# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"
cohort = "GSE204791"

# Input paths
in_trait_dir = "../DATA/GEO/Aniridia"
in_cohort_dir = "../DATA/GEO/Aniridia/GSE204791"

# Output paths
out_data_file = "./output/preprocess/1/Aniridia/GSE204791.csv"
out_gene_data_file = "./output/preprocess/1/Aniridia/gene_data/GSE204791.csv"
out_clinical_data_file = "./output/preprocess/1/Aniridia/clinical_data/GSE204791.csv"
json_path = "./output/preprocess/1/Aniridia/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # The series includes mRNA expression, so we consider it gene expression data.

# 2. Variable Availability and Data Type Conversion
#   2.1 Determine row indices for 'trait', 'age', 'gender'
#       Based on the sample characteristics dictionary:
#         - 0: ['age: 59', 'age: 28', ... ]
#         - 1: ['gender: F', 'gender: M']
#         - 2: ['disease: KC', 'disease: healthy control']
#         - 3: [ ... staging info ... ]
#       We are looking for "Aniridia" but our dictionary lists "KC" or "healthy control" for disease.
#       Hence, we do not have data for "Aniridia." So trait_row = None.

trait_row = None  # No data on "Aniridia" found
age_row = 0       # Multiple ages are present
gender_row = 1    # "F" and "M" are present

#   2.2 Write conversion functions
def convert_trait(val: str):
    """
    Attempt to parse 'Aniridia' or 'control' from the string after the colon.
    Since 'Aniridia' is not actually in our sample data, return None.
    """
    return None

def convert_age(val: str):
    """
    Parse the value after the colon and convert to float.
    Non-numeric or invalid data is converted to None.
    """
    parts = val.split(':')
    if len(parts) < 2:
        return None
    raw_value = parts[1].strip()
    try:
        return float(raw_value)
    except ValueError:
        return None

def convert_gender(val: str):
    """
    Parse the value after the colon and convert:
      F -> 0
      M -> 1
      Otherwise -> None
    """
    parts = val.split(':')
    if len(parts) < 2:
        return None
    raw_value = parts[1].strip().upper()
    if raw_value == 'F':
        return 0
    elif raw_value == 'M':
        return 1
    else:
        return None

# 3. Save Metadata
#    Trait data availability is determined by whether trait_row is None.
is_trait_available = (trait_row is not None)

#    Perform initial filtering (is_final=False).
#    This will record metadata if data fails initial filtering.
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
#    Since trait_row is None, clinical data extraction is skipped.
# 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])
# These identifiers appear to be microarray probe IDs or custom probe identifiers rather than standard human gene symbols.
# Therefore, they need to be mapped to gene symbols.

requires_gene_mapping = True
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# Gene Identifier Mapping

# 1. Identify which columns correspond to the expression data's probe ID and to the gene symbols.
#    From the annotation preview, "ID" appears to match the probe identifiers (e.g., "A_19_P..."),
#    and "GENE_SYMBOL" appears to be the gene symbol column.

probe_id_col = "ID"
gene_symbol_col = "GENE_SYMBOL"

# 2. Extract the mapping information between probe IDs and gene symbols.
gene_mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)

# 3. Apply the mapping to convert probe-level data into gene-level data.
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# STEP 7: Data Normalization and Linking

import pandas as pd

# Since in previous steps we determined trait_row = None (no available trait data),
# we cannot link clinical data or perform trait-based filtering. Hence, we skip steps
# that depend on clinical or trait information.

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. No trait data found; skip clinical linking, missing-value handling, and bias assessment.
print("No trait data found. Skipping clinical linking, missing-value handling, and bias assessment.")

# 3. Conduct final quality validation and save metadata.
#    Since there's no trait data, we must pass some dummy DataFrame and a boolean for is_biased
#    to avoid the ValueError in final mode.
dummy_df = pd.DataFrame()
is_biased_dummy = False  # Arbitrary placeholder since we can't assess bias
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased_dummy,
    df=dummy_df,
    note="Trait data not found; dataset cannot be used for trait-based analysis."
)

# 4. Because we don't have usable trait data, skip saving the linked data
if is_usable:
    # This case should not occur since there's no trait data
    pass
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")