# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pancreatic_Cancer"
cohort = "GSE120127"

# Input paths
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE120127"

# Output paths
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE120127.csv"
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE120127.csv"
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE120127.csv"
json_path = "./output/preprocess/3/Pancreatic_Cancer/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
# Based on series title and genotype info, this appears to be gene expression data from pancreatic cancer cell lines
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
# Trait can be inferred from genotype (feature 2) - KrasG12D vs KO
trait_row = 2

# Gender can be found in feature 0
gender_row = 0

# Age not available for cell lines
age_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert genotype to binary trait"""
    if not value or not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip()
    # Bap1 KO vs WT
    if 'KO' in value:
        return 1
    elif 'WT' in value:
        return 0
    return None

def convert_gender(value):
    """Convert gender to binary"""
    if not value or not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().upper()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

convert_age = None

# 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:
    # Extract features using the library function
    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
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)
# From the row identifiers and examining the number format, these appear to be Agilent probe IDs
# These will need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Extract gene annotation data using a different prefix pattern for correct platform
gene_annotation = filter_content_by_prefix(soft_file, prefixes_a=['^FEATURES'], unselect=True, source_type='file', 
                                         return_df_a=True)[0]

# Get mapping between probe IDs and gene symbols
gene_mapping = gene_annotation.loc[:, ['ID', 'Gene Symbol']]
gene_mapping = gene_mapping.rename(columns={'Gene Symbol': 'Gene'}).astype({'ID': 'str'})

# Convert probe-level measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols using library function
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Convert probe-level measurements to gene expression values using library function
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save normalized gene data
# Remove "-mRNA" suffix from gene symbols before normalization
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data and trait
# First get selected clinical features using the extraction function from previous step
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
)

# Debug data structures before linking
print("\nPre-linking data shapes:")
print("Clinical data shape:", selected_clinical.shape)
print("Gene data shape:", gene_data.shape)
print("\nClinical data preview:")
print(selected_clinical.head())

# Transpose gene data to match clinical data orientation
gene_data_t = gene_data.T
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)

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

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save metadata
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_biased,
    df=linked_data,
    note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
)

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