# Path Configuration
from tools.preprocess import *

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

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

# Output paths
out_data_file = "./output/preprocess/1/Fibromyalgia/GSE67311.csv"
out_gene_data_file = "./output/preprocess/1/Fibromyalgia/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/1/Fibromyalgia/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/1/Fibromyalgia/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. Determine if gene expression data is available
is_gene_available = True  # From the background info, this is a gene expression dataset

# 2. Identify row keys for trait, age, and gender, and define conversion functions

# We see that sample characteristics dictionary at row 0 includes:
# 'diagnosis: fibromyalgia', 'diagnosis: healthy control'.
# This matches our trait-of-interest (Fibromyalgia) vs. Healthy control cohorts, so:
trait_row = 0

# There's no apparent age or gender data in the dictionary, so:
age_row = None
gender_row = None

# Define trait conversion function (binary: fibromyalgia=1, healthy=0)
def convert_trait(value: str):
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    val_lower = value.lower()
    if val_lower == 'fibromyalgia':
        return 1
    elif val_lower == 'healthy control':
        return 0
    return None

# Age and gender are unavailable, so set the converting functions to None
convert_age = None
convert_gender = None

# 3. Save metadata using initial filtering
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 if trait data is available
if trait_row is not None:
    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=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )

    # Preview and save extracted clinical features
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)

    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])
# The given identifiers look like numeric probe IDs rather than standard human gene symbols, 
# so they likely require mapping to gene symbols.

print("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 which columns in the gene_annotation dataframe match our probe identifiers and gene symbols.
#    From the preview, the "ID" column in gene_annotation corresponds to the same probe IDs
#    used in gene_data (both numeric, e.g. '7896736', '7896738'), and "gene_assignment" stores text containing gene symbols.

# 2. Get a gene mapping dataframe: extract the "ID" column (probe ID) and "gene_assignment" column (gene text).
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="gene_assignment"
)

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

print("Gene-level expression data shape:", gene_data.shape)
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)

# 2) Try reading the clinical CSV file (trait data). If it's empty or unreadable, treat trait as unavailable.
if os.path.exists(out_clinical_data_file):
    try:
        tmp_df = pd.read_csv(out_clinical_data_file, header=0)
        # If successfully read, check the number of rows to rename their index properly.
        row_count = tmp_df.shape[0]
        if row_count == 1:
            tmp_df.index = [trait]
        elif row_count == 2:
            tmp_df.index = [trait, "Gender"]

        selected_clinical_df = tmp_df

        # Link the clinical and gene expression data
        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 and gender rows found; no age row."
        )

        # 6) If the dataset is usable, save
        if is_usable:
            final_data.to_csv(out_data_file)

    except (pd.errors.EmptyDataError, ValueError):
        # If file is present but empty or invalid, treat trait data as 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="Trait file is empty or invalid; linking and final dataset output are skipped."
        )
else:
    # If the clinical file does not exist at all, 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 file was found; linking and final dataset output are skipped."
    )