# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE60690"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE60690"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE60690.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE60690.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE60690.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/cohort_info.json"

# STEP1
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)
# 1. Gene Expression Data Availability
#    From the background info ("global gene expression was measured in RNA from LCLs"),
#    it is clear that the dataset contains gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
#    2.1 Data Availability
#    - The "trait" here is "Cystic_Fibrosis", but the background indicates
#      this dataset is entirely CF patients (no variation). Hence treat as not available.
trait_row = None

#    - Age is found in row 2 ("age of enrollment: ...").
age_row = 2

#    - Gender is found in row 0 ("Sex: Male", "Sex: Female").
gender_row = 0

#    2.2 Data Type Conversion
import re

def convert_trait(value: str) -> int:
    """
    Although trait_row is None (trait not available),
    define a function for completeness.
    Returns None if called, as there's no variation here.
    """
    return None

def convert_age(value: str) -> float:
    """
    Convert 'age of enrollment: 38.2' -> 38.2 (float).
    If 'NA', return None.
    """
    # Extract the portion after the colon
    parts = value.split(':')
    if len(parts) < 2:
        return None
    age_str = parts[1].strip()
    if age_str.upper() == 'NA':
        return None
    try:
        return float(age_str)
    except ValueError:
        return None

def convert_gender(value: str) -> int:
    """
    Convert 'Sex: Male' -> 1
            'Sex: Female' -> 0
    Otherwise return None.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().lower()
    if gender_str == 'male':
        return 1
    elif gender_str == 'female':
        return 0
    return None

# 3. Save Metadata
#    trait data availability depends on trait_row.
is_trait_available = trait_row is not None

usable_status = 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 this step because trait_row is None (no trait variation).
#    Thus, we do not call geo_select_clinical_features.
# STEP3
# 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])
print("requires_gene_mapping = True")
# STEP5
# 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))
# STEP6: Gene Identifier Mapping

# 1. Identify the columns in 'gene_annotation' that match the probe IDs in 'gene_data'
#    and the column that stores gene symbols. Based on inspection, "ID" aligns with
#    the probe identifiers in the expression data, and "gene_assignment" stores gene symbols.
probe_col = "ID"
gene_symbol_col = "gene_assignment"

# 2. Extract a mapping dataframe containing these two columns
mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)

# 3. Apply the mapping to convert the probe-level expression data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7

# 1) Normalize the gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# The dataset lacks trait variation (only CF patients), so no clinical data can be used for association.
# We skip linking to clinical data and skip further steps requiring trait info.

# 2) Final validation and saving metadata
#    The library requires non-None boolean for 'is_biased' when is_final=True.
#    Since there's no trait variation, we consider it "biased" for association.
is_biased = True

_ = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=pd.DataFrame(),  # Passing an empty DataFrame as the dataset for final validation
    note="All samples are CF patients; no variation in the trait."
)

# 3) Because the trait is unavailable for association, the dataset is not usable.
#    We therefore do not create or save any linked data CSV file.