module_name.md

# Table of Contents

- [Table of Contents](#table-of-contents)
- [main](#main)
- [:orange\[PINN\]](#orangepinn)
  - [PINN.pinns](#pinnpinns)
  - [PINNd\_p Objects](#pinnd_p-objects)
  - [PINNhd\_ma Objects](#pinnhd_ma-objects)
  - [PINNT\_ma Objects](#pinnt_ma-objects)
- [:orange\[utils\]](#orangeutils)
  - [utils.test](#utilstest)
  - [utils.dataset\_loader](#utilsdataset_loader)
      - [get\_dataset](#get_dataset)
  - [utils.ndgan](#utilsndgan)
    - [DCGAN Objects](#dcgan-objects)
      - [define\_discriminator](#define_discriminator)
      - [generate\_latent\_points](#generate_latent_points)
      - [define\_gan](#define_gan)
      - [summarize\_performance](#summarize_performance)
      - [train\_gan](#train_gan)
  - [utils.data\_augmentation](#utilsdata_augmentation)
  - [dataset Objects](#dataset-objects)
      - [\_\_init\_\_](#__init__)
- [:orange\[nets\]](#orangenets)
  - [nets.envs](#netsenvs)
    - [SCI Objects](#sci-objects)
      - [data\_flow](#data_flow)
      - [init\_seed](#init_seed)
      - [compile](#compile)
      - [train](#train)
      - [inference](#inference)
    - [RCI Objects](#rci-objects)
      - [data\_flow](#data_flow-1)
      - [compile](#compile-1)
  - [nets.dense](#netsdense)
    - [Net Objects](#net-objects)
      - [\_\_init\_\_](#__init__-1)
  - [nets.design](#netsdesign)
      - [B\_field\_norm](#b_field_norm)
  - [nets.deep\_dense](#netsdeep_dense)
    - [dmodel Objects](#dmodel-objects)
      - [\_\_init\_\_](#__init__-2)

<a id="main"></a>

# main

<a id="PINN"></a>

# :orange[PINN]

<a id="PINN.pinns"></a>

## PINN.pinns

<a id="PINN.pinns.PINNd_p"></a>

## PINNd\_p Objects

```python
class PINNd_p(nn.Module)
```

$d \mapsto P$

<a id="PINN.pinns.PINNhd_ma"></a>

## PINNhd\_ma Objects

```python
class PINNhd_ma(nn.Module)
```

$h,d \mapsto m_a $

<a id="PINN.pinns.PINNT_ma"></a>

## PINNT\_ma Objects

```python
class PINNT_ma(nn.Module)
```

$ m_a, U \mapsto T$

<a id="utils"></a>

---
# :orange[utils]

<a id="utils.test"></a>

## utils.test

<a id="utils.dataset_loader"></a>

## utils.dataset\_loader

<a id="utils.dataset_loader.get_dataset"></a>

#### get\_dataset

```python
def get_dataset(raw: bool = False,
                sample_size: int = 1000,
                name: str = 'dataset.pkl',
                source: str = 'dataset.csv',
                boundary_conditions: list = None) -> _pickle
```

Gets augmented dataset

**Arguments**:

- `raw` _bool, optional_ - either to use source data or augmented. Defaults to False.
- `sample_size` _int, optional_ - sample size. Defaults to 1000.
- `name` _str, optional_ - name of wanted dataset. Defaults to 'dataset.pkl'.
- `boundary_conditions` _list,optional_ - y1,y2,x1,x2.

**Returns**:

- `_pickle` - pickle buffer

<a id="utils.ndgan"></a>

## utils.ndgan

<a id="utils.ndgan.DCGAN"></a>

### DCGAN Objects

```python
class DCGAN()
```

<a id="utils.ndgan.DCGAN.define_discriminator"></a>

#### define\_discriminator

```python
def define_discriminator(inputs=8)
```

function to return the compiled discriminator model

<a id="utils.ndgan.DCGAN.generate_latent_points"></a>

#### generate\_latent\_points

```python
def generate_latent_points(latent_dim, n)
```

generate points in latent space as input for the generator

<a id="utils.ndgan.DCGAN.define_gan"></a>

#### define\_gan

```python
def define_gan(generator, discriminator)
```

define the combined generator and discriminator model

<a id="utils.ndgan.DCGAN.summarize_performance"></a>

#### summarize\_performance

```python
def summarize_performance(epoch, generator, discriminator, latent_dim, n=200)
```

evaluate the discriminator and plot real and fake samples

<a id="utils.ndgan.DCGAN.train_gan"></a>

#### train\_gan

```python
def train_gan(g_model,
              d_model,
              gan_model,
              latent_dim,
              num_epochs=2500,
              num_eval=2500,
              batch_size=2)
```

function to train gan model

<a id="utils.data_augmentation"></a>

## utils.data\_augmentation

<a id="utils.data_augmentation.dataset"></a>

## dataset Objects

```python
class dataset()
```

Creates dataset from input source

<a id="utils.data_augmentation.dataset.__init__"></a>

#### \_\_init\_\_

```python
def __init__(number_samples: int,
             name: str,
             source: str,
             boundary_conditions: list = None)
```

_summary_

**Arguments**:

- `number_samples` _int_ - _description_
- `name` _str_ - _description_
- `source` _str_ - _description_
- `boundary_conditions` _list_ - y1,y2,x1,x2

<a id="nets"></a>

# :orange[nets]

<a id="nets.envs"></a>

## nets.envs

<a id="nets.envs.SCI"></a>

### SCI Objects

```python
class SCI()
```

<a id="nets.envs.SCI.data_flow"></a>

#### data\_flow

```python
def data_flow(columns_idx: tuple = (1, 3, 3, 5),
              idx: tuple = None,
              split_idx: int = 800) -> torch.utils.data.DataLoader
```

Data prep pipeline

**Arguments**:

- `columns_idx` _tuple, optional_ - Columns to be selected (sliced 1:2 3:4) for feature fitting. Defaults to (1,3,3,5).
- `idx` _tuple, optional_ - 2|3 indexes to be selected for feature fitting. Defaults to None. Use either idx or columns_idx (for F:R->R idx, for F:R->R2 columns_idx)
  split_idx (int) : Index to split for training
  

**Returns**:

- `torch.utils.data.DataLoader` - Torch native dataloader

<a id="nets.envs.SCI.init_seed"></a>

#### init\_seed

```python
def init_seed(seed)
```

Initializes seed for torch optional()

<a id="nets.envs.SCI.compile"></a>

#### compile

```python
def compile(columns: tuple = None,
            idx: tuple = None,
            optim: torch.optim = torch.optim.AdamW,
            loss: nn = nn.L1Loss,
            model: nn.Module = dmodel,
            custom: bool = False) -> None
```

Builds model, loss, optimizer. Has defaults

**Arguments**:

- `columns` _tuple, optional_ - Columns to be selected for feature fitting. Defaults to (1,3,3,5).
  optim - torch Optimizer
  loss - torch Loss function (nn)

<a id="nets.envs.SCI.train"></a>

#### train

```python
def train(epochs: int = 10) -> None
```

Train model
If sklearn instance uses .fit()

<a id="nets.envs.SCI.inference"></a>

#### inference

```python
def inference(X: tensor, model_name: str = None) -> np.ndarray
```

Inference of (pre-)trained model

**Arguments**:

- `X` _tensor_ - your data in domain of train
  

**Returns**:

- `np.ndarray` - predictions

<a id="nets.envs.RCI"></a>

### RCI Objects

```python
class RCI(SCI)
```

<a id="nets.envs.RCI.data_flow"></a>

#### data\_flow

```python
def data_flow(columns_idx: tuple = (1, 3, 3, 5),
              idx: tuple = None,
              split_idx: int = 800) -> torch.utils.data.DataLoader
```

Data prep pipeline

**Arguments**:

- `columns_idx` _tuple, optional_ - Columns to be selected (sliced 1:2 3:4) for feature fitting. Defaults to (1,3,3,5).
- `idx` _tuple, optional_ - 2|3 indexes to be selected for feature fitting. Defaults to None. Use either idx or columns_idx (for F:R->R idx, for F:R->R2 columns_idx)
  split_idx (int) : Index to split for training
  

**Returns**:

- `torch.utils.data.DataLoader` - Torch native dataloader

<a id="nets.envs.RCI.compile"></a>

#### compile

```python
def compile(columns: tuple = None,
            idx: tuple = (3, 1),
            optim: torch.optim = torch.optim.AdamW,
            loss: nn = nn.L1Loss,
            model: nn.Module = PINNd_p,
            lr: float = 0.001) -> None
```

Builds model, loss, optimizer. Has defaults

**Arguments**:

- `columns` _tuple, optional_ - Columns to be selected for feature fitting. Defaults to None.
- `idx` _tuple, optional_ - indexes to be selected Default (3,1)
  optim - torch Optimizer
  loss - torch Loss function (nn)

<a id="nets.dense"></a>

## nets.dense

<a id="nets.dense.Net"></a>

### Net Objects

```python
class Net(nn.Module)
```

4 layer model, different activations and neurons count on layer

<a id="nets.dense.Net.__init__"></a>

#### \_\_init\_\_

```python
def __init__(input_dim: int = 2, hidden_dim: int = 200)
```

Init

**Arguments**:

- `input_dim` _int, optional_ - Defaults to 2.
- `hidden_dim` _int, optional_ - Defaults to 200.

<a id="nets.design"></a>

## nets.design

<a id="nets.design.B_field_norm"></a>

#### B\_field\_norm

```python
def B_field_norm(Bmax, L, k=16, plot=True)
```

Returns vec B_z

**Arguments**:

- `Bmax` _any_ - maximum B in thruster
  k - magnetic field profile number

<a id="nets.deep_dense"></a>

## nets.deep\_dense

<a id="nets.deep_dense.dmodel"></a>

### dmodel Objects

```python
class dmodel(nn.Module)
```

4 layers Torch model. Relu activations, hidden layers are same size.

<a id="nets.deep_dense.dmodel.__init__"></a>

#### \_\_init\_\_

```python
def __init__(in_features=1, hidden_features=200, out_features=1)
```

Init

**Arguments**:

- `in_features` _int, optional_ - Input features. Defaults to 1.
- `hidden_features` _int, optional_ - Hidden dims. Defaults to 200.
- `out_features` _int, optional_ - Output dims. Defaults to 1.