# COND¶

Describes tissue conductivity values

## Initialization¶

• Python

```from simnibs import sim_struct
s = sim_struct.SESSION()
gm_cond = tdcs_list.cond
```

• MATLAB

```s = sim_struct('SESSION');
s.poslist{1} = sim_struct('TDCSLIST');
gm_cond = s.poslist{1}.cond(2)
```

## Attributes¶

• value: float

• Description: Conductivity value, in S/m

• Name: string (Python)/character array (MATLAB), optional

• Description: Name of the tissue

• distribution_type: ‘uniform’, ‘normal’, ‘beta’ or None/unset (Python/MATLAB), optional
• Description: type of distribution for Uncertainty Quantification. Default: None/unset

Warning

Setting this property will trigger a run of the UQ algorithm. Please see the tutorial on Uncertainty Quantification for more information

• distribution_parameters: list/array of floats (Python/MATLAB)
• Description: Sets the parameters for distribution defined in distribution_type.

• if distribution_type is ‘uniform’: [min_value, max_value]

• if distribution_type is ‘normal’: [mean, standard_deviation]

• if distribution_type is ‘beta’: [p, q, min_value, max_value]

## Examples¶

Note

In this examples, we will consider a list/array of COND structures

• Change the value of gray matter conductivity. See the Standard conductivity values.

• Python

```# In Python, we have to take the cond struct
# with the index tissue_number - 1
cond.value = 0.2
```

• MATLAB

```cond(2).value = 0.2;
```

• Add the conductivity of 10 S/m and a name for a new tissue, labeled as 11

• Python

```cond.name = 'Awesome tissue'
cond.value = 10
```

• MATLAB

```cond(11).name = 'Awesome tissue';
cond(11).value = 10;
```