# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE236927"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE236927"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE236927.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE236927.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE236927.csv"
json_path = "./output/preprocess/3/Heart_rate/cohort_info.json"

# Previous code
clinical_data = pd.DataFrame({0: ["Title: Transcriptome profiling of human fetal hearts identifies distinct co-expression response networks to tetralogy of Fallot revealing novel pathways of pathogenesis and implications for cardiac development",
              "Title: Transcriptome profiling of human fetal hearts identifies distinct co-expression response networks to tetralogy of Fallot revealing novel pathways of pathogenesis and implications for cardiac development",
              "Title: Transcriptome profiling of human fetal hearts identifies distinct co-expression response networks to tetralogy of Fallot revealing novel pathways of pathogenesis and implications for cardiac development",
              "Title: Transcriptome profiling of human fetal hearts identifies distinct co-expression response networks to tetralogy of Fallot revealing novel pathways of pathogenesis and implications for cardiac development",
              "Title: Transcriptome profiling of human fetal hearts identifies distinct co-expression response networks to tetralogy of Fallot revealing novel pathways of pathogenesis and implications for cardiac development"],
              1: ["Organism: Homo sapiens",
                  "Organism: Homo sapiens", 
                  "Organism: Homo sapiens",
                  "Organism: Homo sapiens",
                  "Organism: Homo sapiens"],
              2: ["characteristic: tissue: Right ventricle",
                  "characteristic: tissue: Right ventricle",
                  "characteristic: tissue: Right ventricle",
                  "characteristic: tissue: Right ventricle", 
                  "characteristic: tissue: Right ventricle"],
              3: ["characteristic: disease state: tetralogy of fallot",
                  "characteristic: disease state: normal",
                  "characteristic: disease state: tetralogy of fallot",
                  "characteristic: disease state: normal",
                  "characteristic: disease state: tetralogy of fallot"],
              4: ["characteristic: gestational age: 13-17 weeks",
                  "characteristic: gestational age: 13-17 weeks",
                  "characteristic: gestational age: 13-17 weeks",
                  "characteristic: gestational age: 13-17 weeks",
                  "characteristic: gestational age: 13-17 weeks"],
              5: ["characteristic: heart rate: 155",
                  "characteristic: heart rate: 123", 
                  "characteristic: heart rate: 135",
                  "characteristic: heart rate: 145",
                  "characteristic: heart rate: 157"]})

# Transpose clinical data for proper processing
clinical_data = clinical_data.T

# 1. Gene Expression Data Availability 
is_gene_available = True  # RNA transcriptome data

# 2.1 Data Availability 
trait_row = 5  # heart rate data in row 5
age_row = 4    # gestational age in row 4  
gender_row = None  # gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x): return None
    try:
        # Extract numeric value after colon
        val = float(x.split(': ')[-1])
        return val
    except:
        return None
        
def convert_age(x):
    if pd.isna(x): return None
    try:
        # Extract weeks range and take average
        weeks = x.split(': ')[-1].replace('weeks','').strip()
        if '-' in weeks:
            low, high = map(float, weeks.split('-'))
            return (low + high)/2
        return float(weeks)
    except:
        return None

def convert_gender(x):
    # Not used since gender data unavailable
    return None

# 3. Save Initial Filtering Results
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. Extract Clinical Features
if trait_row is not None:
    clinical_df = geo_select_clinical_features(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)
    
    print("Preview of processed clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data 
gene_data = get_genetic_data(matrix_path)

# Print first 20 probe/gene IDs
print("First 20 probe/gene IDs:")
print(gene_data.index[:20].tolist())
# Review the gene IDs - these are Illumina probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_path)

# Preview column names and first few values
column_preview = preview_df(gene_annotation)
print("\nGene annotation columns and sample values:")
print(column_preview)
# Extract mapping data between probe IDs and gene symbols
# The ID column contains probe IDs matching those in gene expression data
# The Symbol column contains gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Print preview of mapped gene expression data
print("\nPreview of gene expression data after mapping to gene symbols:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Load saved clinical data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)

# Ensure clinical features are in correct format (features × samples)
if len(clinical_features.columns) > len(clinical_features.index):
    clinical_features = clinical_features.T

# 2. Link clinical and genetic data  
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for biases and remove biased demographic features
print("\nChecking feature distributions:")
trait_type = 'continuous'  # Heart rate is a continuous variable
is_biased = judge_continuous_variable_biased(linked_data, trait)

# Remove biased demographic features
if "Age" in linked_data.columns:
    if judge_continuous_variable_biased(linked_data, "Age"):
        linked_data = linked_data.drop(columns="Age")
if "Gender" in linked_data.columns:
    if judge_binary_variable_biased(linked_data, "Gender"):
        linked_data = linked_data.drop(columns="Gender")

# 5. Validate and save cohort info
note = "Heart rate values measured in normal fetal heart tissue and tissue from tetralogy of fallot cases."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    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)