# Clothing

Use Humanform to grade a garment against a population: stature, chest, waist, hip, and the lengths and breadths that set a pattern. The result is a population estimate for the country, gender, and age you choose. It is not a fit model and it is not a person's body.

## Notable measurements

| Measurement | Catalog id | Typical use |
| --- | --- | --- |
| Stature | `Stature` | Overall height grade |
| Chest circumference, nipple | `ChestCircumferenceNipple` | Chest grade |
| Waist circumference, abdominal | `WaistCircumferenceAbdominal` | Waist grade |
| Hip circumference, max | `HipCircumferenceMax` | Hip grade |
| Crotch height | `CrotchHeight` | Rise and inseam reference |
| Shoulder breadth (bideltoid) | `ShoulderBreadthBideltoid` | Shoulder width |
| Neck circumference | `NeckCircumference` | Collar |
| Hand length | `HandLength` | Glove length |
| Foot length | `FootLength` | Footwear length |

## Preset

The `apparel` preset is named Apparel grade in the app. It includes stature, chest circumference at the nipple, abdominal waist circumference, maximum hip circumference, and crotch height.

## Command line

```
humanform-cli predict Stature --country UK --gender female --age 30 --percentile 50
humanform-cli measurements --query waist
humanform-cli accommodate --pack apparel --country UK --gender female --age-min 18 --age-max 65 --target 90
```

`accommodate` requires Humanform Pro.

To see how many people in a sample fall inside a size you already defined, use `population`. The minimum and maximum are your size limits, in millimetres.

```
humanform-cli population WaistCircumferenceAbdominal \
  --country UK --gender female --age-min 18 --age-max 65 \
  --min <lower> --max <upper>
```

## MCP

```
Use the Humanform accommodate tool with pack apparel, country UK, gender female, ageMin 18, ageMax 65, and target 90. List each solved limit with its catalog id. Then use measurements with query "hand" and predict HandLength at the 5th, 50th, and 95th percentiles for the same population. Say these are population estimates for grading, not a fit model.
```