semvec/pypi-packages huggingface dataset:
mdas-python-sdk 0.1.3
Python SDK for MDAS API.
License: MIT
Requires Python: >=3.7

# MDAS Python SDK

A Python SDK for interacting with the MDAS API. This SDK provides an easy-to-use interface for accessing market data and account information.

## Installation

### From PyPI

```bash
pip install mdas-python-sdk
```

## Setup and Configuration

### Basic Setup

```python
from mdas_python_sdk import MdasClient

# Create a client instance
client = MdasClient(
    base_url="https://mdas-api.viewtrade.tech/swagger/index.html",  # Replace with your API endpoint
    username="your-username",                       # Replace with your username
    password="your-password"                        # Replace with your password
)
```

The client will automatically authenticate and obtain a token upon initialization. You can now access various services through the client.

### Available Services

- `client.quote`: Access market data quotes, charts, and other financial information
- `client.account`: Access user account information and settings

## Sample Usage

### Get Level 1 Quotes

```python
# Get level 1 quote for a single symbol
tesla_quote = client.quote.get_level1_quote("TSLA")
print(tesla_quote)

# Get level 1 quote for multiple symbols
multi_quotes = client.quote.get_level1_quote(["AAPL", "MSFT", "GOOG"])
print(multi_quotes)
```

### Using Response Models

You can convert API responses to model objects for easier access:

```python
from mdas_python_sdk.models.quote import QuoteResponse

# Get quotes and convert to model
quotes_data = client.quote.get_level1_quote(["AAPL", "MSFT"])
quote_response = QuoteResponse.from_dict(quotes_data)

# Access data through model
for quote in quote_response.data:
    print(f"{quote.symbol}: ${quote.last_px} ({quote.change_percent}%)")
```

### Get Historical Data

```python
# Get historical minute chart data
historical_data = client.quote.get_historical_minute_chart("TSLA", "2023-05-12")

# Get historical day chart data
day_chart = client.quote.get_historical_day_chart("AAPL", year="2023")
```

### Working with User Account Data

```python
# Get user information
user_info = client.account.get_user_information_by_id("user123")

# Check if session is active
session_status = client.account.is_session_alive()
```

### Handling Authentication

If your token expires during a long session, you can refresh it:

```python
client.refresh_token()
```

## Error Handling

The SDK handles authentication errors automatically by refreshing tokens when they expire. For other errors, you should implement appropriate try/except blocks:

```python
try:
    quote = client.quote.get_level1_quote("INVALID_SYMBOL")
except Exception as e:
    print(f"An error occurred: {e}")
```

## Available APIs

For a complete list of available API endpoints, see [API.md](API.md).
moo-sim-interface 0.4.0
A generic interface for Modelica/FMU simulation and optimization
License: MIT
Requires Python: >=3.10


moo-sim-interface
====

moo-sim-interface is a Python package for the simulation of Modelica-based models, either as FMUs `Functional Mock-up
Units <https://fmi-standard.org/>` or using the OpenModelica or Dymola Python APIs. It allows for easily configurable
simulation setup, execution and evaluation via a generic text-based interface. Its main purpose is parallelization of
simulations and parameter sweeps. Furthermore, it provides a generic interface for Multi-Objective-Optimization.