python hypothesis ghostwriter

Ghostwriter Demo

Tired of writing tests? Hypothesis has you covered!

Wait, what?

It's an interactive demo for the Hypothesis Ghostwriter , a Python tool which helps you to get started with property-based testing . Thanks to Pyodide , we can run Python client-side to both generate and execute tests - and even pip install missing packages! If you're not sure how to get started, try the gzip.compress example and replacing data=st.nothing() with data=st.binary() to generate bytestrings.

Want to install Hypothesis locally? pip install "hypothesis[cli]" and you'll get the Python library and a hypothesis write command which can do all this and more. If you're already using Hypothesis, check out HypoFuzz : it's a world-class fuzzing workflow for your test suite.

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Your Data Guide

How to Perform Hypothesis Testing Using Python

Step into the intriguing world of hypothesis testing, where your natural curiosity meets the power of data to reveal truths!

This article is your key to unlocking how those everyday hunches—like guessing a group’s average income or figuring out who owns their home—can be thoroughly checked and proven with data.

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I am going to take you by the hand and show you, in simple steps, how to use Python to explore a hypothesis about the average yearly income.

By the time we’re done, you’ll not only get the hang of creating and testing hypotheses but also how to use statistical tests on actual data.

Perfect for up-and-coming data scientists, anyone with a knack for analysis, or just if you’re keen on data, get ready to gain the skills to make informed decisions and turn insights into real-world actions.

Join me as we dive deep into the data, one hypothesis at a time!

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What is a hypothesis, and how do you test it?

A hypothesis is like a guess or prediction about something specific, such as the average income or the percentage of homeowners in a group of people.

It’s based on theories, past observations, or questions that spark our curiosity.

For instance, you might predict that the average yearly income of potential customers is over $50,000 or that 60% of them own their homes.

To see if your guess is right, you gather data from a smaller group within the larger population and check if the numbers ( like the average income, percentage of homeowners, etc. ) from this smaller group match your initial prediction.

You also set a rule for how sure you need to be to trust your findings, often using a 5% chance of error as a standard measure . This means you’re 95% confident in your results. — Level of Significance (0.05)

There are two main types of hypotheses : the null hypothesi s, which is your baseline saying there’s no change or difference, and the alternative hypothesis , which suggests there is a change or difference.

For example,

If you start with the idea that the average yearly income of potential customers is $50,000,

The alternative could be that it’s not $50,000—it could be less or more, depending on what you’re trying to find out.

To test your hypothesis, you calculate a test statistic —a number that shows how much your sample data deviates from what you predicted.

How you calculate this depends on what you’re studying and the kind of data you have. For example, to check an average, you might use a formula that considers your sample’s average, the predicted average, the variation in your sample data, and how big your sample is.

This test statistic follows a known distribution ( like the t-distribution or z-distribution ), which helps you figure out the p-value.

The p-value tells you the odds of seeing a test statistic as extreme as yours if your initial guess was correct.

A small p-value means your data strongly disagrees with your initial guess.

Finally, you decide on your hypothesis by comparing the p-value to your error threshold.

If the p-value is smaller or equal, you reject the null hypothesis, meaning your data shows a significant difference that’s unlikely due to chance.

If the p-value is larger, you stick with the null hypothesis , suggesting your data doesn’t show a meaningful difference and any change might just be by chance.

We’ll go through an example that tests if the average annual income of prospective customers exceeds $50,000.

This process involves stating hypotheses , specifying a significance level , collecting and analyzing data , and drawing conclusions based on statistical tests.

Example: Testing a Hypothesis About Average Annual Income

Step 1: state the hypotheses.

Null Hypothesis (H0): The average annual income of prospective customers is $50,000.

Alternative Hypothesis (H1): The average annual income of prospective customers is more than $50,000.

Step 2: Specify the Significance Level

Significance Level: 0.05, meaning we’re 95% confident in our findings and allow a 5% chance of error.

Step 3: Collect Sample Data

We’ll use the ProspectiveBuyer table, assuming it's a random sample from the population.

This table has 2,059 entries, representing prospective customers' annual incomes.

Step 4: Calculate the Sample Statistic

In Python, we can use libraries like Pandas and Numpy to calculate the sample mean and standard deviation.

SampleMean: 56,992.43

SampleSD: 32,079.16

SampleSize: 2,059

Step 5: Calculate the Test Statistic

We use the t-test formula to calculate how significantly our sample mean deviates from the hypothesized mean.

Python’s Scipy library can handle this calculation:

T-Statistic: 4.62

Step 6: Calculate the P-Value

The p-value is already calculated in the previous step using Scipy's ttest_1samp function, which returns both the test statistic and the p-value.

P-Value = 0.0000021

Step 7: State the Statistical Conclusion

We compare the p-value with our significance level to decide on our hypothesis:

Since the p-value is less than 0.05, we reject the null hypothesis in favor of the alternative.

Conclusion:

There’s strong evidence to suggest that the average annual income of prospective customers is indeed more than $50,000.

This example illustrates how Python can be a powerful tool for hypothesis testing, enabling us to derive insights from data through statistical analysis.

How to Choose the Right Test Statistics

Choosing the right test statistic is crucial and depends on what you’re trying to find out, the kind of data you have, and how that data is spread out.

Here are some common types of test statistics and when to use them:

T-test statistic:

This one’s great for checking out the average of a group when your data follows a normal distribution or when you’re comparing the averages of two such groups.

The t-test follows a special curve called the t-distribution . This curve looks a lot like the normal bell curve but with thicker ends, which means more chances for extreme values.

The t-distribution’s shape changes based on something called degrees of freedom , which is a fancy way of talking about your sample size and how many groups you’re comparing.

Z-test statistic:

Use this when you’re looking at the average of a normally distributed group or the difference between two group averages, and you already know the standard deviation for all in the population.

The z-test follows the standard normal distribution , which is your classic bell curve centered at zero and spreading out evenly on both sides.

Chi-square test statistic:

This is your go-to for checking if there’s a difference in variability within a normally distributed group or if two categories are related.

The chi-square statistic follows its own distribution, which leans to the right and gets its shape from the degrees of freedom —basically, how many categories or groups you’re comparing.

F-test statistic:

This one helps you compare the variability between two groups or see if the averages of more than two groups are all the same, assuming all groups are normally distributed.

The F-test follows the F-distribution , which is also right-skewed and has two types of degrees of freedom that depend on how many groups you have and the size of each group.

In simple terms, the test you pick hinges on what you’re curious about, whether your data fits the normal curve, and if you know certain specifics, like the population’s standard deviation.

Each test has its own special curve and rules based on your sample’s details and what you’re comparing.

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Introducing Ghostwriter: Part 2

Christopher Maddalena

Posts By SpecterOps Team Members

Part 1 introduced Ghostwriter. This article expands upon the making of Ghostwriter, the technology stack, and the thought process that went into the development. If you have not read Part 1, check out Ghostwriter here:

Introducing Ghostwriter - Christopher Maddalena - Medium

For the past year the specterops team has been working on a problem: project management. it is not the most glamorous…, stack overview.

Ghostwriter is a web application written in Python with the Django web framework. It is a collection of Python 3.7, HTML, JavaScript, CSS, Jinja, and Django code compartmentalized into multiple Django applications. This compartmentalization helps keep the code organized and easy to peruse during customization or development efforts.

The application uses a PostgreSQL backend that Django natively supports. However, should users desire to switch to a different type of backend, the Django settings.py can be updated to use SQLite, Oracle, or MySQL without any additional libraries. Django makes it easy to modify the Ghostwriter database models as well. Migrations are usually smooth and trouble-free, especially if you are customizing the models prior to using Ghostwriter in production.

Finally, Ghostwriter uses Django Q and Redis for automated queue management processing (AQMP). Ghostwriter automates a number of things like updating domain categorization data and DNS records. These tasks are handed off to Redis for background processing.

Setup is relatively simple. Ghostwriter can be run locally (for an individual consultant) or on a low-power server. The host only needs Redis and Python 3.7. Ghostwriter uses a relatively small number of non-stock Python and Django libraries. Ghostwriter is packaged with Docker, so everything can be deployed using Docker Compose and one command. The full details are available in the GitHub wiki.

Some details concerning why these specific technologies were selected is covered in this post:

Being a Good Domain Shepherd: Part 2

Domain management for red teams.

posts.specterops.io

Database Models

Even though Ghostwriter is comprised of multiple applications, they all talk to each other and share a single database.

As seen below, the database models are all linked to each other to tie everything together. The documentation in the wiki provides more detail and individual UML diagrams for each application.

A table exists for each type of object and we have included Django “fixtures” (predefined datasets) for many of these models. These fixtures are loaded into the Ghostwriter database to pre-populate the necessary models to get a new deployment up and running in minutes. Ghostwriter supports CSV file imports for other models, like domain names and servers.

The Making Of Ghostwriter

While evaluating past experiences, current workflows, and currently available solutions, we quickly identified things that caused us irritation or negatively impacted efficiency and productivity. The key here was investing time into this evaluation. We took a week in the SpecterOps offices to look inward and discuss what we liked, did not like, and wanted in a new tool.

When a team implements a new process there will (almost) always be some suboptimal workarounds, hacks, or peculiar decisions paired with good intentions to fix them later. Those good intentions rarely transform into corrective actions. If these issues aren’t smoothed out they have a tendency to just become the accepted norm and fester.

Think of this as an effect of the “Principle of Least Resistance.” Cal Newport describes this principle in his book Deep Work :

In a business setting, without clear feedback on the impact of various behaviors to the bottom line, we will tend toward behaviors that are easiest in the moment.

As previously stated, project management and process workflows are not glamorous, so it is understandable that when a team has time to optimize something they aren’t going to gravitate towards reporting. The dark side of this is the entire team continues to be collectively irritated by a dozen little things, all self-inflicted. Eventually these grievances are accepted as necessary speed bumps, bogging everyone down until someone takes corrective action or the eventual heat-death of the universe: whichever comes first.

So, the SpecterOps team set out with an ambitious plan: build our dream tool. That was a lofty goal, but a necessary one. By shooting for that distant star we hoped to at least hit the moon 😉. We called this project the SpecterOps Automated Reporting platform, or SOAR, and dedicated ourselves to seeing it through.

With the increased popularity of “Security Orchestration and Response” as a term, it’s a good thing we weren’t too attached to that name; however, SOAR would have been a great name for this platform. C’est la vie.

We named it Ghostwriter instead.

We laid out several important tenets for the project:

Flexibility

One of the first failures of custom management tools is a lack of flexibility. The tool is created to address the most immediate concerns with little regard for what will happen if the process ever changes in the future or needs to be enhanced. We knew Ghostwriter would have to be capable of being easily changed to fit alterations in report designs and requirements.

This was also important for releasing Ghostwriter as an open source tool. It is easier for others to adopt Ghostwriter as a member of their team when they do not need to invest hours into rewriting code to make a simple report design change.

In support of this tenet we included JSON reporting early on to make report alterations simple, allow bringing Ghostwriter projects into other reporting engines (if desired), and normalize report generation across all of the different types of reports (e.g. docx, xlsx, pptx). We use Office documents as templates for the report styles. This way no code needs to be edited to make adjustments to most of the fonts, colors, or header styles. The majority of all of that is managed using a familiar and ubiquitous tool, Microsoft Office.

Extensibility

Extensibility is equally important as flexibility. We knew we would want a solution that could be extended down the road. One of Ghostwriter’s primary objectives is keeping everything in one place. If Ghostwriter could not be easily extended then that might mean a second application or platform could come into play for tracking some new future requirement. That may sound like a little thing; however, it could lead to increased unhappiness over time.

We did not want Ghostwriter to end up so rigid in construction that it could only ever serve a limited set of functions. In our experience, even great custom applications go stale and, at times, become a burden to use. A burden that saves you time is still a joyless thing.

The ability to add new applications that could integrate into existing Ghostwriter applications is a blessing. We are already working towards adding new applications to Ghostwriter, such as an application for the blue team that leverages the reporting engine to generate Alerting and Detection Strategies (ADS).

Not only is an application that generates ADS reports useful for our team, the integration with the existing applications means the “Blue Team Application” is also aware of all of the “Red Team Applications” and accompanying client information, projects, reports, and activities recorded by other team members — and vice versa. Extending Ghostwriter in one direction not only adds new capabilities but also enriches the whole platform.

After all, Ghostwriter is a friendly purple ghost and just wants to help everyone.

One of the early fears we had was building Ghostwriter using $ Some_Library and then having $ Some_Library change in a way that would be a negative for Ghostwriter. So, we committed to the concept of “resiliency” for the first version of everything. Everything in this current version of Ghostwriter is built using core Python and Django libraries. There are a few exceptions, but outside of Django Q (which is well supported) most of the third-party libraries support features that could change without breaking Ghostwriter. Even those are well-used and supported libraries, like Requests. Otherwise, the only other outside dependencies are web frameworks, jQuery and Bootstrap, which are both popular and well supported.

That all means Ghostwriter is easier to install and use and won’t break or act up if an outside library is changed or the wrong version is installed. This made development slightly more difficult in some cases. We chose to leverage jQuery instead of third-party Django libraries, for example. This added a learning curve and some initial difficulties, but the end product is ultimately better off as a result.

Perhaps the most noticeable trade-off is the lack of certain bells and whistles, like WYSIWYG text editors. WYSIWYG editors would certainly be nice, but we deliberately chose to avoid them to make sure there would be a strong foundation in case whichever editor plugin we chose were to vanish one day. At least then we could excise the non-functional WYSIWYG plugin and know the basic text boxes would work. (Also, WYSIWYG editors are coming.)

We wanted Ghostwriter to be able to automate certain processes that would be tedious or those we knew that humans would forget about or ignore. This tenet speaks for itself, but it was important for us to make this a priority early on in the development process. Every time we implemented a new action a user could take we had to ask, “Is any part of this process something we should just automate based on the user’s input?”

Once we had functioning automation using triggers and scheduled tasks (easily done thanks to Django Q and Redis), we could also have fun spitballing new ideas for automation, like Slack notifications and DNS checks for domains.

Eat the Dog Food

Finally, we knew the best — and only — way to make sure we were on the right track was to use Ghostwriter as early as possible. The goal was always to use Ghostwriter, but we started using it within two months instead of waiting for a “beta” or some sort of “v1.0” release. Back then it was just a Python library and a script run from a command line that would generate a report. It was a proof of concept for some of the earliest ideas. That grew into a basic web front end created using Python’s Flask web framework. Research and testing eventually led to the switch to Django.

During each phase we tried different interface designs, tested new ideas and concepts, and just generally experimented. There were quite a few duds, but this process was so important . To quote Mark Frauenfelder, founding editor in chief of Make magazine, from Adam Savage’s book Every Tool is a Hammer :

You’ve got to do at least six iterations, minimum, of any project before it starts getting good enough to share it with other people.

We passed Ghostwriter’s sixth iteration a while back. This gif shows what Ghostwriter, or SOAR as it was known at the time, looked like on September 19, 2018. We hope you will agree with us, the waiting and numerous iterations was worth it.

Written by Christopher Maddalena

A maker and a hacker

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Intro to Ghostwriter

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Complete code.

You'll learn to use Ghostwriter to automagically complete the code you're working on!

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You'll never have to tut at the lack of comments on someone else's code again, explain code does the heavy lifting for you!

Transform Code

Completely refactor code in multiple ways. Used iteration but you'd prefer recursion - let Ghostwriter work its spooky magic

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Ghostwriter: Try it out!

Replit Ghostwriter Review

Ghostwriter, the AI-powered chat feature in the Replit IDE is here to make your coding life easier. With Ghostwriter, you can automate repetitive coding tasks, get suggestions and fixes, and even generate code. It's like having a coding buddy right at your fingertips!

In addition to automating tasks, Ghostwriter also supports multiple programming languages and can be used in unfamiliar codebases. So whether you're a beginner just starting out or an experienced developer diving into new code, Ghostwriter has got your back. And with its lightning-fast response time of just 500ms, you won't have to wait long for those helpful suggestions and fixes.

If you're interested in trying out Ghostwriter, you're in luck! It's currently available for free during the beta period with a Ghostwriter or Pro subscription. And the best part? The team behind Ghostwriter has big plans for its future, including mobile app support and continuous improvement based on user feedback. So get ready to take your coding to the next level with Ghostwriter! Want to learn more about how Ghostwriter can improve your codebase readability and style? Keep reading our article to dive into all the details.

Improving Codebase Readability and Style with Ghostwriter

In the world of programming, one of the biggest challenges developers face is maintaining a clean and readable codebase. A messy codebase can lead to confusion, errors, and wasted time. However, with the introduction of Ghostwriter, an AI-powered chat feature in the Replit IDE, developers now have a powerful tool to improve codebase readability and style.

What is Ghostwriter?

Ghostwriter is an innovative AI-powered feature that is designed to assist developers in their coding journey. It acts as a virtual assistant, helping automate repetitive coding tasks and offering suggestions and fixes to improve the quality of code. With Ghostwriter, developers can save valuable time and effort by letting the AI take care of the mundane tasks, while they focus on the more creative aspects of programming.

Features of Ghostwriter

Ghostwriter offers a range of features aimed at enhancing codebase readability and style. Let's take a closer look at some of the key features:

Automated coding tasks

With Ghostwriter, you can automate repetitive coding tasks such as generating code snippets, writing test cases, and refactoring components. By automating these tasks, Ghostwriter frees up your time, allowing you to focus on more important aspects of your development process.

Suggestion and fix generation

Ghostwriter is equipped with advanced algorithms that analyze your code and provide intelligent suggestions and fixes to common coding mistakes. Whether it's optimizing an algorithm, improving code efficiency, or suggesting alternative coding approaches, Ghostwriter offers valuable insights to help you write cleaner and more efficient code.

Support for multiple programming languages

Ghostwriter supports multiple programming languages, including Python, JavaScript, C++, and many others. This means that no matter which language you prefer to code in, Ghostwriter has got you covered. By providing language-specific suggestions and fixes, Ghostwriter ensures that your code follows the best practices and conventions of the respective language ecosystem.

Usage in unfamiliar codebases

One of the biggest challenges for developers is working with unfamiliar codebases. Ghostwriter comes to the rescue by analyzing the codebase and providing contextual suggestions and fixes. Whether you're collaborating on an open-source project or diving into a legacy codebase, Ghostwriter will help you navigate through the code and make meaningful contributions without getting overwhelmed.

Fast response time

Ghostwriter aims to provide a seamless coding experience with minimal disruption. With an average response time of 500 milliseconds, Ghostwriter delivers near-instantaneous suggestions and fixes, ensuring that you can maintain your coding flow without any interruptions.

Code Generation with Ghostwriter

Another significant feature offered by Ghostwriter is the ability to generate code snippets. Whether you need a simple if-else statement, a loop, or a more complex function, Ghostwriter can generate the code you need with just a few simple commands. This not only saves time but also reduces the chances of errors and increases code consistency.

In addition to code snippets, Ghostwriter can also assist in writing test cases. Writing comprehensive test cases is crucial for ensuring the functionality and reliability of your code. Ghostwriter can generate test cases based on the code you've written, making it easier to achieve comprehensive test coverage and identify potential edge cases.

Furthermore, Ghostwriter can help with refactoring components. Refactoring is the process of restructuring existing code to improve its readability, maintainability, and performance. Ghostwriter can analyze your code and provide suggestions on how to refactor it, helping you create cleaner and more maintainable code.

Improving Codebase Readability

Code readability plays a vital role in the maintainability and collaboration of a codebase. With Ghostwriter, you can improve code readability by performing the following tasks:

Formatting code

Ghostwriter can automatically format your code according to the best practices of the programming language you're using. Proper indentation, consistent spacing, and organized code structure greatly enhance code readability and make it easier for you and other developers to understand and work with your code.

Consistent naming conventions

Ghostwriter suggests and enforces consistent naming conventions for variables, functions, and classes. By following consistent naming conventions, your code becomes easier to navigate and understand. Ghostwriter takes the guesswork out of naming and ensures that your code follows industry-standard practices.

Documentation generation

Documentation is essential for understanding the functionality and usage of code. Ghostwriter can assist in generating documentation for your code, making it easier for other developers to understand and work with your codebase. With Ghostwriter, you don't have to spend hours writing lengthy documentation – it's all done for you, leaving you with more time to focus on actual coding.

Enhancing Code Style with Ghostwriter

In addition to improving code readability, Ghostwriter can also enhance the overall style and aesthetics of your code. Whether you're working on a website, an application, or any other user interface, Ghostwriter can help you create a visually appealing and user-friendly experience. Some ways in which Ghostwriter can assist in enhancing code style include:

Applying CSS styles

Ghostwriter can generate CSS styles based on your preferred design or the specifications of your project. By automating the process of generating CSS styles, Ghostwriter saves time and ensures consistent styling across your codebase. This not only improves code quality but also enhances the visual appeal of your website or application.

Optimizing website layout

Website layout plays a crucial role in user experience. Ghostwriter can help optimize website layout by suggesting improvements based on best practices of user interface design. Whether it's aligning elements, optimizing spacing, or improving the overall flow of the page, Ghostwriter provides valuable suggestions to create an intuitive and user-friendly website layout.

Designing user interfaces

User interfaces are the bridge between users and code. Ghostwriter can assist in designing user interfaces by generating code for buttons, forms, menus, and other UI components. By automating the process of UI design, Ghostwriter ensures consistency and reduces the chances of errors, resulting in a polished and professional-looking user interface.

Getting Started with Ghostwriter

Getting started with Ghostwriter is quick and easy. Simply sign up for an account and access Ghostwriter directly from the Replit IDE. During the beta period, Ghostwriter is available for free with a Ghostwriter or Pro subscription, allowing you to experience its powerful features without any additional cost.

Using Ghostwriter for Codebases

Whether you're starting a new project or working on an existing one, Ghostwriter can seamlessly integrate into your development workflow. You can use Ghostwriter on existing projects by simply importing the code into the Replit IDE and enabling the Ghostwriter feature. With Ghostwriter, you can iterate on your code, make improvements, and get valuable suggestions to enhance your codebase.

Expanding Ghostwriter's Functionality

Ghostwriter has a bright future ahead with plans to introduce mobile app support for on-the-go coding assistance. This will allow developers to access the power of Ghostwriter from their mobile devices, making coding even more convenient and accessible.

Furthermore, Ghostwriter values user feedback and is committed to continuously improving its functionality based on user suggestions and requirements. As more and more developers use Ghostwriter and provide feedback, the future potential of Ghostwriter is limitless. With continuous updates and improvements, Ghostwriter aims to be a valuable tool in every developer's arsenal.

Ghostwriter is a game-changer in the world of programming, offering developers a powerful AI-powered tool to improve codebase readability and style. With its features like automated coding tasks, suggestion generation, support for multiple programming languages, and fast response time, Ghostwriter enables developers to write cleaner, more efficient code with ease.

By generating code snippets, enhancing code readability and style, and seamlessly integrating into your development workflow, Ghostwriter empowers developers to focus on the creative aspects of programming while leaving the tedious and repetitive tasks to the AI. With its future plans for mobile app support and constant improvement based on user feedback, Ghostwriter is set to revolutionize the way we write code and make a significant impact on code readability and style. So why not give Ghostwriter a try and experience the benefits for yourself?

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ghostwriter 0.1

pip install ghostwriter Copy PIP instructions

Released: Apr 3, 2020

GhostWriter lets you edit a website via lektor, a flexible and powerful static content management system for building complex and beautiful websites out of flat files - for people who do not want to make a compromise between a CMS and a static blog engine.

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License: GNU General Public License v3 or later (GPLv3+) (GPL v3)

Author: Hiro

Maintainer: Hiro

Tags lektor, website, docker, container, ghostwriter, onionshare, onion, tor, anonymous, web server

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• X11 Applications :: Qt
• End Users/Desktop
• OSI Approved :: GNU General Public License v3 or later (GPLv3+)
• OS Independent
• Python :: 3
• Security :: Cryptography
• Software Development :: Code Generators

Project description

If you want to edit a website with the ease of a CMS and share it as static pages GhostWriter starts a lektor server locally and include some git functionalities to transparently upload your changes or retrieve updates from an upstream repository. GhostWriter also allow you to share your work via .onion, by using OnionShare or a nginx webserver running on a Docker container. Everything is always hosted on your machine, and disappear when you shutdown GhostWriter. The .onion can be accessed via Tor Browser.

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