# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE230164"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE230164.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE230164.csv"
json_path = "./output/z1/preprocess/Allergies/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
# 1) Gene expression data availability
is_gene_available = True  # "Gene expression profiling of asthma" indicates mRNA expression, not miRNA/methylation.

# 2) Variable availability and converters
# From the Sample Characteristics Dictionary: {0: ['gender: male', 'gender: female']}
trait_row = None   # No trait (Allergies) info available or inferable from the provided characteristics
age_row = None     # No age info in the provided characteristics
gender_row = 0     # Gender available and non-constant

def _extract_after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    return parts[1].strip() if len(parts) > 1 else s.strip()

def convert_trait(x):
    # Generic binary mapping for allergy/asthma-related fields if ever present; otherwise returns None.
    v = _extract_after_colon(x)
    if v is None:
        return None
    v_low = v.lower()
    # Common positive indicators
    pos = {"yes", "y", "1", "true", "positive", "pos", "case", "asthma", "allergy", "allergies", "atopy", "atopic"}
    neg = {"no", "n", "0", "false", "negative", "neg", "control", "healthy", "non-asthma", "nonallergy", "non-allergy"}
    if v_low in pos:
        return 1
    if v_low in neg:
        return 0
    # Heuristics for phrases
    if "asthma" in v_low or "allerg" in v_low or "atopy" in v_low or "atopic" in v_low:
        # If clearly indicating presence or diagnosis, map to 1. Ambiguous terms will return None.
        if any(t in v_low for t in ["yes", "diagnosed", "patient", "case", "positive"]):
            return 1
    if any(t in v_low for t in ["healthy", "control", "no", "negative", "non-asthma", "nonallergy"]):
        return 0
    return None

def convert_age(x):
    v = _extract_after_colon(x)
    if v is None:
        return None
    v_low = v.lower()
    # Extract a numeric value
    import re
    m = re.search(r'[-+]?\d*\.?\d+', v_low)
    if not m:
        return None
    num = float(m.group())
    # Normalize to years if unit hints present
    if "month" in v_low or "mo" in v_low:
        return num / 12.0
    if "day" in v_low or "d " in v_low or v_low.endswith("d"):
        return num / 365.25
    # Assume years by default
    return num

def convert_gender(x):
    v = _extract_after_colon(x)
    if v is None:
        return None
    v_low = v.lower()
    if v_low in {"male", "m", "man", "men", "boy"}:
        return 1
    if v_low in {"female", "f", "woman", "women", "girl"}:
        return 0
    # Handle possible coded values
    if v_low in {"1"}:
        return 1
    if v_low in {"0"}:
        return 0
    return None

# 3) Save metadata (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 (skip because trait_row is 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_df(selected_clinical_df, n=5)
    # 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
requires_gene_mapping = True
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 the columns for probe IDs and gene symbols based on the annotation preview
probe_col = 'ID'       # Matches probe IDs like ILMN_1343291 in the expression data
gene_symbol_col = 'Symbol'  # Contains gene symbols

# Build the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Step 7: Data Normalization and Linking
import os

# 1. Normalize and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Proceed only if clinical features with the trait exist; otherwise record accurate metadata without triggering abnormality override
if ('selected_clinical_data' in globals()) and (trait in selected_clinical_data.index):
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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 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 with correct availability flags
    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="INFO: Linked data created and processed."
    )

    # 6. If the linked data is usable, save it
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        unbiased_linked_data.to_csv(out_data_file)
else:
    # Trait not available; accurately record metadata without abnormality override
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )