# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE67698"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE67698"

# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE67698.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/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

# 1) Gene expression availability
is_gene_available = True  # Two-color transcriptional profiling (mRNA), not miRNA/methylation

# 2) Variable availability and converters
# Based on the sample characteristics dictionary:
# {0: ['cell line: polarized CFBE41o-cell line'],
#  1: ['transduction: TranzVector lentivectors containing deltaF508 CFTR (CFBE41o-deltaF508CFTR)',
#      'transduction: TranzVector lentivectors containing wildtype CFTR (CFBE41o-CFTR)']}
trait_row = 1
age_row = None
gender_row = None

def _extract_value(x):
    if x is None:
        return None
    s = str(x)
    return s.split(':', 1)[1].strip() if ':' in s else s.strip()

def convert_trait(x):
    # Map CF (deltaF508 mutation) -> 1, wildtype -> 0
    v = _extract_value(x)
    if v is None:
        return None
    vlo = v.lower()
    # Detect deltaF508 / F508del variants (including unicode delta)
    if ('deltaf508' in vlo) or ('f508del' in vlo) or ('df508' in vlo) or ('del f508' in vlo) or ('Δf508' in v) or ('∆f508' in v):
        return 1
    # Detect wildtype/WT
    if ('wildtype' in vlo) or (re.search(r'\bwt\b', vlo) is not None):
        return 0
    return None

def convert_age(x):
    v = _extract_value(x)
    if v is None:
        return None
    m = re.search(r'(-?\d+(\.\d+)?)', v)
    return float(m.group(1)) if m else None

def convert_gender(x):
    v = _extract_value(x)
    if v is None:
        return None
    vlo = v.lower()
    if vlo in {'female', 'f', 'woman', 'women'}:
        return 0
    if vlo in {'male', 'm', 'man', 'men'}:
        return 1
    # Handle encoded forms
    if 'female' in vlo:
        return 0
    if 'male' in vlo:
        return 1
    return None

# 3) Save initial 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
)

# 4) Clinical feature extraction (only if trait data is available)
if is_trait_available:
    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 if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    preview = preview_df(selected_clinical_df)
    print(preview)

    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 columns for probe IDs and gene symbols based on annotation preview
probe_col = 'ID'
gene_symbol_col = 'GENE_SYMBOL'

# 2) Build mapping dataframe from annotation
gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# 1) Normalize gene symbols and save gene expression 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) Link clinical and genetic data
try:
    selected_clinical_df
except NameError:
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

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) Bias check on trait and demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5) Final validation and save cohort info (ensure native Python types for JSON)
is_gene_available_flag = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
if trait in selected_clinical_df.index:
    trait_series = selected_clinical_df.loc[trait]
    is_trait_available_flag = bool(pd.Series(trait_series).notna().any())
else:
    is_trait_available_flag = False

# Sanitize df to avoid numpy types in metadata
df_for_meta = unbiased_linked_data.copy()
df_for_meta.columns = list(df_for_meta.columns)

note = "INFO: Trait inferred from CFTR status in CFBE41o cell lines; two-color microarray; in vitro dataset."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available_flag,
    is_trait_available=is_trait_available_flag,
    is_biased=bool(is_trait_biased),
    df=df_for_meta,
    note=note
)

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