen depth (y) on top of a selectable Wikimedia-hosted background image. |
| Default Image Scatterplot | Interactive adaptation of Observable Plot’s plot-image-scatterplot-2 gallery example, using Plot.image to map Wikimedia-hosted presidential portraits across total favorable (x) and total unfavorable (y) opinion percentages. |
| Image Dodge Timeline | Interactive adaptation of Observable Plot’s image-dodge gallery example, plotting Wikimedia-hosted U.S. |
| Olympic Medal Image Plot | Interactive adaptation of the Observable Plot image-medals example pattern, using Plot.image to place Wikimedia-hosted country flag images on a time-versus-medal chart. |
| Presidential Favorability Image Scatterplot | Interactive adaptation of Observable Plot’s image-scatterplot example, using Plot.image to position Wikimedia-hosted U.S. |
Interaction
| Tool | Description |
|---|---|
| Color Crosshair | An interactive scatterplot demonstrating Observable Plot’s color crosshair feature, inspired by the Plot color crosshair gallery example. |
| Crosshair Explorer | An interactive scatterplot demonstrating Observable Plot’s crosshair mark. |
| CrosshairX Explorer | An interactive line chart demonstrating Observable Plot’s crosshairX mark, inspired by the Plot crosshairX gallery example. |
| Tips with Additional Channels | An interactive scatterplot demonstrating Observable Plot’s channels option for adding extra data fields to tooltips without mapping them to visual encodings, inspired by the Plot tips with additional channels gallery example. |
| Tips with Longer Text | An interactive scatterplot demonstrating Observable Plot’s tip mark with longer, paragraph-length text content, inspired by the Observable Plot tips with longer text gallery example. |
| Line Chart with Interactive Tip | An interactive line chart inspired by the Observable Plot line chart with interactive tip example. |
| Map & Tips | An interactive US map demonstrating Observable Plot’s tip mark with geographic centroid positioning, inspired by the Plot maps tips gallery example. |
| Multi-Series Line Chart with Interactive Tips | An interactive multi-series line chart inspired by the Observable Plot multi-series line chart with interactive tips example. |
| One-Dimensional Crosshair | An interactive one-dimensional distribution chart inspired by the Observable Plot one-dimensional crosshair example. |
| One-Dimensional Pointing | An interactive histogram inspired by the Observable Plot one-dimensional pointing example. |
| Pointer Modes: X, Y, and XY | An interactive line chart demonstrating Observable Plot’s three pointer modes (pointerX, pointerY, and pointer/XY), inspired by the Plot pointer modes gallery example. |
| Pointer Target Position | An interactive stock price line chart demonstrating Observable Plot’s pointer target position concept, inspired by the Plot pointer target position gallery example. |
| Pointer Transform Explorer | An interactive scatterplot demonstrating Observable Plot’s pointer transform, inspired by the Plot pointer transform gallery example. |
| Static Annotations | An interactive line chart with static text annotations, inspired by the Observable Plot static annotations gallery example. |
| Tip Format | An interactive scatterplot demonstrating Observable Plot’s tip format option, inspired by the Plot tip format gallery example. |
| Tips with Paired Channels | An interactive histogram demonstrating Observable Plot’s tip behavior with paired channels (x1/x2 from binX, y1/y2 from stackY), inspired by the Plot tips paired channels gallery example. |
Line
| Tool | Description |
|---|---|
| Bollinger Band Pulse | Interactive Bollinger bands chart modeled after the Observable Plot bollinger-bands gallery example, using deterministic daily close values stored in a bf-backed table. |
| Connected Scatterplot Explorer | Interactive connected scatterplot inspired by the Observable Plot connected-scatterplot example, using deterministic yearly driving-versus-gasoline seed data in a bf-backed table. |
| Indexed Stock Performance Chart | Interactive line chart modeled after the Observable Plot index chart pattern, using deterministic monthly close prices for AAPL, AMZN, GOOG, and IBM in a bf-backed table. |
| Labeled Multi-Line Stock Chart | Interactive multi-series stock close chart inspired by the Observable labeled multi-line example, using deterministic bf seed data for AAPL, AMZN, GOOG, and IBM over monthly dates. |
| Simple Line Chart Explorer | Interactive single-series line chart of stock closing price by date using deterministic seed data in a bf table. |
| Line Chart Percent Change Explorer | Interactive single-series percent-change line chart modeled after the Observable Plot line-chart-percent-change example, using deterministic monthly closing prices in a bf-backed seed table. |
| Crimean War Line Chart With Markers | Interactive multi-series line chart inspired by the Observable line-chart-with-markers example, using deterministic monthly Crimean-war-style deaths data in a bf-backed table with wide cause columns that are pivoted to long-form in-app. |
| Line Chart With Gaps Explorer | Interactive time-series line chart inspired by the Observable line-chart-with-gaps example, using deterministic monthly closing values from 2024 through 2025 stored in a bf-backed seed table. |
| Sparse Line Aggregation Explorer | Interactive line chart inspired by the Observable Plot sparse-line example, using deterministic timestamped observations stored in a bf-backed seed table. |
| Marey’s Trains Stringline Explorer | Interactive stringline chart inspired by the Observable Plot Marey’s trains example, using deterministic bf-backed Caltrain-style seed data with station names, route distance, train identifier, direction, service class (normal, limited, bullet), schedule type (weekday/weekend), and formatted time strings. |
| Temperature Anomaly Moving Average Explorer | Interactive line chart inspired by the Observable Plot moving-average example, using deterministic monthly temperature anomaly values stored in a bf-backed table. |
| Metro Unemployment Multi-Line Chart | Interactive multi-series line chart of monthly unemployment rates using deterministic seed data stored in a bf table. |
| Non-Temporal Line Chart Explorer | Interactive non-temporal line chart modeled after the Observable Plot non-temporal line example, using deterministic stage-profile seed data in a bf-backed table with numeric distance, elevation, and gradient columns. |
| Phone Feature Radar Chart | Interactive radar chart inspired by the Observable Plot radar-chart gallery example, implemented with a polar-style azimuthal projection, concentric guide rings, radial axis links, and closed polygon outlines for each phone model. |
| Faceted Radar Chart Small Multiples | Interactive faceted radar chart inspired by the Observable Plot radar-chart-faceted gallery example, using deterministic bf-backed vehicle profile seed data with six metrics (Price, Efficiency, Performance, Safety, Comfort, and Cargo) normalized per metric and rendered as one radar panel per vehicle. |
| Random Walk Drift Explorer | Interactive random walk line chart inspired by the Observable Plot random-walk example, using deterministic seeded step increments stored in a bf-backed table. |
| Government Receipts Slope Chart | Interactive slope chart inspired by the Observable Plot slope-chart gallery example, using deterministic bf-backed country data for government receipts (% of GDP) at two time points (1970 and 2020). |
| Cancer Survival Rates Slope Chart | Interactive slope-style cancer survival chart inspired by the Observable Plot cancer-survival-rates example, using deterministic bf-backed seed data with four categorical follow-up horizons (5 Year, 10 Year, 15 Year, 20 Year) for multiple cancer types. |
Raster
| Tool | Description |
|---|---|
| Mandelbrot Set Explorer | Interactive fractal visualization of the Mandelbrot set using Observable Plot’s raster mark with a computed fill function. |
| Projected Raster — Water Vapor | Global precipitable water vapor visualization rendered as a projected raster using Observable Plot’s raster mark. |
| Volcano Raster | Topographic heatmap of a volcanic surface rendered with Observable Plot’s raster mark. |
Rect
| Tool | Description |
|---|---|
| Band Chart with Rule | Displays monthly temperature ranges as vertical rule bands where each mark spans from minimum temperature (y1) to maximum temperature (y2) at a date on the x-axis, matching the Observable Plot band-chart-with-rule pattern and including a horizontal zero-temperature baseline. |
| Binned Box Plot of Diamond Prices | Displays diamond price distributions as box plots grouped into carat bins, following the Observable Plot binned box pattern by applying fx interval binning to the carat field and plotting price on the y-axis. |
| California COVID-19 Disparities Bullet Graph | Displays a faceted bullet-graph style comparison by race and age using two horizontal bars per row: a gray background bar for population share and a colored foreground bar for COVID-19 case share, mirroring the Observable Plot bullet graph gallery composition. |
| Candlestick Price Explorer | Displays daily OHLC price movement using the Observable Plot candlestick pattern: one ruleX mark for each session’s low-to-high wick and a second thick ruleX mark for the open-to-close body, colored by direction (down, flat, up). |
| County Bounding Boxes Explorer | Displays deterministic county-style geographic bounding rectangles as rect marks projected with Albers USA, following the Observable Plot county boxes pattern of drawing [x1, y1, x2, y2] coordinate bounds. |
| Cumulative Histogram Explorer | Displays athlete weight data as a cumulative histogram using Plot rectY with Plot.binX and the cumulative setting, following the Observable Plot cumulative histogram gallery pattern. |
| Cumulative Distribution of Poverty | Displays a cumulative poverty distribution as stacked rectangles using country population on the x-axis and the share of people living below a poverty threshold on the y-axis, matching the Observable Plot rectY + stackX gallery pattern. |
| Impact of Vaccines by State and Year | Displays disease case intensity as a state-by-year rectangular timeline, matching the Observable Plot vaccine-impact pattern where each state row is segmented by yearly intervals and colored by cases per 100,000 people. |
| Marimekko Revenue Mix by Market and Segment | Displays a Marimekko chart where each market occupies horizontal width proportional to total revenue, and each segment fills vertical share within that market, following the Observable Plot marimekko rect composition pattern. |
| Normal Histogram Explorer | Displays a histogram of deterministic near-normal sample values using Plot rectY with binning on the x-axis, along with an overlaid normal reference curve derived from the sample mean and standard deviation. |
| Overlapping Histograms | Compares deterministic male and female athlete weight distributions as overlapping histograms using Plot rectY with Plot.binX, closely following the Observable Plot overlapping histogram gallery pattern. |
| Percentogram Explorer | Displays deterministic numeric observations as a percentogram using Plot rectY with percentile-derived thresholds, so each bin contains an equal share of points while bin widths vary with the data distribution. |
| Pre-binned Histogram Explorer | Displays a pre-binned histogram using Plot rectY with explicit lower and upper bin boundaries (x0/x1) and precomputed frequencies, following the Observable Plot pre-binned histogram gallery pattern. |
| Crimean War Deaths by Cause | Displays monthly deaths from the Crimean War as stacked rectangular marks with one segment per cause (Disease, Wounds, Other), following the Plot rectY gallery pattern with monthly intervals. |
| Global Warming Stripes | Shows annual temperature anomaly values as contiguous yearly stripes, using one horizontal strip where each year is encoded by a diverging color from cooler to warmer anomalies. |
Rule Tick
| Tool | Description |
|---|---|
| Population Barcode Chart | A barcode-style chart showing US state population distributions by age group, inspired by the Observable Plot barcode example. |
| Germany Traffic Patterns | A sorted-groups tick chart showing hourly vehicle counts at 12 German highway counting stations, inspired by the Observable Plot sorted-groups example. |
| Band Chart with Rule | A temperature band chart showing daily min/max temperature ranges for a full year of synthetic weather data. |
| Bar & Tick Chart | A letter frequency chart combining translucent bars with tick marks to show exact values, inspired by the Observable Plot bar-and-tick example. |
| Candlestick Chart | An OHLC candlestick chart displaying ~65 days of synthetic stock price data. |
Text
| Tool | Description |
|---|---|
| Hexbin Text for Athlete Height and Weight | Interactive adaptation of Observable Plot’s hexbin-text example, plotting seeded athlete body metrics on weight (x) and height (y) axes. |
| US State Labels Explorer | Interactive adaptation of Observable Plot’s plot-state-labels example, showing U.S. |
| Caltrain Stem-and-Leaf Schedule | Interactive adaptation of Observable Plot’s caltrain-schedule example, plotting deterministic Caltrain-like departures as text leaves around an hourly stem. |
| IPO Text Dodge Timeline | Interactive adaptation of Observable Plot’s text-dodge example, plotting seeded IPO events by date and market capitalization. |
| Text Spiral Explorer | Interactive adaptation of Observable Plot’s plot-text-spiral example, plotting seeded spiral coordinates generated from deterministic index-based math. |
| This Is Just To Say — Interactive Text Layout | Interactive adaptation of Observable Plot’s this-is-just-to-say example, rendering William Carlos Williams’s poem lines as Plot.text marks inside a framed chart. |
| Voronoi Labels for Airport Points | Interactive adaptation of Observable Plot’s voronoi-labels example, plotting seeded U.S. |
Tree
| Tool | Description |
|---|---|
| Cluster Dendrogram | A cluster dendrogram of biological taxonomy data (Kingdom → Phylum → Class → Order → Family → Genus → Species) rendered using Observable Plot’s cluster mark. |
| Indented Tree Diagram | An indented tree diagram of a project file system hierarchy rendered using Observable Plot’s tree mark with a custom indented layout. |
| Tree Tidy Layout | A hierarchical tree diagram of the Flare visualization toolkit rendered using Observable Plot’s tree mark with the Reingold–Tilford “tidy” algorithm. |