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Computes a base-100 rebased index of mining energy intensity (energy per BTC mined) across a supplied time series of up to 200 rows.

Computes and compares mining energy intensity (kWh per BTC) for two scenarios — before and after — given user-supplied net BTC mined and energy consumed.

Computes a benchmark correlation report comparing mining energy intensity (kWh per BTC mined) data rows against user-supplied benchmark thresholds.

Computes a statistical distribution summary of energy consumed per BTC mined from an array of mining operation data points.

Computes IQR-based outlier flags for mining energy intensity (kWh per BTC) across a dataset of mining operation rows.

Compares named mining energy intensity scenarios by computing energy consumed per BTC mined across a baseline and up to 20 labeled alternative configurations.

Generates a one-input sensitivity table showing how Bitcoin mining energy intensity (kWh per BTC) varies across a range of supplied values for a single variable.

Ranks a list of mining operation records by energy intensity (kWh per BTC mined) and returns a bounded ranking report.

Computes a rolling mean of energy intensity (kWh per BTC mined) over a sliding window of mining data rows

Computes a sequential (waterfall) attribution report showing how changes in energy consumption and BTC mined contribute to the change in mining energy intensity (kWh per BTC).

Calculates the energy intensity of Bitcoin mining (energy consumed per BTC mined) given user-supplied kWh and net BTC values

Calculates the gap between a user-supplied target and the computed mining energy intensity (energy per BTC mined), given net BTC and energy consumption inputs.

Generates a two-dimensional scenario grid showing mining energy intensity (energy per BTC) across combinations of energy consumption and net BTC mined values.

Computes adjacent-period changes in total facility energy consumption (IT load × PUE) for mining operations across a supplied sequence of time periods.

Calculates total facility energy consumption from IT load and Power Usage Effectiveness (PUE) for up to 200 mining facility configurations in a single batch call.

Computes a benchmark correlation report comparing mining facility total energy consumption (IT load × PUE) against user-supplied benchmark values across up to 200 row pairs.

Bins up to 200 mining facility total energy consumption observations (IT load × PUE) into a distribution histogram with configurable bucket count.

Accepts up to 200 rows of mining facility IT energy (kWh) and PUE values and returns IQR-based statistical outlier flags for each row.

Analyzes ordered trends in mining facility energy consumption factoring in Power Usage Effectiveness (PUE) across multiple data rows.

Computes a rolling mean of total facility energy consumption (IT load × PUE) across a sequence of data points for mining hardware deployments

Calculates total energy consumption of a mining facility by applying a Power Usage Effectiveness (PUE) multiplier to IT equipment power draw (kWh).

Classifies rows of mining facility IT energy and PUE data as above, below, or within user-defined lower/upper thresholds, returning a bounded classification report.

Generates a two-dimensional scenario grid report exploring how IT kilowatt-hours and PUE combinations affect total mining facility energy consumption.

Computes a weighted arithmetic mean of total facility energy consumption (IT load × PUE) across multiple mining facility scenarios.

Computes adjacent-period changes in total overhead energy consumption for a mining facility given IT load (kWh) and PUE values across multiple periods.

Generates a distribution histogram of total overhead (facility) energy consumption for a set of mining operation records, given IT energy load and PUE values.

Detects IQR-based statistical outliers in mining facility overhead energy consumption data using PUE and IT kWh inputs.

Computes the local finite-change elasticity of total overhead energy with respect to IT load (it_kwh) or PUE for a data-center / mining facility scenario, given a baseline configuration and a step size.

Compares named scenarios for mining facility overhead energy consumption using PUE (Power Usage Effectiveness) and IT load inputs, returning a bounded scenario comparison report.

Generates a one-input sensitivity table for mining facility overhead energy, varying either IT load (it_kwh) or Power Usage Effectiveness (pue) across user-supplied values against a fixed baseline.

Computes a rolling mean of total overhead energy consumption for a mining facility given IT load and PUE inputs over a sliding window.

Computes a sequential (waterfall) attribution of how changes in PUE and IT energy consumption drive changes in a mining facility's total overhead energy, comparing a before and after scenario.

Calculates total overhead energy consumption for a mining facility given IT load (kWh) and Power Usage Effectiveness (PUE) ratio.

Calculates the gap between a target value and the computed overhead energy for a mining facility given PUE and IT energy consumption inputs.

Generates a two-dimensional scenario grid showing mining facility total overhead energy across combinations of IT load (it_kwh) and Power Usage Effectiveness (pue) values.

Computes adjacent period-over-period changes in total power consumption (kW) for a configurable mining fleet, given rows of unit counts and per-unit wattage.

Computes a base-100 indexed power consumption report for a mining fleet given per-unit wattage inputs, rebasing all values relative to a chosen baseline row.

Calculates total power consumption (kW) for multiple mining hardware configurations in a single batch call, given unit counts and wattage per unit.

Computes and compares total power consumption (kW) of a cryptocurrency mining fleet before and after a configuration change, given unit counts and wattage per unit.

Generates a distribution histogram of total power consumption across a mining fleet, given per-unit wattage and unit counts across multiple miner rows.

Compares named power consumption scenarios for a mining fleet, calculating total kW usage differences across up to 20 labeled configuration variants against a baseline.

Analyzes ordered power consumption trends across a mining fleet by computing aggregate kW statistics for rows of mining units with specified per-unit wattage.

Computes a rolling mean of total power consumption (kW) across multiple mining fleet row configurations over a specified window size.

Computes a waterfall attribution report showing how sequential changes to mining fleet units and per-unit wattage affect total power consumption.

Calculates the total power consumption in kilowatts for a mining fleet given the number of units and per-unit wattage.

Classifies total power consumption of a mining fleet against user-defined lower and upper thresholds, returning a bounded classification report.

Generates a two-input scenario grid for mining fleet total power consumption given varying unit counts and per-unit wattage assumptions.

Computes a weighted mean of total power consumption (kW) across rows of mining hardware units, using supplied weights and watt-per-unit inputs.

Computes a Base-100 rebased index of mining fleet spare coverage ratios across up to 200 time periods or scenarios, anchored to a user-selected base observation.

Computes and compares spare coverage ratios for a mining fleet before and after a configuration change, given spare and operating unit counts.

Generates a distribution histogram showing the spread of spare-to-operating unit coverage ratios across a fleet of mining operations.

Computes a statistical distribution summary of spare unit coverage ratios across multiple mining fleet configurations, given spare and operating unit counts.

Applies interquartile range (IQR) outlier detection to mining fleet spare-coverage ratios across up to 200 rows of spare-unit and operating-unit pairs

Compares named what-if scenarios for mining fleet spare unit coverage ratios against a baseline configuration, returning a bounded report per call.

Fits a straight-line trend to a time series of mining fleet spare-unit coverage ratios and returns a bounded ordered trend analysis report.

Computes a sequential (waterfall) attribution report showing how changes to spare_units and operating_units individually shift the mining fleet spare coverage ratio.

Calculates the spare unit coverage ratio for a mining fleet given the number of spare units and operating units.

Classifies mining fleet spare-coverage ratios against user-defined lower/upper thresholds for up to 200 equipment rows

Rebases a multi-row mining hardware purchase budget to a base-100 index, normalizing costs across units, unit price, and shipping to a chosen reference row.

Calculates the total purchase budget for multiple mining hardware configurations in a single batch, given unit counts, unit prices, and shipping costs per row.

Compares two mining hardware purchase budget scenarios (before and after) by computing total fleet cost differences based on supplied unit counts, unit prices, and shipping costs.

Computes a benchmark correlation report for a set of mining hardware purchase budget rows against supplied benchmark values, using a fixed formula with disclosed units.

Generates a distribution histogram of total mining hardware purchase budget across a set of line-item rows (units × unit price + shipping), binned into a configurable number of buckets.

Compares up to 20 named override scenarios against a baseline mining hardware purchase budget, returning a bounded scenario comparison report with total cost calculations.

Computes a sequential (waterfall) attribution report showing how each individual input change—units, unit price, shipping—contributes to the total delta in a mining hardware purchase budget.

Calculates the total purchase budget for a mining hardware fleet given unit count, unit price, and shipping cost

Generates a two-input scenario grid showing how total mining hardware purchase budget varies across combinations of unit count, unit price, and shipping cost.

Computes adjacent period changes in mining net capital requirements given hardware, installation, and rebate costs across multiple time periods.

Compares two sets of mining capital assumptions (before and after) to produce a bounded net capital requirement report showing the change.

Generates a distribution histogram of net capital requirements across multiple mining operation profiles using supplied hardware, installation, and rebate cost assumptions.

Computes a statistical distribution summary for mining net capital requirements given hardware, installation, and rebate cost inputs across multiple scenarios.

Identifies IQR-based statistical outliers across a batch of mining net capital requirement calculations using hardware, installation, and rebate cost inputs.

Computes a waterfall attribution showing how sequential changes to mining capital inputs (hardware, installation, rebates) individually shift the net capital requirement.

Calculates the net capital requirement for a mining operation given hardware cost, installation cost, and rebates in USD.

Generates a two-dimensional scenario grid showing mining net capital requirements across combinations of hardware cost, installation cost, and rebate values.

Computes period-over-period changes in mining operating cash contribution (revenue minus electricity, maintenance, and other costs) across up to 200 time periods.

Calculates operating cash contribution (revenue minus electricity, maintenance, and other costs) for up to 200 mining operations in a single batch call.

Computes and compares mining operating cash contribution (revenue minus electricity, maintenance, and other costs) for two scenarios — a 'before' and an 'after' — returning a bounded comparison report.

Generates a binned distribution histogram of mining operating cash contribution (revenue minus electricity, maintenance, and other costs) across up to 200 supplied data rows.

Computes IQR-based outlier flags for mining operating cash contribution metrics across a batch of operations using revenue, electricity, maintenance, and other costs.

Generates a one-input sensitivity table showing how a single cost or revenue variable affects mining operating cash contribution, given a baseline set of mining assumptions.

Ranks a submitted list of mining operation records by their operating cash contribution (revenue minus electricity, maintenance, and other costs), returning a bounded ranking report.

Computes a rolling mean of mining operating cash contribution (revenue minus electricity, maintenance, and other costs) over a sliding window across a series of time-period rows.

Performs a waterfall attribution analysis showing how sequential changes in mining revenue, electricity, maintenance, and other costs drive the difference in operating cash contribution between two scenarios.

Classifies mining operating cash contribution (revenue minus electricity, maintenance, and other costs) as above or below user-defined lower/upper thresholds across multiple rows of mining data.

Calculates the required operating cash reserve for mining operations across multiple scenarios in a single batch call, given daily cost and reserve duration inputs.

Computes and compares mining operating cash reserve totals for two scenarios (before and after) given daily operating costs and desired reserve days.

Generates a distribution histogram of mining operating cash reserve values across multiple mining operations, given daily costs and reserve days.

Detects IQR-based statistical outliers in a set of mining operation cash reserve profiles (daily cost × reserve days).

Compares named scenarios for mining operating cash reserve requirements given daily cost and reserve day assumptions

Generates a one-input sensitivity table showing how mining operating cash reserve changes as either daily operating cost or reserve days varies across a user-supplied range of values.

Computes a rolling mean of mining operating cash reserve values across a series of daily-cost and reserve-day data points

Calculates the total operating cash reserve required for a mining operation given daily cost and number of reserve days.

Classifies a mining operation's cash reserve level as below, within, or above a defined threshold band based on daily cost and reserve days inputs.

Generates a two-input scenario grid report for mining operating cash reserve calculations, varying daily cost and reserve days across user-defined value ranges.

Calculates adjacent period changes in mining operating contribution margin rate given pairs of revenue and contribution data rows.

Computes a base-100 rebased index of mining operating contribution margin (contribution_usd / revenue_usd) across a series of time periods, anchored to a chosen base row.

Computes and compares mining operating contribution margin rates for two scenarios (before and after), returning a bounded comparison report based on supplied revenue and contribution figures.

Generates a bounded distribution histogram of mining operating contribution margin rates from supplied revenue and contribution data rows.

Computes a distribution summary of the operating contribution margin rate across a set of mining operation records, given paired revenue and contribution USD values.