Forem: GeorgeMurphy-1

Exploring the Impact of Early Project Task Completion: A Stochastic Modeling Journey

GeorgeMurphy-1 — Wed, 15 Nov 2023 21:53:34 +0000

Part 1: Unveiling the Stochastic Simulation

Introduction
Welcome to the first part of our exploration into the impact of early task completion on project success. In this installment, we unveil the foundational stochastic simulation model that will serve as the canvas for our mathematical journey. Prepare to immerse yourself in the realm of differential equations and Python scripting.

The Hypothesis
At the heart of our investigation lies a hypothesis: initiating a project with a high task completion rate can positively influence overall project success. To test this, we turn to the world of stochastic processes and differential equations.

The Stochastic Differential Equation
Our primary tool is a stochastic differential equation, a mathematical framework that elegantly combines deterministic and stochastic components. The equation takes the form:

dP(t)=(μ(t)P(t)(1−P(t)))dt+(σ(t)P(t)(1−P(t)))dW(t)

Let's break it down:

P(t): Probability of task completion at time

μ(t): Deterministic part of the growth rate.
σ(t): Volatility term, representing uncertainty.
dW(t): Wiener process, introducing randomness.

Implementing the Simulation

Coding the Model
Our journey commences with translating the math to Python. I use NumPy and Matplotlib to create the simulation of the time series for completion rates.

python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

# Function to simulate task completion rates
def simulate_completion_rate(num_steps, dt, a, b, c, noise_type):
    # Initialize arrays to store time and completion rate
    time = np.zeros(num_steps)
    completion_rate = np.zeros(num_steps)

    # Initial values
    time[0] = 0
    completion_rate[0] = 0.2  # Initial completion rate (adjust as needed)

    # Generate Wiener process (Brownian motion)


    if noise_type == 'normal':
        dW = np.random.normal(0, np.sqrt(dt), 1)
    elif noise_type == 'brownian':

        dW = np.random.normal(0,np.random.standard_normal(1) * np.sqrt(dt), num_steps - 1)
    else:
        raise ValueError("Invalid noise type. Choose 'normal' or 'brownian'.")


    # Simulation loop
    for i in range(1, num_steps):
        # Deterministic part of the growth rate
        mu = a + b * time[i - 1]

        # Volatility term
        sigma = c * completion_rate[i - 1]

        # Stochastic differential equation
        dP = mu * completion_rate[i - 1] * (1 - completion_rate[i - 1]) * dt + sigma * completion_rate[i - 1] * (1 - completion_rate[i - 1]) * dW[i - 1]

        # Update time and completion rate
        time[i] = time[i - 1] + dt
        completion_rate[i] = completion_rate[i - 1] + dP

    return time, completion_rate

# Simulation parameters
num_steps = 1000
dt = 0.1
a = 0.1
b = 0.02
c = 0.1

# Create figure and axes
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)  # Adjust the bottom to make room for sliders

# Plot the initial results
time, completion_rate = simulate_completion_rate(num_steps, dt, a, b, c, noise_type="brownian")
line, = ax.plot(time, completion_rate)
ax.set_title('Task Completion Rate Over Time')
ax.set_xlabel('Time')
ax.set_ylabel('Completion Rate')

# Add sliders for adjusting parameters
axcolor = 'lightgoldenrodyellow'
ax_a = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
ax_b = plt.axes([0.25, 0.05, 0.65, 0.03], facecolor=axcolor)
ax_c = plt.axes([0.25, 0.00, 0.65, 0.03], facecolor=axcolor)

s_a = Slider(ax_a, 'a', 0.01, 1.0, valinit=a)
s_b = Slider(ax_b, 'b', 0.01, 0.1, valinit=b)
s_c = Slider(ax_c, 'c', 0.01, 1.0, valinit=c)

def update(val):
    a = s_a.val
    b = s_b.val
    c = s_c.val
    time, completion_rate = simulate_completion_rate(num_steps, dt, a, b, c, noise_type="brownian")
    line.set_ydata(completion_rate)
    fig.canvas.draw_idle()

s_a.on_changed(update)
s_b.on_changed(update)
s_c.on_changed(update)

plt.show()

Tuning Parameters
To ensure the accuracy and relevance of our simulation, selecting appropriate parameters is crucial. The baseline completion rate (a), rate of change (b), and volatility (c) need data to find . This may involve data analysis, historical project insights, or expert input.

Running the Simulation
Executing the Python script sets the simulation in motion. The output, a dynamic plot depicting completion rates over time, serves as the visual foundation for our exploration.

Next Steps
As we embark on this mathematical journey, part two will delve into scenario exploration, statistical analysis, and the interpretation of our simulation results. Brace yourself for a deeper dive into the intricacies of project dynamics and mathematical modeling.

Stay tuned for the next installment, where we dissect the simulation results and unravel the mathematical nuances of early project task completion.

Continue to Part 2: Analyzing Scenarios and Statistical Metrics

Setting Up A FRESH Headless Wordpress site

GeorgeMurphy-1 — Wed, 15 Nov 2023 16:53:43 +0000

LocalWP

Get local from https://localwp.com/
Run the install

Once installed, in the top left corner menu you will find the option to spin up a new site.

Create a new site using the gui

You should now have you site up and running at the localhost port.

Now log-in at http://localhost:xxxx/wp-login.php where xxxx is your port number provided from the local app.

Or Select one-click admin and use the wp admin button to log in.

GO to plugins section, add a new plug in called wpgraphql.

Wait, why use a plug-in instead of the default RestAPi?

Wordpress as a content management platform can expose a Rest API to allow frontend or other services to access and manage its data. But, in doing so we get a response that contains essentially unfiltered data. To access the rest end points on your newly setup wordpress site go to this address but change example.com to your wordpress site url
http://example.com/?rest_route=/

It is helpful to get a JSON formatter for humans to use in the browser so to be able to make sense of the output. Unless you are a machine.

Graphql improves upon this. How? By improving upon how you access the data and what data is returned!
Lest break that down with an example,

if one wanted to get the post data but only cared about the description one would have to work around the extra data attributes like tags, categories ect.
In addition, you are being sent data that is not needed but taking up time and space.

We can access data much easier by specifing what Well by using a query language and returning only the specified data/attributes. Grahql allows us to delve into the exact data through an easy to use declarative description of what data we want. Thus we can lower web traffic bandwidth.

Rest works

With WPGraphQL installed you can use the IDE to create your queries.

query GetPosts { posts { nodes { id title date } } }

Now taking the desired post id one can compose a query that allows for only the retrieval of the description for this post from the sever using this code.

query GetSinglePost { post(id: "cG9zdDox") { id } }

In the query body under post we would like describe what we want back as fields. We do not want the id so we remove that from the query fields. To identify the post we pass the id as an argument.

query GetSinglePost { post(id: "cG9zdDox") { description } }

To Remove the annoying extension field add this snippet in your functions.php.

add_filter( 'graphql_request_results', function( $response ) { if ( is_array( $response ) && isset( $response['extensions'] ) ) { unset( $response['extensions'] ); } if ( is_object( $response ) && isset( $response->extensions ) ) { unset( $response->extensions ); } return $response; }, 99, 1 );

To get to functions.php, go to WPlocal app and find the go to folder buttoon for your wordpress site.

add this snippet to end of the file and save.

Go through app-> public-> wp-content-> themes-> twentytwentyfour

if you are using a different theme change twentytwentyfour to that theme folder.

Copy the address. Open command line and cd to that address.
Next run nvim functions.php

Add code to end of file. Save and close the file.

Restart WPLocal and test your query again.

You now have a FRESH headless Wordpress site ready to go!

Feel free to contact me if you have any questions or edits!
murfg25@gmail.com

Turning on REST API for WooCommerce

GeorgeMurphy-1 — Thu, 21 Sep 2023 23:09:53 +0000

What's up, folks!

In this article, you'll discover how to activate the REST API for your WooCommerce site.

Ready to dive in? Let's roll:

Head over to your WordPress Admin Dashboard.
In order to access REST API end points you must change your permalinks style from plain to any other format.
Navigate to WooCommerce -> Settings.
Inside WooCommerce Settings, find the "Advanced" tab.
In the "Legacy API" section, you'll come across an option labeled "Enable REST API." Don't check this box. This API is deprecated and not suitable for new projects. The WooCommerce Legacy REST API uses a different JSON format for request and response bodies compared to the modern WooCommerce REST API. It also relies on Basic Authentication instead of OAuth for security.
Save Changes:
Once you've made sure not to enable the deprecated REST API, scroll down to the bottom of the page and hit the "Save changes" button. This step activates the REST API for your WooCommerce site.

Voilà! You've successfully enabled the REST API for your WooCommerce site, opening the doors to communication with various applications like a React front-end or mobile apps for Android or iOS.

Don't forget to configure authentication and permissions for your API to ensure data security and control over its functionality.

Stay tuned for more WooCommerce wisdom!

-George Murphy