# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE65161"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE65161"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE65161.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE65161.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE65161.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) Determine if the dataset likely contains gene expression data
is_gene_available = True  # From the background, this seems to be gene expression data, not miRNA/methylation.

# 2) Identify available keys for trait, age, gender
#    None of these fields appear in the sample characteristics dictionary.
trait_row = None
age_row = None
gender_row = None

# 2.2) Define data type conversion functions (though not used here, must be defined)
def convert_trait(value: str):
    # No trait data is available in this dataset
    return None

def convert_age(value: str):
    # No age data is available in this dataset
    return None

def convert_gender(value: str):
    # No gender data is available in this dataset
    return None

# 3) Conduct initial filtering on dataset usability and record metadata
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) Since trait_row is None, we skip clinical feature extraction
# 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 gene identifiers shown (e.g., "1007_s_at", "1053_at") are typical Affymetrix probe IDs,
# which are not standard human gene symbols and thus require mapping.
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))
# STEP6: Gene Identifier Mapping

# 1. Identify the columns in `gene_annotation` that correspond to the probe IDs (matching the expression data)
#    and the columns that correspond to gene symbols.
#    From the previous step, we observed that 'ID' matches the expression data,
#    and 'Gene Symbol' holds the gene symbols.
probe_col = "ID"
gene_symbol_col = "Gene Symbol"

# 2. Build the mapping dataframe (probe_to_gene)
probe_to_gene = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Use the mapping to convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(gene_data, probe_to_gene)
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."
    )