Circuit Simulation In Python
Using SPICE to simulate an electrical circuit is a common enough practice in engineering that "SPICEing a circuit" is a perfectly valid phrase in the lexicon. SPICE as a software tool has been around since the 70s, and its open source nature means there are more SPICE tools around now to count. It also means it is straightforward enough to use with other software as well, like integrating LTspice with Python for some interesting signal processing circuit simulation.
[Michael]'s latest project involves simulating filters in LTspice (a SPICE derivative) and then using Python/NumPy to both provide the input signal for the filter and process the output data from it. Basically, it allows you to "plug in" a graphical analog circuit of any design into a Python script and manipulate it easily, in any way needed. SPICE programs aren't without their clumsiness, and being able to write your own tools for manipulating circuits is a powerful tool.
This project is definitely worth a look if you have any interest in signal processing (digital or analog) or even if you have never heard of SPICE before and want an easier way of simulating a circuit before prototyping one on a breadboard.
How To Document Python Code Using Docstrings
© Provided by MUOWithout proper documentation, it can be difficult to understand, maintain, and debug your Code. In Python, you can use docstrings to provide concise descriptions and examples of how the code works.
What Are Docstrings?Docstrings are strings you can add to your Python code to explain what it does and how to use it. The piece of code can be a Python function, a module, or a class.
Docstrings look a lot like standard Python comments, but they have some differences. Python comments provide additional information to developers about the code's inner workings, such as implementation details or caveats to keep in mind when extending the code.
On the other hand, docstrings mostly provide information to users of the code who may not necessarily need to know its implementation details but need to understand what it does and how to use it.
How to Write DocstringsYou typically include docstrings at the beginning of the block of code you want to document. You must enclose them in triple quotes ("""). You can write one-line docstrings or multi-line docstrings.
One-line docstrings are suitable for simple code that doesn't require a lot of documentation.
Below is an example of a function called multiply. The docstring explains the multiply function takes two numbers, multiples them, and returns the result.
def multiply(a, b):
"""Multiplies two numbers and returns the result"""
return a * b
Use multi-line docstrings for more complex code that needs detailed documentation.
Consider the following Car class:
class Car:
"""
A class representing a car object.
Attributes:
mileage (float): The current mileage of the car.
Methods:
drive(miles): Drives the car for the specified number of miles.
"""
def __init__(self, mileage):
self.Mileage = mileage
def drive(self, miles):
"""
Drives the car for the specified number of miles.
Args:
miles (float): The number of miles to drive.
Returns:
None
"""
self.Mileage += miles
The docstring for the above class describes what the class represents, its attributes, and its methods. Meanwhile, the docstrings for the drive method provide information about what the method does, the arguments it expects, and what it returns.
This makes it easier for anyone working with this class to understand how to use it. The other benefits of using docstrings include:
A good docstring should describe what a piece of code does, the arguments it expects, and implementation details if necessary. It should especially include any edge cases anyone using the code should be aware of.
A basic docstring format has the following sections:
This is just a basic format as there are other formats you can choose to base your docstrings. The most popular ones are Epytext, reStructuredText (also known as reST), NumPy, and Google docstrings. Each of these formats has its own syntax as shown in the following examples:
EpytextA docstring that follows the Epytext format:
def multiply(a, b):
"""
Multiply two numbers together.
@param a: The first number to multiply.
@type a: int
@param b: The second number to multiply.
@type b: int
@return: The product of the two numbers.
@rtype: int
"""
return a * b
reStructuredText (reST)A docstring that follows the reST format:
def multiply(a, b):
"""
Multiply two numbers together.
:param a: The first number to multiply.
:type a: int
:param b: The second number to multiply.
:type b: int
:return: The product of the two numbers.
:rtype: int
"""
return a * b
NumPyA docstring that follows the NumPy format:
def multiply(a, b):
"""
Multiply two numbers together.
Parameters
----------
a : int
The first number to multiply.
b : int
The second number to multiply.
Returns
-------
int
The product of the two numbers.
"""
return a * b
GoogleA docstring that follows the Google format:
def multiply(a, b):
"""
Multiply two numbers together.
Args:
a (int): The first number to multiply.
b (int): The second number to multiply.
Returns:
int: The product of the two numbers.
"""
return a * b
Although all four docstring formats provide useful documentation for the multiply function, the NumPy and Google formats are easier to read than the Epytext and reST formats.
How to Include Tests in the DocstringsYou can include testing examples in your docstrings using the doctest module. The doctest module searches the docstring for text that looks like interactive Python sessions and then executes them to verify that they work as they should.
To use doctests, include the sample inputs and expected outputs in the docstring. Below is an example of how you would do that:
def multiply(a, b):
"""
Multiply two numbers together.
Parameters
----------
a : int
The first number to multiply.
b : int
The second number to multiply.
Returns
-------
int
The product of the two numbers.
Examples
--------
>>> multiply(2, 3)
6
>>> multiply(0, 10)
0
>>> multiply(-1, 5)
-5
"""
return a * b
The Examples section contains three function calls with different arguments and specifies the expected output for each. When you run the doctest module as shown below, it will execute the examples and compare the actual output to the expected output.
python -m doctest multiply.PyIf there are any differences, the doctest module reports them as failures. Using doctests with docstrings like this helps you verify that the code is working as expected. Note that doctests are not a replacement for more comprehensive unit tests and integration tests for your Python code.
How to Generate Documentation From DocstringsYou've learned the basics of using docstrings to document your Python code and the importance of high-quality documentation. To take it up a notch, you may want to generate documentation for your modules and functions from their respective docstrings.
One of the most popular documentation generators you can use is Sphinx. It supports reST docstring format by default but you can configure it to work with the Google or NumPy format.
Python: The 'equalizer' For Advanced Data Analytics
© Pixabay nullData offers businesses an almost endless list of benefits, from increasing revenue and customer retention to improving decision-making and streamlining operations. This makes it an incredibly valuable asset from day to day, but even more so during tough economic times, such as those experienced across the world during the past few years. But many organizations face difficulties in extracting the value from their data - from failing to ensure that analysts, data scientists, developers and engineers work together effectively, to having such relentless demand for business insights that the in-house data team is overwhelmed.
CONSTELLATION BRANDS, INC.
Python is an 'equalizer' which can help every part of a data operation to work together. Python is now the most popular language for data science, used by 15.7 million developers globally. It provides an open source framework that enables data teams to deliver cutting-edge data insights rapidly and efficiently. For business leaders, it can be a key differentiator for advanced data analytics.
An introduction to PythonPython can be seen across many aspects of our lives, however, not everyone may realize it. It is the basis of the Netflix algorithm and the software that controls self-driving cars that you see on the streets. As a general-purpose language, Python is designed to be used in a range of applications, including data science, software and web development, and automation. It's this versatility along with its beginner-friendliness that makes it accessible to everyone, allowing teams of machine learning (ML) and data engineers, and data scientists to collaborate with ease.
Python has a rich ecosystem of open source libraries that are often targeted for cyber attacks. That is the reason why it is important to proactively address how users access and interact with open source tooling in an organization. Python is developed under an open source license, making it freely usable and distributable. For businesses, an open source approach offers distinct advantages. There is a vast community of developers contributing to Python projects, making it easier for organizations to collaborate and achieve their goals. With its rich ecosystem of open source packages, businesses can leverage Python to accelerate projects, without having to deal with the complexity of deploying third-party applications. It's for these reasons that Python has become so popular in the data science field.
Another key aspect of Python's appeal is speed. In many data analytics use cases, the Python code tends to be simple – requiring just a few lines — which means that time to market is reduced. This makes Python a natural fit for artificial intelligence (AI) and its algorithmic density. In Python, developers can build logic with as much as 75% less code than other comparable languages.
Embracing Python for data science and MLAccording to the latest Python Developers Survey, data analysis is now the single most popular usage for Python, cited by 51% of developers, with ML also among the top uses of the language, cited by 38%. Python provides data scientists with over 70,000 libraries that can be used in any given task. These libraries contain bundles of code, which can be used repeatedly in different programs, making Python programing simpler and more convenient as data scientists will rarely have to start from scratch. Take Streamlit as an example. As a Python-based library, it's specifically designed for developers and ML engineers to rapidly build and share ML and data science apps.
For businesses hoping to get to grips with ML for the first time, Python is a clear winner. It offers concise code, allowing developers to write reliable ML solutions faster. This means developers can place all of their efforts into solving an ML problem, rather than focusing on the technical nuances of the language. It's platform-independent, allowing it to run on almost every operating system, which makes it perfect for organizations that don't want to be locked into a proprietary system. As a result, Python improves how cross-functional teams of data scientists, data engineers, and application developers can collaborate in taking ML models from experiments into production - which is one of the key challenges ML practitioners face according to the Anaconda State of Data Science report.
Showing its value across industriesAcross industries, Python is making a fundamental difference in how businesses operate, saving time, money, and better utilizing their employees' skills. For example, in healthcare, the principal application of Python is based on ML and natural language processing (NLP) algorithms. Such applications include image diagnostics, NLP of medical documents, and the prediction of diseases using human genetics. Patient data is highly confidential, so secure and well-governed processing of such data is essential: this is a key challenge for organizations in the healthcare sector.
The industry widely recognizes the importance of Python, having set up the NHS Python Community. Led by enthusiasts and advocates of practice, the community champions the use of the Python programming language and open code in the NHS and healthcare sector.
Elsewhere, in the utility sector, Python is being adopted to open up new applications to help customers save money and energy. Take EDF as an example - the energy giant moved away from legacy systems in order to have a more unified view of its data. A crucial aspect of this involved utilizing Python to enable data scientists to bring ML models into production. By taking an integrated approach, the company is able to better understand the requirements of its customers and develop new products via ML techniques. As a result, EDF can better support financially vulnerable customers, setting up strategies if they start to face difficulties, and predicting it before it happens.
For most scenarios, whether its analytics, machine learning or app development, Python is not the only language being used. Rather it's often paired with SQL, Java and other languages used by different teams. Integrating Python into data platforms provides organizations with a unique way to create their own applications to derive business value from their data across teams and programming language boundaries. Doing so in a streamlined single cloud service removes much of the expense and complexity traditionally associated with building and managing data-intensive applications catering to different programming language preferences from different teams. Using a cloud data platform — along with the languages that developers are already comfortable with — offers a simpler, faster way to derive business insights from data.
Looking to the futureBusiness leaders need to ensure they are taking advantage of their data while empowering their data scientists, data engineers and developers to collaborate effectively. They also need to be proactive in how open source is used to ensure sensitive data is protected. Python offers data teams the flexibility, performance and speed to turn data into actionable insights, providing an invaluable competitive edge. Going forward, it will be an essential tool for any business looking to operationalize ML insights and grow their business, even in the toughest of times.
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