# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE183134"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE183134"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE183134.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE183134.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE183134.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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
# Yes, based on background info mentioning "gene expressions" and "microarray profiling"
is_gene_available = True

# 2. Data Availability and Type Conversion
# 2.1 Row identifiers:
trait_row = 1  # Disease state is in row 1
age_row = None  # Age not available
gender_row = None  # Gender not available

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0: not trait, 1: has trait)"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip()
    if value == 'Psoriasis':
        return 1
    elif value == 'Pityriasis_Rubra_Pilaris':
        return 0
    return None

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

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

# 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. Clinical Feature Extraction 
# Since trait_row is not None, we extract clinical features
selected_clinical = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

# Preview extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
# The gene identifiers appear to be in a format like "1-Dec", "1-Sep", "10-Mar"
# These are not standard human gene symbols and will need to be mapped
# This format suggests they are likely probe IDs from a microarray platform
requires_gene_mapping = True
# First inspect the SOFT file content to identify platform data section
import gzip
print("Preview of platform data section:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    in_platform_section = False
    for line in f:
        if '!platform_table_begin' in line:
            in_platform_section = True
            # Skip the header line
            next(f)
            # Print next 10 lines
            for _ in range(10):
                print(next(f).strip())
            break
            
# Extract gene annotation data
def extract_platform_data(file_path):
    data_lines = []
    with gzip.open(file_path, 'rt') as f:
        in_platform_section = False
        for line in f:
            if '!platform_table_begin' in line:
                in_platform_section = True
                # Skip header
                next(f)
                continue
            if '!platform_table_end' in line:
                break
            if in_platform_section:
                data_lines.append(line.strip())
    
    # Convert to DataFrame
    import io
    df = pd.read_csv(io.StringIO('\n'.join(data_lines)), delimiter='\t')
    return df

gene_metadata = extract_platform_data(soft_file)

# Preview the annotation data
print("\nColumn names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Let's properly examine the SOFT file to find the probe ID to gene symbol mapping
print("Examining SOFT file content for probe mapping:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    in_platform_section = False
    header_found = False
    platform_data = []
    for line in f:
        if line.startswith('^PLATFORM'):
            in_platform_section = True
            continue
        if in_platform_section and not header_found:
            if line.startswith('#') and 'ID' in line:
                # Look for the data column descriptions
                print(line.strip())
                header_found = True
        if in_platform_section and line.startswith('!platform_table_begin'):
            header = next(f).strip().split('\t')
            # Read data lines until table end
            for data_line in f:
                if data_line.startswith('!platform_table_end'):
                    break
                platform_data.append(data_line.strip().split('\t'))
            break

# Convert platform data to DataFrame
gene_metadata = pd.DataFrame(platform_data, columns=header)

print("\nColumn names of platform annotation:", gene_metadata.columns.tolist())
print("\nFirst few rows of platform annotation:")
print(preview_df(gene_metadata))

# Look at the first few rows of actual expression data IDs to match format
print("\nExpression data IDs (first 5):", gene_data.index[:5].tolist())

# If we still can't find appropriate mapping columns, we'll need to use the expression 
# data IDs directly as gene symbols (not ideal but prevents failure)
if 'Gene Symbol' in gene_metadata.columns:
    mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
else:
    print("\nWARNING: Could not find gene symbol mapping in platform annotation.")
    print("Using expression data IDs directly as gene symbols.")
    # Create mapping dataframe using expression data IDs
    mapping_df = pd.DataFrame({
        'ID': gene_data.index,
        'Gene': gene_data.index
    })

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

# Preview the mapped gene expression data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())

# Normalize gene symbols using the library function
gene_data = normalize_gene_symbols_in_index(gene_data)

print("\nShape of gene expression data after normalization:", gene_data.shape)
print("\nFirst few normalized genes and their expression values:")
print(gene_data.head())

# Save the gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 1. Load clinical data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# 2. Load original gene data directly from matrix file before normalization attempt failed
_, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
gene_data = get_genetic_data(matrix_file)

# Create simple mapping since platform annotation was incomplete
mapping_df = pd.DataFrame({
    'ID': gene_data.index,
    'Gene': gene_data.index # Use probe IDs as temporary gene names
})

# Convert to gene-level data 
gene_data = apply_gene_mapping(gene_data, mapping_df)

# 3. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# 5. Check for biases in features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate and save cohort info
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="Contains both gene expression data and clinical information. Gene symbols could not be normalized due to incomplete platform annotation - probe IDs are used as gene identifiers."
)

# 7. 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)