# Key examples

see these for a quick overview:

• generic
• pathological

# 1D

## example of simple 1D SAS data, I(Q)

```SASroot
SASentry
SASdata
@Q_indices=0
@I_axes="Q"
I: float[100]
Q: float[100]
```

(see the Discussion page for an XML representation with the recommended minimum content)

## example of simple 1D SAS data in a time series, I(Q, t)

```SASroot
SASentry
SASdata
@Q_indices=1
@I_axes="Time,Q"
@Time_indices=0
I: float[nTime,100]
Q: float[100]
Time: float[nTime]
```

## example of generic 1D SAS data in a time series, I(Q(t), t)

```SASroot
SASentry
SASdata
@Q_indices=0,1
@I_axes="Time,Q"
@Time_indices=0
I: float[nTime,100]
Q: float[nTime,100]
Time: float[nTime]
```

# 2D

## example of simple 2D (image) SAS data, I(Q)

```SASroot
SASentry
SASdata
@Q_indices=0,1
@I_axes="Q,Q"
I: float[100, 512]
Qx: float[100, 512]
Qy: float[100, 512]
```

## example of a simple masked 2D (image) SAS data, I(Q)

```SASroot
SASentry
SASdata
@Q_indices=0,1
@I_axes="Q,Q"
I: float[100, 512]
Qx: float[100, 512]
Qy: float[100, 512]
```

## example of generic 2D SAS data, I(Q)

Could use this model, for example, to describe data from multiple detectors (by listing individual pixels off all detectors). Or, could describe data from one detector of any geometry. This is the most flexible.

```SASroot
SASentry
SASdata
@Q_indices=0
@I_axes="Q"
I: float[100*512]
Qx: float[100*512]
Qy: float[100*512]
Qz: float[100*512]
```

## example of simple 2D SAS/WAS data, Isas(Q) & Iwas(Q)

Consider the multi-technique experiment that produces small-angle and wide-angle scattering data images. The reduced data results in images as well. Each image might be described separately (see [[2012_Data_Discussion_Examples#example_of_SAS_data_with_several_detectors.2C_I.28Q.29 | example of SAS data with several detectors]] for an alternative). Here the SAS data image is 100 x 512 pixels. The WAS data (not covered by this canSAS standard) is 256 x 256 pixels.

```SASroot
SASentry
SASdata
@name="sasdata"
@Q_indices=0,1
@I_axes="Q,Q"
I: float[100, 512]
Qx: float[100, 512]
Qy: float[100, 512]
SASdata
@name="wasdata"
@Q_indices=0,1
@I_axes="Q,Q"
I: float[256, 256]
Qx: float[256, 256]
Qy: float[256, 256]
```

## example of 2D SANS and 2D SAXS, In(Q) & Ix(Q)

Consider the multi-technique experiment that produces small-angle neutron and X-ray scattering data. Here the SANS data image is 100 x 512 pixels and the SAXS data is 256 x 256 pixels.

```SASroot
SASentry
SASdata
@name="sans"
@Q_indices=0
@I_axes="Q"
I: float[100*512]
Qx: float[100*512]
Qy: float[100*512]
SASdata
@name="saxs"
@Q_indices=0
@I_axes="Q"
I: float[256*256]
Qx: float[256*256]
Qy: float[256*256]
```

# 2D with additional varied parameters

## example of generic 2D SAS data in a time series, I(Q(t),t)

```SASroot
SASentry
SASdata
@Q_indices=0,1
@I_axes="Time,Q"
@Time_indices=0
I: float[nTime,100*512]
Qx: float[nTime,100*512]
Qy: float[nTime,100*512]
Qz: float[nTime,100*512]
Time: float[nTime]
```

## example of 2D SAS data as images in a time series with a time-independent mask, I(Q(t),t)

This example explores a bit of complexity added to the previous example.

```SASroot
SASentry
SASdata
@Q_indices=0,1,2
@I_axes="Time,Q,Q"
@Time_indices=0
I: float[nTime,100,512]
Qx: float[nTime,100,512]
Qy: float[nTime,100,512]
Qz: float[nTime,100,512]
Time: float[nTime]
```

## example of generic 2D SAS data in a time, T, & P series, I(t,T,P,Q(t,T,P))

```SASroot
SASentry
SASdata
@Q_indices=0,1,2,3
@I_axes="Time,Temperature,Pressure,Q"
@Time_indices=0
@Temperature_indices=1
@Pressure_indices=2
I: float[nTime,nTemperature,nPressure,100*512]
Qx: float[nTime,nTemperature,nPressure,100*512]
Qy: float[nTime,nTemperature,nPressure,100*512]
Qz: float[nTime,nTemperature,nPressure,100*512]
Time: float[nTime]
Temperature: float[nTemperature]
Pressure: float[nPressure]
```

## example of generic 2D SAS data (images) in a time, T, & P series, I(T,t,P,Q(t))

```SASroot
SASentry
SASdata
@Q_indices=1,3,4
@I_axes="Temperature,Time,Pressure,Q,Q"
@Time_indices=1
@Temperature_indices=0
@Pressure_indices=2
I: float[nTemperature,nTime,nPressure,100,512]
Qx: float[nTime,100,512]
Qy: float[nTime,100,512]
Qz: float[nTime,100,512]
Time: float[nTime]
Temperature: float[nTemperature]
Pressure: float[nPressure]
```

## example of SAS data with several detectors, I(Q)

Here, the data are appended to common data objects. This hypothetical case has reduced data derived from three detectors, Ia(Q), Ib(Q), and Ic(Q):

• Ia(Q) is derived from a 2D detector (100 x 512 pixels)
• Ib(Q) is derived from a 1D detector (2000 pixels)
• Ic(Q) is derived from a 2D detector (256 x 256 pixels)

Data from a SAXS/MAXS/WAXS instrument might be represented thus.

```SASroot
SASentry
SASdata
@Q_indices=0
@I_axes="Q"
I: float[100*512  + 2000 + 256*256]
Qx: float[100*512 + 2000 + 256*256]
Qy: float[100*512 + 2000 + 256*256]
Qz: float[100*512 + 2000 + 256*256]
```

# invalid case

## example of **over-simple** 2D (image) SAS data, I(Q)

Invalid because the method of addressing the Q values is different from all the above.

```SASroot
SASentry
SASdata
@Q_indices="*,*"
@I_axes=" ??? "
I: float[100, 512]
Qx: float[100]
Qy: float[512]
```

# terms

## SASroot

same use as original 1D format

## SASentry

some changes from the original 1D format

needs a version attribute that describes the version of the canSAS definition of SASentry (use version="1.0")

## SASdata

different use from original 1D format, refers to a single reduced data set that can be represented thus (such as from one detector)

SASdata has some possible attributes, as shown in this example:

```@Q_indices=1,3,4
@I_axes="Temperature,Time,Pressure,Q,Q"
```

To indicate the dependency relationships of other varied parameters, use attributes similar to @Mask_indices (such as @Temperature_indices or @Pressure_indices).

### @Q_indices

Array attribute that describes which indices (of the I data object) are used to reference Q. The items in this array use zero-based indexing.

### @I_axes

Comma-separated list that describes the names of the data objects that correspond to the indices of the I object.