# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriatic_Arthritis"
cohort = "GSE141934"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE141934"

# Output paths
out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE141934.csv"
out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE141934.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE141934.csv"
json_path = "./output/preprocess/3/Psoriatic_Arthritis/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
# This dataset contains T cell transcriptional data according to background, so gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Row Identification
trait_row = 6  # working_diagnosis contains Psoriatic Arthritis
age_row = 2    # Age data available
gender_row = 1  # Gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    # Binary: 1 for Psoriatic Arthritis, 0 for others
    if not value or ':' not in value:
        return None
    diagnosis = value.split(': ')[1].strip()
    if diagnosis == 'Psoriatic Arthritis':
        return 1
    elif diagnosis in ['Rheumatoid Arthritis', 'Reactive Arthritis', 'Crystal Arthritis', 
                      'Osteoarthritis', 'Non-Inflammatory', 'Undifferentiated Inflammatory Arthritis',
                      'Other Inflammatory Arthritis', 'Enteropathic Arthritis', 
                      'Undifferentiated Spondylo-Arthropathy', 'Unknown']:
        return 0
    return None

def convert_age(value: str) -> float:
    # Continuous
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> int:
    # Binary: 0 for female, 1 for male
    if not value or ':' not in value:
        return None
    gender = value.split(': ')[1].strip()
    if gender == 'F':
        return 0
    elif gender == 'M':
        return 1
    return None

# 3. Initial Validation
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 data
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.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])
# The identifiers start with "ILMN_", indicating these are Illumina probe IDs
# We need to map these probe IDs to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

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

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping data - 'ID' is probe ID, 'Symbol' is gene symbol
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Apply gene mapping to convert probe-level measurements to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print dimensions after mapping
print("\nShape after mapping:", gene_data.shape)

# Preview first few gene symbols 
print("\nFirst few genes:")
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 = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
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)