semvec/pypi-packages huggingface dataset:
pico-sdk 0.1.4
High performance, gap-free streaming from any Pico Technology oscilloscope
Requires Python: >=3.6

# pico_sdk

### High performance, gap-free streaming from any Pico Technology oscilloscope.

> **Note**: This is not an official Pico Technology package

This package wraps all current Pico oscilloscope drivers in a high-level, common
API written in Rust. This API is compiled to native code and exposed to Python
through simple C bindings.

## Supported platforms

Python >= 3.6 on:

- Windows (32/64bit)
- macOS (64bit)
- Linux (64bit)

```python
from pico_sdk import PicoDevice

# List the available Pico devices
found = PicoDevice.enumerate()
for device in found:
    print("PicoScope " + device.variant + " with serial " + device.serial)

# Open the only connected device using locally installed Pico SDKs
device = PicoDevice.open()

# Open a specific device by serial number using locally installed Pico SDKs
device = PicoDevice.open("ABC/123")

# Open a device by serial number and automatically download missing drivers
device = PicoDevice.open("ABC/123", True)

# Get device details
print("Device Serial: " + device.get_serial())
print("Device variant: " + device.get_variant())
print("Valid ranges for channel A: " +
      ", ".join(device.get_channel_ranges("A")))

# String case and white space are ignored
device.enable_channel("A", "200mV")
device.enable_channel("b", "20 v", "dc")


def callback(data):
    # Callback data is a dictionary containing an entry for each channel
    # Channel data is via numpy.ndarray containing float values in volts
    print("Received streaming data...")
    for channel in data:
        print("Channel " + channel + " has " +
              str(data[channel].size) + " samples")


device.set_callback(callback)
samples_per_second = device.start_streaming(1_000_000)

print("Streaming started with " + str(samples_per_second) + " samples per second")
input('Hit ENTER to stop and exit\n')
```
tunepy2 0.0.3
A package containing a set of bitstring optimizers.
Requires Python: >=3.7

[![Build Status](https://travis-ci.org/efortner/tunepy.svg?branch=master)](https://travis-ci.org/efortner/tunepy)
[![Coverage Status](https://coveralls.io/repos/github/efortner/tunepy/badge.svg?branch=master)](https://coveralls.io/github/efortner/tunepy?branch=master)
# tunepy2
Provides a variety of extensible and easy to use bitstring optimizers as well as several machine learning convenience
utilities for model optimization.

## Dependencies
* python >= 3.7
* numpy >= 1.19.2
* scikit-learn >= 0.23.2

## Installation
```
pip install tunepy2
```

## Basic Usage
All bitstring optimizers extend the Abstract Base Class (ABC) [AbstractOptimizer](https://github.com/efortner/tunepy/blob/fe28cfde8f08438d21d9c58d879b24a327b5e737/tunepy2/interfaces/abstract_optimizer.py).
The most basic versions of all optimizers are accessed through static build methods.

### Importing tunepy2
```python
import tunepy2 as tp
```

### Basic Prebuilt Optimizers
* Random Restart Hill Climber ([`tp.new_random_restart_hill_climber`](https://github.com/efortner/tunepy/blob/master/tunepy2/prebuilt/prebuilt_optimizers.py#L16))
* Simulated Annealer ([`tp.new_simulated_annealer`](https://github.com/efortner/tunepy/blob/master/tunepy2/prebuilt/prebuilt_optimizers.py#L78))
* Basic Hill Climber ([`tp.new_hill_climber`](https://github.com/efortner/tunepy/blob/master/tunepy2/prebuilt/prebuilt_optimizers.py#L133))
* Genetic Algorithm ([`tp.new_genetic_optimizer`](https://github.com/efortner/tunepy/blob/master/tunepy2/prebuilt/prebuilt_optimizers.py#L180))

Fitness functions are assumed have an array-like passed in as the first argument and return a fitness value.