# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE12295"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE12295"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE12295.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE12295.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE12295.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 background info about "spotted oligonucelotide platelet-focused arrays"

# 2) Variable Availability and Data Type Conversion

# From the sample characteristics dictionary provided:
# {0: ['Essential thrombocythemia Patient Platelet', 'Reactive Thrombocytosis Patient platelets', 'Normal Patient Platelets']}
# We see multiple categories indicating different conditions, including "Essential thrombocythemia".
# Hence, trait data is available at key 0 (multiple unique values). No indication of age or gender keys.

trait_row = 0
age_row = None
gender_row = None

def convert_trait(value: str):
    """Binary conversion for ET vs. non-ET."""
    if not isinstance(value, str):
        return None
    # Extract substring after colon if it exists
    val = value.split(':')[-1].strip().lower()
    # Label sample as 1 if it is 'Essential thrombocythemia', else 0 if recognized
    if 'essential thrombocythemia' in val:
        return 1
    elif val:
        return 0
    return None

def convert_age(value: str):
    """Stub for age conversion; here we have no age data."""
    return None

def convert_gender(value: str):
    """Stub for gender conversion; here we have no gender data."""
    return None

# 3) Save Metadata (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:
    # 'clinical_data' is assumed to be the DataFrame containing sample characteristics
    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 and save
    print("Clinical features preview:", preview_df(selected_clinical_df))
    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 listed identifiers (e.g., '1001', '1002', etc.), they appear to be numeric IDs
# rather than official human gene symbols. Thus, gene symbol mapping is likely required.

print("These IDs are numeric and not standard human gene symbols.\nrequires_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 matching columns for gene expression ID and gene symbol from the annotation dataframe.
#    The 'ID' column in gene_annotation matches the numeric identifier used in gene_data.
#    The 'Gene Symbol' column corresponds to the actual gene symbols.

# 2. Create a mapping dataframe.
gene_mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID',             # Matches the index of gene_data
    gene_col='Gene Symbol'     # Column containing gene symbols
)

# 3. Convert probe-level data to gene-level data.
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_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
    # Since we only have the trait row (and no age/gender),
    # read the one-row CSV with header=0, then rename index to [trait].
    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
    # We expect exactly one row: the trait
    if len(tmp_df) == 1:
        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="Only trait row present; no age/gender rows."
    )

    # 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."
    )