# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE120899"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE120899"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE120899.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE120899.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE120899.csv"
json_path = "./output/preprocess/1/Eczema/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 2: Dataset Analysis and Clinical Feature Extraction

# 1. Decide whether gene expression data is available
is_gene_available = True  # Based on the presence of mRNA expression data in the background info

# 2. Determine data availability and define row indices
# From the sample characteristics dictionary, row 1 has "lesional skin", "non-lesional skin", "Normal"
# We can interpret this as "Eczema vs. Normal" (case vs. control). Hence:
trait_row = 1
age_row = None
gender_row = None

# 2.2 Define data-type conversion functions
def convert_trait(value: str):
    # First, handle non-string or NaN cases
    if not isinstance(value, str):
        return None
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'normal':
        return 0
    elif val in ['lesional skin', 'non-lesional skin']:
        return 1
    else:
        return None

def convert_age(value: str):
    if not isinstance(value, str):
        return None
    return None  # Not available in this dataset

def convert_gender(value: str):
    if not isinstance(value, str):
        return None
    return None  # Not available in this dataset

# 3. Perform initial filtering and save metadata
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
)

# 4. Clinical feature extraction (only if trait data is available)
if trait_row is not None:
    import pandas as pd
    temp_dict = {
        0: ['batch_date: 2016-02-01', 'batch_date: 2016-01-12', 'batch_date: 2016-01-20', 'batch_date: 2016-01-25'],
        1: ['tissue: lesional skin', 'tissue: non-lesional skin', 'tissue: Normal'],
        2: ['week: 0', 'week: 12', 'week: NA'],
        3: ['treatment: APRMST-30', 'treatment: Placebo', 'treatment: APRMST-40', 'treatment: NA'],
        4: ['patient id: 31007', 'patient id: 61001', 'patient id: 61007', 'patient id: 61013']
    }
    clinical_data = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in temp_dict.items()])).T

    extracted_clinical = 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 = preview_df(extracted_clinical, n=5, max_items=200)
    print("Preview of extracted clinical features:", preview)

    extracted_clinical.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 provided probe IDs (e.g., "1007_s_at"), they are Affymetrix probe set IDs, not human gene symbols.
# Hence, they require mapping to gene symbols.
print("\nrequires_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))
# STEP: Gene Identifier Mapping

# 1. Identify the columns in gene_annotation that match the probe identifiers in the expression data 
#    (i.e., "ID") and the column for gene symbols (i.e., "Gene Symbol").
probe_col = "ID"
symbol_col = "Gene Symbol"

# 2. Extract the corresponding mapping between probe IDs and gene symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

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

# (Optional) Preview the resulting gene_data to verify the shape and a few rows.
print("Mapped gene_data shape:", gene_data.shape)
print("Preview of mapped gene_data:\n", gene_data.head())
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)

# 2) Read the clinical data with the default header, then re-align columns with gene_data.
selected_clinical_df = pd.read_csv(out_clinical_data_file)  # Expect shape (1, Ncolumns)
# Ensure we only take the first row in case there are unexpected extra rows
if selected_clinical_df.shape[0] > 1:
    selected_clinical_df = selected_clinical_df.iloc[:1, :]
    
num_clin_cols = selected_clinical_df.shape[1]
num_gene_cols = len(normalized_gene_data.columns)

# Match columns up to the smaller dimension
if num_clin_cols <= num_gene_cols:
    new_col_names = list(normalized_gene_data.columns[:num_clin_cols])
else:
    # If clinical has more columns, constrain to gene_data's column count
    selected_clinical_df = selected_clinical_df.iloc[:, :num_gene_cols]
    new_col_names = list(normalized_gene_data.columns)

selected_clinical_df.columns = new_col_names
# Assign the single row index to the trait
selected_clinical_df.index = [trait]

# 3) Link clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4) Handle missing values in the linked data
final_data = handle_missing_values(linked_data, trait_col=trait)

# 5) Evaluate bias in the trait and remove any biased features (like Age or Gender) if needed
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 6) Final validation and metadata saving
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="Adjusted clinical data column alignment to match gene expression sample IDs."
)

# 7) If the dataset is usable, save it
if is_usable:
    final_data.to_csv(out_data_file)