> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.conductorquantum.com/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.conductorquantum.com/_mcp/server.

# Using the Turn-On Models

> Learn how to use the Turn-On Models

## Installation

We recommend using pip, poetry, or uv to install the package.

**`pip`**

```bash title="pip"
pip install conductorquantum
```

**`poetry`**

```bash title="poetry"
poetry add conductorquantum
```

**`uv`**

```bash title="uv"
uv add conductorquantum
```

## Authentication

The SDK requires an API key for authentication.  [Sign in and create a new API key](/control/getting-started/api-keys). Remember, your API key is your access secret - keep it safe
with environment variables.

Using environment variables:

**`Python`**

```python title="Python"
from dotenv import load_dotenv
import os
from conductorquantum import ConductorQuantum

# Load API key from .env file
load_dotenv()
TOKEN = os.getenv("CONDUCTOR_API_KEY")

# Initialize client
client = ConductorQuantum(token=TOKEN)
```

Or provide the API key directly:

**`Python`**

```python title="Python"
from conductorquantum import ConductorQuantum

# Initialize client with API key
client = ConductorQuantum(token="YOUR_API_KEY")
```

## Usage Examples

### Using the Turn-On Classifier

The Turn-On Classifier is a model that can classify a given current measurement as either
in the Turn-On regime or not.

You can [download](https://storage.googleapis.com/conductor-docs-example-files/turn-on-classifier-v0.npy) an example file to follow along with the example:

**`Python`**

```python title="Python"
from dotenv import load_dotenv
import os
import numpy as np
from conductorquantum import ConductorQuantum


# Load API key from .env file
load_dotenv()
TOKEN = os.getenv("CONDUCTOR_API_KEY")

# Initialize client
client = ConductorQuantum(token=TOKEN)

# Load Turn-on data (current measurement)
data = np.load("turn-on-classifier-v0.npy")  # shape (n, )

# Detect Turn-on
result = client.control.models.run(
    model="turn-on-classifier-v0",
    data=data
)

# Access the classification result
is_turn_on = result.output["classification"]
print(f"Is turn-on: {is_turn_on}")
```

**`Output`**

```python title="Output"
Is turn-on: True
```

### Using the Turn-On Parameter Extractor

The Turn-On Parameter Extractor is a model that can extract the parameters of a given current measurement.

You can [download](https://storage.googleapis.com/conductor-docs-example-files/turn-on-parameter-extractor-v0.npy) an example file to follow along with the example:

**`Python`**

```python title="Python"
from dotenv import load_dotenv
import os
import numpy as np
from conductorquantum import ConductorQuantum


# Load API key from .env file
load_dotenv()
TOKEN = os.getenv("CONDUCTOR_API_KEY")

# Initialize client
client = ConductorQuantum(token=TOKEN)

# Load Turn-on data (current measurement)
data = np.load("turn-on-parameter-extractor-v0.npy")  # shape (n, )

# Detect Turn-on
result = client.control.models.run(
    model="turn-on-parameter-extractor-v0",
    data=data
)

# Access the peak locations (indices)
threshold_index = result.output["threshold_idx"]
print(f"Threshold index: {threshold_index}")
```

**`Output`**

```python title="Output"
Threshold index: 52
```

#### Plotting the Output

**`Python`**

```python title="Python"
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 5), dpi=300)
plt.plot(data, 'k-')
plt.axvline(x=result.output["threshold_idx"], color='b', linestyle='--', label='Threshold Index')
plt.xlabel('Voltages (Indices)')
plt.ylabel('Current (a.u.)')
plt.legend()
plt.show('turn-on-parameter-extractor.png')

```

![Turn-on Parameter Extractor](/_fern-img/ce765a75513de87c120b1144d2e5e637ef987150c5e1b0886c662d4600172d76.webp)

## Important Notes for Turn-On Models

* **Input dimensions**: Input dimensions should ideally match the specified resolution of the model for the best results. You may need to interpolate or downsample your data to match the required shape. You can find the required input shapes for each model on the [models overview](/control/models/overview) page.
* **Data format**: Input data should be a 1D numpy array representing current measurements as a function of gate voltage.
* **Output format**:
  * The classifier outputs a boolean value (`True` or `False`) indicating whether the measurement exhibits turn-on behavior.
  * The parameter extractor outputs a dictionary with a `threshold_idx` key, which indicates the index where the turn-on threshold occurs.
* **Model versions**: Higher version numbers typically indicate improved accuracy and performance. Check the [models overview](/control/models/overview) for the latest available versions.