# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE55976"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE55976"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE55976.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE55976.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE55976.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)
# Step 1: Check if gene expression data is available
# Based on the description ("cDNA microarray analysis ... gene expression profile ..."),
# we conclude that gene expression data is available.
is_gene_available = True

# Step 2: Determine data availability and define row indices
# From the sample characteristics dictionary, row 0 covers "subject condition" with multiple MPNs,
# including "Essential thrombocythemia". Hence trait data is available in row 0.
trait_row = 0
age_row = None
gender_row = None

# Step 2.2: Define data type conversion functions
def convert_trait(x: str):
    if not x or ':' not in x:
        return None
    value = x.split(':', 1)[1].strip().lower()
    # Convert "Essential thrombocythemia" to 1, otherwise 0
    if 'essential thrombocythemia' in value:
        return 1
    else:
        return 0

def convert_age(x: str):
    # Not used (age_row is None), but a stub is provided
    return None

def convert_gender(x: str):
    # Not used (gender_row is None), but a stub is provided
    return None

# Step 3: Initial filtering and metadata saving
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
)

# Step 4: Extract clinical features if trait_row is available
if trait_row is not None:
    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 the extracted clinical data
    preview_dict = preview_df(selected_clinical_df, n=5, max_items=200)
    print("Preview of selected clinical features:", preview_dict)

    # Save the clinical features to CSV
    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 numeric probe-like identifiers, these are not standard human gene symbols.
# Therefore, they 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 columns in 'gene_annotation' corresponding to the probe ID and gene symbol
#    Based on the preview, 'ID' matches the probe IDs in the expression data
#    and 'GENE SYMBOL' corresponds to the gene symbols.

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE SYMBOL")

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
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 single-row clinical CSV without forcing an index column
    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
    tmp_df.index = [trait]
    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 data successfully extracted; row renamed to trait for linking."
    )

    # 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."
    )