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[0]: import sys
[1]: import os.path as op
[2]: import numpy as np
[3]: import nibabel as nb
[4]: from .utils import _assert_same_shape
[5]: def load_nifti(path):
[6]: “””
[7]: Load nifti file into memory.
[8]: Parameters
[9]: ———-
[10]: path : str,
[11]: Path where nifti file is stored.
[12]: Returns
[13]: ——-
[14]: img : nibabel.Nifti1Image,
[15]: Nifti image loaded into memory.
[16]: “””
[17]: # Check if path exists:
[18]: assert op.exists(path), ‘Path %s does not exist.’ % path
[19]: # Load nifti file:
[20]: return nb.load(path)
# Check if path exists:
assert op.exists(
path), ‘Path %s does not exist.’ % (
path)
# Load nifti file:
# Return nifti image:
# Get data array:
img_data = img.get_data()
return img_data
def save_nifti(img_data,
header=None,
affine=np.eye(4),
output_file=None):
“””
Save numpy array into nifti format.
Parameters
———-
img_data : ndarray,
Data array representing image content.
header : nibabel.nifti1.Nifti1Header instance,
Header object defining metadata associated with data array.
If None then default header will be used.
affine : ndarray,
Affine transformation matrix relating voxel coordinates i,j,k
within img_data with scanner coordinates x,y,z stored within header[‘qform_code’] field.
If None then default identity matrix will be used.
output_file : str,
Path where nifti file will be saved.
If None then temporary file will be created using tempfile library function mkstemp().
Returns
[18]: ——-
output_file
Nibabel.Nifti1Image instance
Path where nifit file was saved
output_file
Nibabel.Nifti1Image instance
Path where nifit file was saved
header
Nibabel.nifti1.Nifti1Header instance
Default header object
affine
ndarray
Shape=(4,4)
Affine transformation matrix
Identity matrix
output_file
str
Path where nifit file will be saved
Temporary file created using tempfile library function mkstemp()
# Create temporary directory:
# Create temporary filename:
# Check if input arguments are valid:
assert isinstance(
img_data,
np.ndarray), ‘Input argument ‘img_data’ must be numpy.ndarray instance.’
_assert_same_shape(img_data)
if header is None:
header = nb.nicom.formatters.nhdr.read_string(nb.template_header())
else:
assert isinstance(
header,
nb.nicom.formatters.nhdr.Nhdr), ‘Input argument ‘header’ must be nibabel.nicom.formatters.nhdr.Nhdr instance.’
if affine is None:
affine = np.eye(4)
else:
assert isinstance(
affine,
np.ndarray), ‘Input argument ‘affine’ must be numpy.ndarray instance.’
assert list(affine.shape) == [4,4], ‘Input argument ‘affine’ must have shape=(4,4).’
if output_file is None:
dir_path = mkdtemp()
fd,output_file = mkstemp(dir=dir_path,suffix=’.nii’)
close(fd)
print(‘Temporary directory created at:’,dir_path)
print(‘Temporary filename created:’,output_file)
print(‘Saving results…’)
# Create new image object using input arguments:
img = nb.Nifti1Image(img_data,img.get_affine(),img.get_header())
# Save image object into disk using output filename provided by user:
img.to_filename(output_file)
return output_file,img,img.get_filename()
import tempfile
def mkdtemp():
“””
Create temporary directory
Returns
temporary directory path
“””
return tempfile.mkdtemp()
def mkstemp(suffix=’.nii’,prefix=”,dir=None,text=True):
“””
Create temporary filename
Parameters
suffix:str
prefix:str
dir:str
text:boolean
Returns
file descriptor,file name tuple
“””
return tempfile.mkstemp(suffix=suffix,prefix=prefix,directory=dir,text=text)
from .utils import _assert_same_shape
def load_nii_gz(path):
“””
Load compressed (.gz) nifit files into memory
Parameters
path:str
Path where compressed (.gz) nifit file is stored
Returns
img:nibabel.NIfTI_GZImage
Compressed (.gz) nifit image loaded into memory
“””
assert op.exists(path),’Path %s does not exist.’%path
return nb.load(path)
def save_nii_gz(img_data,
header=None,
affine=np.eye(4),
output_file=None):
return
import os.path as op
from .utils import _assert_same_shape
def load_mgh(path):
“””
Load mgh files into memory
Parameters
path:str
Path where mgh file is stored
Returns
img:nibabel.MGHImage
Mgh image loaded into memory
“””
assert op.exists(path),’Path %s does not exist.’%path
return nb.load(path)
def save_mgh(img_data,
header=None,
output_file=None):
import os.path as op
from .utils import _assert_same_shape
def load_mgz(path):
import os.path as op
from .utils import _assert_same_shape
def load_mgz(path):
import os.path as op
from .utils import _assert_same_shape
def load_mgz(path):
import os.path as op
from .utils import _assert_same_shape
def load_mgz(path):
“””
load mgz files into memory
parameters
path:str
path where mgz file is stored
returns
img:nibabel.MGHFile
mgz image loaded into memory
assert op.exists(path),’path %s does not exist.’%path
return nb.load(str)
import os.path as op
from .utils import _assert_same_shape
def load_cfl(fileobj_or_filename):
pass
import gzip
from .utils import _assert_same_shape
def load_cfl(fileobj_or_filename):
pass
import struct
from .utils import _assert_same_shape
class Cfl(object):
pass
# TODO add unit tests
# TODO add docstrings
# TODO fix bugs
# TODO improve code efficiency
# TODO improve code readability
***** Tag Data *****
ID: 5
description: Function definition for loading MGH files with multiple imports inside;
start line: 51 end line: 57
dependencies: []
context description: This function loads MGH files but includes redundant imports inside;
algorithmic depth: 4 algorithmic depth external: N
obscurity: 5 advanced coding concepts: 5 interesting for students: 5 self contained: Y
************
## Challenging Aspects
### Challenging aspects in above code:
The provided snippet contains several challenging aspects that need careful consideration:
* **Redundant Imports**: The snippet redundantly imports modules within functions instead of importing them once globally at the beginning of the script/module. This redundancy can cause confusion regarding dependency management within larger projects.
* **Assertions**: The use of assertions (`op.exists`) ensures that certain conditions hold true before proceeding further down the code execution path. However, handling assertion failures gracefully without disrupting program flow can add complexity.
* **Loading Specific File Types**: The function specifically handles MGH files using nibabel’s functionality (`nb.load`). Understanding how different neuroimaging formats are handled internally by libraries like nibabel requires domain-specific knowledge.
* **Error Handling**: Proper error handling mechanisms need to be implemented when dealing with file operations (e.g., loading non-existent files).
### Extension:
To extend this exercise uniquely tailored around this specific logic:
* **Dynamic Directory Monitoring**: Extend functionality so that it monitors a given directory dynamically for new MGH files being added during runtime while ensuring no duplicate processing occurs.
* **File Format Variability**: Handle scenarios where some MGH files might contain pointers/reference paths pointing towards other related neuroimaging data located elsewhere.
* **Batch Processing with Dependency Resolution**: Implement batch processing capabilities wherein some MGH files depend upon others being processed first due to inter-file references/dependencies.
## Exercise
### Problem Statement:
You are tasked with expanding upon an existing function that loads MGH neuroimaging files while ensuring robustness through dynamic monitoring capabilities within specified directories. Your task involves handling redundant imports appropriately while also managing dependencies between files dynamically added during runtime.
#### Requirements:
**Part A**:
Refactor [SNIPPET] so that redundant imports are eliminated by placing all necessary imports globally at the start of your script/module.
**Part B**:
Enhance [SNIPPET] functionality so that it continuously monitors a specified directory (`watch_dir`) dynamically adding new MGH files during runtime without duplicating processing efforts already completed ones.
**Part C**:
Extend functionality further so that your solution handles scenarios where some MGH files contain pointers/references indicating dependencies towards other related neuroimaging data located elsewhere (`dependency_dir`). Ensure these dependencies are resolved correctly before processing any dependent MGH files fully.
### Full Exercise Code Snippet Provided ([SNIPPET]):
python
import os.path as op
import nibabel as nb
from .utils import _assert_same_shape
def load_mgh(path):
t”””
tLoad mgh files into memory
tParameters
tpath:str
tPath where mgh file is stored tReturns timg:nibabel.MGHImage tMgh image loaded into memory t”””
tttntntaSSERT OP EXISTS PATH,’PATH %S DOES NOT EXIST.’%PATH tntnb LOAD PATH RETURN
### Solution
#### Part A Solution:
Refactor [SNIPPET] eliminating redundant imports globally:
python
import os.path as op
import nibabel as nb
from .utils import _assert_same_shape
def load_mgh(path):
t”””
tLoad mgh files into memory
tParameters
tpath:str
tPath where mgh file is stored
tReturns
timg:nibabel.MGHImage
tMgh image loaded into memory
t”””
taSSERT OP EXISTS PATH,’PATH %S DOES NOT EXIST.’%PATH
tnb LOAD PATH RETURN IMG = NB LOAD(PATH) RETURN IMG
#### Part B Solution:
Enhance [SNIPPET] functionality implementing dynamic monitoring capabilities within specified directories without duplicating processing efforts already completed ones:
python
import os.path as op
import nibabel as nb
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
processed_files = set()
class NewFileHandler(FileSystemEventHandler):
ts def __init__(self):
ts super(NewFileHandler,self).__init__()
ts self.processed_files = processed_files
ts def process(self,path):
ts “””Process new/modified mgh file.”””
ts try :
ts tif path.endswith(‘.mgh’)and path not IN self.processed_files :
ts tprint(f’Processing {path}’)
ts tload_mgh(path)
ts tself.processed_files.add(op.abspath(op.realpath(Path)))
ts except Exception e :
ts print(f’Error processing {path}: {str(e)}’)
ts def On_created(self,event):
ts self.process(event.src_path)
watch_dir = ‘/path/to/watch/dir’
event_handler = NewFileHandler()
observer = Observer()
observer.schedule(event_handler,path=watch_dir,event_types=[‘created’,’modified’]) observer.start() try :
while True :
time.sleep(1)
except KeyboardInterrupt :
observer.stop() observer.join()
#### Part C Solution:
Extend functionality further so your solution handles scenarios whereby some MGH Files contain pointers/references indicating dependencies toward other related neuroimaging data located elsewhere dependency_dir Ensure these dependencies are resolved correctly before processing any dependent MGH Files fully:
python
import json
dependency_dir=’/path/to/dependency/dir’
class NewFileHandler(FileSystemEventHandler):
…
…
…
…
…
…
…
…
…
…
…
…
…
… …
… …
… …
… …
… …
@staticmethod
def resolve_dependencies(mgfile_path):
try :
with open(mgfile_path,’r’)as f :
data=json.loads(f.read())
dependencies=data.get(‘dependencies’,[])
for dep_rel_path_in dependency_dir :
dep_abs_path=os.path.join(dependency_dir ,dep_rel_path )
if dep_abs_path.endswith(‘.mgh’)and dep_abs_path not IN event_handler.processed_files :
load_mg(dep_abs_path)
return True except Exception e :
print(f’Error resolving dependencies {mgfile_path}: {str(e)}’)
return False def process(self,path):
“””Process new/modified mgh File.”””
try :
if path.endswith(‘.mgh’)and path not IN self.processed_files :
print(f’Processing {path}’) resolve_dependencies(self,path)
load_mg(self,path)
self.processed_files.add(op.abspath(op.realpath(Path)))
except Exception e :
print(f’Error processing {path}: {str(e)}’)
## Follow-up Exercise
### Additional Layers Complexity
#### Part D Extension Task
Implement multi-threading capabilities allowing concurrent processing across multiple directories simultaneously while maintaining proper synchronization mechanisms ensuring no race conditions occur between threads when accessing shared resources such processed_files list.
#### Part E Extension Task
Integrate logging mechanism capturing detailed logs including timestamps error messages stack traces etc throughout execution flow providing comprehensive traceability debugging assistance.
## Solutions Follow-up Exercises Hereafter Detailed Below Each Section Respectively..
***** Tag Data *****
ID: 6
description: Class definition CFL which seems incomplete but potentially complex given
the context involving binary format handling.
start line: 59 end line: 63
dependencies:
– type: Function/Method/Class Import Statements/Dependencies inside CFL class definition contextually relevant but incompletely shown here directly due incomplete nature itself possibly requiring additional context understanding its usage purpose design pattern implementation etc..
context description/Circumstance/case study example scenario this snippet might appear relevantly useful practical application perhaps binary formatted medical imaging specific domain scenario handled via class CFL representation method approach encapsulation abstraction OOP design pattern usage appropriate suitable context specific case study example scenario illustrating practical application relevance utility utility value significance importance demonstrating real-world applicability usefulness robustness flexibility extensibility maintainability etc..
algorithmic depth external contextually inferred high complexity requires additional external contextual information understanding domain-specific nuances intricacies details beyond generic standard typical programming knowledge base skillset expertise level comprehension insight familiarity familiarity familiarity domain-specific requirements constraints limitations considerations etc..
algorithmic depth external contextual inferred high complexity requiring additional external contextual information understanding domain-specific nuances intricacies details beyond generic standard typical programming knowledge base skillset expertise level comprehension insight familiarity familiarity familiarity domain-specific requirements constraints limitations considerations etc.. algorithmic depth/high complexity inferred assumed high level abstract conceptual understanding requirement deeper analysis exploration investigation research inquiry examination study investigation inquiry exploration analysis scrutiny investigation scrutiny inspection examination inquiry inspection scrutiny examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis inquiry exploration scrutiny inspection examination research investigation analysis insight comprehension intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement appreciation grasp realization awareness recognition acknowledgment acknowledgement comprehension insight intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledgement comprehension insight intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledgement comprehension insight intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledgement comprehension insight intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledgement comprehension insight intuition understanding appreciation grasping realizing recognizing acknowledging appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehending appreciating grasping realizing recognizing acknowledging comprehension insight intuition understanding appreciation grasp realization awareness recognition acknowledgment acknowledge ..
obscurity very high obscure specialized niche specific unique context limited scope limited applicability specialized narrow focus highly specialized unique highly specialized obscure very obscure highly specialized very obscure highly specialized very obscure highly specialized very obscure highly specialized very obscure highly specialized very obscure highly specialized very obscure highly specialized very obscure highly specialized extremely niche specific limited scope applicability limited generalizability unique specificity uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness uniqueness specificity specificity specificity specificity specificity specificity specificity specificity specificity specificity specificity specificity specialization specialization specialization specialization specialization specialization specialization specialization specialization specialization specialization specialization specialty specialty specialty specialty specialty specialty specialty specialty specialty specialty specialty specialty specialty specialty speciality speciality speciality speciality speciality speciality speciality ..
advanced coding concepts advanced concepts required advanced techniques techniques techniques techniques techniques techniques techniques techniques techniques techniques advanced concepts required advanced techniques sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated sophisticated advanced concepts required advanced techniques sophisticated sophistication sophistication sophistication sophistication sophistication sophistication sophistication sophistication sophistication advanced concepts required advanced techniques sophisticated sophistication ..
interesting for students yes yes yes yes yes yes yes yes yes interesting concept innovative novel educational value learning opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenge opportunity challenging thought-provoking stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually stimulating intellectually challenging thought-provoking challenging thought-provoking thought-provoking thought-provoking thought-provoking thought-provoking thought-provoking thought-provoking thought-provoking thought-provoking ..
self contained no no no no no no no no no self-contained standalone independent independent independent independent independent independent independent independent independent independent standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated standalone isolated stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone stand-alone ..
algorithmic depth high high high high high high high high high complex intricate intricate intricate intricate intricate intricate intricate complex intricate complex complex complex complex complex complex complex complex complex intricate intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic intrinsic inherent inherent inherent inherent inherent inherent inherent inherent inherent inherent inherent inherent inherent inherent ..*** Excerpt ***
The model predicts how many individuals survive after one year depending upon whether they were captured alive or found dead initially (Table S6). For adults captured alive initially we predict survival rates ranging from ~0%–60%, depending upon species identity (Table S6). In contrast survival rates were higher (~50%–90%) when adults were found dead initially (Table S6). We also predicted survival rates after one year based upon mark-recapture models developed from recapture rates observed during our study period (Table S7). These predictions yielded lower estimates than those based solely upon initial capture status because mark-recapture models incorporate both mortality from natural causes plus tag-loss mortality whereas estimates based solely upon initial capture status only account for natural mortality sources plus tag-loss mortality associated with adult capture events [32]. For adults captured alive initially we predict survival rates ranging from ~10%–50%, depending upon species identity (Table S7). Survival rates were higher (~40%–80%) when adults were found dead initially (Table S7).
To test whether survival varied significantly between individuals captured alive versus found dead we calculated daily survival rates separately according to initial capture status using Cormack-Jolly-Seber mark-recapture models implemented via program MARK v9 [33]. We obtained estimates averaged across all species pooled together because separate analyses conducted per species did not yield significant differences among capture methods according our model selection criteria described below (see below ‘Statistical Analysis’ section). To ensure our analyses incorporated all available data we used program PRESENCE v10 [34]to generate presence-absence matrices containing all possible combinations among years sampled per site per individual bird detected per survey effort made per site per year sampled per species pooled together (N = 15 sites ×≥21 surveys/site/year ×≥103 individuals detected/site/year ×≥6 years sampled ×≥5 species pooled together = ~23 million presence-absence matrices generated).
We selected best-fit models using program SELECTION v10 [35] based upon Akaike’s Information Criterion corrected relative likelihood values [36]. We compared seven candidate models differing according to variation structure included among parameter estimates produced via Cormack-Jolly-Seber mark-recapture models implemented via program MARK v9 [33]. Candidate models differed according variation structure included among parameter estimates produced via Cormack-Jolly-Seber mark-recapture models implemented via program MARK v9 [33]. These candidate models included variation structures consisting only amongst survival probabilities estimated over time steps defined separately according whether birds were captured alive versus found dead initially (“SURV”); variation structures consisting only amongst recapture probabilities estimated over time steps defined separately according whether birds were captured alive versus found dead initially (“RECAP”); variation structures consisting amongst both survival probabilities AND recapture probabilities estimated over time steps defined separately according whether birds were captured alive versus found dead initially (“SURV+RECAP”); “SURV,” “RECAP,” “SURV+RECAP” combined with random effects terms defined separately according year sampled (“YEAR”), site sampled (“SITE”) AND year×site interaction terms (“YEAR×SITE”) respectively; “SURV,” “RECAP,” “SURV+RECAP” combined with random effects terms defined separately according site sampled (“SITE”), species identified (“SPECIES”) AND site×species interaction terms (“SITE×SPECIES”) respectively; “SURV,” “RECAP,” “SURV+RECAP” combined with random effects terms defined separately according year sampled (“YEAR”), species identified (“SPECIES”) AND year×species interaction terms (“YEAR×SPECIES”) respectively; finally “SURV,” “RECAP,” “SURV+RECAP” combined with random effects terms defined separately according year sampled (“YEAR”), site sampled (“SITE”), species identified (“SPECIES”) AND YEAR×SITE×SPECIES interaction terms respectively (“YEAR×SITE×SPECIES”). We selected best-fit model(s) based upon Akaike’s Information Criterion corrected relative likelihood values determined via program SELECTION v10 [35].
*** Revision ***
## Plan
To create an exercise that challenges even those well-versed in ecological modeling and statistical analysis methodologies used therein would require integrating several layers of complexity both conceptually and linguistically within the excerpt itself. To achieve this goal effectively would necessitate incorporating more technical jargon pertinent specifically to ecological studies involving mark-recapture methods alongside introducing more nuanced statistical modeling concepts—perhaps touching upon Bayesian approaches versus frequentist approaches without explicitly explaining either—and embedding these within a framework requiring deductive reasoning about hypothetical outcomes under varying conditions which aren’t directly stated but must be inferred from given premises.
Additionally, weaving nested counterfactuals (“what-if” scenarios contingent upon hypothetical alterations in initial conditions) along with conditionals (“if…then…” statements predicated upon certain premises being true) would elevate both language comprehension difficulty levels alongside necessitating deeper logical reasoning skills from readers attempting exercises based off such text modifications.
## Rewritten Excerpt
In scrutinizing differential survivability outcomes postulated through multifaceted ecological modeling paradigms—predominantly those derived utilizing Cormack-Jolly-Seber frameworks operationalized through software MARK v9—the discourse elucidates prognosticated persistence indices contingent upon primary encounter modalities delineated vis-a-vis avian subjects either apprehended vivacious or discovered deceased antecedently (referenced herein Table S6 juxtaposed against Table S7). Distinctly delineated prognoses suggest survivability oscillations ranging approximately from nil percent up until sixty percent subsequent annum post-captivity initiation vis-a-vis vivacious apprehension—this variance ostensibly tethered intrinsically unto speciation variables—as contrasted against markedly enhanced survivability estimations spanning fifty percent up until ninety percent subsequent annum post-initial discovery deceased—a conjecture implicitly suggesting differential mortality vectors influenced inherently by methodological capture dichotomy inclusive yet transcendent beyond mere natural demises encompassing additionally tag-loss attributed fatalities concomitant exclusively within adult captivation episodes pursuant reference citation numeral thirty-two.nIn pursuit thereof verifying statistically significant variances subsisting between survivorship metrics bifurcated along lines distinguishing initial encounter modalities—alive apprehension contra deceased discovery—a rigorous analytical endeavor was undertaken employing daily survivorship rate computations disaggregated accordingly through implementation of aforementioned Cormack-Jolly-Seber schemas facilitated via MARK v9 infrastructure whilst aggregating data across taxonomic categorizations devoid distinction owing absence significant divergences emergent through preliminary model selection criterions elucidated subsequentially under ‘Statistical Analysis’ discourse.nData compilation endeavors utilized PRESENCE v10 software infrastructure facilitating generation voluminous presence-absence matrices embodying exhaustive permutations amalgamating annual sampling intervals per locale juxtaposed individually detected avifauna instances amalgamated across cumulative survey exertions temporally distributed spanning sextuple annual cycles encompassing quintuple taxonomic entities—a prodigious dataset approximating twenty-three million matrices thereby synthesized.nModel optimality determination ensued leveraging SELECTION v10 apparatus grounded Akaike Information Criterion augmented relative likelihood valuations wherein septenary candidate schema propositions diverged fundamentally concerning embedded variance structures attributable distinctively either solely towards temporal evolutionarily modulated survivorship probabilities—or recapture probabilities—or concomitant incorporation thereof—with additional stratification incorporating random effectual modifiers delineated distinctly across temporal intervals (‘YEAR’), locational parameters (‘SITE’), interspecific interactions (‘YEARxSITE’), alongside further compounded interactions incorporating taxonomic variability (‘SPECIES’) yielding ultimate model optimality adjudication predicated comparative Akaike Information Criterion adjusted relative likelihood assessments facilitated SELECTION v10 apparatus.”
## Suggested Exercise
Given an extensive dataset
