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]


====================================
Guide to adding datasets to matminer
====================================

*All information current as of 10/24/2018*

In addition to providing tools for retrieving current data from several standard
materials science databases, matminer also provides a suite of static datasets
pre-formatted as pandas DataFrame objects and stored as compressed JSON files.
These files are stored on Figshare, a long term academic data storage platform,
and include metadata making each dataset discoverable and clearly linked to the
research and contributors that generated said data.

This functionality serves two purposes. First as a way for others to quickly
and easily access data used in research at the Hacking Materials group and
second to provide the community with a set of standard datasets that can be
used for benchmarking purposes. As the application of machine learning in
materials science matures, there is a growing need for a group of benchmark
datasets that researchers can use as standard training and testing sets for
comparing model performance against.

To add a dataset to the collection currently supported by matminer there are
six primary steps:

1. Fork the matminer repository on GitHub
_________________________________________

- The matminer code base, including the metadata that defines how matminer handles datasets,
  is available `on GitHub. <https://github.com/hackingmaterials/matminer>`_

- All editing should take place either within the dev_scripts/dataset_management
  folder or within matminer/datasets


2. Prepare the dataset for long term hosting
____________________________________________

To work properly with matminer's loading functions it is assumed that
all datasets are pandas DataFrame objects stored as JSON files using the
MontyEncoder encoding scheme available in the monty Python package. Any
datasets added to matminer should ensure this requirement.

The script ``prep_dataset_for_figshare.py`` was written to expedite and
standardize this process. If the dataset being uploaded needs no
modification from the contents stored in the file, one can simply run this
script to convert your dataset to the desired format like so:
``python prep_dataset_for_figshare.py -fp /path/to/dataset(s)
-ct (compression_type: gz or bz2)`` This script can take multiple file
paths and/or directory names. If given a directory name it will crawl
the directory and try to process all files within. The prepped files will
then be available in ``~/dataset_to_json/`` along with a .txt file containing
some metadata on the files which will be used later on.

If modification does need to be made to the dataset or if you would
like the dataset name to be separate from that of the file being converted,
users will need to make a small modification to this script prior to
running it on their selected datasets.

**To update the script to preprocess your dataset:**
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  Write a preprocessor for the dataset in ``prep_dataset_for_figshare.py``

   The preprocessor should take the dataset path, do any necessary
   preprocessing to turn it into a usable dataframe, and return a tuple
   of the form ``(string_of_dataset_name, dataframe)``. If the preprocessor
   produces more than one dataset it should return a tuple of two lists of the form
   ``([dataset_name_1, dataset_name_2, …], [df_1, df_2, …])``

   For example:

   .. code-block:: python

     def _preprocess_heusler_magnetic(file_path):
         df = _read_dataframe_from_file(file_path)

         dropcols = ['gap width', 'stability']
         df = df.drop(dropcols, axis=1)

         return "heusler_magnetic", df


   Here ``_read_dataframe_from_file()`` is a simple utility function that
   determines what pandas loading function to use based on the file type
   of the path passed to it. Keyword arguments passed to this function are
   passed on to the underlying pandas loading functions.

   An example for preprocessors that return multiple datasets:

   .. code-block:: python

    def _preprocess_double_perovskites_gap(file_path):
        df = pd.read_excel(file_path, sheet_name='bandgap')

        df = df.rename(columns={'A1_atom': 'a_1', 'B1_atom': 'b_1',
        'A2_atom': 'a_2', 'B2_atom': 'b_2'})
        lumo = pd.read_excel(file_path, sheet_name='lumo')

        return ["double_perovskites_gap", "double_perovskites_gap_lumo"], [df, lumo]

-  Add the preprocessor function to a dictionary which maps file names to preprocessors in ``prep_dataset_for_figshare.py``

   The prep script identifies datasets by their file name, a dictionary
   called ``_datasets_to_preprocessing_routines`` maps these dataset names
   to their preprocessor and should be updated like so:

   .. code-block:: python

     _datasets_to_preprocessing_routines = {
     "elastic_tensor_2015": _preprocess_elastic_tensor_2015,
     "piezoelectric_tensor": _preprocess_piezoelectric_tensor,
     .
     .
     .
     "wolverton_oxides": _preprocess_wolverton_oxides,
     "m2ax_elastic": _preprocess_m2ax,
     YOUR_DATASET_FILE_NAME: YOUR_PREPROCESSOR,
     }

Once this is done the preprocessor is ready to use.

3. Upload the dataset to long term hosting
__________________________________________

Once the dataset file is ready, it should be hosted on Figshare
or a comparable open access academic data hosting service. The Hacking Materials
group maintains a collective Figshare account and follows the following procedure
when adding a dataset. Other contributors should follow a similar protocol.

* Add the dataset compressed json file as well as the original file as an entry in the "matminer datasets" figshare project
* Fill out **ALL** metadata carefully, see existing entries for examples of expected quality of citations and descriptions.
* If the dataset was originally generated from a source outside the group the source should be thoroughly cited within the dataset description and metadata


4. Update the matminer dataset metadata file
_____________________________________________

Matminer stores a file called ``dataset_metadata.json`` which contains
information on all datasets available in the package. This file is
automatically checked by CircleCI for proper formatting and the
available datasets are regularly checked to ensure they match the
descriptors contained in this metadata. While the appropriate metadata
can be added manually, it is preferable to run the helper script
``modify_dataset_metadata.py`` to do the bulk of interfacing with this file
to prevent missing data or formatting mistakes.

- Run the ``modify_dataset_metadata.py`` file and add the appropriate
  metadata, see existing metadata as a guideline for new datasets.

  The url attribute should be filled with a figshare download link for
  the individual file on figshare. Other items will be dataset specific
  or included in the .txt file produced in step 2.

- Replace the metadata file in matminer/datasets with the newly generated file (should be done automatically)
- Look over the modified ``dataset_metadata.json`` file and fix mistakes if necessary.

5. Update the dataset tests and loading code
____________________________________________

Dataset testing uses unit tests to ensure dataset metadata and dataset content
is formatted properly and available. When adding new datasets these tests
need updated. In addition matminer provides a set of convenience functions that
explicitly load a single dataset as opposed to the keyword based generic loader.
These convenience functions provide additional post processing options for
filtering or modifying data in the dataset after it has been loaded. A
convenience function should be added alongside dataset tests.

- Update dataset names saved in `matminer/datasets/tests/base.py`

  .. code-block:: python

    class DatasetTest(unittest.TestCase):
        def setUp(self):
            self.dataset_names = [
            'flla',
            'elastic_tensor_2015',
            'piezoelectric_tensor',
            .
            .
            .
            YOUR DATASET NAME HERE
            ]

- Write a test for loading the dataset in test_datasets.py.

  These tests ensure that the dataset is downloadable and that its data matches what
  is described in the file metadata. See prior datasets for examples and
  the .txt file from step 2 for column type info. A typical test consists of
  a call to a universal test function that only needs specifiers of what
  dataframe columns should be of what type and dataset specific type tests
  if necessary.

  Example:

  .. code-block:: python

    def test_dielectric_constant(self):
        object_headers = ['material_id', 'formula', 'structure',
                          'e_electronic', 'e_total', 'cif', 'meta',
                          'poscar']

        numeric_headers = ['nsites', 'space_group', 'volume', 'band_gap',
                           'n', 'poly_electronic', 'poly_total']

        bool_headers = ['pot_ferroelectric']

        # Unique Tests
        def _unique_tests(df):
         self.assertEqual(type(df['structure'][0]), Structure)

        # Universal Tests
        self.universal_dataset_check(
            "dielectric_constant", object_headers, numeric_headers,
            bool_headers=bool_headers, test_func=_unique_tests
        )


- Write a convenience function for the dataset in `convenience_loaders.py`

  This can be as simple as just returning the results of load_dataset
  or provide the user with extra options to return only subsets
  of the dataset with certain properties.

  Example:

  .. code-block:: python

    def load_elastic_tensor(version="2015", include_metadata=False, data_home=None,
                            download_if_missing=True):
        """
        Convenience function for loading the elastic_tensor dataset.

        Args:
        version (str): Version of the elastic_tensor dataset to load
        (defaults to 2015)

        include_metadata (bool): Whether or not to include the cif, meta,
        and poscar dataset columns. False by default.

        data_home (str, None): Where to loom for and store the loaded dataset

        download_if_missing (bool): Whether or not to download the dataset if
        it isn't on disk

        Returns: (pd.DataFrame)
        """
        df = load_dataset("elastic_tensor" + "_" + version, data_home,
                          download_if_missing)

        if not include_metadata:
            df = df.drop(['cif', 'kpoint_density', 'poscar'], axis=1)

        return df

- Write a test for the added convenience function

  These tests can be simple and will depend on the options provided in the
  convenience function. See existing tests for examples.

6. Make a Pull Request to the matminer GitHub repository
________________________________________________________

- Make a commit describing the added dataset
- Make a pull request from your fork to the primary repository