# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE129604"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE129604"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE129604.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE129604.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE129604.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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 the background information, this is a blood gene expression study
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# For trait - VitD (binary control/treatment)
trait_row = 2  # agent row shows treatment groups
def convert_trait(x):
    if not x or ':' not in x:
        return None
    val = x.split(':')[1].strip()
    if 'VitD' in val:  # Any group with VitD treatment
        return 1
    elif 'Placebo' in val:  # Control group
        return 0
    return None  # Other treatment groups not relevant

# For age - not available in sample characteristics
age_row = None
convert_age = None

# For gender - available in Sex field
gender_row = 0
def convert_gender(x):
    if not x or ':' not in x:
        return None
    val = x.split(':')[1].strip().lower()
    if 'female' in val:
        return 0
    elif 'male' in val:
        return 1
    return None

# 3. Save Initial 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:
    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)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The identifiers shown are probe IDs from an Affymetrix microarray, not human gene symbols
# They begin with "AFFX-" which is a standard Affymetrix control probe prefix
# These need to be mapped to proper gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# The gene identifiers in expression data are probe IDs starting with "AFFX-"
# The 'SPOT_ID.1' column contains the rich gene annotation text from which we can extract gene symbols
prob_col = 'ID'
gene_col = 'SPOT_ID.1'

# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Preview result
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index[:5]))

# Save processed gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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=is_trait_biased,
    df=linked_data,
    note=note
)

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