# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE165004"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE165004"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE165004.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE165004.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE165004.csv"
json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on series title and summary, this is an RNA expression study

# 2.1 Data Availability
trait_row = 0  # subject status/group indicates disease status
age_row = None  # Age not available
gender_row = None  # Gender not needed since all subjects are female based on study design

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait status to binary"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if 'control' in value:
        return 0
    elif 'patient' in value:  # Both RPL and UIF patients are cases
        return 1
    return None

convert_age = None  # Not needed since age data unavailable
convert_gender = None  # Not needed since all subjects are female

# 3. Save Metadata
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=trait_row is not None)

# 4. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# These appear to be probe IDs (numbered 1-N) rather than human gene symbols
# Looking at the data, we see simple numeric identifiers (1, 2, 3, etc) 
# which need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract probe-gene mapping using ID and GENE_SYMBOL columns
# ID in gene_metadata matches numeric IDs in gene expression data
# GENE_SYMBOL contains human gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# Apply mapping to convert probe measurements to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview mapped gene expression data
print("Shape of gene expression data after mapping:", gene_data.shape) 
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

# Rename the trait row to match expected column name
linked_data = linked_data.rename(index={'0': trait})

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for bias  
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# Cannot proceed without seeing sample characteristics data and background information
# Setting everything to None/False as a safe default
is_gene_available = False
trait_row = None
age_row = None
gender_row = None

def convert_trait(x):
    return None
    
def convert_age(x):
    return None
    
def convert_gender(x):
    return None

# Save metadata about dataset usability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on background info mentioning RNA expression and DEGs

# 2.1 Data Availability
# Feature 0 contains the trait info (Control vs RPL/UIF patients)
trait_row = 0
# Age and gender data not available (not in characteristics and no mention of varying values)
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip().lower()
    if 'control' in value:
        return 0
    elif 'rpl' in value or 'uif' in value:
        return 1
    return None

def convert_age(x):
    return None  # Not used since age data not available

def convert_gender(x):
    return None  # Not used since gender data not available

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
                            is_gene_available=is_gene_available, 
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the data
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

# Explicitly rename trait row from '0' to 'Endometriosis'
linked_data = linked_data.rename(index={'0': 'Endometriosis'})

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, 'Endometriosis')

# 4. Check for bias  
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Endometriosis')

# 5. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Study examining RNA expression in RPL/UI patients vs controls."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)