# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE26927"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE26927"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE26927.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE26927.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE26927.csv"
json_path = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the series summary, this dataset uses Illumina whole genome array.

# 2. Variable Availability
# Checking the sample characteristics dictionary, we found:
#   - trait_row = 0, because row 0 has "disease: ..." entries including "Amyotrophic lateral sclerosis".
#   - age_row = 2, because row 2 has "age at death (in years): ..." entries.
#   - gender_row = 1, because row 1 has "gender: M" or "gender: F".
trait_row = 0
age_row = 2
gender_row = 1

# 2.2 Data Type Conversions
def convert_trait(value: str) -> int:
    """
    Convert disease field to binary indicating ALS (1) vs non-ALS (0).
    Unknown values are converted to None.
    Example input: "disease: Amyotrophic lateral sclerosis"
    """
    parts = value.split(":")
    if len(parts) < 2:
        return None
    disease_str = parts[1].strip().lower()
    if "amyotrophic lateral sclerosis" in disease_str:
        return 1
    else:
        return 0

def convert_age(value: str) -> Optional[float]:
    """
    Convert age field to continuous (float).
    Unknown values are converted to None.
    Example input: "age at death (in years): 70"
    """
    parts = value.split(":")
    if len(parts) < 2:
        return None
    age_str = parts[1].strip()
    try:
        return float(age_str)
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Convert gender field to binary: female -> 0, male -> 1.
    Unknown values are converted to None.
    Example input: "gender: M"
    """
    parts = value.split(":")
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().lower()
    if gender_str == 'f':
        return 0
    elif gender_str == 'm':
        return 1
    else:
        return None

# 3. Save Metadata (initial filtering)
is_trait_available = (trait_row is not None)
is_final = False  # initial filtering
validate_and_save_cohort_info(
    is_final=is_final,
    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_row is not None)
if trait_row is not None:
    # Assume 'clinical_data' is the DataFrame with sample characteristics loaded from a previous step.
    selected_clinical_df = geo_select_clinical_features(
        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 and save
    print("Preview of Selected Clinical Features:", preview_df(selected_clinical_df))
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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])
# These identifiers, such as 'ILMN_10000', are Illumina microarray probe IDs rather than standard gene symbols.
# Hence, they require mapping to gene symbols.
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))
# STEP: Gene Identifier Mapping

# 1. Identify the corresponding columns for probe ID and gene symbol in 'gene_annotation'.
#    From the previews, 'ID' matches the probe identifiers used in the gene expression data (e.g., 'ILMN_10000'),
#    and 'SYMBOL' stores the gene symbols.
probe_col = 'ID'
symbol_col = 'SYMBOL'

# 2. Get a mapping dataframe from 'gene_annotation'.
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

# 3. Convert probe-level measurements to gene-level expression data using 'apply_gene_mapping'.
#    This handles the many-to-many relationships by dividing expression across genes and summing contributions per gene.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For verification, let's output a small preview of the resulting gene_data: shape and first few gene indices.
print("Mapped gene_data shape:", gene_data.shape)
print("First 10 genes after mapping:", gene_data.index[:10].to_list())
# STEP 7: Data Normalization and Linking

import pandas as pd

# 1. Normalize 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)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Read the previously saved clinical data and correctly restore the row/column structure
#    After step 2, the CSV has 3 rows (trait, Age, Gender) and columns = sample IDs (header=0).
#    We can directly set the new index labels because we know row 0 = trait, row 1 = Age, row 2 = Gender.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
if selected_clinical_df.shape[0] == 3:
    selected_clinical_df.index = [trait, 'Age', 'Gender']
else:
    print("Warning: The clinical data does not have 3 rows as expected. Check the saved CSV format.")

# 3. Link clinical and gene expression data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4. Handle missing values (drop samples missing trait, drop high-missing genes/samples, impute remaining)
linked_data = handle_missing_values(linked_data, trait_col=trait)

# 5. Check for biased features (trait, age, gender) and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Final quality validation and metadata saving
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=trait_biased,
    df=linked_data,
    note="Final data pipeline completed."
)

# 7. Save final linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Dataset is not usable for trait-based association. Skipping final output.")