# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE61629"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE61629"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE61629.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE61629.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE61629.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Yes, this appears to be microarray gene expression data based on background info
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 0  # Disease state is recorded in row 0
age_row = None  # Age is not available 
gender_row = None  # Gender is not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert disease state to binary (0=control, 1=ET)"""
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip().upper()
    if value == 'ET':
        return 1
    elif value == 'CONTROL':
        return 0
    return None

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

def convert_gender(value: str) -> int:
    """Convert gender to binary - not used since gender not available"""
    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 (since trait_row is not None)
clinical_features = geo_select_clinical_features(clinical_data, 
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

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

# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Get gene mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Preview result to verify the conversion
print("\nFirst few genes and their expression values:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)

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

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in Essential Thrombocythemia patients and controls. " 
        "The data contains expression measurements from whole blood samples.")
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=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# From series title and summary, this is a microarray gene expression dataset
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
# Trait (ET vs control) is in row 0 under "disease state"
trait_row = 0
# Age and gender are not provided in sample characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary: ET=1, control=0"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].strip().upper()
    if value == 'ET':
        return 1
    elif value == 'CONTROL':
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    if pd.isna(value):
        return None
    try:
        age = float(value.split(": ")[1].strip())
        return age
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: female=0, male=1"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save Metadata
# The trait_row is not None, so trait data is available
validate_and_save_cohort_info(is_final=False, 
                             cohort=cohort,
                             info_path=json_path,
                             is_gene_available=is_gene_available,
                             is_trait_available=True)

# 4. Clinical Feature Extraction
# Since trait data is available, extract clinical features
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 extracted features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# Based on the probe IDs shown (e.g. '1007_s_at', '1053_at'), these appear to be Affymetrix probe IDs 
# rather than human gene symbols. They need to be mapped to standard gene symbols.

requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Get gene mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Preview result to verify the conversion
print("\nFirst few genes and their expression values:")
preview_dict = preview_df(gene_data)
for col, values in preview_dict.items():
    print(f"{col}: {values}")
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)

# 3-4. Handle missing values and check for bias
linked_data = handle_missing_values(linked_data, trait)
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
note = ("This dataset studies gene expression profiles in Essential Thrombocythemia patients and controls. "
        "The data contains expression measurements from whole blood samples.")
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=note
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Based on background info mentioning "Microarrays were used to assess gene expression"
# and "gene expression index calculation", this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Row Identification
# Trait (ET vs control) can be found in row 0 under "disease state"
trait_row = 0

# Age and gender are not available in the sample characteristics
age_row = None  
gender_row = None

# 2.2 Conversion Functions
def convert_trait(value):
    """Convert disease state to binary (0=control, 1=ET)"""
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip().lower()
    if value == 'et':
        return 1
    elif value == 'control':
        return 0
    # PMF and PV are other diseases, not relevant for ET study
    return None

def convert_age(value):
    """Placeholder function since age is not available"""
    return None

def convert_gender(value):
    """Placeholder function since gender is not available"""
    return None

# 3. Save Metadata
# Initial filtering - only checking data availability
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, extract clinical features
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 extracted clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Read the gene data that was saved in previous step
gene_data = pd.read_csv(out_gene_data_file, index_col=0)

# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical and gene data files
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0)

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

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

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
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=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")