# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE118336"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE118336"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE118336.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.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)
# Step 1: Determine if gene expression data is available
# Based on the series description (HTA2.0 array - a gene expression microarray), we assume it contains gene expression data.
is_gene_available = True

# Step 2: Identify availability of trait, age, and gender, and set row indices accordingly
# From the sample characteristics dictionary:
# {
#   0: ['cell type: iPSC-MN'],
#   1: ['genotype: FUSWT/WT', 'genotype: FUSWT/H517D', 'genotype: FUSH517D/H517D'],
#   2: ['time (differentiation from motor neuron precursor): 2 weeks', 'time (differentiation from motor neuron precursor): 4 weeks']
# }
# We interpret row=1 as the ALS status (presence/absence of mutation) => trait
trait_row = 1

# There's no clear row for age or gender
age_row = None
gender_row = None

# Step 2.2: Define conversion functions for trait, age, and gender

def convert_trait(value: str):
    # Typically "genotype: something"
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    # Map genotype to binary: "FUSWT/WT" -> 0 (control), else -> 1 (ALS)
    if val == "FUSWT/WT":
        return 0
    elif "H517D" in val:
        return 1
    else:
        return None

def convert_age(value: str):
    # Not applicable here, return None
    return None

def convert_gender(value: str):
    # Not applicable here, return None
    return None

# Step 3: Initial filtering for dataset usability
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
)

# Step 4: Clinical Feature Extraction (only if trait data is available)
if trait_row is not None:
    # Here we assume 'clinical_data' is already loaded as a pandas DataFrame
    selected_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
    )
    
    # Preview the extracted clinical data
    preview = preview_df(selected_clinical_df, n=5)
    print("Clinical Data Preview:", preview)
    
    # Save clinical data 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])
# Based on biomedical knowledge, these "xxx_st" identifiers appear to be probe set IDs (likely from an Affymetrix array),
# not human gene symbols. Therefore, they require mapping to gene symbols.

# Conclusion:
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 6: Gene Identifier Mapping (Revised)

# We will attempt to map the probe identifiers in gene_data (e.g. "2824546_st") to those in the
# gene_annotation DataFrame (e.g. "TC01000001.hg.1"). The original attempt concluded there
# was no match and skipped the mapping entirely. Here, we'll demonstrate a more thorough check:
# 1) Direct match
# 2) Partial match by stripping "_st"
# If no matches are found, we conclude that no mapping can be performed and retain the original data.

# Copy the annotation so we can manipulate it safely
annot_df = gene_annotation.copy()

# Identify columns to use for probe ID and gene assignment (gene symbol or similar).
probe_col = 'ID'
gene_col = 'gene_assignment'

# Create the mapping DataFrame
mapping_df = get_gene_mapping(
    annotation=annot_df,
    prob_col=probe_col,
    gene_col=gene_col
)

# 1) Direct match between expression index and annotation ID:
expr_ids = set(gene_data.index)
annot_ids = set(mapping_df['ID'])
common_ids = expr_ids.intersection(annot_ids)

if len(common_ids) > 0:
    # Some probes match directly; proceed with standard mapping
    print("Direct matches found. Proceeding with gene symbol mapping using apply_gene_mapping...")
    gene_data = apply_gene_mapping(gene_data, mapping_df)
else:
    # 2) Attempt partial match: removing '_st' from expression IDs before checking
    print("No direct matches found. Attempting partial match by stripping '_st'...")
    # Create a dictionary to map stripped IDs -> original IDs
    stripped_to_orig = {}
    for idx in gene_data.index:
        stripped = idx.replace('_st', '').strip()
        stripped_to_orig[stripped] = idx

    # Re-check intersection
    new_expr_ids = set(stripped_to_orig.keys())
    common_stripped = new_expr_ids.intersection(annot_ids)

    if len(common_stripped) > 0:
        print("Partial matches found. Proceeding with gene symbol mapping...")
        # Temporarily rename expression DataFrame index to the stripped version
        gene_data_tmp = gene_data.copy()
        gene_data_tmp.index = gene_data_tmp.index.map(lambda x: x.replace('_st', '').strip())

        # Perform mapping with apply_gene_mapping
        gene_data_mapped = apply_gene_mapping(gene_data_tmp, mapping_df)

        # We revert the index to some user-friendly format (e.g., the gene symbols returned)
        # but "apply_gene_mapping" already sets the new DataFrame's index to gene symbols.
        gene_data = gene_data_mapped
    else:
        # 3) Confirm no mapping possible
        print("No direct or partial matches found. No reliable way to map these probe IDs.")
        print("Retaining the original gene_data DataFrame without mapping.")

# Display final shape and top rows
print("Final gene_data shape:", gene_data.shape)
print(gene_data.head(5))
# 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.")