# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE182798"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE182798"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE182798.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE182798.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE182798.csv"
json_path = "./output/z1/preprocess/Asthma/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
# Step 1: Determine gene expression availability
is_gene_available = True  # "Transcriptomic profiling" indicates gene expression data (not miRNA-only or methylation-only)

# Step 2: Determine availability rows based on the provided Sample Characteristics Dictionary
trait_row = 0  # diagnosis field with values including 'adult-onset asthma', 'healthy', 'IEI'
age_row = 2    # age field with diverse numeric values
gender_row = None  # only 'Female' observed (constant), considered not available

# Step 2.2: Define conversion functions
def _extract_value(cell):
    if cell is None:
        return None
    try:
        # Extract value after the last colon to be robust to multiple colons
        return str(cell).split(":", 1)[1].strip()
    except Exception:
        return str(cell).strip()

def convert_trait(cell):
    v = _extract_value(cell)
    if v is None:
        return None
    vl = v.lower()
    # Map asthma vs non-asthma
    if "asthma" in vl:
        return 1
    if vl in {"healthy", "control", "normal"}:
        return 0
    if "iei" in vl:  # Idiopathic Environmental Intolerance is not asthma
        return 0
    return None

def convert_age(cell):
    v = _extract_value(cell)
    if v is None:
        return None
    try:
        val = float(v)
        # Filter unreasonable ages
        if 0 <= val < 120:
            return val
        return None
    except Exception:
        return None

def convert_gender(cell):
    v = _extract_value(cell)
    if v is None:
        return None
    vl = v.lower()
    if vl in {"female", "f", "woman", "women"}:
        return 0
    if vl in {"male", "m", "man", "men"}:
        return 1
    return None

# Step 3: Initial filtering and save metadata
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
)

# Step 4: Clinical feature extraction (only if trait data 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=None
    )
    preview = preview_df(selected_clinical_df, n=5)
    # Optionally print to observe preview during execution
    print("Preview of selected clinical features:", preview)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# 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
# Determine appropriate columns for probe IDs and gene symbols
candidate_id_cols = [col for col in ['ID', 'SPOT_ID'] if col in gene_annotation.columns]
symbol_col = 'GENE_SYMBOL' if 'GENE_SYMBOL' in gene_annotation.columns else None

# Fallback checks
if not candidate_id_cols or symbol_col is None:
    raise ValueError("Required columns for mapping not found in gene annotation.")

# Select the probe ID column with the highest overlap with expression data indices
overlaps = {}
gene_index_set = set(gene_data.index.astype(str))
for col in candidate_id_cols:
    ann_ids = gene_annotation[col].dropna().astype(str).str.strip()
    overlaps[col] = len(gene_index_set.intersection(set(ann_ids)))

id_col = max(overlaps, key=overlaps.get) if overlaps else 'ID'

# Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=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 the obtained gene data 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 the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, 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 some 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.
note = "INFO: Gender unavailable/constant female; mixed tissues (PBMC and Nasal biopsy) present."
is_usable = validate_and_save_cohort_info(
    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)