Data x402 offerings

Builds the cohort table from the events you pass: users grouped by the period of their first event, counted again in each following period. Returns the table per cohort, a retention curve across all cohorts, and, where events carry a value, cumulative value and value per user. The curve counts only cohorts old enough for a given period, so a young cohort is not scored as churn. Day, week and month are real calendar periods. Nothing is fetched, nothing is stored.

Reads one conversion experiment two ways at once. Frequentist: two-proportion test, p value, confidence interval on the absolute lift, and the sample size the requested minimum detectable effect needs at the requested power. Bayesian: Beta-Binomial posteriors, probability the variant is better, expected loss either way, credible intervals - closed form, not sampled, so the numbers repeat exactly. looks_taken carries the adjusted threshold and the rate you actually ran at.

Repariert die typischen Fehler aus Sprachmodell-Ausgaben und Fremdsystemen: Markdown-Zaun, haengende Kommas, einfache Anfuehrungszeichen, unquotierte Schluessel, Kommentare, typografische Anfuehrungszeichen, unsichtbare Zeichen, fehlende Klammern, NaN und Infinity. Liefert das geparste Objekt und ein Protokoll jeder einzelnen Reparatur.

Runs three detectors over one series and reports what each saw: a robust z score on median and median absolute deviation, a robust seasonal-trend decomposition with a z score on the residual, and an isolation forest over value and step-to-previous. Every score is returned per point, so callers set their own threshold instead of trusting a verdict. The forest draws from a fixed seed: same request, same result. Under three full cycles the seasonal figure is marked as not robust.

Walks the events you pass through the ordered steps you name and reports per step: how many arrived, conversion from the previous step and from the first, drop-off in users and as a rate, and time to the next step as median, p90 and mean. A step counts only if it happened after the one before and inside the window. Where events carry a segment, each is tested against the largest with the same two-proportion test the A/B endpoint uses. Nothing is stored.

Prüft am Objekt paginierung den Befund technische Indexierbarkeit.

Updates one shared beta prior with each variant’s supplied clicks and non-clicks, reports posterior means and bounded normal-approximation 95% intervals, and ranks by posterior mean. It does not run an experiment, correct assignment bias, prove significance or predict future performance under distribution shift.

Counts supplied outcomes, computes Pearson’s chi-square statistic, and estimates an upper-tail p-value with the Wilson-Hilferty normal approximation. It tests aggregate frequencies only; it cannot prove draw independence, seed fairness, code integrity, or manipulation, especially when expected counts are small.

Applies caller-supplied rename and numeric scale/offset rules to scalar attributes, optionally retaining unmapped fields, then hashes the canonical mapped payload. It does not transfer ownership, call a chain, validate either game, preserve semantics, or authorize asset use.

Computes dense Farneback optical flow between two consecutive grayscale frames and returns the horizontal and vertical displacement of every pixel. Unlike sparse tracking this covers the whole image, which is what you want for measuring motion in regions with no distinct features to track — smoke, water, crowds, a panning background. Both frames must be the same size and should come from the same camera at a short interval.

Given paired calibration observations and the intrinsics of both cameras, this computes the rotation and translation between them and derives everything a stereo pipeline needs downstream: rectification transforms for both views, both projection matrices, and the disparity-to-depth mapping. The RMS error in pixels says how well the rig geometry actually fits the data.

Solves for the rigid transform between an object's coordinate frame and the camera, given 3D landmark coordinates, where those landmarks appear in the image, and the camera matrix and distortion. Returns the rotation and translation together with the reprojection RMSE, so a caller can tell a well-constrained pose from one that merely converged. This is the standard step for placing a known object in a scene or locating a camera against a known marker.

Takes matched object and image points from multiple views of a planar calibration target and returns the camera matrix, five distortion coefficients and the RMS reprojection error that the fit actually achieved. The error figure is the point: it tells a caller whether the calibration is usable before anything is built on top of it, instead of handing back numbers that look plausible.

Fits a 2D projective transform between two point sets with RANSAC at a caller-chosen reprojection threshold, and returns both the homography and the inlier mask. The mask is what makes this useful beyond a plain fit: bad correspondences are named rather than quietly averaged in, so a caller can re-run detection, drop a frame or raise an alarm. Suitable for document rectification, planar tracking and image stitching where the scene really is flat.

Triangulates 3D positions from corresponding points in two calibrated views using the supplied projection matrices. Degenerate cases are reported honestly: a correspondence whose rays are near-parallel has no finite intersection, and it comes back as null with its position in the finite mask, instead of as an enormous coordinate that silently poisons whatever consumes it. That makes the result safe to feed into a fitting step without a sanity filter of your own.

Solves the hand-eye problem: given a series of gripper-to-base poses read from the robot and the matching target-to-camera poses seen by the camera, it recovers the rigid transform between camera and gripper that stays constant as the arm moves. This is what lets a robot act on what its camera sees. The poses must be genuinely synchronized — a pairing that is off by one sample produces a confident and entirely wrong transform, and nothing in the data reveals it.

Runs semi-global block matching over a rectified grayscale stereo pair and returns per-pixel disparity together with the count of pixels that could actually be matched. Pixels with no reliable match stay unmatched rather than being interpolated, so occlusions and textureless regions are visible in the output instead of hidden by plausible-looking values. Disparities are positive and bounded at 16 pixels, which suits small-baseline rigs and close range.

Resamples a grayscale image to remove radial and tangential lens distortion, using the camera matrix and five distortion coefficients you supply — typically from a planar calibration. Dimensions and camera matrix are preserved, so the corrected image drops into an existing pipeline without any change to geometry downstream. Because the output frame is unchanged, strongly distorted corners are pushed outside the image and are simply lost rather than being retained in a larger canvas.

Adds structured delivery-note extraction alongside the existing invoice extractor and purchase-order matcher; does not duplicate invoice-extract or filing-to-json. Strict namespace-aware XML parsing, no model or foreign API. Only supplied source fields are returned; optional fields are null. Validates extraction bounds, dates, unique line IDs and decimal quantities, but does not claim full UBL XSD or legal conformity. No invoice, PDF or OCR support.

Also supports unencrypted text-layer PDFs <=187500 bytes/10 pages/120000 extracted characters. Conservative English/German labels for invoice number, dates and totals; returns page/line evidence and complete bounded text lines. Unrecognized fields and item tables remain null/empty, partial=true; dollar signs do not imply USD. Unsupported or ambiguous core fields fail422 without settlement. Local Poppler only, no model, OCR or external API. 20 second timeout.

Does the page declare schema.org Question structured data? Parses every JSON-LD block (and microdata itemtype attributes) on the page and returns whether a Question object is present, how many, and the Question object(s) themselves (trimmed). Rich-result eligibility and data extraction in one call. $0.01 per page.

Site-wide tallies: published episodes, shows, checked claims and cited studies.

Dataset metadata for this Japanese (JP) corporate-number API — how many corporations are loaded and which prefectures are covered.

Extract a <table> from an HTML string as JSON rows (header-keyed) or CSV. Useful for prices, schedules, sports stats, or anything an agent has already fetched as HTML. If multiple tables match, the first is used.