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]


#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Define ray sensors; sensors that cast rays into the world and return the range of the nearest object that were
intersected with these ones.
"""

import numpy as np

from pyrobolearn.robots.sensors.links import LinkSensor
from pyrobolearn.utils.transformation import get_rotated_point_from_quaternion

__author__ = "Brian Delhaisse"
__copyright__ = "Copyright 2019, PyRoboLearn"
__credits__ = ["Brian Delhaisse"]
__license__ = "GNU GPLv3"
__version__ = "1.0.0"
__maintainer__ = "Brian Delhaisse"
__email__ = "briandelhaisse@gmail.com"
__status__ = "Development"


class RaySensor(LinkSensor):
    r"""Ray sensor

    This sensor casts a single ray into the world, check for intersection, and return the range of the nearest object.
    """

    def __init__(self, simulator, body_id, to_position, link_id=-1, noise=None, ticks=1, latency=None, position=None,
                 orientation=None):
        """
        Initialize the Ray sensor.

        Args:
            simulator (Simulator): simulator instance.
            body_id (int): unique body id.
            to_position (np.array[float[3]]): position where the ray should stop with respect to the new local link
                frame (specified by :attr:`position` and :attr:`orientation`).
            link_id (int): unique id of the link.
            noise (None, Noise): noise to be added.
            ticks (int): number of steps to wait/sleep before acquisition of the next sensor value.
            latency (int, float, None): latency time / step.
            position (np.array[float[3]], None): local position of the sensor with respect to the given link. If None,
                it will be the zero vector.
            orientation (np.array[float[4]], None): local orientation of the sensor with respect to the given link
                (expressed as a quaternion [x,y,z,w]). If None, it will be the unit quaternion [0,0,0,1].
        """
        super(RaySensor, self).__init__(simulator, body_id=body_id, link_id=link_id, noise=noise, ticks=ticks,
                                        latency=latency, position=position, orientation=orientation)
        if isinstance(to_position, (tuple, list)):
            to_position = np.asarray(to_position)
        if not isinstance(to_position, np.ndarray):
            raise TypeError("Expecting the given 'to_position' to be a np.array, but got instead: "
                            "{}".format(type(to_position)))
        if to_position.shape != (3,):
            raise ValueError("Expecting the shape of the given 'to_position' to be (3,), but got instead a shape of: "
                             "{}".format(to_position.shape))
        self.to_position = to_position

    def _sense(self, apply_noise=True):
        """
        Sense using the ray sensor.

        Args:
            apply_noise (bool): if we should apply the noise or not. Note that the sensor might already have some noise.

        Returns:
            float: hit fraction along the ray in range [0,1] along the ray.
        """
        if self.simulator.supports_sensors("ray"):
            return self.simulator.get_sensor("ray", self.body_id, self.link_id).sense()
        position = self.position + get_rotated_point_from_quaternion(self.orientation, self.to_position)
        hit = self.sim.ray_test(from_position=self.position, to_position=position)[2]
        if apply_noise:
            hit = self._noise(hit)
        return hit

    def render(self, enable=True, color=None):
        """Render the ray in the simulator; they are only visual and attached to the sensor (link). The visual shape
        is updated at runtime (each time you call this function).

        Args:
            enable (bool): if we should render or not.
            color (None, tuple/list[float[4]], np.ndarray[float[4]]): RGBA color of all the rays, where each channel
                is between 0 and 1.
        """
        pass


class RayBatchSensor(LinkSensor):
    r"""Ray batch sensor.

    This sensor casts a batch of rays into the world, check for intersections, and return the range of the nearest
    objects. This can be used for sonars, laser scanning range sensors (such as LIDAR), and others.

    Note that the number of rays must be smaller than `simulator.MAX_RAY_INTERSECTION_BATCH_SIZE`. In pybullet, this
    is currently set to 16,384.
    """

    def __init__(self, simulator, body_id, to_positions, link_id=-1, noise=None, ticks=1, latency=None, position=None,
                 orientation=None):
        """
        Initialize the Ray batch sensor.

        Args:
            simulator (Simulator): simulator instance.
            body_id (int): unique body id.
            to_positions (np.array[N,3]): position where each ray should stop with respect to the new local link frame
                (specified by :attr:`position` and :attr:`orientation`).
            link_id (int): unique id of the link.
            noise (None, Noise): noise to be added.
            ticks (int): number of steps to wait/sleep before acquisition of the next sensor value.
            latency (int, float, None): latency time / step.
            position (np.array[float[3]], None): local position of the sensor with respect to the given link. If None,
                it will be the zero vector.
            orientation (np.array[float[4]], None): local orientation of the sensor with respect to the given link
                (expressed as a quaternion [x,y,z,w]). If None, it will be the unit quaternion [0,0,0,1].
        """
        super(RayBatchSensor, self).__init__(simulator, body_id=body_id, link_id=link_id, noise=noise, ticks=ticks,
                                             latency=latency, position=position, orientation=orientation)
        if isinstance(to_positions, (tuple, list)):
            to_positions = np.asarray(to_positions)
        if not isinstance(to_positions, np.ndarray):
            raise TypeError("Expecting the given 'to_positions' to be a np.array, but got instead: "
                            "{}".format(type(to_positions)))
        if to_positions.ndim != 2:
            raise ValueError("Expecting the given 'to_positions' to be 2D array, but got instead a {}D "
                             "array".format(to_positions.ndim))
        if to_positions.shape[1] != 3:
            raise ValueError("Expecting the shape of the given 'to_positions' to be (N,3), but got instead a shape "
                             "of: {}".format(to_positions.shape))
        if len(to_positions) > self.sim.MAX_RAY_INTERSECTION_BATCH_SIZE:
            raise ValueError("The number of 'to_positions' (={}) is bigger than the maximum amount authorized "
                             "(={})".format(len(to_positions), self.sim.MAX_RAY_INTERSECTION_BATCH_SIZE))
        self.to_positions = to_positions

    def _sense(self, apply_noise=True):
        """
        Sense using the ray batch sensor.

        Args:
            apply_noise (bool): if we should apply the noise or not. Note that the sensor might already have some noise.

        Returns:
            np.array[float[N]]: hit fractions along each ray in range [0,1] along the ray.
        """
        if self.simulator.supports_sensors("ray_batch"):
            return self.simulator.get_sensor("ray_batch", self.body_id, self.link_id).sense()
        position = self.position + get_rotated_point_from_quaternion(self.orientation, self.to_positions)
        rays = self.sim.ray_test_batch(from_positions=self.position, to_positions=position)
        hit = np.array([ray[2] for ray in rays])
        if apply_noise:
            hit = self._noise(hit)
        return hit

    def render(self, enable=True, color=None):
        """Render the batch of rays in the simulator; they are only visual and attached to the sensor (link). The
        visual shape of each ray is updated at runtime (each time you call this function).

        Args:
            enable (bool): if we should render or not.
            color (None, tuple/list[float[4]], np.ndarray[float[4]]): RGBA color of all the rays, where each channel
                is between 0 and 1.
        """
        pass


class HeightmapSensor(LinkSensor):
    r"""Heightmap Sensor

    Sensor that detects the heights of its surrounding using a grid map.

    Warnings: this is only valid in the simulator.
    """

    def __init__(self, simulator, body_id, link_id, width, height, num_rays_width=2, num_rays_height=2,
                 max_ray_length=100, position=None, orientation=None):  # TODO: use orientation initially
        """
        Initialize the heightmap sensor. This is only valid in the simulator.

        Note that `num_rays_width * num_rays_height` has to be smaller than `simulator.MAX_RAY_INTERSECTION_BATCH_SIZE`.
        In pybullet, this is currently set to 16,384.

        Args:
            simulator (Simulator): simulator instance.
            body_id (int): unique id of the body
            link_id (int): unique id of the link
            width (float): width of the map (along the left-right axis (i.e. y axis) of the body, measured in meters)
            height (float): height of the map (along the front-back axis (i.e. x axis) of the body, measured in meters)
            num_rays_width (int): number of rays along the width dimension (left-right axis). This will be the 'width'
                of the returned heightmap. This must be bigger or equal to 2.
            num_rays_height (int): number of rays along the height dimension (front-back axis). This will be
                the 'height' of the returned heightmap. This must be bigger or equal to 2.
            max_ray_length (float): maximum length of each ray.
            position (np.array[float[3]], None): local position of the sensor with respect to the given link. If None,
                it will be the zero vector. This position represents the center of the map.
            orientation (np.array[float[4]], None): local orientation of the sensor with respect to the given link
                (expressed as a quaternion [x,y,z,w]). If None, it will be the unit quaternion [0,0,0,1].
        """
        super(HeightmapSensor, self).__init__(simulator, body_id=body_id, link_id=link_id, position=position,
                                              orientation=orientation)

        # Check arguments

        # set num_rays_width and num_rays_height
        num_rays_width, num_rays_height = int(num_rays_width), int(num_rays_height)
        if num_rays_width * num_rays_height > self.sim.MAX_RAY_INTERSECTION_BATCH_SIZE:  # pybullet = 16,384
            raise ValueError("num_rays_width * num_rays_height can not be bigger"
                             " than {}".format(self.sim.MAX_RAY_INTERSECTION_BATCH_SIZE))
        if num_rays_width < 2:
            raise ValueError("num_rays_width must be equal or bigger than 2, but got: {}".format(num_rays_width))
        if num_rays_height < 2:
            raise ValueError("num_rays_height must be equal or bigger than 2, but got: {}".format(num_rays_height))
        self._num_rays_width = num_rays_width
        self._num_rays_height = num_rays_height

        # set max_ray_length
        if not isinstance(max_ray_length, (float, int)):
            raise TypeError("Expecting 'max_ray_length' to be an int or float, but got instead: "
                            "{}".format(type(max_ray_length)))
        max_ray_length = float(max_ray_length)
        if max_ray_length <= 0.:
            raise ValueError("Expecting 'max_ray_length' to be positive, but got instead: {}".format(max_ray_length))
        self._max_ray_length = max_ray_length

        # set width and height
        width, height = float(width), float(height)
        if width <= 0.:
            raise ValueError("Expecting the 'width' to be bigger than 0, but got: {}".format(width))
        if height <= 0.:
            raise ValueError("Expecting the 'height' to be bigger than 0, but got: {}".format(height))
        self._width = width
        self._height = height

        self._z_array = np.ones(self._num_rays_width * self._num_rays_height)

    def get_ray_from_to_positions(self):
        """
        Return the world positions for the rays to start and end.

        Returns:
            np.array[float[N,3]]: list of starting positions for the rays
            np.array[float[N,3]]: list of ending positions for the rays
        """
        pos = self.position
        w2, h2 = self._width / 2., self._height / 2.

        x, y = np.meshgrid(np.linspace(pos[1] - w2, pos[1] + w2, self._num_rays_width),
                           np.linspace(pos[0] - h2, pos[0] + h2, self._num_rays_height))
        x, y = x.ravel(), y.ravel()
        from_z = pos[2] * self._z_array
        to_z = from_z - self._max_ray_length

        from_positions = np.vstack((x, y, from_z)).T  # (N, 3)
        to_positions = np.vstack((x, y, to_z)).T  # (N, 3)

        return from_positions, to_positions

    def _sense(self, apply_noise=True):
        """
        Return the heightmap.

        Returns:
            np.array[float[width, height]]: Height map with shape [width, height] where the values are the hit
                fractions [0,1], you can multiply it by :attr:`max_ray_length` to get the depth in meters.
        """
        from_positions, to_positions = self.get_ray_from_to_positions()
        rays = self.sim.ray_test_batch(from_positions=from_positions, to_positions=to_positions)
        hit = np.array([ray[2] for ray in rays]).reshape(self._num_rays_width, self._num_rays_height)
        if apply_noise:
            hit = self._noise(hit)
        return hit

    def render(self, enable=True, color=None):
        """Render the grid map in the simulator; each point/intersection in the grid is represented as a visual
        sphere. The position of these spheres is updated at runtime (each time you call this function).

        Args:
            enable (bool): if we should render or not.
            color (None, tuple/list of 4 float, np.ndarray[float[4]]): RGBA color of all the rays, where each channel
                is between 0 and 1.
        """
        pass