{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "85965b97", "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"Ocular_Melanomas\"\n", "cohort = \"GSE60464\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Ocular_Melanomas\"\n", "in_cohort_dir = \"../../input/GEO/Ocular_Melanomas/GSE60464\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Ocular_Melanomas/GSE60464.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Ocular_Melanomas/gene_data/GSE60464.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Ocular_Melanomas/clinical_data/GSE60464.csv\"\n", "json_path = \"../../output/preprocess/Ocular_Melanomas/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "bf174a8a", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "13e8f4b7", "metadata": {}, "outputs": [], "source": [ "# 1. Check what files are actually in the directory\n", "import os\n", "print(\"Files in the directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# 2. Find appropriate files with more flexible pattern matching\n", "soft_file = None\n", "matrix_file = None\n", "\n", "for file in files:\n", " file_path = os.path.join(in_cohort_dir, file)\n", " # Look for files that might contain SOFT or matrix data with various possible extensions\n", " if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n", " soft_file = file_path\n", " if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n", " matrix_file = file_path\n", "\n", "if not soft_file:\n", " print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if gz_files:\n", " soft_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "if not matrix_file:\n", " print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n", " elif len(gz_files) == 1 and not soft_file:\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# 3. Read files if found\n", "if soft_file and matrix_file:\n", " # Read the matrix file to obtain background information and sample characteristics data\n", " background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", " clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", " \n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " \n", " # Obtain the sample characteristics dictionary from the clinical dataframe\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " \n", " # Explicitly print out all the background information and the sample characteristics dictionary\n", " print(\"Background Information:\")\n", " print(background_info)\n", " print(\"Sample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", " except Exception as e:\n", " print(f\"Error processing files: {e}\")\n", " # Try swapping files if first attempt fails\n", " print(\"Trying to swap SOFT and matrix files...\")\n", " temp = soft_file\n", " soft_file = matrix_file\n", " matrix_file = temp\n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " print(\"Background Information:\")\n", " print(background_info)\n", " print(\"Sample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", " except Exception as e:\n", " print(f\"Still error after swapping: {e}\")\n", "else:\n", " print(\"Could not find necessary files for processing.\")\n" ] }, { "cell_type": "markdown", "id": "50469b0a", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "7d404a74", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "import gzip\n", "from typing import Optional, Callable, Dict, Any, List\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the Series title and summary, this appears to be a gene expression dataset\n", "# The Series_overall_design mentions \"expression profiles of a total of 9,829 unique genes\"\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Identifying data rows\n", "\n", "# For trait (cerebrotropism):\n", "# Row 2 contains cerebrotropic status with values 0 or 1\n", "trait_row = 2\n", "\n", "# For age:\n", "# There is no age information in the sample characteristics\n", "age_row = None\n", "\n", "# For gender:\n", "# There is no gender information in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"Convert cerebrotropic status to binary 0/1\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if \":\" in value:\n", " parts = value.split(\":\", 1)\n", " if len(parts) > 1:\n", " value = parts[1].strip()\n", " \n", " # Extract the actual value after \"1=Yes; 0 =No: \"\n", " if \"1=Yes; 0 =No:\" in value:\n", " value = value.split(\"1=Yes; 0 =No:\", 1)[1].strip()\n", " \n", " try:\n", " return int(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age to continuous value\"\"\"\n", " # This function is not needed as age data is not available\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n", " # This function is not needed as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Read the sample characteristics from the matrix file\n", " sample_ids = []\n", " characteristics_data = {}\n", " \n", " with gzip.open(os.path.join(in_cohort_dir, \"GSE60464_series_matrix.txt.gz\"), 'rt') as file:\n", " for line in file:\n", " line = line.strip()\n", " if line.startswith(\"!Sample_geo_accession\"):\n", " sample_ids = line.split('\\t')[1:]\n", " # Initialize the data structure for each sample\n", " for sample_id in sample_ids:\n", " characteristics_data[sample_id] = {}\n", " \n", " # Extract characteristic data\n", " elif line.startswith(\"!Sample_characteristics_ch\"):\n", " parts = line.split('\\t')\n", " if len(parts) <= 1:\n", " continue\n", " \n", " values = parts[1:]\n", " \n", " # Make sure we have the right number of values\n", " if len(values) != len(sample_ids):\n", " continue\n", " \n", " # Determine which row this characteristic belongs to\n", " for i, (sample_id, value) in enumerate(zip(sample_ids, values)):\n", " # Store in the dictionary with row numbers as keys\n", " characteristics_data[sample_id][int(line.split(\"ch\")[1].split(\"\\t\")[0])] = value\n", " \n", " # Create DataFrame from the collected data\n", " clinical_data_dict = {}\n", " for row_idx in range(10): # Check rows 0-9\n", " if any(row_idx in sample_data for sample_data in characteristics_data.values()):\n", " clinical_data_dict[row_idx] = []\n", " for sample_id in sample_ids:\n", " value = characteristics_data[sample_id].get(row_idx, None)\n", " clinical_data_dict[row_idx].append(value)\n", " \n", " # Create a properly formatted DataFrame for geo_select_clinical_features\n", " clinical_data = pd.DataFrame(clinical_data_dict, index=sample_ids)\n", " \n", " # Select and process clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the processed clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of processed clinical data:\")\n", " print(preview)\n", " \n", " # Save the processed clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "10d4ca78", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "6b025a32", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# We need to first read the clinical data to assess its content\n", "clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", "if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " print(f\"Clinical data loaded, shape: {clinical_data.shape}\")\n", " \n", " # Preview the first few rows to understand the structure\n", " print(\"\\nPreview of clinical data:\")\n", " sample_char_keys = list(clinical_data.index)\n", " print(f\"Sample characteristics keys: {sample_char_keys}\")\n", " \n", " # Display unique values for each row to help identify relevant variables\n", " for idx in clinical_data.index:\n", " unique_values = clinical_data.loc[idx].unique()\n", " print(f\"Row {idx} unique values: {unique_values}\")\n", "else:\n", " clinical_data = None\n", " print(\"Clinical data file not found\")\n", "\n", "# 1. Gene Expression Data Availability\n", "# Assuming gene expression data is available if a matrix file exists\n", "gene_matrix_path = os.path.join(in_cohort_dir, \"gene_expression_matrix.csv\")\n", "is_gene_available = os.path.exists(gene_matrix_path)\n", "if is_gene_available:\n", " print(\"Gene expression data is available\")\n", "else:\n", " print(\"Gene expression data is not available\")\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# Based on the output, we'll identify rows for trait, age, and gender\n", "\n", "# For demonstration, let's assume:\n", "trait_row = 0 # Example: if ocular melanoma status is in row 0\n", "age_row = None # Example: if age is not available\n", "gender_row = None # Example: if gender is not available\n", "\n", "def convert_trait(value: str) -> int:\n", " \"\"\"\n", " Convert ocular melanoma trait value to binary format.\n", " \"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (assuming 1 represents having the condition)\n", " value = value.lower()\n", " if 'melanoma' in value or 'positive' in value or 'yes' in value:\n", " return 1\n", " elif 'normal' in value or 'negative' in value or 'no' in value or 'control' in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"\n", " Convert age value to continuous format.\n", " \"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Try to extract numeric age\n", " try:\n", " # Extract digits from the value\n", " import re\n", " digits = re.findall(r'\\d+', value)\n", " if digits:\n", " return float(digits[0])\n", " else:\n", " return None\n", " except:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert gender value to binary format (0 for female, 1 for male).\n", " \"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " value = value.lower()\n", " if 'female' in value or 'f' in value:\n", " return 0\n", " elif 'male' in value or 'm' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata for initial filtering\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None and clinical_data is not None:\n", " print(\"Extracting clinical features...\")\n", " \n", " # Extract and process clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age if age_row is not None else None,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender if gender_row is not None else None\n", " )\n", " \n", " # Preview the processed clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"\\nPreview of processed clinical data:\")\n", " print(preview)\n", " \n", " # Save the processed clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "else:\n", " print(\"Clinical data extraction skipped - trait data not available\")\n" ] }, { "cell_type": "markdown", "id": "8367570e", "metadata": {}, "source": [ "### Step 4: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "11ad6431", "metadata": {}, "outputs": [], "source": [ "# 1. First get the path to the soft and matrix files\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Looking more carefully at the background information\n", "# This is a SuperSeries which doesn't contain direct gene expression data\n", "# Need to investigate the soft file to find the subseries\n", "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n", "\n", "# Open the SOFT file to try to identify subseries\n", "with gzip.open(soft_file, 'rt') as f:\n", " subseries_lines = []\n", " for i, line in enumerate(f):\n", " if 'Series_relation' in line and 'SuperSeries of' in line:\n", " subseries_lines.append(line.strip())\n", " if i > 1000: # Limit search to first 1000 lines\n", " break\n", "\n", "# Display the subseries found\n", "if subseries_lines:\n", " print(\"Found potential subseries references:\")\n", " for line in subseries_lines:\n", " print(line)\n", "else:\n", " print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n", "\n", "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(\"\\nGene data extraction result:\")\n", " print(\"Number of rows:\", len(gene_data))\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n" ] }, { "cell_type": "markdown", "id": "f75215a2", "metadata": {}, "source": [ "### Step 5: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "d6c2d641", "metadata": {}, "outputs": [], "source": [ "# Examining the gene identifiers in the gene expression data\n", "# The identifiers starting with \"ILMN_\" are Illumina probe IDs, not human gene symbols\n", "# These probe IDs need to be mapped to gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "9e51787f", "metadata": {}, "source": [ "### Step 6: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "65f21d8c", "metadata": {}, "outputs": [], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "c8df67bc", "metadata": {}, "source": [ "### Step 7: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "54c09ed5", "metadata": {}, "outputs": [], "source": [ "# 1. Examining the gene annotation dictionary to identify relevant columns\n", "# From the preview of gene annotation data, we can see:\n", "# - 'ID' column contains identifiers matching the ones in gene expression data (ILMN_*)\n", "# - 'Symbol' column appears to contain gene symbols\n", "\n", "# 2. Create the mapping dataframe linking probe IDs to gene symbols\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n", "\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 3. Convert probe-level data to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "print(\"\\nGene expression data after mapping:\")\n", "print(f\"Number of genes: {len(gene_data)}\")\n", "print(\"First 10 gene symbols:\")\n", "print(gene_data.index[:10].tolist())\n", "\n", "# Normalize gene symbols to ensure consistency\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(\"\\nGene expression data after normalization:\")\n", "print(f\"Number of genes: {len(gene_data)}\")\n", "print(\"First 10 gene symbols:\")\n", "print(gene_data.index[:10].tolist())\n", "\n", "# Save the gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "1275b51a", "metadata": {}, "source": [ "### Step 8: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "d1b84798", "metadata": {}, "outputs": [], "source": [ "# 1. Normalize gene symbols in the gene expression data (already done in step 7)\n", "# We need to load the gene data that was saved in the previous step\n", "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", "print(f\"Loaded gene expression data with shape: {gene_data.shape}\")\n", "\n", "# 2. Reload clinical data to properly process it for the Ocular Melanomas dataset\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "\n", "# Let's examine the original data to understand the correct cerebrotropism values\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "print(\"Sample Characteristics Dictionary:\")\n", "for key, value in sample_characteristics_dict.items():\n", " print(f\"Row {key}: {value}\")\n", "\n", "# Define conversion function specifically for cerebrotropism status\n", "def convert_trait(value):\n", " \"\"\"Convert cerebrotropic status to binary 0/1\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if \":\" in value:\n", " parts = value.split(\":\", 1)\n", " if len(parts) > 1:\n", " value = parts[1].strip()\n", " \n", " # Extract the actual value after \"1=Yes; 0 =No: \"\n", " if \"1=Yes; 0 =No:\" in value:\n", " value = value.split(\"1=Yes; 0 =No:\", 1)[1].strip()\n", " \n", " try:\n", " return int(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "# Extract the clinical data using the appropriate row (row 2) based on our analysis\n", "clinical_df = geo_select_clinical_features(\n", " clinical_data,\n", " trait=trait,\n", " trait_row=2, # Row 2 contains cerebrotropic status (from the sample characteristics dictionary)\n", " convert_trait=convert_trait,\n", " # No age or gender data available in this dataset\n", " age_row=None,\n", " convert_age=None,\n", " gender_row=None,\n", " convert_gender=None\n", ")\n", "\n", "print(\"Clinical data preview:\")\n", "print(preview_df(clinical_df))\n", "\n", "# Save the clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_df.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Handle sample ID format to ensure proper linking\n", "# Strip quotes from sample IDs if present\n", "gene_data.columns = gene_data.columns.str.strip('\"')\n", "if isinstance(clinical_df.columns, pd.Index):\n", " clinical_df.columns = clinical_df.columns.str.strip('\"')\n", "\n", "# Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n", "print(f\"Shape of linked data: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values in the linked data\n", "linked_data_cleaned = handle_missing_values(linked_data, trait)\n", "print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n", "\n", "# 5. Check if the trait and demographic features are biased\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n", "\n", "# 6. Validate the dataset and save cohort information\n", "note = \"Dataset contains gene expression data from metastatic melanoma samples. The trait variable represents cerebrotropic status (early brain metastasis): 1=early cerebrotropic patients (brain metastases within 6 months), 0=non-cerebrotropic patients (no brain metastases for over 18 months).\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True,\n", " is_biased=is_trait_biased,\n", " df=unbiased_linked_data,\n", " note=note\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_data.to_csv(out_data_file)\n", " print(f\"Saved processed linked data to {out_data_file}\")\n", "else:\n", " print(\"Dataset validation failed. Final linked data not saved.\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }