You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Why this python class is not working with numba jitclass?

I have written the following code with the help of numpy and I want to improve the performance with numba. I am not sure why it is not working as I have set all the variables as per numba system. I am trying to speed up this code as I would be working with large data sets in the future.
import numpy as np
import math
from numba import jitclass 
from numba import float64,int64

spec =[
       ('spacing',float64),
       ('n_iterations',int64),
       ('np_emptyhouses',float64[:,:]),
       ('np_agenthouses',float64[:,:]),
       ('similarity_threshhold',float64),
       ('n_changes',int64)
       ]

@jitclass(spec)
class geo_schelling_update:

    def __init__(self,n_iterations,spacing,np_agenthouses,np_emptyhouses,similarity_threshhold):
        self.spacing=spacing
        self.n_iterations=n_iterations
        self.np_emptyhouses=np_emptyhouses
        self.np_agenthouses=np_agenthouses
        self.similarity_threshhold=similarity_threshhold

    def distance_vectorize(self,pointA1, pointA2,agent):
        x_square=np.square(pointA1-agent[0])
        y_square=np.square(pointA2-agent[1])
        dist=np.sqrt(np.array(x_square,dtype=np.float32)+np.array(y_square,dtype=np.float32))
        return np.round(dist,4)

    def is_unsatisfied_vectorize(self,x,y):
        race = np.extract(np.logical_and(np.equal(self.np_agenthouses[:,0],x),np.equal(self.np_agenthouses[:,1],y)),self.np_agenthouses[:,2])[0]
        euclid_distance1=round(math.hypot(self.spacing,self.spacing),4)
        euclid_distance2=self.spacing
        total_agents=np.extract(np.logical_or(np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance1),np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance2)),self.np_agenthouses[:,2])
        if total_agents.size ==0:
            return False
        else:
            return total_agents[total_agents==race].size/total_agents.size<self.similarity_threshhold    

    def move_to_empty(self,x,y):
        race = np.extract(np.logical_and(np.equal(self.np_agenthouses[:,0],x),np.equal(self.np_agenthouses[:,1],y)),self.np_agenthouses[:,2])[0]
        x_new,y_new=self.np_emptyhouses[np.random.choice(self.np_emptyhouses.shape[0],1),:][0]
        self.np_agenthouses=self.np_agenthouses[~(np.logical_and(self.np_agenthouses[:,0]==x, self.np_agenthouses[:,1]==y))]
        self.np_agenthouses=np.vstack([self.np_agenthouses,[x_new,y_new,race]])
        self.np_emptyhouses=self.np_emptyhouses[~(np.logical_and(self.np_emptyhouses[:,0]==x_new, self.np_emptyhouses[:,1]==y_new))]
        self.np_emptyhouses=np.vstack([self.np_emptyhouses,[x,y]])

    def update_helper(self,agent):
        if self.is_unsatisfied_vectorize(agent[0],agent[1]):
            self.move_to_empty(agent[0],agent[1])
            return 1
        else:
            return 0

    def update(self):
        for i in np.arange(self.n_iterations):
            np_oldagenthouses=self.np_agenthouses.copy()
            n_changes=0
            for row in np_oldagenthouses:
                n=self.update_helper(row)
                n_changes+=n
            print(n_changes)
            print(i)
            if n_changes == 0:
                break

np_agenthouses=np.array([[-71.8,    41.4,   2.0],
                        [-71.6, 41.4,   2.0],
                        [-71.6, 41.6,   2.0],
                        [-71.4, 41.6,   1.0],
                        [-71.6, 41.8,   1.0],
                        [-71.4, 41.8,   2.0],
                        [-71.6, 42.0,   2.0],
                        [-71.4, 42.0,   1.0],
                        [-71.4, 41.4,   2.0],
                        [-71.2, 41.4,   1.0]])

np_emptyhouses=np.array([[-71.8,  41.3],[-71.8,  41.4],[-71.5,  41.5],
                [-71.5,  41.6],[-71.7,  41.8],[-71.7,  41.9],
                [-71.5,  41.9],[-71.2,  41.4],[-71.6,  41.7]])

spacing=0.1
similarity_threshhold=0.65
n_iterations=100
schelling= geo_schelling_update(n_iterations,
                         spacing,
                         np_agenthouses,
                         np_emptyhouses,similarity_threshhold)
schelling.update() 

Here is the error I am getting:  
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<function round_ at 0x000001909ED270D0>) with argument(s) of type(s): (array(float64, 1d, C), Literal[int](4))
 * parameterized
In definition 0:
    All templates rejected with literals.
In definition 1:
    All templates rejected without literals.
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: resolving callee type: Function(<function round_ at 0x000001909ED270D0>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (42)

File "test2.py", line 42:
    def is_unsatisfied_vectorize(self,x,y):
        <source elided>
        euclid_distance2=self.spacing
        total_agents=np.extract(np.logical_or(np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance1),np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance2)),self.np_agenthouses[:,2])
        ^

[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'is_unsatisfied_vectorize') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (57)

[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'is_unsatisfied_vectorize') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (57)

File "test2.py", line 57:
    def update_helper(self,agent):
        if self.is_unsatisfied_vectorize(agent[0],agent[1]):
        ^

[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update_helper') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (68)

[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update_helper') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (68)

File "test2.py", line 68:
    def update(self):
        <source elided>
            for row in np_oldagenthouses:
                n=self.update_helper(row)
                ^

[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at <string> (3)

[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at <string> (3)

Also I am running this code in IDE. If the above code doesn't work with numba then what is the best way to make this code work to get the same result.

A:

The issue is with np.round. It's not entirely clear from the documentation, but you can see from looking at the source, that if you are using the function on an array input, you need to provide all 3 arguments. So the following does not work:
nb.jit(nopython=True)
def func(x):
    return np.round(x)

but the following works as expected:
nb.jit(nopython=True)
def func(x):
    out = np.empty_like(x)
    np.round(x, 0, out)
    return out

See the docs for np.around for the full description. I'm going to raise an issue on the numba issue tracker since this isn't obvious from looking at the docs.