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Ranks multiple mining operation records by their break-even energy tariff (maximum cost per kWh at which mining remains profitable), given revenue and non-energy cost assumptions.

Computes how changes to mining revenue, non-energy costs, and energy consumption individually shift the break-even energy tariff, using sequential waterfall attribution.

Calculates the gap between a computed mining break-even energy tariff and a user-supplied target value, given mining revenue, non-energy costs, and energy consumption inputs.

Computes the weighted mean of break-even energy tariff values across multiple cryptocurrency mining scenarios using user-supplied revenue, non-energy cost, and energy consumption inputs.

Computes adjacent-period changes in mining break-even hashprice given supplied hashrate and daily operating cost inputs across multiple time rows.

Calculates a base-100 rebased index of mining break-even hashprice across multiple miner configurations, given daily costs and active terahash rates.

Calculates and compares mining break-even hashprice for two scenarios (before and after) given hashrate and daily cost assumptions

Computes benchmark correlation statistics for mining break-even hashprice given daily operating costs and active terahashes per second across a set of hashprice benchmark levels.

Calculates and returns a distribution summary of break-even hashprice values given mining cost and hash rate assumptions across multiple operational profiles.

Identifies IQR-based statistical outliers in mining break-even hashprice calculations given daily cost and active terahash inputs across multiple rows.

Analyzes ordered trends in mining break-even hashprice given sequences of daily cost and active terahash data points

Ranks multiple mining operations by their break-even hashprice given daily operating costs and active terahash, returning a bounded comparative ranking report.

Computes a sequential input-change attribution report showing how changes in active hashrate and daily cost affect Bitcoin mining break-even hashprice.

Classifies whether a set of mining operations' break-even hashprice falls within a user-defined lower/upper threshold range, given daily cost and active terahash inputs.

Computes a weighted mean of break-even hashprice values across multiple mining operation scenarios given daily costs and active terahash inputs.

Calculates period-over-period changes in Bitcoin mining proceeds given user-supplied BTC amounts and USD prices across multiple time periods.

Generates a Base-100 rebase index report for Bitcoin mining proceeds at user-supplied BTC quantities and USD prices, normalizing values to a baseline row.

Calculates Bitcoin mining proceeds for multiple BTC amounts at supplied USD prices in a single batch request

Computes a benchmark correlation report for BTC mining proceeds calculated from user-supplied BTC quantities and USD prices against a set of benchmark thresholds.

Generates a distribution histogram of Bitcoin mining proceeds calculated at a user-supplied BTC price, bucketed into configurable bins.

Computes a statistical distribution summary of Bitcoin mining proceeds across multiple BTC quantity and price scenarios.

Computes the local finite-change elasticity of BTC mining proceeds with respect to a supplied BTC/USD price, given user-defined mining assumptions and a percentage step size.

Analyzes a series of BTC mining proceeds rows (BTC amount + USD price) and returns an ordered trend analysis report showing directional patterns over the supplied data.

Ranks up to 200 supplied BTC mining output records by calculated USD proceeds value given user-specified BTC amounts and BTC/USD prices.

Calculates a rolling mean of Bitcoin mining proceeds (in USD) over a sliding window across a series of BTC quantity and price data points.

Calculates estimated BTC mining proceeds in USD given a BTC amount and a supplied BTC/USD price, using fixed disclosed mining assumptions.

Classifies a set of BTC mining proceeds rows as below, within, or above a defined threshold range, given a user-supplied BTC amount and USD price per row.

Generates a two-input scenario grid showing BTC mining proceeds across combinations of BTC amount mined and BTC/USD price inputs.

Computes a weighted mean of BTC mining proceeds (in USD) across multiple rows of mined BTC quantities and BTC/USD prices using user-supplied weights.

Calculates mining contribution (profit) per kilowatt-hour of energy consumed across multiple input rows in a single batch call, using supplied mining assumptions and a disclosed formula.

Computes and compares mining contribution (USD) per kWh of energy consumed before and after a scenario change, returning a bounded comparison report.

Generates a distribution histogram showing how mining contribution (USD) per kilowatt-hour varies across a supplied dataset of mining operation rows.

Identifies IQR-based statistical outliers in mining contribution per kWh (profit per energy unit) across up to 200 supplied data rows.

Analyzes the ordered trend of mining contribution (profit) per kilowatt-hour across a series of data rows, returning a bounded trend analysis report.

Computes a rolling mean of mining contribution (USD) per energy unit (kWh) over a sliding window across a series of data rows.

Computes a sequential waterfall attribution report showing how changes to energy consumption and mining contribution inputs affect the mining profit-per-kWh metric.

Calculates mining profit contribution per kilowatt-hour of energy consumed, given a USD contribution amount and energy usage in kWh.

Classifies whether mining profit per kWh falls below, within, or above a defined threshold range, given supplied contribution and energy data.

Computes a weighted result mean of mining contribution (USD) per unit of energy (kWh) across multiple data rows using caller-supplied weights.

Computes period-over-period changes in electricity costs for cryptocurrency mining cooling systems across up to 200 data rows.

Computes a base-100 rebase index for mining cooling electricity costs across multiple scenarios, normalising each row relative to a chosen baseline entry.

Calculates the electricity cost of mining cooling systems for multiple scenarios in a single batch request, given cooling power draw, hours of operation, and electricity rate.

Computes and compares mining cooling electricity costs between two scenarios (before and after), returning a bounded comparison report based on user-supplied cooling power, hours, and electricity rate.

Generates a distribution histogram of electricity costs for mining cooling loads across multiple scenarios defined by cooling power, operating hours, and electricity rate.

Computes a statistical distribution summary (min, max, mean, percentiles, etc.) for mining cooling electricity costs given arrays of cooling power, hours, and electricity rate inputs.

Detects IQR-based statistical outliers in a dataset of mining cooling electricity cost records given cooling power, hours, and electricity rate inputs.

Computes the local finite-change elasticity of mining cooling electricity cost with respect to a chosen input variable (cooling power, hours, or electricity rate) given a baseline scenario.

Generates a one-input sensitivity table showing how mining cooling electricity cost varies across a range of values for a chosen variable (cooling_kw, hours, or usd_per_kwh).

Analyzes ordered trends in mining cooling electricity costs across multiple scenarios defined by cooling power (kW), hours of operation, and electricity rate (USD/kWh).

Ranks a list of mining cooling scenarios by electricity cost, given cooling power draw, operating hours, and electricity rate per scenario.

Performs sequential (waterfall) attribution analysis on mining cooling electricity costs, showing how changes in cooling power, hours, and electricity rate each contribute to total cost differences.

Calculates the electricity cost of cooling a cryptocurrency mining operation given cooling power, operating hours, and electricity rate.

Classifies mining cooling electricity cost values as below, within, or above a defined threshold range, given cooling power, hours, and electricity price inputs.

Generates a two-dimensional scenario grid of electricity costs for mining cooling systems across combinations of cooling power, operating hours, and electricity rate.

Computes the relative percentage change in mining difficulty between adjacent periods for a sequence of difficulty values

Computes a base-100 rebased index series of mining difficulty relative changes from pairs of new/old difficulty values.

Calculates the relative change between old and new mining difficulty values for up to 200 row pairs in a single batch request

Computes and compares the relative percentage change in mining difficulty across two scenario pairs (before and after), returning a side-by-side comparison report.

Computes the Pearson (or similar) correlation between a series of mining difficulty relative changes and a set of user-supplied benchmark values, returning a bounded correlation report.

Generates a binned distribution histogram of relative changes between old and new Bitcoin mining difficulty values across a dataset of up to 200 row pairs.

Computes a distribution summary (statistical summary) of relative changes between old and new Bitcoin/crypto mining difficulty values across a batch of rows.

Analyzes an ordered sequence of mining difficulty relative changes (period-over-period) and produces a bounded trend analysis report.

Accepts multiple pairs of old/new mining difficulty values and returns a ranked report of their relative percentage changes.

Computes a rolling mean of mining difficulty relative changes across a series of difficulty adjustment data points.

Classifies whether the relative change between two mining difficulty values falls within a specified lower/upper threshold range, returning a bounded classification report.

Generates a two-input scenario grid showing how mining difficulty relative change varies across combinations of new_difficulty and old_difficulty values.

Computes the weighted mean of relative mining difficulty changes across multiple difficulty transitions using user-supplied row data and weights.

Computes a Base-100 rebase index for mining downtime contribution loss across multiple rows, expressing each row's loss relative to a chosen baseline row.

Calculates the total contribution (revenue) lost due to mining equipment downtime for up to 200 row entries in a single batch request.

Computes and compares mining downtime contribution loss (in USD) between two scenarios — a 'before' and 'after' state — using supplied downtime hours and contribution rate per hour.

Generates a distribution histogram of mining downtime contribution losses given arrays of downtime hours and contribution rates per hour.

Computes a distribution summary of contribution loss (in USD) caused by mining equipment downtime across multiple supplied data rows.

Identifies IQR-based statistical outliers in mining downtime contribution loss data across a set of input rows

Analyzes ordered trends in mining downtime contribution losses given a sequence of downtime hours and contribution rates per hour.

Ranks a batch of mining operation records by their estimated contribution loss (downtime hours × contribution rate) using preset formulas.

Computes a rolling mean of mining downtime contribution loss (in USD) over a sliding window across a series of downtime observations

Performs waterfall/sequential attribution analysis showing how changes in mining downtime hours and contribution per hour drive overall contribution loss, for before-and-after scenario comparison.

Calculates the target gap for mining downtime contribution loss given downtime hours and contribution rate, returning a bounded report for $0.01 USDC.

Classifies the total contribution loss from mining downtime against user-defined lower and upper thresholds, using supplied downtime hours and contribution rate inputs.

Generates a two-dimensional scenario grid showing contribution loss from mining downtime across combinations of downtime hours and contribution USD per hour.

Computes period-over-period changes in mining electricity costs given arrays of energy consumption and per-kWh price inputs.

Calculates electricity cost for multiple cryptocurrency mining scenarios in a single batch, given energy consumption (kWh) and electricity rate (USD/kWh) per row.

Computes and compares mining electricity costs before and after a change in energy consumption or rate, returning a bounded report of the difference.

Generates a one-input sensitivity table showing how mining electricity charges vary across a range of energy consumption (kWh) or electricity rate (USD/kWh) values, given a baseline mining configuration.

Computes a rolling mean of electricity cost for cryptocurrency mining operations given a time series of energy consumption and price data.

Computes a sequential (waterfall) attribution of how changes in energy consumption (kWh) and electricity rate (USD/kWh) each contribute to the difference in a miner's total electricity bill.

Calculates the electricity cost for a cryptocurrency mining operation given energy consumption in kWh and a per-kWh rate in USD.

Generates a two-dimensional scenario grid of mining electricity charges by varying energy consumption (kWh) and electricity rate (USD/kWh) across user-specified ranges.

Calculates a weighted mean of mining electricity charges across up to 200 energy/rate data points using supplied weights

Calculates period-over-period changes in mining energy consumption (kWh) given power, hours, and uptime inputs across multiple time periods.

Computes a base-100 rebase index for mining energy consumption (kWh) across multiple mining operation profiles, normalizing all values relative to a chosen baseline row.

Computes and compares mining energy consumption (kWh) across two scenarios—before and after—based on supplied power draw, hours, and uptime assumptions.

Computes a statistical distribution summary of energy consumption (kWh) across a set of mining hardware rows defined by power draw, operating hours, and uptime percentage.

Calculates how sensitively total mining energy consumption (kWh) responds to small finite changes in power draw, operating hours, or uptime percentage at a given baseline.

Analyzes ordered trends in mining energy consumption (kWh) across multiple operational rows, given power draw, hours, and uptime inputs.

Ranks a list of mining operation records by energy consumption (kWh) based on supplied power, hours, and uptime assumptions.

Calculates the gap between actual and target energy consumption (kWh) for a mining operation given power, hours, and uptime inputs.

Classifies computed mining energy consumption (kWh) values as below, within, or above a user-defined threshold range, given power, hours, and uptime inputs.

Generates a two-dimensional grid of mining energy consumption (kWh) scenarios by varying two of three inputs: power draw (kW), operating hours, and uptime percentage.

Calculates a weighted mean of mining energy consumption (kWh) across multiple mining operation records, each defined by power draw, operating hours, and uptime percentage.