# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE185658"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE185658"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE185658.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE185658.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
import os
import re
import pandas as pd

# 1. Gene Expression Data Availability
is_gene_available = True  # Affymetrix microarrays -> gene expression data present

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics:
# 0: time: DAY14/DAY4
# 1: group: AsthmaHDM / Healthy / AsthmaHDMNeg
# 2: donor: unique IDs
# Operationalize 'Allergies' as HDM sensitization using the 'group' field.
trait_row = 1
age_row = None
gender_row = None

def _after_colon(value: str) -> str:
    if value is None or (isinstance(value, float) and pd.isna(value)):
        return ""
    s = str(value)
    return s.split(":", 1)[1].strip() if ":" in s else s.strip()

def convert_trait(x):
    """
    Map 'group' to Allergies (binary):
    - AsthmaHDM -> 1 (allergic / HDM-sensitized)
    - Healthy, AsthmaHDMNeg -> 0 (non-allergic for HDM)
    Unknowns -> None
    """
    val = _after_colon(x).lower()
    if val in {"asthmahdm"}:
        return 1
    if val in {"healthy", "asthmahdmneg"}:
        return 0
    # Heuristics
    if "hdm" in val and "neg" in val:
        return 0
    if "healthy" in val:
        return 0
    if "hdm" in val and ("pos" in val or "+" in val):
        return 1
    return None

def convert_age(x):
    """
    Extract continuous age from strings like 'age: 45', 'age: 45 years'
    """
    val = _after_colon(x)
    m = re.search(r"[-+]?\d*\.?\d+", val)
    if m:
        try:
            return float(m.group())
        except Exception:
            return None
    return None

def convert_gender(x):
    """
    Map gender to binary: female->0, male->1
    """
    val = _after_colon(x).strip().lower()
    if val in {"female", "f", "woman", "women"}:
        return 0
    if val in {"male", "m", "man", "men"}:
        return 1
    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 (only if trait 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=None,
        gender_row=gender_row,
        convert_gender=None
    )
    preview = preview_df(selected_clinical_df, n=5)
    print(preview)
    # Save clinical features
    out_dir = os.path.dirname(out_clinical_data_file)
    os.makedirs(out_dir, 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
# Based on the observed numeric probe-like IDs (e.g., '7892501'), mapping to human gene symbols is required.
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 columns for mapping: probe IDs are in 'ID'; gene symbols can be parsed from 'gene_assignment'
probe_col = 'ID'
gene_symbol_col = 'gene_assignment'

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

# Apply 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 gene symbols and save
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. 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 demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "INFO: Trait operationalized as HDM sensitization from 'group' field: AsthmaHDM=1; Healthy/AsthmaHDMNeg=0."
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 if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)