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]


# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Processes tests and creates new Anomaly entities.

This module contains the ProcessTest function, which searches the recent
points in a test for potential regressions or improvements, and creates
new Anomaly entities.
"""
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import logging

from google.appengine.ext import deferred
from google.appengine.ext import ndb

from dashboard import email_sheriff
from dashboard import find_change_points
from dashboard.common import utils
from dashboard.models import alert_group
from dashboard.models import anomaly
from dashboard.models import anomaly_config
from dashboard.models import graph_data
from dashboard.models import histogram
from dashboard.models import subscription
from dashboard.sheriff_config_client import SheriffConfigClient
from tracing.value.diagnostics import reserved_infos

# Number of points to fetch and pass to FindChangePoints. A different number
# may be used if a test has a "max_window_size" anomaly config parameter.
DEFAULT_NUM_POINTS = 50


@ndb.synctasklet
def ProcessTests(test_keys):
  """Processes a list of tests to find new anoamlies.

  Args:
    test_keys: A list of TestMetadata ndb.Key's.
  """
  yield ProcessTestsAsync(test_keys)


@ndb.tasklet
def ProcessTestsAsync(test_keys):
  # Using a parallel yield here let's the tasklets for each _ProcessTest run
  # in parallel.
  yield [_ProcessTest(k) for k in test_keys]


@ndb.tasklet
def _ProcessTest(test_key):
  """Processes a test to find new anomalies.

  Args:
    test_key: The ndb.Key for a TestMetadata.
  """
  # We're dropping clank support, which goes through the old recipe_bisect
  # system. For now, we're simply disabling alert generation and stopping
  # bisects from getting kicked off. We'll follow up with a more thorough
  # removal of all old bisect related code.
  # crbug.com/937230
  if test_key.id().startswith('ClankInternal'):
    raise ndb.Return(None)

  test = yield test_key.get_async()

  config = yield anomaly_config.GetAnomalyConfigDictAsync(test)
  max_num_rows = config.get('max_window_size', DEFAULT_NUM_POINTS)
  rows_by_stat = yield GetRowsToAnalyzeAsync(test, max_num_rows)

  ref_rows_by_stat = {}
  ref_test = yield _CorrespondingRefTest(test_key)
  if ref_test:
    ref_rows_by_stat = yield GetRowsToAnalyzeAsync(ref_test, max_num_rows)

  for s, rows in rows_by_stat.items():
    if rows:
      logging.info('Processing test: %s', test_key.id())
      yield _ProcessTestStat(config, test, s, rows, ref_rows_by_stat.get(s))


def _EmailSheriff(sheriff, test_key, anomaly_key):
  test_entity = test_key.get()
  anomaly_entity = anomaly_key.get()

  email_sheriff.EmailSheriff(sheriff, test_entity, anomaly_entity)


@ndb.tasklet
def _ProcessTestStat(config, test, stat, rows, ref_rows):
  test_key = test.key

  # If there were no rows fetched, then there's nothing to analyze.
  if not rows:
    logging.error('No rows fetched for %s', test.test_path)
    raise ndb.Return(None)

  # Get anomalies and check if they happen in ref build also.
  change_points = FindChangePointsForTest(rows, config)

  if ref_rows:
    ref_change_points = FindChangePointsForTest(ref_rows, config)

    # Filter using any jumps in ref
    change_points = _FilterAnomaliesFoundInRef(change_points, ref_change_points,
                                               test_key)

  anomalies = yield [
      _MakeAnomalyEntity(c, test, stat, rows) for c in change_points
  ]

  # If no new anomalies were found, then we're done.
  if not anomalies:
    raise ndb.Return(None)

  logging.info('Created %d anomalies', len(anomalies))
  logging.info(' Test: %s', test_key.id())
  logging.info(' Stat: %s', stat)

  # Get all the sheriff from sheriff-config match the path
  client = SheriffConfigClient()
  subscriptions, err_msg = client.Match(test.test_path)
  subscription_names = [s.name for s in subscriptions or []]

  # Breaks the process when Match failed to ensure find_anomaly do the best
  # effort to find the subscriber. Leave retrying to upstream.
  if err_msg is not None:
    raise RuntimeError(err_msg)

  if not subscriptions:
    logging.warning('No subscription for %s', test.test_path)

  for a in anomalies:
    a.subscriptions = subscriptions
    a.subscription_names = subscription_names
    a.internal_only = (
        any([
            s.visibility != subscription.VISIBILITY.PUBLIC
            for s in subscriptions
        ]) or test.internal_only)
    a.groups = alert_group.AlertGroup.GetGroupsForAnomaly(a, subscriptions)

  yield ndb.put_multi_async(anomalies)

  # TODO(simonhatch): email_sheriff.EmailSheriff() isn't a tasklet yet, so this
  # code will run serially.
  # Email sheriff about any new regressions.
  for anomaly_entity in anomalies:
    if anomaly_entity.bug_id is None and not anomaly_entity.is_improvement:
      deferred.defer(_EmailSheriff, anomaly_entity.subscriptions, test.key,
                     anomaly_entity.key)


@ndb.tasklet
def _FindLatestAlert(test, stat):
  query = anomaly.Anomaly.query()
  query = query.filter(anomaly.Anomaly.test == test.key)
  query = query.filter(anomaly.Anomaly.statistic == stat)
  query = query.order(-anomaly.Anomaly.end_revision)
  results = yield query.get_async()
  if not results:
    raise ndb.Return(None)
  raise ndb.Return(results)


@ndb.tasklet
def _FindMonitoredStatsForTest(test):
  del test
  # TODO: This will get filled out after refactor.
  raise ndb.Return(['avg'])


@ndb.synctasklet
def GetRowsToAnalyze(test, max_num_rows):
  """Gets the Row entities that we want to analyze.

  Args:
    test: The TestMetadata entity to get data for.
    max_num_rows: The maximum number of points to get.

  Returns:
    A list of the latest Rows after the last alerted revision, ordered by
    revision. These rows are fetched with t a projection query so they only
    have the revision, value, and timestamp properties.
  """
  result = yield GetRowsToAnalyzeAsync(test, max_num_rows)
  raise ndb.Return(result)


@ndb.tasklet
def GetRowsToAnalyzeAsync(test, max_num_rows):
  # If this is a histogram based test, there may be multiple statistics we want
  # to analyze
  alerted_stats = yield _FindMonitoredStatsForTest(test)

  latest_alert_by_stat = dict(
      (s, _FindLatestAlert(test, s)) for s in alerted_stats)

  results = {}
  for s in alerted_stats:
    results[s] = _FetchRowsByStat(test.key, s, latest_alert_by_stat[s],
                                  max_num_rows)

  for s in results.keys():
    results[s] = yield results[s]

  raise ndb.Return(results)


@ndb.tasklet
def _FetchRowsByStat(test_key, stat, last_alert_future, max_num_rows):
  # If stats are specified, we only want to alert on those, otherwise alert on
  # everything.
  if stat == 'avg':
    query = graph_data.Row.query(projection=['revision', 'timestamp', 'value'])
  else:
    query = graph_data.Row.query()

  query = query.filter(
      graph_data.Row.parent_test == utils.OldStyleTestKey(test_key))

  # The query is ordered in descending order by revision because we want
  # to get the newest points.
  if last_alert_future:
    last_alert = yield last_alert_future
    if last_alert:
      query = query.filter(graph_data.Row.revision > last_alert.end_revision)
  query = query.order(-graph_data.Row.revision)

  # However, we want to analyze them in ascending order.
  rows = yield query.fetch_async(limit=max_num_rows)

  vals = []
  for r in list(reversed(rows)):
    if stat == 'avg':
      vals.append((r.revision, r, r.value))
    elif stat == 'std':
      vals.append((r.revision, r, r.error))
    else:
      vals.append((r.revision, r, getattr(r, 'd_%s' % stat)))

  raise ndb.Return(vals)


def _FilterAnomaliesFoundInRef(change_points, ref_change_points, test):
  change_points_filtered = []
  test_path = utils.TestPath(test)
  for c in change_points:
    # Log information about what anomaly got filtered and what did not.
    if not _IsAnomalyInRef(c, ref_change_points):
      logging.info('Nothing was filtered out for test %s, and revision %s',
                   test_path, c.x_value)
      change_points_filtered.append(c)
    else:
      logging.info('Filtering out anomaly for test %s, and revision %s',
                   test_path, c.x_value)
  return change_points_filtered


@ndb.tasklet
def _CorrespondingRefTest(test_key):
  """Returns the TestMetadata for the corresponding ref build trace, or None."""
  test_path = utils.TestPath(test_key)
  possible_ref_test_paths = [test_path + '_ref', test_path + '/ref']
  for path in possible_ref_test_paths:
    ref_test = yield utils.TestKey(path).get_async()
    if ref_test:
      raise ndb.Return(ref_test)
  raise ndb.Return(None)


def _IsAnomalyInRef(change_point, ref_change_points):
  """Checks if anomalies are detected in both ref and non ref build.

  Args:
    change_point: A find_change_points.ChangePoint object to check.
    ref_change_points: List of find_change_points.ChangePoint objects
        found for a ref build series.

  Returns:
    True if there is a match found among the ref build series change points.
  """
  for ref_change_point in ref_change_points:
    if change_point.x_value == ref_change_point.x_value:
      return True
  return False


def _GetImmediatelyPreviousRevisionNumber(later_revision, rows):
  """Gets the revision number of the Row immediately before the given one.

  Args:
    later_revision: A revision number.
    rows: List of Row entities in ascending order by revision.

  Returns:
    The revision number just before the given one.
  """
  for (revision, _, _) in reversed(rows):
    if revision < later_revision:
      return revision
  assert False, 'No matching revision found in |rows|.'


def _GetRefBuildKeyForTest(test):
  """TestMetadata key of the reference build for the given test, if one exists.

  Args:
    test: the TestMetadata entity to get the ref build for.

  Returns:
    A TestMetadata key if found, or None if not.
  """
  potential_path = '%s/ref' % test.test_path
  potential_test = utils.TestKey(potential_path).get()
  if potential_test:
    return potential_test.key
  potential_path = '%s_ref' % test.test_path
  potential_test = utils.TestKey(potential_path).get()
  if potential_test:
    return potential_test.key
  return None


def _GetDisplayRange(old_end, rows):
  """Get the revision range using a_display_rev, if applicable.

  Args:
    old_end: the x_value from the change_point
    rows: List of Row entities in asscending order by revision.

  Returns:
    A end_rev, start_rev tuple with the correct revision.
  """
  start_rev = end_rev = 0
  for (revision, row, _) in reversed(rows):
    if revision == old_end and hasattr(row, 'r_commit_pos'):
      end_rev = row.r_commit_pos
    elif revision < old_end and hasattr(row, 'r_commit_pos'):
      start_rev = row.r_commit_pos + 1
      break
  if not end_rev or not start_rev:
    end_rev = start_rev = None
  return start_rev, end_rev


@ndb.tasklet
def _MakeAnomalyEntity(change_point, test, stat, rows):
  """Creates an Anomaly entity.

  Args:
    change_point: A find_change_points.ChangePoint object.
    test: The TestMetadata entity that the anomalies were found on.
    stat: The TestMetadata stat that the anomaly was found on.
    rows: List of Row entities that the anomalies were found on.

  Returns:
    An Anomaly entity, which is not yet put in the datastore.
  """
  end_rev = change_point.x_value
  start_rev = _GetImmediatelyPreviousRevisionNumber(end_rev, rows) + 1
  display_start = display_end = None
  if test.master_name == 'ClankInternal':
    display_start, display_end = _GetDisplayRange(change_point.x_value, rows)
  median_before = change_point.median_before
  median_after = change_point.median_after

  suite_key = test.key.id().split('/')[:3]
  suite_key = '/'.join(suite_key)
  suite_key = utils.TestKey(suite_key)

  queried_diagnostics = yield (
      histogram.SparseDiagnostic.GetMostRecentDataByNamesAsync(
          suite_key,
          set([
              reserved_infos.BUG_COMPONENTS.name, reserved_infos.OWNERS.name,
              reserved_infos.INFO_BLURB.name
          ])))

  bug_components = queried_diagnostics.get(reserved_infos.BUG_COMPONENTS.name,
                                           {}).get('values')
  if bug_components:
    bug_components = bug_components[0]
    # TODO(902796): Remove this typecheck.
    if isinstance(bug_components, list):
      bug_components = bug_components[0]

  ownership_information = {
      'emails':
          queried_diagnostics.get(reserved_infos.OWNERS.name, {}).get('values'),
      'component':
          bug_components,

      # Info blurbs should be a single string, and we'll only take the firs
      #  element of the list of values.
      'info_blurb':
          queried_diagnostics.get(reserved_infos.INFO_BLURB.name,
                                  {}).get('values', [None])[0],
  }

  # Compute additional anomaly metadata.
  def MinMax(iterable):
    min_ = max_ = None
    for val in iterable:
      if min_ is None:
        min_ = max_ = val
      else:
        min_ = min(min_, val)
        max_ = max(max_, val)
    return min_, max_

  earliest_input_timestamp, latest_input_timestamp = MinMax(
      r.timestamp for unused_rev, r, unused_val in rows)

  new_anomaly = anomaly.Anomaly(
      start_revision=start_rev,
      end_revision=end_rev,
      median_before_anomaly=median_before,
      median_after_anomaly=median_after,
      segment_size_before=change_point.size_before,
      segment_size_after=change_point.size_after,
      window_end_revision=change_point.window_end,
      std_dev_before_anomaly=change_point.std_dev_before,
      t_statistic=change_point.t_statistic,
      degrees_of_freedom=change_point.degrees_of_freedom,
      p_value=change_point.p_value,
      is_improvement=_IsImprovement(test, median_before, median_after),
      ref_test=_GetRefBuildKeyForTest(test),
      test=test.key,
      statistic=stat,
      internal_only=test.internal_only,
      units=test.units,
      display_start=display_start,
      display_end=display_end,
      ownership=ownership_information,
      earliest_input_timestamp=earliest_input_timestamp,
      latest_input_timestamp=latest_input_timestamp)
  raise ndb.Return(new_anomaly)


def FindChangePointsForTest(rows, config_dict):
  """Gets the anomaly data from the anomaly detection module.

  Args:
    rows: The Row entities to find anomalies for, sorted backwards by revision.
    config_dict: Anomaly threshold parameters as a dictionary.

  Returns:
    A list of find_change_points.ChangePoint objects.
  """
  data_series = [(revision, value) for (revision, _, value) in rows]
  return find_change_points.FindChangePoints(data_series, **config_dict)


def _IsImprovement(test, median_before, median_after):
  """Returns whether the alert is an improvement for the given test.

  Args:
    test: TestMetadata to get the improvement direction for.
    median_before: The median of the segment immediately before the anomaly.
    median_after: The median of the segment immediately after the anomaly.

  Returns:
    True if it is improvement anomaly, otherwise False.
  """
  if (median_before < median_after
      and test.improvement_direction == anomaly.UP):
    return True
  if (median_before >= median_after
      and test.improvement_direction == anomaly.DOWN):
    return True
  return False