# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obstructive_sleep_apnea"
cohort = "GSE133601"

# Input paths
in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE133601"

# Output paths
out_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/GSE133601.csv"
out_gene_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/gene_data/GSE133601.csv"
out_clinical_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/clinical_data/GSE133601.csv"
json_path = "./output/preprocess/3/Obstructive_sleep_apnea/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning "gene expression in peripheral blood mononuclear cells" and CD1D/RAB20 genes
is_gene_available = True

# 2.1 Data Availability
# trait: Can be inferred from timepoint (pre vs post CPAP)
trait_row = 2  

# Age and gender are not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Convert Functions
def convert_trait(value: str) -> int:
    """Convert pre/post CPAP to binary OSA status
    pre-CPAP: 1 (has OSA)
    post-CPAP: 0 (treated OSA)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'pre-cpap' in value:
        return 1
    elif 'post-cpap' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    return None  # Not available

def convert_gender(value: str) -> int:
    """Convert gender to binary"""
    return None  # Not available

# 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. Extract clinical features
if trait_row is not None:
    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 clinical data:")
    print(preview_df(clinical_df))
    
    # Save to CSV
    clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# These appear to be Affymetrix probe IDs (ending in "_at"), not gene symbols
# Need to map these IDs to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data from between Platform table markers
gene_annotation = filter_content_by_prefix(soft_file_path, 
                                         prefixes_a=['!Platform_table_begin'], 
                                         prefixes_b=['!Platform_table_end'],
                                         unselect=True,
                                         source_type='file',
                                         return_df_a=True,
                                         return_df_b=False)[0]

# Display column names and preview data 
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))

# Print first few rows to see full data structure
print("\nFirst few rows of annotation data:")
print(gene_annotation.head())
# Get gene annotation and create mapping
gene_annotation = get_gene_annotation(soft_file_path)
probe_gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')

# Apply mapping to convert probe-level to gene-level data
gene_data = apply_gene_mapping(genetic_data, probe_gene_mapping)

# Preview results
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
print("\nFirst 10 gene symbols:")
print(list(gene_data.index)[:10])
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)