# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"
cohort = "GSE60861"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE60861"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE60861.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE60861.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE60861.csv"
json_path = "./output/preprocess/1/Chronic_kidney_disease/cohort_info.json"

# STEP1
from tools.preprocess import *

# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
    soft_file, matrix_file = None, None

if soft_file is None or matrix_file is None:
    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
    # 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. Determine if the dataset likely contains gene expression (mRNA) data
#    From the background info, the title explicitly states analysis of mRNA-expression,
#    so we set is_gene_available = True.

is_gene_available = True

# 2. Determine availability and parsing of the variables 'trait', 'age', and 'gender'.

#    2.1 Data Availability
#    The dataset is entirely about chronic kidney disease (CKD) subdivided by specific nephropathies.
#    Hence CKD is constant across all samples and offers no variation for associating gene expression.
#    Therefore, "trait_row" should be None (treated as not available).
trait_row = None

#    For the 'age' variable, key=1 in the sample characteristics has well-defined numeric values
#    with multiple distinct entries. Thus we set age_row=1.
age_row = 1

#    For the 'gender' variable, key=0 has entries "gender: male" and "gender: female" (and one mentioning "tissue"),
#    which still shows at least 2 distinct gender values. Hence gender_row=0.
gender_row = 0

#    2.2 Data Type Conversion
#    - Trait: not available (trait_row=None), but we still define the function.
#    - Age: continuous.
#    - Gender: binary (female->0, male->1).

def convert_trait(value: str) -> int:
    # Not actually used since trait_row is None, but defined for completeness.
    # We consider it unavailable, so return None to skip usage.
    return None

def convert_age(value: str) -> float:
    # Extract the part after the colon and convert to float
    # Unknown or malformed entries become None
    parts = value.split(":")
    if len(parts) < 2:
        return None
    try:
        return float(parts[-1].strip())
    except ValueError:
        return None

def convert_gender(value: str) -> int:
    # Extract the part after the colon
    parts = value.split(":")
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    if val == "male":
        return 1
    elif val == "female":
        return 0
    else:
        return None

# 3. Save metadata with initial filtering.
#    trait_row is None => is_trait_available = False
#    gene expression is likely => is_gene_available = True
#    Perform the initial validation.
is_trait_available = (trait_row is not None)

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
#    We only extract clinical features if trait_row is not None. Here trait_row = None (no variation),
#    so we skip the extraction step.
#    (No further action regarding clinical data extraction.)
# STEP3
# Attempt to read gene expression data; if the library function yields an empty DataFrame,
# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
# place actual expression rows under lines that begin with '!').

gene_data = get_genetic_data(matrix_file)
if gene_data.empty:
    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
    import gzip

    # Locate the marker line first
    skip_rows = 0
    with gzip.open(matrix_file, 'rt') as file:
        for i, line in enumerate(file):
            if "!series_matrix_table_begin" in line:
                skip_rows = i + 1
                break

    # Read the data again, this time not treating '!' as comment
    gene_data = pd.read_csv(
        matrix_file,
        compression="gzip",
        skiprows=skip_rows,
        delimiter="\t",
        on_bad_lines="skip"
    )
    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
    gene_data.set_index("ID", inplace=True)

# Print the first 20 row IDs to confirm data structure
print(gene_data.index[:20])
# Based on their format (e.g., "A_23_P100001"), these identifiers appear to be probe IDs from a microarray platform,
# rather than standard human gene symbols. Therefore, they need to be mapped to gene symbols.

print("These identifiers are microarray probe IDs, not standard human 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))
# STEP6: Gene Identifier Mapping

# 1 & 2. Decide which columns in the gene annotation dataframe correspond to the probe IDs and gene symbols.
#    From inspection, "ID" matches the probe IDs (e.g., "A_23_P100001") in our expression data,
#    and "GENE_SYMBOL" contains the gene symbols.
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

# 3. Convert probe-level measurements to gene expression data by applying the mapping:
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP7: Data Normalization and Linking

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

# 2) Load clinical data only if it exists and is non-empty
if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
    # Read the file
    clinical_temp = pd.read_csv(out_clinical_data_file)

    # Adjust row index to label the trait, age, and gender properly
    if clinical_temp.shape[0] == 3:
        clinical_temp.index = [trait, "Age", "Gender"]
    elif clinical_temp.shape[0] == 2:
        clinical_temp.index = [trait, "Age"]
    elif clinical_temp.shape[0] == 1:
        clinical_temp.index = [trait]

    # 2) Link the clinical and normalized genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

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

    # 4) Check for severe bias in the trait; remove biased demographic features if present
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5) Final quality validation and save metadata
    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=f"Final check on {cohort} with {trait}."
    )

    # 6) If the linked data is usable, save it
    if is_usable:
        linked_data.to_csv(out_data_file)
else:
    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
    is_usable = 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,  # Force a fallback so that it's flagged as unusable
        df=pd.DataFrame(),
        note=f"No trait data found for {cohort}, final metadata recorded."
    )
    # Per instructions, do not save a final linked data file when trait data is absent.