# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_Fatigue_Syndrome"
cohort = "GSE67311"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE67311"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_Fatigue_Syndrome/GSE67311.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/1/Chronic_Fatigue_Syndrome/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)
import pandas as pd
import numpy as np

# 1. Gene Expression Data Availability
# Based on the background information, this dataset uses an Affymetrix Gene 1.1 ST array,
# indicating it is gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics dictionary in the provided output, we see:
#   - For "Chronic_Fatigue_Syndrome", data is found at row 8 with multiple values ("Yes", "No", "-", nan).
#   - No row for "age".
#   - No row for "gender".

trait_row = 8
age_row = None
gender_row = None

# 2.2 Data Type Conversion
# "Chronic_Fatigue_Syndrome" is a binary variable (Yes/No).
def convert_trait(value):
    if not isinstance(value, str):
        return None
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'yes':
        return 1
    elif val == 'no':
        return 0
    # Handle missing or ambiguous values
    return None

# Since we do not have age or gender data, we define dummy functions returning None.
def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3. Save Metadata with initial filtering
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_row is not None)
if trait_row is not None:
    # Assume "clinical_data" is the DataFrame containing the characteristics, already in scope.
    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=None,
        gender_row=gender_row,
        convert_gender=None
    )
    preview = preview_df(selected_clinical_df)
    print("Preview of Clinical Features:")
    print(preview)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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 biomedical knowledge and the numeric nature of these identifiers,
# they are not standard human gene symbols and require mapping to gene symbols.

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 which columns in the annotation are equivalent to the expression data IDs and which store gene symbols.
#    From the preview, the "ID" column corresponds to the expression data IDs,
#    and the "gene_assignment" column contains the gene symbol information.
probe_id_column = "ID"
gene_symbol_column = "gene_assignment"

# 2. Get the gene mapping dataframe by extracting these two columns.
mapping_df = get_gene_mapping(gene_annotation, probe_id_column, gene_symbol_column)

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

# For inspection, let's check the shape of the resulting gene_data.
print("Mapped gene expression data shape:", gene_data.shape)
print("First 5 rows of mapped gene expression data:")
print(gene_data.head())
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.