Survey Data CSV Row Completeness Checker
Annotated per-row survey CSV required-field completeness; not an automatic CSVparser or validation of actual field truth. Actual independently witnessed criterion columns: fromstation field present, tostation field present, distance field present, azimuth field present, inclination field present. Unknowns retained; not readiness, authenticity, legality, diagnosis or safety certification.
Description
Survey CSV completeness concerns whether the required fields have documented evidence.
Survey CSV completeness concerns whether the required fields have documented evidence.1
Annotated presence is distinct from parsing the file or verifying that a recorded measurement is true.
When to use Survey Data CSV Row Completeness Checker
Use this tool to obtain witnessedCriteria, knownMissingCriteria, unknownCriteria from the supplied records using the stated calculation. Establish the survey or observation scope, reference frame, units and independently applicable calibration or qualified plan.
How to use Survey Data CSV Row Completeness Checker and understand its results
- Establish the survey or observation scope, reference frame, units and independently applicable calibration or qualified plan.
- Enter independentlyWitnessedCriterionFlags, criterionEvidenceKnownFlags, using the field meanings and rules below, then run the tool.
- Read witnessedCriteria, knownMissingCriteria and unknownCriteria separately. Unknown evidence remains unresolved rather than passing or failing the criterion.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| independentlyWitnessedCriterionFlags | Independently witnessed criterion flags | list of number | Required |
| criterionEvidenceKnownFlags | Criterion evidence known flags; binary 1 means the matched observation is known, 0 means unknown | list of number | Required |
Supply one binary observation flag and one binary known flag for each fixed criterion in the table, in exactly that order. A known one enters witnessedCriteria; a known zero enters knownMissingCriteria; an unknown item enters unknownCriteria regardless of its placeholder observation flag. All flags must be zero or one. The tool accepts wholly unknown records and performs no measurement or automatic verification.
| Zero-based column | Criterion |
|---|---|
| 0 | fromstation field present |
| 1 | tostation field present |
| 2 | distance field present |
| 3 | azimuth field present |
| 4 | inclination field present |
witnessed={label_i:known_i=1 and flag_i=1}; knownMissing={label_i:known_i=1 and flag_i=0}; unknown={label_i:known_i=0} Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Qualified observed calculation or explicit reference | object |
| result.witnessedCriteria | Fixed criterion labels whose flags are known and present. | list |
| result.knownMissingCriteria | Fixed criterion labels whose flags are known and absent. | list |
| result.unknownCriteria | Fixed criterion labels whose observations remain unknown. | list |
{
"input": {
"independentlyWitnessedCriterionFlags": [
1,
1,
1,
1,
1
],
"criterionEvidenceKnownFlags": [
1,
1,
1,
1,
1
]
},
"output": {
"result": {
"witnessedCriteria": [
"fromstation field present",
"tostation field present",
"distance field present",
"azimuth field present",
"inclination field present"
],
"knownMissingCriteria": [],
"unknownCriteria": []
}
}
}Limitations and assumptions
The actual observation coverage, calibration and qualified plan determine applicability. A numerical result does not establish survey accuracy, route suitability, atmosphere conditions or equipment adequacy beyond the declared comparison.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries, subject to the narrower lengths and code rules above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Shot Data Unit Consistency Checker: A survey record's distance and angle tags declare how its numbers are interpreted. For a complementary comparison, use LRUD Passage Sketch Dimension Completeness Checker: LRUD means left, right, up and down passage dimensions under the adopted survey frame.
References
-
Survey Data CSV Row Completeness Checker — tool.io
Similar or alternative tools
- Shot Data Unit Consistency Checker
A survey record's distance and angle tags declare how its numbers are interpreted.
- LRUD Passage Sketch Dimension Completeness Checker
LRUD means left, right, up and down passage dimensions under the adopted survey frame.