# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE159514"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE159514"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE159514.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE159514.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE159514.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 series title and summary, this dataset is gene expression data on an Affymetrix platform.

# 2. Variable Availability and Data Type Conversion

# From sample characteristics dictionary:
# {0: ['disease: PPV', 'disease: Overt-PMF', 'disease: PET', 'disease: Pre-PMF'],
#  1: ['driver mutation: JAK2V617F', 'driver mutation: CALR Type 1', 'driver mutation: MPL',
#      'driver mutation: TN', 'driver mutation: CALR Type 2', 'driver mutation: CALR', 'driver mutation: JAK2 ex12']}

# 2.1 Identify data availability
# We see multiple values under key=0 for "disease", so we infer that is the trait row.
trait_row = 0  # This row contains "PET" which we interpret as (post) essential thrombocythemia
age_row = None  # Not available
gender_row = None  # Not available

# 2.2 Data Type Conversion
def convert_trait(value: str) -> int:
    """
    Convert the disease field to a binary indicator of essential thrombocythemia (ET).
    Here we regard 'PET' (post-ET) as having the trait.
    """
    # Split at colon and strip (take last part to handle potential "disease: PET" format).
    parts = value.split(':')
    disease_label = parts[-1].strip().lower()
    if 'pet' in disease_label:
        return 1
    # For other disease subtypes, return 0
    return 0

# Since age and gender are unavailable, we won't define conversion functions for them.
convert_age = None
convert_gender = 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 proceed if trait_row is not None
if trait_row is not None:
    # Suppose 'clinical_data' is the DataFrame containing the sample characteristics.
    # In a real scenario, we'd have it from previous steps or from reading a file.
    # For demonstration, let's assume 'clinical_data' is already loaded.
    # (We'll just reference it here and expect it to exist in this environment.)

    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
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))

    # Save
    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 (e.g., "11715100_at") appear to be Affymetrix microarray probe set IDs.
# These are not standard human gene symbols and thus need mapping to official gene symbols.

print("They are Affymetrix microarray probe set IDs, not standard human 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 the appropriate columns in the gene annotation:
#    - The probe identifiers correspond to the "ID" column.
#    - The gene symbols correspond to the "Gene Symbol" column.

# 2. Create a gene mapping dataframe.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="Gene Symbol"
)

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

# For verification, let's quickly check the shape of the resulting gene_data.
print("Mapped gene_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)

# 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."
    )