---
title: "mlab-fileio — Mova code model"
description: "Mova code model of mlab/src/fileio.rs: components with their labels, obernants and the model text."
url: "https://groupants.elamur.ai/free/en/mova/mlab/src/fileio.rs.groupant/"
---
Elamur: [Mova](https://md.mova.elamur.ai/free/en/) · [Dictionary](https://md.dictionary.elamur.ai/free/en/) · [Research](https://md.research.elamur.ai/free/en/) · **Groupants**

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# mlab-fileio

The groupant beside mlab/src/fileio.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.

## Facts

- **kind:** groupant
- **status:** partial source bound model
- **source:** [`mlab/src/fileio.rs`](https://github.com/ElamurAI/mova-free/blob/main/mlab/src/fileio.rs)
- **source SHA-256:** `547a259326432436…`
- **repository folder:** [`mlab/src/fileio.rs.groupant`](https://github.com/ElamurAI/mova-free/tree/main/mlab/src/fileio.rs.groupant)
- **repository commit:** `48f2e6f74146`

> **Implementation material.** A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.

## Components

| component | label | where |
|---|---|---|
| **approximant** | partial | [approximant](#approximant) |
| **ast** | partial | `syntax.json` |
| **composition** | partial | [composition](#composition) |
| **cost** | symbolic source derived | [invariant](#invariant) |
| **descriptant** | present | [descriptant](#descriptant) |
| **invariant** | partial | [invariant](#invariant) |
| **tests** | executed bounded checks | [tests](#tests) |

## Obernants

The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.

| id | type | label | where |
|---|---|---|---|
| `behavior_complement` | behavior_complement | partial source derived model | [behavior_complement](#behavior-complement) |
| `reverse_reconstruction` | reverse_reconstruction | partial source derived model | [reverse_reconstruction](#reverse-reconstruction) |
| `state_invariant` | state_invariant | partial source derived model | [state_invariant](#state-invariant) |
| `failure_precedence` | failure_precedence | partial source derived model | [failure_precedence](#failure-precedence) |
| `representation_transport` | representation_transport | partial source derived model | [representation_transport](#representation-transport) |
| `resource_cost` | resource_cost | partial source derived model | [resource_cost](#resource-cost) |

## Parts

- [mlab-fileio-csv-parser](https://md.groupants.elamur.ai/free/en/mova/mlab/src/fileio.rs.groupant/csv-parser.groupant/)
- [mlab-fileio-json-table](https://md.groupants.elamur.ai/free/en/mova/mlab/src/fileio.rs.groupant/json-table.groupant/)

## Model text


### descriptant

#### mlab-fileio

CSV and JSON parsers, inferred table columns and matrix/value serialization without filesystem effects.

##### Modeling epoch — 2026-10-31

Prospective source read preceded edits. Bounded synthetic checks and selected existing safe tests exercise actual code; paired debug/release results and compiled negative-control assertions are retained externally. Frozen scenario, corpus, external measurement, full-school and training effects are unexecuted. Partial lexical syntax supplies no macro-expanded or semantic proof.

##### Cross-review refinement — 2026-10-31

Table CSV encodes numeric/date NaN as empty fields, whereas matrix CSV emits NaN text. Logical NaN becomes JSON true by nonzero conversion; numeric nonfinite values become null. Recursive JSON rendering repeatedly copies child text, so cumulative subtree sizes, rather than final output alone, determine work. CRLF/LF ambiguity is limited to record separators; quoted payload retains CR.

### approximant

#### Forward relation

CSV scans Unicode chars after leading BOMs, tracks line/column, decodes quoted fields and validates table width before inference. Numeric inference precedes boolean/date/text and quoted-present forces text. JSON parser preserves object member sequence; table conversion unions first-seen keys, picks first duplicate and fills missing null. Writers emit canonical bytes.

##### Domain

Valid Rust value/storage invariants and available bounded resources; exact branch and effect observations only.

##### Executable approximant gap

No useful percentage-error surrogate is established for these categorical, textual or effectful observations. Supported future caps are 1%, 2%, 5%, 10%; no executable approximation or production substitution is claimed.

### invariant

#### Invariants and costs

CSV coordinates are one-based Unicode-scalar columns and decoded line counts. Matrix output uses row order over column-major storage. Parsing allocates values but performs no host/VFS effects.

##### Failure ordering

CSV syntax errors precede table width errors; quoted unterminated error reports field origin, unexpected postquote reports current location. JSON parser is permissive f64 grammar rather than strict RFC number validator; recursion/allocation unbounded.

##### Cost

CSV O(B+cells) time/storage. JSON character parsing O(B), error position rescans prefix; key union and per-column row lookups can require O(rows*keys^2). json_text copies child strings at each nesting level: cumulative work is the sum of serialized subtree sizes, bounded by O(depth * output bytes), plus table/cell work. A depth-D singleton-array chain can require O(D^2) copying for O(D) output; recursion depth is unchecked.

### composition

#### Composition

CSV scans Unicode chars after leading BOMs, tracks line/column, decodes quoted fields and validates table width before inference. Numeric inference precedes boolean/date/text and quoted-present forces text. JSON parser preserves object member sequence; table conversion unions first-seen keys, picks first duplicate and fills missing null. Writers emit canonical bytes.

Numeric/date NaN table cells serialize empty CSV; write_matrix emits literal NaN through num_text. Numeric-class nonfinite JSON values become null, while logical-class values use nonzero truth, so logical NaN becomes true. Booleans with missing values become numeric; dates encode day values. Empty table CSV is newline and JSON is [] newline; read_matrix skips at most one nonnumeric header.

##### csv-parser

Strip leading BOM chars, skip blank LF/CRLF lines; each field records starting line/col, quoted strings decode doubled quotes and embedded LF before delimiter/end handling.

Boundary: Field coordinates are Unicode scalar indices, one-based; each record retains start line and end column.

##### json-table

Require array of objects, collect keys in first-encounter order, sanitize/dedupe names; each column uses first matching member per row or Null and ordered type tests.

Boundary: Output column order follows first key encounter; all output columns have one element per input object.

##### Interaction limit

Local fragment relations compose only with valid Value storage, caller error propagation and delegated providers. They do not establish whole-interpreter or host-filesystem guarantees.

### models

## Typed models — mlab-fileio

Source-derived partial relations; finite checks do not prove universality.

### behavior_complement

Quoted numeric CSV fields exclude numeric inference for their column; entirely missing column excludes text inference. J::Num and numeric-class nonfinite JSON serialization excludes numeric literals and becomes null; logical-class values instead use x!=0, so logical NaN serializes true. Successful parse_json excludes trailing nonwhitespace.

### reverse_reconstruction

Canonical tables admit many CSV antecedents differing quoting, headers sanitized/deduped and numeric spelling. JSON table preimages include absent versus null and later duplicate keys. Vector JSON cannot reconstruct row versus column orientation or complex imaginary data.

### state_invariant

CSV coordinates are one-based Unicode-scalar columns and decoded line counts. Matrix output uses row order over column-major storage. Parsing allocates values but performs no host/VFS effects.

### failure_precedence

CSV syntax errors precede table width errors; quoted unterminated error reports field origin, unexpected postquote reports current location. JSON parser is permissive f64 grammar rather than strict RFC number validator; recursion/allocation unbounded.

### representation_transport

Numeric/date NaN table cells serialize empty CSV; write_matrix emits literal NaN through num_text. Numeric-class nonfinite JSON values become null, while logical-class values use nonzero truth, so logical NaN becomes true. Booleans with missing values become numeric; dates encode day values. Empty table CSV is newline and JSON is [] newline; read_matrix skips at most one nonnumeric header.

### resource_cost

CSV O(B+cells) time/storage. JSON character parsing O(B), error position rescans prefix; key union and per-column row lookups can require O(rows*keys^2). json_text copies child strings at each nesting level: cumulative work is the sum of serialized subtree sizes, bounded by O(depth * output bytes), plus table/cell work. A depth-D singleton-array chain can require O(D^2) copying for O(D) output; recursion depth is unchecked.

### tests

#### Evidence and limitations

Selected named outcomes: fileio::wave30_tests::csv_inference_and_json_loss. Each ran twice debug and twice release with sorted identical nonempty outcomes and numeric exit 0. Source-specific compiled control exercised "fileio::wave30_tests::csv_inference_and_json_loss": compile exit 0, assertion-red exit 101, exact-restored rerun exit 0. External receipts: octopus-wave30-io-2026-10-31 (native-commands.json, positive-receipts.json, controls.json).

Scope: CSV coordinates are one-based Unicode-scalar columns and decoded line counts. Matrix output uses row order over column-major storage. Parsing allocates values but performs no host/VFS effects.


AST projection generated twice to fresh files and compared byte-for-byte. Runtime evidence is finite and does not establish every branch, allocation failure, backend implementation or effect path. No optimization or speed claim.