# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE174060"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE174060"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE174060.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE174060.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE174060.csv"
json_path = "./output/preprocess/1/Essential_Thrombocythemia/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
is_gene_available = True  # Based on the series title mentioning "Gene expression data"

# 2. Variable Availability and Data Type Conversion
# After examining the sample characteristics dictionary, we identified:
trait_row = 4     # diagnosis: [ET, PV, PMF, pPV-MF, pET-MF, healthy control]
age_row = 2       # age: [Various numbers]
gender_row = 3    # Sex: [F, M]

# Define converters
def convert_trait(value: str) -> int:
    """
    Convert diagnosis information to a binary indicator of Essential Thrombocythemia (ET).
    If the diagnosis is ET or pET-MF, return 1; otherwise 0.
    Unknown values return None.
    """
    # Example value: "diagnosis: ET"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    diag = parts[1].strip()
    if diag in ["ET", "pET-MF"]:
        return 1
    else:
        return 0

def convert_age(value: str) -> Optional[int]:
    """
    Convert age information into integer value.
    If parsing fails, return None.
    """
    # Example value: "age: 41"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    try:
        return int(parts[1].strip())
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Convert gender information to binary.
    Female (F) -> 0, Male (M) -> 1, unknown -> None
    """
    # Example value: "Sex: F"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().upper()
    if gender_str == "F":
        return 0
    elif gender_str == "M":
        return 1
    else:
        return None

# Determine trait data availability
is_trait_available = trait_row is not None

# 3. Save Metadata (initial filtering) and store the returned usability
is_usable = 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 (trait_row is not None)
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,            # Provided DataFrame with sample characteristics
        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
    )
    # Observe output
    preview_output = preview_df(selected_clinical_df, n=5)
    print("Preview of extracted clinical features:", preview_output)
    
    # Save clinical data
    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])
# Based on the given identifiers (e.g., "TC01000001.hg.1"), they do not appear to be standard human gene symbols.
# They likely require mapping to known gene symbols.
requires_gene_mapping = True
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. Identify the columns in 'gene_annotation' that match our probe IDs and gene symbols.
#    From the preview, the 'ID' column matches the probe identifiers (e.g. "TC01000001.hg.1").
#    The 'gene_assignment' column contains text with gene symbols that we need to parse.
probe_id_col = "ID"
gene_symbol_col = "gene_assignment"

# 2. Get a gene mapping dataframe from these two columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)

# 3. Convert probe-level measurements to gene expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For verification, let's print the first 20 genes now present in 'gene_data'.
print("First 20 gene symbols in the mapped gene_data:")
print(gene_data.index[:20])
import os
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
if os.path.exists(out_clinical_data_file):
    # 2) Link the clinical and gene expression data
    # Load the three-row clinical CSV (trait, age, gender) without forcing an index column
    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
    # Rename these three rows
    tmp_df.index = [trait, "Age", "Gender"]
    selected_clinical_df = tmp_df

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 3) Handle missing values
    final_data = handle_missing_values(linked_data, trait_col=trait)

    # 4) Evaluate bias in the trait (and remove biased demographics if any)
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation
    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=final_data,
        note="Trait, Age, and Gender rows are properly indexed and linked."
    )

    # 6) If the dataset is usable, save
    if is_usable:
        final_data.to_csv(out_data_file)
else:
    # If the clinical file does not exist, the trait is unavailable
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="No trait data was found; linking and final dataset output are skipped."
    )