# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE53441"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE53441"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE53441.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE53441.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE53441.csv"
json_path = "./output/preprocess/1/Sickle_Cell_Anemia/cohort_info.json"

# STEP1
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 1: Determine gene expression data availability
# Based on the metadata ("Affymetrix Human Genome U133 Plus 2.0 array"), we conclude:
is_gene_available = True

# Step 2.1: Variable Availability
# From the sample characteristics dictionary:
# {0: ['diagnosis: sickle cell anemia (SCA)', 'diagnosis: normal'],
#  1: ['tissue: PBMC']}
# Row 0 has two unique values indicating different diagnoses ("normal" vs "SCA"), which aligns with our trait.
trait_row = 0
age_row = None
gender_row = None

# Step 2.2: Data Type Conversion Functions

def convert_trait(value: str) -> Optional[int]:
    # Extract the text after the colon
    parts = value.split(":", 1)
    val = parts[1].strip() if len(parts) > 1 else value.strip()
    
    # Heuristic: treat "normal" as 0, and "sickle cell anemia" (SCA) as 1
    val_lower = val.lower()
    if "normal" in val_lower:
        return 0
    elif "sickle cell" in val_lower:
        return 1
    else:
        return None

def convert_age(value: str) -> Optional[float]:
    # Since age data is not found (age_row is None), this function won't be used.
    # We define it for completeness. Return None by default.
    return None

def convert_gender(value: str) -> Optional[int]:
    # Since gender data is not found (gender_row is None), this function won't be used.
    # We define it for completeness. Return None by default.
    return None

# Step 3: Conduct initial filtering and save metadata 
# Trait data is available because trait_row is not None. So is_trait_available = True.
is_trait_available = (trait_row is not None)

initial_usable = 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
)

# Step 4: Clinical Feature Extraction (Only if trait_row is not None)
if trait_row is not None:
    # Assume 'clinical_data' is the DataFrame containing sample characteristics
    # (It was mentioned in the instructions as previously obtained.)
    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
    preview = preview_df(selected_clinical_df, n=5, max_items=200)
    print("Preview of the selected clinical features:", preview)
    
    # Save the clinical features to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# These identifiers appear to be Affymetrix probe IDs, not standard gene symbols.
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1 & 2. Identify the columns in the annotation that match probe IDs and gene symbols, then create a mapping dataframe
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 3. Convert probe-level measurements to gene expression data by applying the mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# (Optional) Print shape or preview to verify result
print("Mapped gene expression data shape:", gene_data.shape)
# STEP7
# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
#    Fixing the variable name from 'selected_clinical_data' to 'selected_clinical_df'
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the 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_trait_biased,
    df=linked_data
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)