# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE139384"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE139384"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE139384.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE139384.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE139384.csv"
json_path = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # Microarray transcriptome data is provided.

# 2. Variable Availability and Data Type Conversion

# Based on the sample characteristics dictionary, we identify the following:
# - The trait (ALS) can be inferred from row 0, which contains "clinical phenotypes: ALS", "ALS+D", "PDC", etc.
trait_row = 0

# - Age data appears in row 2 (and row 3). We'll choose row 2 for this example, as it lists multiple ages.
age_row = 2

# - Gender data can be found in row 1, which has both "gender: Female" and "gender: Male".
gender_row = 1

# Define data type conversion functions

def convert_trait(value: str) -> int:
    """
    Convert raw trait strings into a binary label:
      1 if the text contains 'ALS', else 0.
    Unknown or non-matching entries -> None
    """
    parts = [x.strip() for x in value.split(':', 1)]
    if len(parts) < 2:
        return None
    val = parts[1].upper()  # convert to uppercase for easy matching
    if 'ALS' in val:
        return 1
    elif 'PDC' in val or 'AD' in val or 'HEALTHY CONTROL' in val or 'ALZHEIMER' in val:
        return 0
    else:
        return None

def convert_age(value: str) -> float:
    """
    Convert raw age strings into a continuous (float) variable.
    Unknown entries -> None
    """
    parts = [x.strip() for x in value.split(':', 1)]
    if len(parts) < 2:
        return None
    if not parts[0].lower().startswith('age'):
        return None
    try:
        return float(parts[1])
    except ValueError:
        return None

def convert_gender(value: str) -> int:
    """
    Convert gender strings into a binary variable:
      female -> 0, male -> 1.
    Unknown entries -> None
    """
    parts = [x.strip() for x in value.split(':', 1)]
    if len(parts) < 2:
        return None
    lab = parts[0].lower()
    val = parts[1].strip().lower()
    if lab.startswith('gender'):
        if 'female' in val:
            return 0
        elif 'male' in val:
            return 1
    return None

# 3. Save Metadata - initial filtering
is_trait_available = (trait_row is not None)
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=is_trait_available
)

# 4. Clinical Feature Extraction (only if trait_row is not None)
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 output
    print("Preview of selected clinical features:", preview_df(selected_clinical_df, n=5))

    # Save the extracted clinical data
    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 (e.g., ILMN_1343291) are Illumina probe IDs and not standard human gene symbols.
# They need to be mapped to gene symbols.

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. We observed that the gene expression data uses Illumina probe IDs like "ILMN_1343291" in its index.
#    In the gene annotation dataframe, these IDs appear in the 'ID' column.
#    The gene symbols are stored in the 'Symbol' column.

# 2. Create the gene mapping dataframe using the 'get_gene_mapping' function
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Apply the mapping to convert probe-level measurements into gene-level data
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final quality validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,   # We do have a trait column
    is_biased=trait_biased,
    df=linked_data,
    note="Cohort data successfully processed with trait-based analysis."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")