# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE48828"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE48828"

# Output paths
out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE48828.csv"
out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE48828.csv"
out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE48828.csv"
json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/cohort_info.json"


# Step 1: Initial Data Loading
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 2: Dataset Analysis and Clinical Feature Extraction
import os
import re
import pandas as pd

# 1) Gene expression data availability
is_gene_available = True  # Affymetrix Human Exon 1.0 ST array indicates mRNA expression profiling

# 2) Variable availability and converters based on provided Sample Characteristics Dictionary
# Keys:
# 0: diagnosis includes 'Duchenne Muscular Dystrophy' among others
# 1: gender: F/M/Not available
# 2: age (yrs): numeric and Not available/na

trait_row = 0
age_row = 2
gender_row = 1

def _after_colon(value):
    if value is None:
        return None
    if isinstance(value, str):
        parts = value.split(":", 1)
        v = parts[1] if len(parts) > 1 else parts[0]
        return v.strip()
    return value

def convert_trait(value):
    v = _after_colon(value)
    if v is None:
        return None
    v_low = v.strip().lower()
    # Map DMD to 1, all others (DM1, DM2, BMD, TMD, Normal, etc.) to 0
    if "duchenne" in v_low:
        return 1
    # If it's a diagnosis but not DMD, map to 0; unknowns remain None
    known_diagnoses_keywords = ["myotonic", "becker", "tibial", "normal", "muscular dystrophy", "dystrophy"]
    if any(k in v_low for k in known_diagnoses_keywords):
        return 0
    return None

def convert_age(value):
    v = _after_colon(value)
    if v is None:
        return None
    v_low = v.lower()
    if v_low in {"na", "not available", "n/a", "unknown", ""}:
        return None
    # Extract numeric (integer or float)
    m = re.search(r"[-+]?\d*\.?\d+", v)
    if m:
        try:
            return float(m.group())
        except Exception:
            return None
    return None

def convert_gender(value):
    v = _after_colon(value)
    if v is None:
        return None
    v_low = v.lower()
    if v_low in {"f", "female"}:
        return 0
    if v_low in {"m", "male"}:
        return 1
    return None

# 3) Save metadata with initial filtering
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 (only if trait_row is available)
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
    preview = preview_df(selected_clinical_df)
    print(preview)

    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
import re

# Given identifiers from previous step
ids = ['2315588', '2315589', '2315591', '2315594', '2315595', '2315596',
       '2315598', '2315602', '2315603', '2315604', '2315607', '2315638',
       '2315639', '2315640', '2315641', '2315642', '2315643', '2315644',
       '2315645', '2315690']

# Consider as gene symbols only if any identifier contains alphabetic characters
requires_gene_mapping = not any(re.search('[A-Za-z]', x) for x in ids)

print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Gene Annotation
# 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
# Decide columns for mapping based on annotation preview:
# - Probe/feature identifiers: 'ID' (matches numeric IDs in expression data)
# - Gene symbols embedded in: 'gene_assignment'
id_col = 'ID'
gene_col = 'gene_assignment'

# 2) Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)

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

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
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(selected_clinical_df, normalized_gene_data)

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

# 4. Determine bias and remove biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
try:
    trait_counts = unbiased_linked_data[trait].value_counts().to_dict()
    note = f"WARNING: Trait distribution after preprocessing: {trait_counts}. Extremely imbalanced if minor class <10%."
except Exception:
    note = "WARNING: Unable to compute trait distribution for note."
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=unbiased_linked_data,
    note=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)