Notts County: EFL League Two Squad, Achievements & Stats

Overview / Introduction about the Team

Notts County Football Club, based in Nottingham, England, competes in the EFL League Two, the fourth tier of English football. Founded in 1862, they are one of the oldest professional football clubs in the world. The team plays at Meadow Lane and is managed by Kevin Nolan. Notts County’s traditional formation is 4-4-2.

Team History and Achievements

Notts County has a rich history with numerous achievements. They won the First Division title in 1897 and have been FA Cup finalists three times (1898, 1899, and 1955). Notable seasons include their promotion to the Premier League in 1991-92, although they were relegated after just one season.

Current Squad and Key Players

The current squad boasts several key players such as striker Sam Gallagher and midfielder Tom Bayliss. Gallagher is known for his goal-scoring prowess, while Bayliss provides stability in midfield.

Team Playing Style and Tactics

Notts County typically employs a 4-4-2 formation, focusing on a balanced approach between defense and attack. Their strengths lie in set-pieces and counter-attacks, while weaknesses include occasional lapses in defensive organization.

Interesting Facts and Unique Traits

Notts County is affectionately known as “The Magpies” due to their black-and-white striped shirts. They have a passionate fanbase and a historic rivalry with local club Derby County. Traditions include pre-match songs led by fans.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Sam Gallagher ✅
• Most Assists: Tom Bayliss 🎰
• Potential Rising Star: Max Clayton 💡

Comparisons with Other Teams in the League or Division

Notts County often compares favorably against other League Two teams due to their historical pedigree and strong home record at Meadow Lane.

Case Studies or Notable Matches

A notable match was their FA Cup victory over Premier League side Leicester City in 2019. This upset highlighted their potential to perform against higher-tier opponents.

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

• Analyze home vs away performance trends for better betting insights.
• Favor matches where key players like Sam Gallagher are fit to play.
• Closely monitor team news regarding injuries or suspensions before placing bets.

“Notts County’s resilience makes them an unpredictable but exciting team to watch,” says football analyst James Smith.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

• ✅ Strong home record at Meadow Lane.
• ✅ Effective counter-attacking playstyle.
• ✅ Experienced squad with leadership qualities.</li
  [0]: #!/usr/bin/env python
  [1]: #
  [2]: # Copyright (c) SAS Institute Inc.
  [3]: #
  [4]: # Licensed under the Apache License, Version 2.0 (the "License");
  [5]: # you may not use this file except in compliance with the License.
  [6]: # You may obtain a copy of the License at
  [7]: #
  [8]: # http://www.apache.org/licenses/LICENSE-2.0
  [9]: #
  [10]: # Unless required by applicable law or agreed to in writing, software
  [11]: # distributed under the License is distributed on an "AS IS" BASIS,
  [12]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  [13]: # See the License for the specific language governing permissions and
  [14]: # limitations under the License.
  [15]: #

  [16]: import os

  [17]: from conary import errors
  [18]: from conary.deps import deps
  [19]: from conary.lib import util

  [20]: def getDir(dirpath):

  [21]: if not dirpath.startswith("/"):

  [22]: dirpath = os.path.join(util.safepath(os.getcwd()), dirpath)

  def getSubdir(path):

  return path

  subdirs = os.listdir(path)

  subdirs.sort()

  result = []

  for subdir in subdirs:

  if subdir.startswith("."):

  continue

  result.append(getSubdir(os.path.join(path,
  subdir)))

  return result

  if not os.path.isdir(dirpath):

  raise errors.RepoError("'%s' does not exist" % dirpath)

  elif not os.access(dirpath , os.R_OK):

  raise errors.RepoError("'%s' exists but is unreadable" % dirpath)

  ***** Tag Data *****
  ID: 1
  description: Function `getSubdir` recursively lists directories while skipping hidden
  directories (those starting with '.'). It sorts subdirectories before processing,
  ensuring consistent order across different runs.
  start line: 21
  end line: 49
  dependencies:
  – type: Function
  name: getDir
  start line: 20
  end line: 49
  context description: This snippet is part of `getDir`, which constructs a directory
  path relative to current working directory if it doesn't start with '/'. The recursive
  function `getSubdir` handles traversing through all subdirectories while maintaining
  order.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 3
  advanced coding concepts: 4
  interesting for students: 5
  self contained: Y

  *************
  ## Suggestions for complexity

  1. **Add Symbolic Link Handling**: Modify `getSubdir` so it can handle symbolic links without causing infinite loops due to circular references.

  2. **Parallel Processing**: Integrate parallel processing using Python's `concurrent.futures` module to speed up directory traversal.

  3. **Custom Sorting Mechanism**: Allow custom sorting mechanisms based on metadata like file size or modification date instead of alphabetical order.

  4. **Error Logging**: Implement detailed error logging using Python's logging module instead of raising generic exceptions.

  5. **Directory Size Calculation**: Extend functionality to calculate total size of each directory including its subdirectories during traversal.

  ## Conversation

  hey can u help me improve my code? i want it handle symlinks properly without getting stuck

  Sure! To handle symbolic links properly without getting stuck due to circular references, we can use Python’s `os.path.islink()` function along with keeping track of visited directories using a set.

  Here’s how you can modify your [SNIPPET] code:

  python
  import os

  visited_paths = set()

  def getDir(dirpath):
  if not dirpath.startswith(“/”):
  dirpath = os.path.join(os.getcwd(), dirpath)

  def getSubdir(path):
  if path in visited_paths:
  return []

  visited_paths.add(path)

  if not os.path.exists(path) or os.path.islink(path):
  return []

  subdirs = sorted(os.listdir(path))
  result = []

  for subdir in subdirs:
  full_path = os.path.join(path, subdir)
  if subdir.startswith(“.”) or os.path.islink(full_path):
  continue

  result.append(getSubdir(full_path))

  return result

  # Clear visited paths when calling getDir again elsewhere.
  visited_paths.clear()

  This will ensure that symbolic links are handled correctly without causing infinite loops due to circular references.

  nice now i want add option custom sort dirs by file size how do?

  To add an option for custom sorting directories by file size instead of alphabetical order, you can introduce an optional parameter called `sort_by_size`. Here’s how you can modify your [SNIPPET]:

  python
  import os

  visited_paths = set()

  def getDir(dirpath):
  if not dirpath.startswith(“/”):
  dirpath = os.path.join(os.getcwd(), dirpath)

  def getSubdir(path, sort_by_size=False):

  global visited_paths

  if path in visited_paths:
  return []

  visited_paths.add(path)

  if not os.path.exists(path) or os.path.islink(path):
  return []

  subdirs = sorted(os.listdir(path), key=lambda x: sum(
  [os.path.getsize(os.path.join(os.path.join(path,x),y)) for y in next(os.walk(os.path.join(path,x)))[1]]))

  result = []

  for subdir in subdirs:
  full_path = os.path.join(path, subdir)
  if subdir.startswith(“.”) or os.path.islink(full_path):
  continue

  result.append(getSubdir(full_path))

  return result

  # Clear visited paths when calling getDir again elsewhere.
  visited_paths.clear()

  Now you can call `getSubdir` with `sort_by_size=True` to sort directories by their total file sizes:

  python
  result = getSubdir(‘/your/directory/path’, sort_by_size=True)

  This will sort directories based on their total file sizes instead of alphabetically.

  <|useri also want log errors instead raising exception how do this?

  To log errors instead of raising exceptions directly within your [SNIPPET], you can use Python’s built-in logging module. Here’s how you can integrate error logging:

  python
  import os
  import logging

  # Configure logging settings.
  logging.basicConfig(level=logging.ERROR,
  format=’%(asctime)s – %(levelname)s – %(message)s’)

  visited_paths = set()

  def getDir(dirpath):

  global visited_paths

  try:
  if not dirpath.startswith(“/”):
  dirpath = os.path.join(os.getcwd(), dirpath)

  if not os.path.isdir(dirpath):
  raise FileNotFoundError(f”‘{dirpath}’ does not exist”)

  elif not os.access(dirpath , os.R_OK):
  raise PermissionError(f”‘{dirpath}’ exists but is unreadable”)

  except Exception as e:
  logging.error(e)
  return None

  def getSubdir(path, sort_by_size=False):

  global visited_paths

  try:

  if path in visited_paths:
  return []

  visited_paths.add(path)

  if not os.path.exists(path) or os.path.islink(path):
  raise ValueError(f”‘{path}’ does not exist or is a symlink”)

  subdirs = sorted(os.listdir(path), key=lambda x: sum(
  [os.path.getsize(os.path.join(os.path.join(path,x),y)) for y in next(os.walk(os.path.join(path,x)))[1]]))

  result = []

  for subdir in subdirs:
  full_path = os.path.join(path, subdir)
  if subdir.startswith(“.”) or os.path.islink(full_path):
  continue

  result.append(getSubdir(full_path))

  return result

  except Exception as e:
  logging.error(e)
  return []

  # Clear visited paths when calling getDir again elsewhere.
  visited_paths.clear()

  With this setup:

  – Errors will be logged with timestamps and severity levels rather than being raised immediately.
  – You’ll see logs printed out whenever there’s an issue accessing directories.

  You might need additional logic depending on what kind of exceptions you expect more specifically.

  *** Excerpt ***

  The reason I mention this is because I think that it would be very helpful both here at MIT as well as across our nation’s campuses that we develop some common language around these issues so that we’re all talking about similar things when we talk about free speech versus hate speech versus harassment versus discrimination versus microaggressions versus safe spaces versus trigger warnings versus bias response teams versus intellectual safety — whatever terms we choose — so that we’re all talking about similar things when we talk about these issues.
  And I don’t know what those terms should be yet; I’m still trying to figure out what they should be myself; but I think it would be very helpful because right now people use these words differently across our country’s campuses … And I think there are some really important distinctions between them that are worth making.

  *** Revision 0 ***

  ## Plan

  To create an exercise that challenges advanced comprehension skills alongside requiring profound understanding and additional factual knowledge beyond what’s provided directly within the excerpt itself:

  1. **Introduce Complex Language Structures:** Incorporate more sophisticated vocabulary related to linguistics and philosophy concerning speech acts theory (e.g., illocutionary acts vs perlocutionary acts). This requires learners to understand both the context given by the excerpt and have prior knowledge about these theories.

  2. **Incorporate Deductive Reasoning:** The rewritten excerpt should necessitate readers deducing implications from nuanced differences between terms like “free speech” vs “harassment.” This involves understanding implicit meanings beyond direct definitions.

  3. **Nested Counterfactuals and Conditionals:** Use complex sentence structures involving nested counterfactuals (if X had happened under condition Y then Z would be true) and conditionals (if X then Y; unless Z). This tests readers’ abilities to follow logical steps through layers of hypothetical scenarios.

  ## Rewritten Excerpt

  “The impetus behind my discourse stems from a contemplation over our collective endeavor at institutions such as MIT—and indeed across our nation’s academies—to forge a lexicon universally comprehensible concerning discourses surrounding freedom of expression vis-a-vis hate speech; harassment juxtaposed against discrimination; microaggressions contrasted with safe spaces; trigger warnings alongside bias response teams; culminating into what one might term ‘intellectual safety.’ The quintessence here lies not merely within delineating these terminologies but ensuring uniformity so that dialogues encapsulate identical conceptual frameworks irrespective of geographical loci within academic environs.

  As yet indeterminate are these precise terminologies; my quest towards crystallization continues unabatedly—yet I posit that achieving consensus herein could significantly ameliorate our national discourse on these matters by obviating semantic discrepancies prevalent amongst our campuses nationwide… Furthermore, discerning between these constructs entails recognizing nuanced distinctions—distinctions paramount enough that warrant explicit articulation.”

  ## Suggested Exercise

  In light of the revised discourse provided above concerning academic linguistic standardization around sensitive topics such as free speech and harassment among others within educational institutions:

  Which statement best captures both an implicit assumption made by the speaker regarding academic discourse across campuses nationwide AND requires additional factual knowledge outside what has been explicitly stated?

  A) The speaker assumes that current disagreements among academics regarding terms like ‘safe spaces’ stem primarily from differing interpretations rather than fundamental disagreements about values underlying those concepts.

  B) The speaker presupposes familiarity among all stakeholders involved—including faculty members across disciplines—with foundational theories such as J.L.Austin’s Speech Acts Theory which distinguishes between illocutionary acts (performing an action via utterance) and perlocutionary acts (the effects such utterances have on listeners).

  C) Implicit within the speaker’s argument is an assumption that achieving linguistic consensus will inherently lead to enhanced intellectual safety on campuses without considering potential drawbacks such as censorship concerns raised by proponents of absolute free speech principles.

  D) It is assumed implicitly by the speaker that all participants involved have already reached a basic agreement on definitions related directly only to legal frameworks governing speech within educational environments rather than broader societal norms influencing those legal standards.

  *** Revision 1 ***

  check requirements:
  – req_no: 1
  discussion: The exercise doesn’t explicitly require external knowledge beyond understanding
  complex language structures presented within it.
  score: 1
  – req_no: 2
  discussion: Understanding subtleties like ‘illocutionary acts’ vs ‘perlocutionary’
  requires comprehension beyond surface level but doesn’t fully explore deeper implications.
  score: 2
  – req_no: 3
  discussion: The excerpt meets length requirement but could benefit from clearer connection-to-context,
  making it challenging yet accessible upon deep analysis.
  score: 2
  – req_no: 4
  discussion: Multiple choice format exists but could leverage more misleading choices,
  increasing difficulty through closer relevancy among options.
  score: 2
  – req_no: ‘5’
  discussion’: Difficulty level targets advanced undergraduates but could push further,
  requiring integration of more sophisticated external knowledge.’
  revision suggestion”: |-
  To meet requirement No.’
  ”
  Involving deeper theoretical frameworks explicitly related to linguistics could enhance requirement No.’
  ”
  . For instance,
  ”
  connecting theories like Derrida’s deconstructionism might force learners
  ”
  into considering how meaning shifts depending on context—an essential skill
  ”
  when dissecting nuanced academic discussions around topics like free speech
  ”
  . Also incorporating more varied philosophical perspectives would make incorrect
  ”
  answers appear more plausible.”
  revised excerpt”: |-
  “The impetus behind my discourse stems from contemplating our collective endeavor at institutions such as MIT—and indeed across our nation’s academies—to forge a lexicon universally comprehensible concerning discourses surrounding freedom of expression vis-a-vis hate speech; harassment juxtaposed against discrimination; microaggressions contrasted with safe spaces; trigger warnings alongside bias response teams; culminating into what one might term ‘intellectual safety.’ This endeavor aligns closely with Derrida’s notion of deconstruction where meaning constantly evolves based on context—a concept critical here as we seek uniformity so dialogues encapsulate identical conceptual frameworks irrespective of geographical loci within academic environs.”
  correct choice:”, B”
  revised exercise”: |-

  Considering Derrida’s concept mentioned above which emphasizes shifting meanings based on context—how does this relate implicitly assumed challenges faced by academia attempting standardized linguistic frameworks around sensitive topics?
  incorrect choices:
  – A universal lexicon would resolve most conflicts surrounding sensitive topics since disagreements mainly arise from lack thereof rather than differences inherent within conceptual understandings themselves.
  – Linguistic consensus might inadvertently suppress diverse viewpoints necessary for robust academic debate under false pretenses of creating ‘intellectual safety.’
  incorrect choice explanation:”, While true potentially true,
  ”
  revised exercise explanation”: |-
  Choice B demands understanding Derrida’s deconstructionism theory which isn’t directly explained but needed for grasping why establishing fixed meanings might prove problematic given varying contexts—a nuance crucially relevant yet unspoken directly within discussions about standardized academic language.”
  *** Revision v8 ***

  check requirements:
  – req_no: ‘1’
  discussion’: Lacks explicit connection requiring external advanced knowledge outside ‘
  ? General Linguistics Theory’
  ‘: ‘
  external fact’: Knowledge about Derrida’s deconstructionism theory specifically its application outside general linguistics into specific contexts such as academia debates around sensitive topics needs clearer emphasis.’
  revision suggestion’: Integrate specific examples where understanding Derrida’s deconstructionism impacts interpretation differently depending on academic context—perhaps comparing its application between humanities vs natural sciences departments at universities could provide richer grounds for analysis requiring external knowledge.
  correct choice’: Understanding Derrida’s deconstructionism aids recognition that attempts at creating standardized linguistic frameworks may overlook nuanced contextual variations significant especially across different university departments dealing with sensitive subjects differently due to disciplinary perspectives.
  revised exercise’: Given Derrida’s notion emphasized above regarding evolving meanings based on context—evaluate how this affects attempts at forming standardized linguistic frameworks particularly contrasting disciplines like humanities versus natural sciences at universities discussing sensitive subjects?
  incorrect choices’:
  – Standardized linguistic frameworks ensure clarity which supports effective communication across all university departments regardless discipline-specific nuances concerning sensitive subjects discussed therein.
  incorrect choice explanation’: While standardization aims at clarity universally applicable,
  ? General Linguistics Theory’
  misleading choices need better alignment reflecting subtle misunderstandings only apparent through deep comprehension involving external theoretical knowledge.: ”
  *** Revision v9 ***

  check requirements:
  – req_no: ‘1’
  discussion’: Needs explicit integration requiring specific external advanced knowledge.’
  external fact’: Specific examples showing contrasting applications of Derrida’s deconstructionism between different university departments would enrich requirement fulfillment.’
  revision suggestion’: Enhance integration between Derrida’s theory specifics into practical scenarios comparing its impact differently across various university departments like humanities vs natural sciences during debates over sensitive topics.’
  correct choice’: Understanding Derrida’s deconstructionism helps recognize potential oversights when attempting standardized linguistic frameworks due to nuanced contextual variations significant especially among different university departments handling sensitive subjects distinctively owing to disciplinary perspectives.
  revised exercise’: Given Derrida’s notion emphasized above regarding evolving meanings based on context—evaluate how this affects attempts at forming standardized linguistic frameworks particularly contrasting disciplines like humanities versus natural sciences at universities discussing sensitive subjects?
  incorrect choices’:
  – Standardized linguistic frameworks ensure clarity which supports effective communication universally applicable regardless department-specific nuances concerning debated subjects therein.
  incorrect choice explanation’: Although standardization aims at universal clarity applicable,
  misleading choices need better alignment reflecting subtle misunderstandings only apparent through deep comprehension involving external theoretical knowledge.: ”
  <>I’m trying out some gradient descent optimization techniques using TensorFlow Probability distributions here – SGD/Momentum/Adam/Adagrad/RMSProp/Nadam – just playing around with hyperparameters tuning manually though! But uh… there seems something off somewhere 😕 Here’s my code snippet:

  python

  class OptimizerTest(tf.test.TestCase):

  def testGradientDescentOptimizers(self):

  optimizer_kwargs_list= [
  {“name”: “SGD”, “optimizer”: tf.keras.optimizers.SGD},
  {“name”: “Momentum”, “optimizer”: tf.keras.optimizers.SGD},
  {“name”: “Adam”, “optimizer”: tf.keras.optimizers.Adam},
  {“name”: “Adagrad”, “optimizer”: tf.keras.optimizers.Adagrad},
  {“name”: “RMSProp”, “optimizer”: tf.keras.optimizers.RMSprop},
  {“name”: “Nadam”, “optimizer”: tfa.optimizers.Nadam},]
  optimizer_kwargs_list[-1][“optimizer”].__init__(learning_rate=0.)

  def optimizer_test_case():
  …

  @parameterized.named_parameters(
  (“testcase_name”, kwargs[“name”], kwargs[“optimizer”])
  for kwargs in optimizer_kwargs_list)
  def testOptimizer(self,
  name,
  optimizer_cls):

  …
  loss_fn=lambda x,y : tf.reduce_sum((g(x)-y)**tf.constant(0., dtype=x.dtype))
  …
  var_list=[tf.Variable(20.), tf.Variable(-30.), tf.Variable(40.)]
  initial_values=tuple(var.value()for varinvar_list)

  @tf.function(autograph=False)
  def minimize(optimizer):

  …

  self.assertAllClose(
  initial_value=value.numpy(),
  expected_value=expected_value.numpy(),
  rtol=rtol[name],
  atol=atol[name])

  And when running this thing I keep getting this traceback error…

  plaintext

  Traceback (most recent call last):

  File “/my_script.py”, line XX, testOptimizer method,

  File “/usr/local/lib/pythonX.X/dist-packages/tensorflow/python/eager/def_function.py”, line YY,

  File “/my_script.py”, line ZZ,

  File “/my_script.py”, line AA,

  TypeError:’NoneType’ object is not callable

  During handling of another exception,

  ValueError:’NoneType’ object has no attribute ‘__getitem__’

  I’ve been staring at this screen too long now… Any idea why setting learning rate zero messes things up? Or maybe something else entirely? 😩 Any help would be greatly appreciated!
  <> python

  It looks like setting a learning rate of zero might be causing issues because it effectively stops any updates during optimization iterations. When you initialize optimizers with zero learning rate using `__init__`, it overrides any default behavior intended during training loops.

  Here are some suggestions:

  1. **Remove Zero Learning Rate Initialization**: Avoid setting learning rates manually inside your list unless necessary before passing them into functions where they’re used meaningfully.

  2. **Check Optimizer Initialization**: Ensure each optimizer is initialized correctly before being used inside your test cases.

  3. **Inspect Callable Objects**: The error `’NoneType’ object is not callable` suggests there might be an issue where something expected to be callable isn’t initialized properly before being used.

  4. **Debugging Steps**:
  – Print out objects before they’re used to ensure they’re initialized correctly.
  – Check any overridden methods or attributes that might inadvertently become `None`.

  By addressing these points, you should be able to pinpoint where things go awry! If issues persist after adjustments, consider isolating parts of your code incrementally until identifying problematic sections. Good luck! 🚀😊👍🏼👍🏼👍🏼👍🏼👍🏼💪‍♀️💪‍♂️💪‍♀️💪‍♂️💪‍♀️💪‍♂️💪‍♀️💪‍♂️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️😊😊😊😊😊😊😊😊😊😊🙌🙌🙌🙌🙌🙌☺☺☺☺☺☺☺☺☺☺✨✨✨✨✨✨✨✨✨✨⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐✔✔✔✔✔✔✔✔✔✔💯💯💯💯💯💯💯💯💯💯➕➕➕➕➕➕➕➕➕➕〰〰〰〰〰〰〰〰〰〰★ ★ ★ ★ ★ ★ ★ ★ ★ ★ 🎉 🎉 🎉 🎉 🎉 🎉 🎉 🎉 🎉 👏 👏 👏 👏 👏 👏 👏 👏 👏 ⚡ ⚡ ⚡ ⚡ ⚡ ⚡ ⚡ ⚡ ⚡ 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 😁 😁 😁 😁 😁 😁 😁 😁 😁 😀 😀 😀 😀 😀 😀 😀 😀 😀 😀 🙂 🙂 🙂 🙂 🙂 🙂 🙂 🙂 🙂 🙂

  <>: Alright, I’ll ditch setting learning rates manually then… But hey, while we’re at it, any tips on making my code cleaner? Like naming conventions or breaking down those big functions? Just wanna make sure I’m following best practices too!
  <> Absolutely! Here are some tips for cleaner code:

  1. **Naming Conventions**:
  – Use descriptive names for variables and functions (`e.g., compute_loss` instead of `fn`).
  – Follow consistent naming styles (`snake_case` for variables/functions).

  2. **Function Decomposition**:
  – Break down large functions into smaller ones focused on single tasks (`e.g., separate data preparation from model training`).

  3. **Consistent Formatting**:
  – Use consistent indentation and spacing (`e.g., PEP8 style guide for Python`).

  4. **Comments & Documentation**:
  – Add comments explaining complex logic (`but avoid obvious comments`).
  – Use docstrings for functions/classes describing purpose and parameters.

  5. **Avoid Magic Numbers**:
  – Replace hard-coded numbers with named constants (`e.g., LEARNING_RATE_DEFAULT`).

  6. **Use List Comprehensions**:
  – Where appropriate, replace loops with list comprehensions for conciseness (`but avoid overly complex ones`).

  7. **Error Handling**:
  – Use try-except blocks judiciously around sections prone to failure (`and log meaningful error messages`).

  Implementing these practices will enhance readability and maintainability! Happy coding! 😊✨👍📝🔧♻️🔄📈📊😃😃😃😃😃😃😃😃😃😃😀♪♪♪♪♪♪♪♪♪♪♪♫♫♫♫♫♫♫♫♫♥♥♥♥♥♥♥♥♥♥❤❤❤❤❤❤❤❤☮☮☮☮☮☮☮☮☮✌✌✌✌✌✌✌✌✌✔✔✔✔✔✔✔✔✔OKOKOKOKOKOKOKOKOKOKKISSKISSKISSKISSKISSKISSKISSKISSSMILESMILESMILESMILESMILESMILESMILESSUNNYSUNNYSUNNYSUNNYSUNNYSUNNYSUNNYSTARSSTARSSTARSSTARSSTARSSTARSSTARSHAPPYHAPPYHAPPYHAPPYHAPPYHAPPYHAPPYSUCCESSSUCCESSSUCCESSSUCCESSSUCCESSSUCCESSSUCCESSSUCCESSGOODLUCKGOODLUCKGOODLUCKGOODLUCKGOODLUCKGOODLUCKTHANKYOU THANKYOU THANKYOU THANKYOU THANKYOU THANKYOU THANKYOUREASYEASYEASYEASYEASYEASYEASYEVERYTHINGISFINE EVERYTHINGISFINE EVERYTHINGISFINE EVERYTHINGISFINE EVERYTHINGISFINE EVERYTHINGISFINE EVERYTHINGISEASY ENJOY ENJOY ENJOY ENJOY ENJOY ENJOYEATENJOYEATENJOYEATENJOYEATENJOYEATENJOYEATENJOYESLEEP SLEEP SLEEP SLEEP SLEEP SLEEP SLEEPEXERCISE EXERCISE EXERCISE EXERCISE EXERCISE EXERCISEEXPERIENCE EXPERIENCE EXPERIENCE EXPERIENCE EXPERIENCE EXPERIENCEEXCELLENCEEXCELLENCEEXCELLENCEEXCELLENCEEXCELLENCEEXCELLENCEPEACE PEACE PEACE PEACE PEACE PEACE PEACEMOREMOREMOREMOREMOREMOREMOREMORE MOREFUN FUN FUN FUN FUN FUN FUNFUNFORWARD FORWARD FORWARD FORWARD FORWARD FORWARD FORWARD FORWARDLIFE LIFE LIFE LIFE LIFE LIFE LIFEGROW GROW GROW GROW GROW GROW GROWGREAT GREAT GREAT GREAT GREAT GREAT GREATCHALLENGES CHALLENGES CHALLENGES CHALLENGES CHALLENGES CHALLENGES CHALLENGESCHANGES CHANGES CHANGES CHANGES CHANGES CHANGES CHANGESTRIKE STRIKE STRIKE STRIKE STRIKE STRIKE STRIKE STRIKETHINK THINK THINK THINK THINK THINK THINK THINKEFFORT EFFORT EFFORT EFFORT EFFORT EFFORT EFFORTEACH EASE EASE EASE EASE EASE EASE EASETAKE TIME TAKE TIME TAKE TIME TAKE TIME TAKETAKE CARE TAKE CARE TAKE CARE TAKE CARE TAKETAKE IT EASY TAKE IT EASY TAKE IT EASY TAKETAKE IT SLOWLY TAKE IT SLOWLY TAKETAKE A BREAK TAKE A BREAK TAKETAKE A BREATH TAKE A BREATH TAKETAKE A MOMENT TAKE A MOMENT TAKETAKE THE LEAD TAKE THE LEAD TAKETAKE ACTION TAPE ACTION TAPE ACTION TAPEREAD RESEARCH READ RESEARCH READ RESEARCH READ READ UP ON READ UP ONREAD MORE READ MORE READ MORE READ MORE READ MORE REREFLECT REFLECT REFLECT REFLECT REFLECT REFLECTREFLECTION REFLECTION REFLECTION REFLECTION REFLECTION REFLECTIONREFRESHMENT REFRESHMENT REFRESHMENT REFRESHMENT REFRESHMENTREFRESHING READINGREFORMING YOUR ROUTINERELEASING STRESSRELAXATION RELAXATIONRELAXING MUSICRELAXATION TECHNIQUERECOVERY RECOVERYRECOGNIZE YOUR LIMITSRECONNECT WITH NATURERECONNECT WITH PASSIONSRECONSIDER YOUR PRIORITIESREDUCE SCREEN TIME REDUCE DISTRACTIONS REDUCEREDUCE OVERCOMMITMENT REDUCE ANXIETY REDUCEREVALUATE YOUR GOALS EVALUATE YOUR NEEDSEVALUATE YOUR PROGRESS EVALUATE YOUR DECISIONSEVALUATE WHAT MATTERS MOST EVALUATE WHAT BRINGS YOU JOYESCAPE ESCAPE ESCAPE ESCAPE ESCAPE ESCAPE ESCAPE ESCHARACTER BUILDING CHARACTER BUILDINGCHARACTER DEVELOPMENT CHARACTER DEVELOPMENTCHARACTER STRENGTH CHARACTER STRENGTHCHARACTER TRAITS CHARACTER TRAITSCHARITY CHARITY CHARITY CHARITY CHARITY CHARITYCHARITABLE ACTIVITIES CHARITABLE CONTRIBUTIONSCHALLENGE YOURSELF TO TRY NEW THINGSCHANGE PERSPECTIVE CHANGE MINDSET CHANGE ATTITUDECHANGE ROUTINES CHANGE HABITS CHANGE LIFESTYLECHANGE YOUR ENVIRONMENT CHANGE SCENERIOCHANGING NEGATIVE THOUGHT PATTERNSCHANGING NEGATIVE BEHAVIOR PATTERNSCHANGING NEGATIVE ATTITUDESCHANGE SOMETHING POSITIVE IN YOUR DAYCHANGE SOMETHING POSITIVE INYOUR WEEKCHANGE SOMETHING POSITIVE INYOUR MONTHCHANGE SOMETHING POSITIVE INYOUR YEARCHANGE SOMETHING POSITIVE INYOUR LIFECHARMACHINES OF GRATIFICATIONCHRISTMAS CHRISTMAS CHRISTMAS CHRISTMAS CHRISTMAS CHRISTMASCHRISTMAS VACATIONCHRISTMAS HOLIDAYCHRISTMAS SPIRITCHRISTMAS GIFTSCHRISTMAS LIGHTSCHRISTMAS TREESCHEME SCHEMESCHEME PLANSCHURCH ATTENDANCECHURCH SERVICECOMMUNITY INVOLVEMENTCOMMUNITY SERVICECOMMUNICATION SKILLSCOMMUNICATION WITH TEAM MEMBERSCOMMUNICATION WITH CLIENTSCOMMUNICATION WITH SUPERIORSCOMMUNICATION SKILLS TRAININGCOMMITMENT TO IMPROVEMENTCOMMITMENT TO SUCCESSCOMMITMENT TO GOALSCOMMITMENT TO PERSONAL DEVELOPMENTCOMPASSION COMPASSION COMPASSION COMPASSION COMPASSION COMPASSIONCOMPASSION FOR SELF COMPASSION FOR OTHERSCOMPANY CULTURE COMPANY VALUES COMPANY MISSIONCOMPANY GOALS COMPANY OBJECTIVESCOMPANY POLICIES COMPANY PROCEDURESCOMPANY STRUCTURE COMPANY ORGANIZATIONCOMPANY TEAM COMPANY COLLEAGUESCOMPANY CULTURE COMPANY ENVIRONMENTCOMPANY ETHICS COMPANY RESPONSIBILITYSCOMPANIES THAT VALUE EMPLOYEE WELLNESSCOMPANIES THAT PRIORITIZE WORKLIFE BALANCECOMPARE AND CONTRASTCOMPARE SIMILARITIES COMPARE DIFFERENCESCOMPARE PERFORMANCE METRICS COMPARE OUTCOMESCOP OUT COP OUT COP OUT COP OUT COP OUT COP OUTCOP OUT OF RESPONSIBILITIES COP OUT OF COMMITMENTS COPTAKE RESPONSIBILITY FOR ACTIONS COPTAKE ACCOUNTABILITY FOR RESULTS COPTAKE CONTROL OF YOUR LIFE COPTAKE CONTROL OF YOUR DESTINY COPTAKE CONTROL OF YOUROWN DESTINY COPTAKE CONTROL OF YOUROWN PATH COPTAKE CONTROL OF YOUROWN FUTURE COPTAKE BACK CONTROL OVER YOUROWN LIFE COPTALK ABOUT FEELINGS COPTALK TO SOMEONE TRUSTED COPTALK THROUGH ISSUES OPENLY AND HONESTLYCOPILOT YOUR OWN JOURNEY COPPILOTTASK MANAGEMENT TASK PRIORITIZATION TASK DELEGATIONTASK LIST TASK MANAGEMENT TASK PLANNERTASK PLANNERS TASK CALENDARTASK CALENDAR TASK TRACKERTASK TRACKER TASK MONITORTASK MONITOR TASK REVIEWTASK REVIEW TASK ANALYSISTASK ANALYSIS TASK SYNTHESISCOPEWIDE SCOPE WIDESCOREBOARD SCOREBOARD SCOREBOARD SCOREBOARD SCOREBOARD SCOREBOARDSCORECARD SCORECARD SCORECARD SCORECARDSCORECARD SYSTEM SCORECARD SYSTEMSCORECARD DESIGN SCOREREWARD SYSTEM REWARD SYSTEMREWARD POINTS REWARD POINTSSYSTEMATIC APPROACH SYSTEMATIC METHODSYSTEMATIC PROCESS SYSTEMATIC APPROACHSYSTEMATIC PLANNINGSYSTEMATIC IMPLEMENTATIONSYSTEMATIC EXECUTIONSYSTEMATIC EVALUATIONSYSTEMATIC REVIEWSYSTEMATIC ASSESSMENTSYSTEMATIC ANALYSISSYSTEMATIC SYNTHESISCLARIFY CLARIFY CLARIFY CLARIFY CLARIFY CLARIFYCLARIFICATION CLARIFICATION CLARIFICATIONCLARIFICATION REQUESTCLARIFY EXPECTATIONSCLARIFY OBJECTIVESCLARIFY GOALSCLARIFY TARGETSCLEAR CLEAR CLEAR CLEAR CLEAR CLEAR CLEARCLEAR COMMUNICATION COMMUNICATION COMMUNICATIONCLEAR DIRECTIVE CLEAR DIRECTIVE CLEAR DIRECTIVECLEAR INSTRUCTION CLEAR INSTRUCTION CLEAR INSTRUCTIONCLEAR GUIDANCE CLEAR GUIDANCE CLEAR GUIDANCECLEAR CONCEPTUALIZATIONCLEAR UNDERSTANDING