# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE65986"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"

# Output paths
out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE65986.csv"
out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE65986.csv"
out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE65986.csv"
json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression data availability
# From background info: "Gene expression in 55 epithelial ovarian cancers ... was analyzed by Affymetrix U133plus2 array"
is_gene_available = True  

# 2.1 Data availability
# For trait (Endometrioid_Cancer):
# Key 0 has cancer histology types including "Endometrioid"
trait_row = 0  

# For age:
# Key 1 has age values 
age_row = 1

# For gender:
# No gender info in characteristics, all samples appear to be female based on ovarian cancer study
gender_row = None

# 2.2 Data type conversion functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'endometrioid' in x:
        return 1
    elif x in ['clear', 'serous']:  # Other cancer types
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    return None  # No gender data

# 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
selected_clinical = 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 processed clinical data
preview_result = preview_df(selected_clinical)
print("Preview of processed clinical data:")
print(preview_result)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Based on the gene expression data preview:
# The identifiers shown are probe IDs from Affymetrix microarray platform 
# (e.g., '1007_s_at', '1053_at' are typical Affymetrix probe formats)
# These need to be mapped to human gene symbols for standardization
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. The 'ID' column in gene_metadata contains probe IDs (e.g., '1007_s_at') matching the gene expression data indices,
#    and 'Gene Symbol' column contains the corresponding gene symbols
prob_col = 'ID'  
gene_col = 'Gene Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Print shape and preview first few rows
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of first few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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)

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

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

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

# 5. Final validation and metadata saving
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 comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
)

# 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)